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248 Commits
Author SHA1 Message Date
Evan ded7840499 api streams 2026-05-08 16:04:25 +01:00
Evan 7ed3eaa617 json state proxy 2026-05-07 15:36:03 +01:00
Evan e113d42ba1 uncap 2026-05-07 13:28:51 +01:00
Evan c5245dd87e libp2p -> zenoh 2026-05-07 11:21:59 +01:00
Alex Cheema edef8004f8 Store custom model cards in State (#2024)
## Why

Workers currently update their custom model-card cache by reacting to
`CustomModelCardAdded` / `CustomModelCardDeleted` events directly. That
is another snapshot footgun: a worker restored from State may never see
the historical add/delete event, so the durable State must include the
desired custom-card set.

## How

- Add `State.custom_model_cards`, keyed by `ModelId`.
- Reduce `CustomModelCardAdded` into State.
- Reduce `CustomModelCardDeleted` into State.
- Add focused reducer tests for add and delete.

This PR only makes custom cards durable in State. A follow-up PR will
make workers reconcile their on-disk custom-card cache from this state
instead of relying on those events directly.

## Tests

- `uv run pytest
src/exo/shared/tests/test_apply/test_apply_custom_model_cards.py
src/exo/shared/tests/test_state_serialization.py`
- `uv run pytest`
- `uv run ruff check src/exo/shared/types/state.py
src/exo/shared/apply.py
src/exo/shared/tests/test_apply/test_apply_custom_model_cards.py`
- `uv run basedpyright`
- `nix fmt`
2026-05-07 09:06:39 +01:00
Alex CheemaandClaude Opus 4.7 a0c00f9dfd fix(placement): gate RDMA on nodeRdmaCtl.enabled at both endpoints (#2014)
## Summary

- Fixes a bug where `POST /place_instance` (and the dashboard UI) would
accept an MlxJaccl/RDMA instance spanning nodes whose
`nodeRdmaCtl.enabled` was `false`, because topology + placement
consulted Thunderbolt-derived RDMA edges without checking the per-node
`rdma_ctl` status.
- Three-layer fix: topology only emits `RDMAConnection` edges when both
endpoints have `nodeRdmaCtl.enabled = true`; flipping a node to disabled
immediately purges every RDMA edge touching it; `place_instance`
additionally rejects RDMA cycles containing any disabled or unobserved
node as a defense-in-depth check on the API/master path.

## Details

- `src/exo/shared/apply.py`
- `MacThunderboltConnections` case now filters out RDMA connections
whose source or sink lacks observed-and-enabled `rdma_ctl` status
(missing entry → treated as disabled).
- `RdmaCtlStatus` case now calls
`topology.remove_all_rdma_connections_touching(node_id)` when the node
reports disabled, so consumers don't have to wait for the next TB poll.
- `src/exo/shared/topology.py`
- New `Topology.remove_all_rdma_connections_touching(node_id)` removes
every RDMA edge incident to the node (incoming and outgoing) while
leaving socket edges intact.
- `src/exo/master/placement.py`
- `place_instance` accepts `node_rdma_ctl: Mapping[NodeId,
NodeRdmaCtlStatus] | None`. The `is_rdma_cycle` filter now also requires
`nodeRdmaCtl.enabled` for every node in the cycle. MlxJaccl placement
raises the existing "no RDMA-connected cycles available" error if no
qualifying cycle remains.
- `src/exo/api/main.py`, `src/exo/master/main.py`
  - Both placement entrypoints now pass `state.node_rdma_ctl` through.

## Tests

- `src/exo/shared/tests/test_apply/test_apply_rdma_gating.py` (new): six
unit tests covering enabled/disabled/missing combinations on apply, the
immediate-purge transition, and that purging RDMA edges leaves socket
edges untouched.
- `src/exo/master/tests/test_placement.py`: existing
`test_tensor_rdma_backend_connectivity_matrix` updated to pass
`node_rdma_ctl`. Two new tests assert MlxJaccl placement is rejected
when any cycle node is `enabled=false` or has no `rdma_ctl` entry.

## Test plan

- [x] `uv run basedpyright` — 0 errors
- [x] `uv run ruff check` — clean
- [x] `nix fmt`
- [x] `uv run pytest` — 429 passed, 1 skipped
- [ ] On a real mixed cluster (s15/s16 disabled, s17/s18 enabled),
confirm:
- [ ] `POST /place_instance` for an RDMA instance including s15 or s16
returns an error
  - [ ] An RDMA instance can still be placed across {s17, s18}
- [ ] `GET /state` shows no `sourceRdmaIface`/`sinkRdmaIface` on s15↔s16
connections
- [ ] Dashboard previews don't surface RDMA-spanning options that
include s15/s16

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-07 07:00:15 +00:00
Drifter4242 89d20c1888 fix(inference): prevent TP collective deadlock via agree_on_tasks order (#2048)
If you have two machines and make two requests at the same time, it can
crash. This is because the tasks can sometimes end up in different
orders on different machines. We need to sort the tasks and
mx_all_gather_tasks already sorts the tasks but the code ignores that
ordering. The fix is to make sure the sort order is preserved.

The rest is written by Sonnet (reviewed by me):

Tensor-parallel inference requires that every rank enqueues tasks in the
same order before running agree_on_tasks collectives. The old
implementation filtered from _maybe_queue:

self._queue.extend(task for task in self._maybe_queue if task in agreed)
self._maybe_queue = [task for task in self._maybe_queue if task in
different]

Because _maybe_queue is independently ordered per-rank (tasks arrive via
gRPC in whatever order the API server sends them), two concurrent
requests could produce different _maybe_queue orderings on rank 0 vs
rank 1. The filter then preserved those different orders into _queue, so
each rank started processing tasks in a different sequence. The next mlx
collective (all_reduce, all_gather, etc.) on rank 0 corresponded to a
different task than on rank 1 → permanent deadlock.

Fix: extend from agreed directly. mx_all_gather_tasks returns agreed as
a list sorted by task_id on all ranks, so every rank appends the same
sequence regardless of local arrival order.

Applies to both SequentialGenerator and BatchGenerator.

## Motivation

`agree_on_tasks` is called on every rank after accumulating new requests
in
`_maybe_queue`. Its job is to run an `all_gather` collective so all
ranks agree
on which tasks to promote to `_queue` before the next inference step.

The old implementation re-imposed **local arrival order** when extending
`_queue`:

```python
self._queue.extend(task for task in self._maybe_queue if task in agreed)
```

`mx_all_gather_tasks` already returns `agreed` sorted by `task_id` — the
same
deterministic order on every rank. But iterating `self._maybe_queue`
instead of
`agreed` discarded that sort and substituted the local gRPC arrival
order, which
differs per rank under concurrent load. Two concurrent requests arriving
in
`[A, B]` order on rank 0 and `[B, A]` on rank 1 caused the first MLX
collective
in the next step to hang permanently: each rank was executing a
different task's
collective and would never match.

## Changes

`SequentialGenerator.agree_on_tasks` and
`BatchGenerator.agree_on_tasks`:

```python
# Before
self._queue.extend(task for task in self._maybe_queue if task in agreed)
self._maybe_queue = [task for task in self._maybe_queue if task in different]

# After
self._queue.extend(agreed)          # preserves mx_all_gather_tasks sort order
self._maybe_queue = list(different) # already in local order; filter was redundant
```

## Why It Works

`mx_all_gather_tasks` (in `utils_mlx.py`) computes the agreed set then
sorts by
`task_id`:

```python
agreed = [local_tasks[tid] for tid in sorted(agreed_ids)]
```

Because `task_id` is a UUID and the sort is lexicographic, every rank
produces
the same `agreed` list regardless of local arrival order. Using `agreed`
directly
preserves this guarantee. The `different` list (tasks not yet seen on
all ranks)
is built by iterating `tasks` in local order, which is already correct.

## Test Plan

### Manual Testing

**Hardware:** 2× Mac Studio M3 Ultra 512 GB, Thunderbolt 5 direct
bridge,
`MlxJaccl` RDMA tensor-parallel (`moonshotai/Kimi-K2.6`, 595 GB INT4, 61
layers).

- Sent concurrent streaming requests; confirmed all complete without
deadlock.
- This hardware configuration (sub-millisecond inter-node latency) is
the most
likely to trigger the race, as requests from separate HTTP connections
can
reach rank 0 and rank 1 in opposite order before `agree_on_tasks` runs.

### Automated Testing

All existing tests pass: `pytest src -m "not slow"
--import-mode=importlib`
— 422/422 passed. The existing `test_event_ordering.py` covers the
`agree_on_tasks` call path with a mock that returns tasks in consistent
order;
the race requires real distributed hardware to reproduce
deterministically.
2026-05-06 12:24:58 +00:00
Evan Quineyandciaranbor dbcceaa50c Initialise _cancelled_tasks in ImageEngine (#2051)
we yielded nonsense chunks from engines; we didn't initialize the image
engine correctly. mostly rewrite of #2049

---------

Co-authored-by: ciaranbor <ciaranborourke-dev@proton.me>
2026-05-05 17:27:57 +01:00
Sam BradburyandSam Bradbury 9c6ff4ce95 feat: update rdma_ctl instructions (#1977)
## Motivation

The RDMA setup instructions were missing a step: after booting to
Recovery mode, users need to open Terminal from the Utilities menu
before they can run the `rdma_ctl` command. Without this step, users
following the instructions wouldn't know how to access a terminal in
Recovery mode. This step was already in the README just not in the UI
notifications.

## Changes

Added a missing instruction step — "Open Terminal from the Utilities
menu" — to three instances of the RDMA setup flow in
`dashboard/src/routes/+page.svelte`.

## Why It Works

N/A copy change only. 

## Test Plan

### Manual Testing
Hardware: MacBook Pro M4 Max 48GB

### Automated Testing
No automated tests affected; this is a UI copy change only.

Co-authored-by: Sam Bradbury <sam@consultbradbury.com>
2026-05-01 11:18:57 +00:00
ecohash-coandJordan Miller b26268dfaf fix(macos-app): disable URL response caching for cluster-state polling (#2005)
Fixes #2004.

`ClusterStateService` polls `/state` at 2 Hz via `URLSession.shared`,
which keeps an on-disk `URLCache` attached by default. Every polled
response body gets persisted under `~/Library/Caches/exolabs.EXO/`,
sustaining ~500–620 KB/sec of file-backed memory dirtied — far above
macOS's ~25 KB/sec per-process daily-average baseline. Six
microstackshot reports observed on a single Mac Studio M3 Ultra over
eight days, with one 15-hour run accumulating 34.36 GB of cache writes.

Heaviest stack on every diagnostic report (96–98% of samples):

```
_dispatch_workloop_worker_thread → _dispatch_block_async_invoke2 →
  __CFURLCache::CreateAndStoreCacheNode → write
```

Full diagnostic data and analysis in #2004.

## What changed

`ClusterStateService` now defaults to an ephemeral, non-caching
`URLSession` instead of `URLSession.shared`. Cluster-state responses are
time-sensitive and small; nothing benefits from being cached on disk.

```swift
private static func makeNonCachingSession() -> URLSession {
    let config = URLSessionConfiguration.ephemeral
    config.urlCache = nil
    config.requestCachePolicy = .reloadIgnoringLocalCacheData
    return URLSession(configuration: config)
}
```

The existing per-request `request.cachePolicy =
.reloadIgnoringLocalCacheData` calls are kept as defense in depth — they
only affect read behavior, but harmless to leave alongside the
session-level config.

## Scope

- **Behavioral**: none. Polled requests still go out at the same
cadence; responses still parse the same; no semantic change to any API
surface.
- **Test injection**: the `session:` parameter remains in `init`, so
tests can still inject a custom mock session unchanged.
- **`BugReportService` and other `URLSession.shared` callers**:
untouched. If maintainers prefer an app-wide URLCache disable instead,
happy to switch the approach (issue body has the alternative spelled
out).

## Verification

Verified locally that compiling EXO with this change produces a working
menubar app and `ClusterStateService` continues to fetch state
correctly. After ~30 min of idle polling, no new entries in
`/Library/Logs/DiagnosticReports/EXO_*.diag` and no growth in
`~/Library/Caches/exolabs.EXO/`.

## Test plan
- [ ] Build EXO from this branch on macOS 26.4
- [ ] Launch, let cluster state polling run for 30+ min
- [ ] Confirm no new microstackshot diagnostic reports
- [ ] Confirm `~/Library/Caches/exolabs.EXO/Cache.db*` does not grow

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Jordan Miller <jordan.d.miller@gmail.com>
2026-05-01 10:41:10 +00:00
ciaranbor 8dae3ecb9a A few targeted tweaks to address HF rate limits (#2009)
## Motivation

- exo bursts ~200 HF Hub-API requests on every cold start, blowing past
the anonymous 500-req/5-min budget.
- The existing retry loop catches 429 generically and gives up in ~3s —
well before HF's reset window.
- `file_meta` and `_download_file` had no 429 handling at all (became
`AssertionError`).
- Disk file-list cache was bypassed on every process restart.

## Changes

All in `src/exo/download/download_utils.py` + tests.

- Parse `t=` from HF's `RateLimit` header on 429; sleep `min(t, 300s) +
jitter`.
- Handle 429 at all three call sites (`_fetch_file_list`, `file_meta`,
`_download_file`).
- `n_attempts`: 3 → 5.
- Disk cache now primary across restarts (24h mtime TTL).
- `?recursive=true` instead of N+1 subdir walks.

## Why It Works

`t=<seconds>` is HF's "wait this long and you'll be unblocked" —
sleeping that long lets the window reset. Disk-cache-as-primary plus
recursive listing cuts cold-start Hub-API traffic by ~10×.

## Test Plan

### Manual Testing

MacBook Pro M1 Max. Tripped the real HF 429. Pre-fix: failed in 3.4s.
Post-fix: slept (HF returned `t=158`) and recovered.

### Automated Testing

- New `test_rate_limit_handling.py` (19 tests) — header parsing,
retry-loop behaviour, plus HTTP-level coverage that mocks aiohttp to
return a 429 and asserts each call site raises
`HuggingFaceRateLimitError(retry_after=52.0)`.
- New `TestFileListCacheTTL` in `test_offline_mode.py` — fresh cache
hits, stale cache refetches.
- 421 tests pass; basedpyright / ruff / nix fmt clean.
2026-04-30 18:06:15 +00:00
Alex CheemaandClaude Opus 4.7 fb12b403ea fix(app): tighten Share Bug Report prompt layout (#2008)
## Summary

Follow-ups to #2003 based on feedback that the Share Bug Report window
felt visually weighty: too much padding above and below, and a
description editor that invited an essay rather than a one-liner.

## Changes (one file)

`app/EXO/EXO/Views/BugReportWindowController.swift`:

- **Auto-size the window to its content.** Switched from `NSHostingView`
+ fixed `contentRect: 480x380` + SwiftUI `frame(minHeight: 320)` to
`NSHostingController` with `sizingOptions = [.preferredContentSize,
.minSize]`. The fixed-min combo was centering the form in dead vertical
space.
- **Smaller, lower-pressure editor.** Field is now labeled `Description
(optional)` with a placeholder hint (`What were you doing when it
broke?`) inside the editor. Editor height fixed at 72pt (was 120pt min).
Replaced the long lead-in paragraph and headline with a single one-line
caption between field and buttons: `Diagnostic logs will be uploaded
with your report.`
- **Tighter spacing.** Outer padding 20 -> 16, root spacing 16 -> 12,
prompting-section spacing 12 -> 8.
- **Remove em dash from copy.**

`BugReportService` and the menu wiring are unchanged.

## Test plan

- [ ] Click `Share Bug Report...` from the menu bar.
- [ ] The window opens centered and sized to its content (no big empty
bands top/bottom).
- [ ] Description editor is visibly compact, with the placeholder hint
showing when empty.
- [ ] The optional-ness is conveyed by the field label (no separate help
paragraph).
- [ ] Caption `Diagnostic logs will be uploaded with your report.`
appears in `.caption` style under the editor, above the buttons.
- [ ] Resize the window: persists across re-opens (frame autosave still
works).
- [ ] Send/Cancel/Try Again/Done flows behave the same as before.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-30 15:10:26 +01:00
Alex CheemaandClaude Opus 4.7 1606e63816 feat(app): open Share Bug Report in a dedicated window (#2003)
## Summary

- Adds a top-level **Share Bug Report…** menu item to the macOS popover
(between *Check for Updates* and *Quit*) with SF Symbol `ladybug`.
- Clicking it opens a dedicated resizable `NSWindow` ("Send a Bug
Report") that hosts the prompting / sending / success / failure flow.
- Removes the description-less duplicate from Settings → Debug Info, and
the dead `debugSection` it nominally lived behind.

## Why

PR #1959 added a user-description prompt to the bug-report flow, but its
trigger lived inside `ContentView.debugSection` — a view that's defined
but never rendered in the body. The path users actually hit was
`SettingsView.sendBugReportButton`, which called
`BugReportService.sendReport(isManual: true)` without ever passing
`userDescription`. So the description prompt was unreachable in the
built app.

## Approach

Per Apple HIG, an action that requires further input before completing
should open a dialog, not transform the menu inline. So:

- Add a top-level menu entry that ends in `…` (HIG: ellipsis indicates
"further input required").
- Move the prompting/sending/success/failure state machine into a
standalone `BugReportWindowController` modeled after the existing
`SettingsWindowController`.
- Single-instance window with frame-autosave name, sensible
`contentMinSize`, resizable, native button layout (`.cancelAction` /
`.defaultAction` keyboard shortcuts), light/dark-mode-correct
`.textBackgroundColor` and `.separatorColor`.
- Auto-focus the description field on open. `Try Again` from failure,
`Open GitHub Issue` + `Done` from success.

## Files

- `app/EXO/EXO/Views/BugReportWindowController.swift` (new) — controller
+ view.
- `app/EXO/EXO/EXOApp.swift` — wire `BugReportWindowController` as a
`@StateObject` and inject as environment object.
- `app/EXO/EXO/ContentView.swift` — replace inline state machine with
menu item that calls `bugReportWindowController.open()`. Remove
now-unused state, helpers, and dead `debugSection`.
- `app/EXO/EXO/Views/SettingsView.swift` — remove duplicate
`sendBugReportButton`, `sendBugReport()`, and related `@State`. Section
"Debug Info" keeps Thunderbolt / interface / RDMA info.

`BugReportService` is unchanged.

## Test plan

- [ ] Open the menu-bar popover → confirm **Share Bug Report…** appears
between *Check for Updates* and *Quit*, with a ladybug icon.
- [ ] Click it → a window titled "Send a Bug Report" appears, centered,
with the description editor focused.
- [ ] Resize the window → size persists across re-opens (frame
autosave).
- [ ] Type a description, press Return → upload succeeds, success card
with **Open GitHub Issue** + **Done** appears.
- [ ] Click **Open GitHub Issue** → browser opens with the description
pre-filled into the issue template.
- [ ] Send with empty description → upload still succeeds.
- [ ] Press Esc from the prompting state → window closes.
- [ ] On failure (e.g., offline) → error card with **Try Again** +
**Close** appears; Try Again returns to the editor with the description
preserved.
- [ ] Open the Settings window → Debug Info section is unchanged except
the Send Bug Report button is gone.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-30 13:05:07 +01:00
667a3bb0e5 feat: keep-models option when uninstalling EXO (#1997)
## Summary

- Adds a **Keep downloaded models (~/.exo/models)** checkbox to the
macOS uninstall confirmation dialog (Settings → Advanced → Danger Zone).
The full `~/.exo` directory is now removed on uninstall by default; if
the checkbox is checked, `~/.exo/models` is preserved.
- The standalone `app/EXO/uninstall-exo.sh` gains a matching
`--keep-models` flag and the same `~/.exo` cleanup so GUI and CLI flows
stay in sync. Resolves the user home via `$SUDO_USER` since the script
runs under `sudo`.

Previously, "Uninstall EXO" only cleaned up system-level components
(LaunchDaemon, network location, logs, app bundle) and left the entire
`~/.exo` directory behind. Now uninstalling actually removes EXO's user
data, with a one-click opt-out for the (potentially many GB) of
downloaded models.

![Uninstall dialog with new
checkbox](https://raw.githubusercontent.com/exo-explore/exo/703b7fbbf13441217ad2903bb199f07e92af4490/uninstall-dialog.png)

> Note: the rendered icon in the screenshot above is the generic system
folder icon because it was captured from a small standalone Swift binary
(no app bundle / icon resource). When triggered from the actual EXO.app,
the EXO app icon is shown.

## Test plan

- [ ] Build EXO.app locally; open Settings → Advanced → Danger Zone →
Uninstall EXO; confirm the new "Keep downloaded models (~/.exo/models)"
checkbox is present and unchecked by default.
- [ ] Uninstall with the checkbox **checked** → `~/.exo/models/`
survives, all other entries under `~/.exo` are gone, system components
removed, app moved to Trash.
- [ ] Uninstall with the checkbox **unchecked** → `~/.exo` is fully
removed.
- [ ] `sudo app/EXO/uninstall-exo.sh --keep-models` → `~/.exo/models/`
is preserved, the rest of `~/.exo` is removed.
- [ ] `sudo app/EXO/uninstall-exo.sh` (no flag) → `~/.exo` is fully
removed.
- [ ] `app/EXO/uninstall-exo.sh --help` prints usage and exits 0;
unknown args exit 2 with a usage hint.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-authored-by: Evan <evanev7@gmail.com>
2026-04-28 01:06:02 +00:00
Evan Quiney c80b10c013 implement engine abstraction for mlx and mflux (#2000)
refactor for future versions.
2026-04-28 00:58:17 +00:00
Alex CheemaandClaude Opus 4.7 18ffe1df23 fix: uninstall-exo.sh removes both current and legacy bridge scripts (#1998)
## Summary

The standalone `app/EXO/uninstall-exo.sh` only knew about the legacy
filename `disable_bridge_enable_dhcp.sh`. On machines installed with
newer EXO versions, the current `/Library/Application
Support/EXO/disable_bridge.sh` was left behind, and the script then
reported `EXO support directory not empty, leaving in place`.

This PR makes the script try both filenames, removing whichever ones
exist. Tolerates **either**, **both**, or **neither** being present
without erroring.

The Swift `NetworkSetupHelper.makeUninstallScript()` already handles
both paths correctly, so the GUI uninstall flow is unaffected — this is
a script-only fix.

Caught while running an end-to-end uninstall on a real machine for
#1997.

## Test plan

Verified the new block in isolation against all four states:

- [x] both `disable_bridge.sh` and `disable_bridge_enable_dhcp.sh`
present → both removed
- [x] only `disable_bridge.sh` present → removed cleanly
- [x] only `disable_bridge_enable_dhcp.sh` present → removed cleanly
(legacy install)
- [x] neither present → prints the existing "already removed?" warning,
exits 0

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 00:28:12 +00:00
rltakashige f0d1371d89 MLX P/D (#1993)
## Motivation

MLX only prefill server for Apple Silicon
2026-04-28 00:12:42 +00:00
5d10188d3a fix: route by in-flight tasks only — completed tasks were skewing load balance (#1989)
The load balancer counted ALL tasks (Complete, Cancelled, TimedOut,
Failed) instead of only Pending/Running ones. With 138 accumulated tasks
and only 7 active, routing decisions were based on historical
distribution, causing one node to appear permanently 'busier' and
starving the other of work.

Co-authored-by: Adam Durham <adam@example.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-27 16:03:12 +00:00
ciaranbor f2a0db4e23 Extend bench/eval tooling (#1905)
## Motivation

Extend bench/eval tooling with robustness features, streaming support,
and align model configs with vllm eval for reproducible comparisons.

## Changes

- **exo_eval**: Checkpoint/resume (JSONL), instance health monitoring +
early abort, `top_k`/`min_p`/`enable_thinking` params, LCB
`--release-version`/`--offset`
- **exo_bench**: Streaming SSE (`--stream`), Kimi tokenizer fix for
transformers 5.x
- **Both tools**: Auto-detect running instances instead of requiring
`--skip-instance-setup`; `--fresh-instance` to override
- **harness**: SSE streaming client, `find_existing_instance()` shared
helper, removed download timeout, settle-timeout default 0→7200s
- **models.toml**: Added `enable_thinking`, aligned `max_tokens`/temps
with vllm, added new models
- **API**: Streaming SSE for `/bench/chat/completions`

## Why It Works

- Checkpoint/resume uses append-only JSONL + skip-on-load so interrupted
evals resume without re-running completed questions
- Health monitoring races an `asyncio.Event` against API calls for fast
abort when the instance dies
- Auto-detection queries `/state` for existing instances matching the
model ID before attempting placement
- Streaming reuses the existing `generate_chat_stream` infrastructure
from the regular chat endpoint
2026-04-27 16:53:43 +01:00
rltakashigeandEvan 37f6f4f6c2 Add DeepSeek V4 Flash/Pro (#1978)
Wait for upstream merge.

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-04-27 15:20:50 +01:00
Adam DurhamandAdam Durham 48a922fd5c fix: map presence_penalty and frequency_penalty from ChatCompletionRequest (#1991)
Upstream PR #1947 added `presence_penalty` and `frequency_penalty` to
`TextGenerationTaskParams` and the mlx-lm generator call sites, but
missed wiring them up in the API adapter so they were silently dropped
from incoming requests. This fixes the API mapping.

Co-authored-by: Adam Durham <adam@example.com>
2026-04-27 08:58:59 +00:00
rltakashige fd707de30b Add more model cards (#1970) 2026-04-23 15:28:40 +01:00
Alex CheemaandClaude Opus 4.7 45248c5c85 chore(app): hardcode bug report presigned-URL endpoint (#1971)
## Motivation

The bug-report presigned-URL endpoint
(`https://reports.exolabs.net/presigned-urls`) was injected at build
time from the `EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT` GitHub Actions
secret into `Info.plist`, then read at runtime by `BugReportService`. It
isn't actually a secret — the POST body is just `{"keys":[...]}` with no
credential (see `app/EXO/EXO/Services/BugReportService.swift:136-142`),
abuse prevention lives server-side on the lambda, and the URL is already
visible in every publicly-distributed DMG's `Info.plist`. Treating it as
a repo secret added plumbing with no security benefit and broke local
dev builds — hitting **Send Bug Report** on an uncustomised `just
build-app` raised "Bug report endpoint is invalid".

## Changes

- `app/EXO/EXO/Info.plist`: replace
`$(EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT)` with the literal URL.
- `.github/workflows/build-app.yml`: drop the
`EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT` job-level env var and the
xcodebuild build-setting passthrough. No other workflow changes.

Swift code is unchanged — `BugReportService` still reads from
`Info.plist`, which leaves an escape hatch if anyone ever needs to
override via `xcodebuild EXOBugReportPresignedUrlEndpoint=...` without
recompiling.

Follow-up: the `EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT` repo secret can
now be deleted in the GitHub Actions settings UI.

## Why It Works

`Info.plist` variable substitution turns `$(FOO)` into whatever build
setting `FOO` resolves to. CI was setting `FOO` via xcodebuild; local
dev wasn't, so the key resolved to an empty string, which
`BugReportService.fetchPresignedUploadUrls` rejects via the
`!trimmedEndpointString.isEmpty` guard at `BugReportService.swift:131`.
Hardcoding the literal string removes the substitution entirely, so
every build — local or CI — gets the right value.

## Test Plan

### Manual Testing
<!-- Hardware: MacBook Pro (macOS app build via Xcode) -->
- `just build-app` with no extra env vars (reproduces the failure path
on `main`).
- `/usr/libexec/PlistBuddy -c "Print :EXOBugReportPresignedUrlEndpoint"
app/EXO/build/Build/Products/Debug/EXO.app/Contents/Info.plist` →
returns `https://reports.exolabs.net/presigned-urls` (was empty before
this change).
- `open app/EXO/build/Build/Products/Debug/EXO.app` → menubar → **Debug
Info** → **Send Bug Report** → type a description → **Send** → upload
succeeds and the **Create GitHub Issue** button appears (was failing
with "Bug report endpoint is invalid" before).
- Cross-check on the Slack side that the uploaded `report.json` lands
under `reports/YYYY/MM/DD/<ts>/` as before.

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
- No new tests. This is a single-string change to `Info.plist` plus a
workflow cleanup. `nix flake check` in CI verifies formatting/lint for
the rest of the tree.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-23 14:08:16 +00:00
rltakashige 290e3fd927 Keep image cache fresh (temporary fix) (#1961)
## Motivation

When a new node joins, it might not have the cache.



Caveat: 
This is potentially fallible if a new node joins and updates real
topology, but the API topology hasn't caught up with this fact and the
user queues up a new text generation. In practice, there is only a split
second where this is the case, and this is only for users of the
dashboard interface. We should fix this properly after the release.
2026-04-23 11:39:36 +01:00
rltakashige 3894cf134e Fix Gemma 4 E2B TP + DeepSeek V32 thinking parsing (#1967) 2026-04-23 01:50:39 +00:00
Alex CheemaandClaude Opus 4.7 8993ccaf09 feat(app): add friendly context message to bug report prompt (#1959)
## Motivation

When a user clicks **Send Bug Report** in the macOS app, we already give
them the option to add more context via an optional text field. But the
current prompt is just a terse label — `"What's the issue? (optional)"`
— which doesn't tell the user why bothering to fill it in matters. A
friendly one-line explanation increases the chance they'll describe what
went wrong, which is the single most useful signal when we triage the
resulting diagnostic bundle.

## Changes

- `app/EXO/EXO/ContentView.swift`: In the `.prompting` phase of
`sendBugReportButton`, replace the single label with a two-line
hierarchy:
  - Primary: `Tell us what went wrong (optional)`
- Helper: `A quick description of what you were doing and what happened
helps us track down the bug for you.`
- The helper uses `.caption2` + `.secondary` + `.opacity(0.8)` +
`.fixedSize(horizontal: false, vertical: true)` so it stays visually
subordinate and wraps cleanly inside the 340pt popover.

No changes to `BugReportService`, the `user_description` payload, or any
other flow.

## Why It Works

The optional description is already plumbed end-to-end (text editor →
`bugReportUserDescription` state → `BugReportService.sendReport(...,
userDescription:)` → `report.json`'s `user_description` field → GitHub
issue pre-fill). The only gap was user-facing motivation, so this is
purely a copy/layout tweak inside the existing `.prompting` case — no
new state, bindings, or service changes.

## Test Plan

### Manual Testing
<!-- Hardware: MacBook Pro (macOS app build via Xcode) -->
- Build the macOS app in Xcode (`app/EXO/EXO.xcodeproj`) and launch it.
- Open the menubar popover → expand **Debug Info** → click **Send Bug
Report**.
- Verify the new primary label and helper sentence both appear above the
text editor and wrap cleanly within the popover width.
- Leave the field empty → click **Send** → upload should succeed (no
`user_description` in payload, same as before).
- Fill in a description → click **Send** → upload succeeds and the
success card with **Create GitHub Issue** appears; clicking it opens
GitHub with the description pre-filled.
- Click **Cancel** from the prompting state → returns to idle.

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
- No new automated tests. This is a SwiftUI copy/layout change; existing
`EXOTests` are smoke-level and don't cover `ContentView` view bodies,
and UI snapshot tests aren't worth adding for a two-line copy tweak.
- `nix fmt` reports 0 files changed after the edit; `nix flake check` in
CI will verify formatting/lint for the rest of the tree.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-22 17:39:36 +00:00
Nadeem Hilal Wani 4939fbe995 feat(dashboard): add Pi integration tab (#1925)
## Summary
Adds a new **Pi** tab to the Integrations page (`/#/integrations`)
alongside the existing Claude Code, OpenCode, Codex, OpenClaw, Open
WebUI, n8n, and Firefox tabs.

[pi](https://pi.dev) (`@mariozechner/pi-coding-agent`) is a terminal
coding agent that supports custom OpenAI-compatible providers via
`~/.pi/agent/models.json`.
This tab gives users a copy-pasteable config to wire pi up to their exo
cluster.

## What's in the tab
- **Model selector** (shown when multiple models are running) — picks
the default model for the generated shell command.
- **Models Config card** — generates `~/.pi/agent/models.json`
registering `exo` as a custom provider:
     - `baseUrl` → `<apiUrl>/v1`
     - `api` → `openai-completions`
     - `apiKey` → `"exo"` (placeholder; exo ignores it)
- `compat.supportsDeveloperRole: false` and
`compat.supportsReasoningEffort: false`, per pi docs recommendation for
local OpenAI-compatible servers
- Auto-populates every running model with `id`, `contextWindow` (from
`/v1/models`), and `input: ["text", "image"]` for vision-capable models
- **Shell Command card** — `pi --provider exo --model <model>` for quick
launch.

The tab gracefully falls back to `your-model-id` when no models are
running, matching the behavior of the other tabs.

   ## Usage

   1. `npm install -g @mariozechner/pi-coding-agent`
   2. Paste the generated config into `~/.pi/agent/models.json`
3. Run `pi` and pick an exo model via `/model` — or run the shell
command directly

   ## Changes

- `dashboard/src/routes/integrations/+page.svelte` — adds `"Pi"` to the
`tabs` tuple, `piModel` state, `piModelsJson` + `piShellCommand`
derivations, and the tab content block.

   Single-file, scoped change — no backend or type changes.

   ## Testing

   - `cd dashboard && npm run build` —  builds cleanly
   - `svelte-check` on the edited file — no new errors
- Manually verified the tab renders, the model selector updates the
generated JSON, and the config reflects `/v1/models` capabilities
(vision → `input: ["text","image"]`,
 `context_length` → `contextWindow`).

   ## Screenshots

<img width="1545" height="1236" alt="pi-tab"
src="https://github.com/user-attachments/assets/38aa179f-4ed9-4a1e-9783-d3baa7738263"
/>
2026-04-22 17:29:47 +00:00
rltakashige 73782ecc65 Fix event mutation causing indexed vs event mismatch (#1964)
Fixes small issue with #1957
2026-04-22 16:12:24 +00:00
rltakashige f6e418ed23 Cleanup on #1952 (#1960) 2026-04-22 17:05:49 +01:00
rltakashigeandEvan 7a312a177b Misc fixes: upstream JACCL all_sum, API, etc. + Add Kimi K2.6 (#1952)
## Motivation

This fixes a bunch of observed model quality issues introduced upstream
in JACCL, as well as API issues and prefix cache calculation.


## Test Plan

### Manual Testing
Tested a bunch

### Automated Testing
Added a test, automated eval tool calls on Kimi K2.6, Minimax M2.7, GPT
OSS and Qwen3.6 models.

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-04-22 15:43:27 +00:00
Evan Quiney 0a549f8846 remove layer loading callback (#1890)
first part of modularising the backend is simplifying some of the
control flow. more tbd.
2026-04-22 14:03:31 +01:00
Evan Quiney df332035ef swap camelcasemodels for frozenmodels globally (#1957) 2026-04-22 11:49:25 +00:00
ciaranbor af673845d3 Ignore HF remote repo changes (temporary fix) (#1958)
## Motivation

Fixes #1918. Downloaded model status reverts from "completed" to
"pending" during each download scan. Reproduced with `zai-org/GLM-5.1`.

## Changes

- `coordinator.py`: In the periodic rescan, don't downgrade
already-completed models; fall back to `resolve_existing_model()`
(safetensors weight check) when per-file size check reports incomplete
- New `test_download_status_not_lost.py`: 3 regression tests

## Why It Works

The rescan compares local file sizes against HF's `main` revision. When
HF updates text files (README, jinja, etc.), remote sizes change but
local files still match the old revision — causing a false "incomplete".
The fix uses the safetensors weight check as ground truth instead.

Long-term: pin the downloaded revision SHA rather than always checking
against `main`.

## Test Plan

### Manual Testing

- Mac Studio M3 Ultra with GLM-5.1 downloaded (natural reproduction of
the issue)
- Confirmed GLM-5.1 stays `DownloadCompleted` through multiple rescan
cycles

### Automated Testing

- 3 new tests: completed-not-downgraded, fallback-to-resolve,
genuinely-incomplete-stays-pending
2026-04-22 11:11:01 +01:00
ciaranbor 49670c8624 Handle missing total_size in safetensors index files (#1956)
## Motivation

Image models fail to load after a mid-download instance deletion and
recreation. The system skips the download and crashes with
`FileNotFoundError: No safetensors files found in .../vae`.

## Changes

- Make `ModelSafetensorsIndexMetadata.total_size` optional (`PositiveInt
| None = None`)
- Add null guard in `fetch_safetensors_size`
- Add regression test

## Why It Works

Exolabs quantized image models have safetensors index files with mflux
metadata (`quantization_level`, `mflux_version`) but no `total_size`.
The required `PositiveInt` field caused Pydantic validation to fail,
which was silently swallowed by `except Exception: continue` in
`_scan_model_directory`. This skipped all weight map checks, making
incomplete models appear complete.

## Test Plan

### Manual Testing

- Hardware: Mac Studio
- Before: `CreateRunner → LoadModel` (crash). After: `CreateRunner →
DownloadModel` (correct).

### Automated Testing

- `test_safetensors_index.py`: 3 cases covering missing, valid, and null
metadata
2026-04-21 16:39:14 +01:00
rltakashige fcc3718efb Add sampling defaults (#1947)
## Motivation

Model quality issues

### Manual Testing
TODO
2026-04-21 06:45:33 +00:00
rltakashige 8ccfd7fcb6 Fix some misc build issues (#1948)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-04-21 07:41:07 +01:00
Evan Quiney 7b416155de bump uv lock for linux builds (#1942)
followup from a rebase issue in #1874
2026-04-20 11:41:40 +00:00
Evan Quiney 93a24748e6 mlx cuda 13 (dgx spark) support (#1874) 2026-04-20 11:49:12 +01:00
Evan Quiney e32829e51d chore: bump versions in line with release (#1941) 2026-04-20 09:39:28 +00:00
rltakashige 09e894dd52 Fix vision models on M5 Pro/Max MacBooks (#1927)
## Motivation

Vision models don't understand images on M5 series MacBooks. The
upstream NAX addmm fix (https://github.com/ml-explore/mlx/pull/3422)
fixes this.

## Why It Works
Same conclusion I came to when I was debugging the issue on an M5 Max.
It works after this fix.

## Test Plan

### Manual Testing
Works for Qwen3.5 27B
2026-04-19 12:55:20 +01:00
rltakashige bf8aacfd41 Improve build CI (#1920)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-04-17 20:55:15 +00:00
af9e847edb fix: force gc + clear_cache after KV prefix cache eviction (#1832)
## Summary
- After `KVPrefixCache` evicts LRU entries, the MLX Metal buffers stay
allocated until Python's GC runs
- This leaks ~3-4 GB between long-context requests, reducing the
effective context ceiling for back-to-back requests
- Adding `gc.collect()` + `mx.clear_cache()` after eviction frees Metal
buffers promptly

## Test plan
- [x] Measured on 2-node PP cluster with Qwen3.5-397B-A17B-4bit at 63K
context
- [x] Before: 108.88 GB retained after eviction (3.78 GB above baseline)
- [x] After: 105.48 GB retained after eviction (0.38 GB above baseline —
draft model KV + minor overhead)
- [x] `gc.collect()` adds ~2-3ms latency, runs once per eviction cycle
(not per token)
- [ ] Verify with `uv run pytest`

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Adam Durham <adam@example.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: rltakashige <rl.takashige@gmail.com>
2026-04-17 17:57:32 +00:00
mlpy0 01598960bd Add model card for Qwen3.6-35B-A3B-8bit (#1917)
Adds the 8bit variant missing from #1907 — the safetensors index is now
live on HF.

- `mlx-community/Qwen3.6-35B-A3B-8bit` (~35 GB)

Architectural fields match the existing 4bit/5bit/bf16 cards.
`storage_size.in_bytes` is taken from `metadata.total_size` of the
upstream `model.safetensors.index.json`.
2026-04-17 10:06:23 +00:00
63b8e64715 Add model cards for Qwen3.6-35B-A3B variants (#1907)
## Motivation

`mlx-community` has just published the new **Qwen3.6-35B-A3B**
multimodal MoE family on HuggingFace. Without static model cards exo
doesn't surface these models in the dashboard picker or match its
placement / prefill logic, so users can't one-click launch them. This PR
adds cards for the three quants whose safetensors indexes are already
live on HF (4bit / 5bit / bf16).

## Changes

Three new TOML files in `resources/inference_model_cards/`:

- `mlx-community--Qwen3.6-35B-A3B-4bit.toml` (~19 GB)
- `mlx-community--Qwen3.6-35B-A3B-5bit.toml` (~23 GB)
- `mlx-community--Qwen3.6-35B-A3B-bf16.toml` (~65 GB)

All three share the same architectural fields (`n_layers = 40`,
`hidden_size = 2048`, `num_key_value_heads = 2`, `context_length =
262144`, capabilities `text, thinking, thinking_toggle, vision`,
`base_model = "Qwen3.6 35B A3B"`) — only `model_id`, `quantization`, and
`storage_size.in_bytes` differ between variants.

## Why It Works

- Qwen3.6-35B-A3B reuses the `qwen3_5_moe` architecture
(`Qwen3_5MoeForConditionalGeneration`) — the same one already wired into
exo's MLX runner at `src/exo/worker/engines/mlx/auto_parallel.py:47` via
`Qwen3_5MoeModel`. The architectural fields are taken verbatim from the
HF `config.json.text_config` and match the existing `Qwen3.5-35B-A3B-*`
cards.
- Storage sizes are the exact `metadata.total_size` read from each
variant's `model.safetensors.index.json` on HF, so download progress and
cluster-memory-fit checks are accurate.
- Vision support is flagged in `capabilities`; the `[vision]` block is
auto-detected by `ModelCard._autodetect_vision` from the upstream
`config.json`, so no hand-written vision config is required.
- The card loader (`_refresh_card_cache` in
`src/exo/shared/models/model_cards.py`) globs every `.toml` in
`resources/inference_model_cards/` on startup, so nothing else needs to
change — the `/models` endpoint and the dashboard picker pick them up
automatically.

The `mxfp4` / `mxfp8` / `nvfp4` variants are still uploading upstream
(index JSONs currently 404) and can be added in a follow-up PR once HF
completes.

## Test Plan

### Manual Testing

Hardware: MacBook Pro M4 Max, 48 GB unified memory.

- Built the dashboard, ran `uv run exo`, waited for the API to come up
on `http://localhost:52415`.
- `curl -s http://localhost:52415/models` returns the three new model
ids (`mlx-community/Qwen3.6-35B-A3B-{4bit,5bit,bf16}`) alongside
existing models.
- Opened the dashboard, clicked SELECT MODEL, typed "Qwen3.6" into the
search box. A single **"Qwen3.6 35B A3B"** group appears showing `3
variants (19GB-65GB)`. Expanding it lists the `4bit` / `5bit` / `bf16`
quants with sizes `19GB` / `23GB` / `65GB`, exactly as expected:

![Qwen3.6 35B A3B in model
picker](https://gist.githubusercontent.com/AlexCheema/68c2c02da9450b44968e6b0e0b1d255e/raw/127119f70382353c65a847188e5a2c9013db68d2/qwen36-picker.png)

- Programmatically loaded each TOML via `ModelCard.load_from_path(...)`
and confirmed the parsed fields (layers / hidden / KV heads / context /
quant / base_model / caps / bytes) match what's written in the files.

### Automated Testing

No code paths were touched — these are pure TOML data files that plug
into the existing model-card loader. The existing pytest suite covers
TOML parsing and card serving; adding new TOMLs doesn't require new test
scaffolding. `uv run ruff check` and `nix fmt` are clean.

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Ryuichi Leo Takashige <rl.takashige@gmail.com>
2026-04-16 23:25:26 +01:00
rltakashige 28c797846a Update mlx and mlx lm to latest (#1906)
Just bumping to the very latest upstream versions.
2026-04-16 10:59:33 +00:00
rltakashige 058bb08261 Allow copying on dashboard even on HTTP (#1902)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-04-15 23:30:12 +01:00
rltakashige 3eead80238 Better environment variables in MacOS app (#1901)
## Motivation

Closes #1858 

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-04-15 20:14:52 +00:00
rltakashige 87329c80ef Add usage stats to tool calls and handle multiple tool calls correctly (#1899)
## Motivation

Tool calls are usually not end tokens, so they didn't have usage stats.
2026-04-15 19:40:00 +01:00
rltakashige 8cdc833892 Drain tokens silently skipped in thinking parsing (#1898)
## Motivation
Closes #1882
2026-04-15 14:23:07 +00:00
rltakashige 2cd66ae4cf Fix out of order event idx causing fatal crashes (#1894)
## Motivation

<img width="828" height="373" alt="Screenshot 2026-04-14 at 22 56 52"
src="https://github.com/user-attachments/assets/f8f48c1d-68c5-4acc-a6de-9d180672da9d"
/>

if is_new_master=True, _elect_loop creates a new EventRouter before the
worker has receivers. Then, event router runs _run_ext_in and
buf.drain_indexed() will pick off events, even though
self.internal_outbound is not populated fully.

Finally, when the worker does try requesting events, the next event it
receives is not the first event, meaning the worker crashes.

## Changes

Start the event router after all the receivers are registered

## Why It Works

self.internal_outbound is populated before the loop begins.

## Test Plan

### Manual Testing
No more crashes observed in testing (it's actually quite easy to
reproduce the issue if you have one node with this fix but the other
node on main).

I'm convinced this is a fix, at least.
2026-04-15 08:46:02 +00:00
rltakashige 2ecefa0cfe Fix Qwen3-VL and autodetect vision config (#1893)
## Motivation

Qwen3 VL TP doesn't work atm, and vision is not behaving.

## Test Plan

### Manual Testing
Works now.
2026-04-14 23:05:55 +01:00
rltakashige b8eaf707a8 Add gemma 4 tensor parallelism (#1891) 2026-04-14 20:31:59 +01:00
rltakashige 8d81811b89 Try harder to clean up processes nicely (#1889)
## Motivation

Model loading is actually quite reliable now. No need to kill if you
have a slow SSD or it's a massive model; the user can shut the instance
down if necessary.

This was a major cause of signal=9 issues although not the only one (can
happen during inference too?).
The reason signal=9 is so bad is that RDMA will no longer work until
restart if this ever happens.

## Changes

- no more model load timeout
- no more crazy sigkills
- try harder to clean up processes on model shutdown

## Test Plan

### Manual Testing
Tested with some RDMA instances
2026-04-14 16:37:49 +01:00
rltakashige f2709dcde6 Add prefix cache flag to exo bench (#1888)
## Motivation
For using Exo-Bench extensively, there are many cases that we could use
prefix caching to speed up the benchmarks, especially when the focus is
on the token generation.

At the same time, it's very clear that prefix caching decode tokens is
not very useful in most current scenarios. Surprisingly, even for
non-thinking models, the chat template means that a continued
conversation will be formatted such that the existing cache is not
effective.

We already (slightly accidentally) do this for the batch generator - we
should do it for the sequential generator too.

## Changes

We can now speed up exo bench by having a use prefix caching flag. Of
course, for most accurate pp results, it is better to not have it, but
this speeds up tg and large benchmarking significantly.
Updated methodology to match

## Test Plan

### Manual Testing
Tested on many configurations that the difference in results is
negligible, even with multiple --pp options.
2026-04-14 11:12:58 +01:00
ciaranbor 77ffe039b3 Complete responses api usage response field (#1885)
## Motivation

The Responses API usage response was missing `input_tokens_details` and
`output_tokens_details`. The chat completions API already reports these.

## Changes

- Added `InputTokensDetails` (`cached_tokens`) and `OutputTokensDetails`
(`reasoning_tokens`) to `ResponseUsage`
- Extracted shared `_build_response_usage()` helper for both streaming
and non-streaming paths

## Test Plan

### Manual Testing

4-node cluster, `Qwen3-30B-A3B-4bit` — verified both detail objects
present with correct values in streaming and non-streaming responses.

### Automated Testing

13 tests in `test_openai_responses_api.py`.
2026-04-13 17:38:33 +00:00
rltakashige 3f0df404a5 Reduce memory consumption by adding Flash Attention to Qwen3.5 and Gemma 4, and fix RotatingKVCache prefix cache memory leak (#1886)
## Motivation

Part 1 of many memory improvements.

## Changes
As written in the title

## Test Plan

### Manual Testing
Gemma 4 26B cache reduced from 54GB -> 10GB per 100k tokens, Qwen3.5 35B
A3B cache reduced from 21GB every 100000 tokens to 7GB.
2026-04-13 18:32:17 +01:00
Evan Quiney 9b381f7bfe bump and simplify flake (#1866)
seems like stablepkgs swiftfmt works now! also bump macmon to 0.7
2026-04-13 15:45:17 +00:00
Alex Cheema d2f67b5d10 dashboard: group Gemma under Google with proper logo (#1883)
## Motivation

In the dashboard model picker sidebar, the Gemma 4 models were showing
up under a "Gemma" family with the generic fallback tick/checkmark icon
(the default case in `FamilyLogos.svelte`), since no dedicated logo
branch existed for `family === "gemma"`. Every other vendor (Meta,
NVIDIA, OpenAI, DeepSeek, Qwen, …) has its own brand mark.

Gemma is Google's model family, so it should live under a **Google**
bucket that future Google-authored models can join, and it should render
with a proper Google logo in the same style as its neighbors.

## Changes

- `dashboard/src/lib/components/FamilyLogos.svelte`: added a `family ===
"google"` branch rendering a monochrome Google "G" as a single `<path>`
inside the shared `24×24` viewBox with `fill="currentColor"`, matching
the other vendor logos.
- `dashboard/src/lib/components/FamilySidebar.svelte`: added `google:
"Google"` to the `familyNames` display map.
- `dashboard/src/lib/components/ModelPickerModal.svelte`: inserted
`"google"` into the `familyOrder` array (next to `"llama"`) so the
vendor has a deterministic sort position.
- `resources/inference_model_cards/mlx-community--gemma-4-*.toml` (16
files): changed `family = "gemma"` → `family = "google"`. `base_model =
"Gemma 4 …"` is unchanged, so the model titles still read "Gemma".

## Why It Works

The sidebar builds its family list from whatever values appear in
`model.family` across the loaded model cards (`ModelPickerModal.svelte`
`uniqueFamilies`). Renaming the family string on the 16 Gemma cards from
`"gemma"` to `"google"` collapses them into a single "Google" bucket,
and the new logo branch + display-name map entry gives that bucket a
real brand mark and label. All other logos share the same `w-6 h-6 /
viewBox="0 0 24 24" / fill="currentColor"` shape, so inheriting
`text-exo-yellow` / `text-white/50` just works.

## Test Plan

### Manual Testing
<!-- Hardware: MacBook Pro M3 Max -->
- `cd dashboard && npm install && npm run build` — dashboard builds
cleanly.
- `uv run exo`, opened `http://localhost:52415`, clicked **SELECT
MODEL**:
- sidebar shows a **Google** entry with a monochrome Google "G" logo in
the same style as Meta / NVIDIA / etc.
  - old "Gemma" entry with the generic tick is gone.
- clicking **Google** filters to the Gemma 4 variants (e2b / e4b / 26B
A4B / 31B).
- hover/selected color states switch between `text-white/50` and
`text-exo-yellow` correctly.

### Automated Testing
- No new tests — this is a cosmetic grouping/logo change. Existing
dashboard build verifies the Svelte + TS compiles.
2026-04-13 14:08:15 +00:00
8973503322 fix: use configured api_port for IP connectivity probes (#1877)
## Motivation

Fixes #1861

When `--api-port` is set to a non-default value (e.g., `--api-port
55555`), the IP connectivity discovery system still probes peers on the
hardcoded default port 52415. Since the API is not listening on 52415,
all reachability checks fail, the topology reports zero reachable nodes,
and the dashboard shows "No valid configurations for current settings."

## Changes

Thread the configured `api_port` from `Args` through `Worker` into the
reachability probe functions:

- `net_profile.py`: `check_reachability()` and `check_reachable()`
accept an `api_port` parameter (default 52415 for backward
compatibility)
- `worker/main.py`: `Worker` stores `api_port` and passes it to
`check_reachable()`, uses it in `Multiaddr` construction and the mDNS
connection filter
- `main.py`: passes `args.api_port` to the `Worker` constructor

## Why It Works

The `/node_id` endpoint used by reachability probes is served by the
FastAPI app, which binds to `args.api_port`. The probes must use the
same port the API is actually listening on. Before this fix, the port
was hardcoded in three places in `net_profile.py` and `worker/main.py`;
now it uses the value from the CLI flag.

## Test Plan

### Manual Testing
<!-- Hardware: not available for multi-node testing -->
- Verified ruff passes on all changed files
- Code inspection: traced `api_port` flow from `Args.parse()` →
`Node.create()` → `Worker.__init__()` → `_poll_connection_updates()` →
`check_reachable()` → `check_reachability()` → HTTP probe URL

### Automated Testing
- No existing automated tests cover the reachability probe code path
- The new `api_port` parameter defaults to `52415`, so all existing
behavior is preserved when `--api-port` is not specified

---------

Co-authored-by: lawrence3699 <lawrence3699@users.noreply.github.com>
Co-authored-by: Evan <evanev7@gmail.com>
2026-04-13 14:01:42 +00:00
Alex CheemaandClaude Opus 4.6 eb9228615f models: add MiniMax M2.7 cards (#1884)
## Motivation

The mlx-community [MiniMax-M2.7
collection](https://huggingface.co/collections/mlx-community/minimax-m27)
landed but exo didn't have model cards for any of the variants yet, so
they weren't selectable from the dashboard model picker. Adding cards
also makes them discoverable under the existing MiniMax family entry.

## Changes

Added 6 new model cards in `resources/inference_model_cards/`, one per
quant of MiniMax M2.7:

- `mlx-community--MiniMax-M2.7.toml` (bf16, full precision — 457 GB)
- `mlx-community--MiniMax-M2.7-4bit.toml` (128 GB)
- `mlx-community--MiniMax-M2.7-4bit-mxfp4.toml` (121 GB)
- `mlx-community--MiniMax-M2.7-5bit.toml` (157 GB)
- `mlx-community--MiniMax-M2.7-6bit.toml` (185 GB)
- `mlx-community--MiniMax-M2.7-8bit.toml` (243 GB)

All six use `family = "minimax"` and share `base_model = "MiniMax M2.7"`
so they collapse into a single group in the picker with the existing
MiniMax logo. Architecture fields (`n_layers = 62`, `hidden_size =
3072`, `num_key_value_heads = 8`, `context_length = 196608`) were read
from each repo's `config.json`; `storage_size.in_bytes` was summed from
the HF tree API per repo.

`capabilities = ["text", "thinking"]` follows the existing MiniMax M2.5
cards — the chat template always emits `<think>` tags (no toggle),
matching M2.5 behavior.

## Why It Works

Model cards in `resources/inference_model_cards/` are auto-loaded by
`src/exo/shared/models/model_cards.py::get_model_cards`. The dashboard
picker groups by `base_model` and filters by `family`, so sharing both
across all six variants gives a single "MiniMax M2.7" group under the
MiniMax sidebar entry, with the quant variants exposed as selectable
sub-options.

## Test Plan

### Manual Testing
<!-- Hardware: MacBook Pro M3 Max -->
- Ran `uv run python -c "…await get_model_cards()…"` and confirmed all 6
new cards load with `family=minimax`, `base_model="MiniMax M2.7"`, and
correct quant + byte sizes.
- `cd dashboard && npm run build` then `uv run exo`, opened the model
picker → **MiniMax** family → **MiniMax M2.7** group shows all six quant
variants.

### Automated Testing
- No new automated tests — these are data files validated by the
existing Pydantic `ModelCard` schema at load time.

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-13 14:47:09 +01:00
MikkoParkkola 4b13735ea3 build: remove pyinstaller temp artifacts (#1868)
removes PyInstaller `build/` leftovers after `just package`
2026-04-11 11:46:03 +00:00
rltakashigeandEvan 196543ce69 Add Gemma 4 + VLM fixes + thinking parsing updates (#1851)
## Motivation
Add support for Gemma 4, including VLM!

## Changes

- Add auto parallel strategies and model cards for Gemma 4
- Normalise Gemma 4's special Vision Transformer handling to be in line
with the rest of our vision processors.
- Also adds reprs to messages and b64 hashes to prevent log spam.

## Test Plan

### Manual Testing
Tested manually on 4bit E2B and 8bit 26B

### Automated Testing
Model onboarding shows small logit diffs.

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-04-11 12:29:33 +01:00
ciaranbor 6172617b00 add env override to macos app (#1869)
## Motivation

- Let users pass/override arbitrary exo env vars from the macOS app
without a code change.

## Changes

- `ExoProcessController.swift`: `CustomEnvironmentVariable` struct +
`@Published` list persisted to `UserDefaults`, injected into the child
process after built-ins.
- `SettingsView.swift`: new **Environment** tab with add/remove rows,
trim + dedup on save, and a POSIX name validator with a warning badge.

## Why It Works

- Custom vars applied last in `makeEnvironment`, so overriding a
built-in works with no special-casing.

## Test Plan

### Manual Testing

- Set `EXO_LIBP2P_NAMESPACE` via the new UI; confirmed override in
`~/.exo/exo_log/exo.log`.
2026-04-10 17:39:55 +01:00
ciaranbor 93a980a61e just package first builds dashboard (#1867)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-04-10 16:25:30 +00:00
ciaranbor 2962ebee60 Fix pdf inputs on Safari (#1865)
## Motivation

PDF attachments weren't working on Safari

## Changes

Create async readable stream if none exists

## Why It Works
pdfjs-dist requires an async readable stream internally

## Test Plan

### Manual Testing
pdf attachments now work on Safari, still work on Firefox
2026-04-10 14:59:34 +00:00
rltakashigeandEvan abd75ae06c Truncate long logs with repr (#1854)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-04-10 15:53:48 +01:00
kaiisfree ee2e505b3c fix: handle BrokenResourceError in download progress callback (#1846)
## Summary
- Wraps progress callback `send()` in try/except to gracefully handle
`BrokenResourceError` when the memory stream is closed
- Prevents unhandled `ExceptionGroup` from crashing the process when the
download consumer disconnects during transfer

## Root Cause
The download progress callback sends updates through an anyio memory
object stream. When the receiving end closes (e.g., client disconnect,
timeout, or task cancellation), `send()` raises `BrokenResourceError`.
Inside an anyio `TaskGroup`, this unhandled exception becomes an
`ExceptionGroup` that propagates up and crashes the coordinator.

## Fix
Catch `BrokenResourceError` (and `ClosedResourceError` for completeness)
in the progress callback and handle gracefully — the download continues
but progress updates are silently dropped for disconnected consumers.

Fixes #1844

🤖 Generated with [Claude Code](https://claude.com/claude-code)
2026-04-10 11:43:42 +00:00
Evan Quiney f2e6b1ef76 prevent some crash loops (#1827)
extension to #1763 that prevents crash looping in some common scenarios.
2026-04-09 11:34:35 +00:00
ciaranbor e2e17eafb7 Fix reasoning_tokens counting for multi-token thinking tag models (#1848)
## Motivation

`reasoning_tokens` is always 0 in usage stats, even when thinking
content streams correctly via `reasoning_content` SSE deltas. The MLX
generators had their own thinking detection comparing individual
detokenized tokens against think tags — this never fires for models
where tags span multiple tokens (e.g. gpt-oss-120b) or are already in
the prompt.

## Changes

- Removed broken per-token thinking detection from `batch_generate.py`
and `generate.py`
- Added `_count_reasoning_tokens` wrapper in `model_output_parsers.py`
that counts `is_thinking=True` responses and patches the total into
Usage on the final response
- Wired it as the outermost stage of `apply_all_parsers`, so it works
regardless of which parser sets `is_thinking`
- Added 3 tests covering `parse_thinking_models` and `parse_gpt_oss`
paths

## Why It Works

The parser pipeline already correctly sets `is_thinking` on each
response. Counting at the output of `apply_all_parsers` means one
counting point that works for all model types, replacing the duplicate
broken logic in two generators.

## Test Plan

### Manual Testing

- 4-node cluster, `mlx-community/gpt-oss-120b-MXFP4-Q8`
- Main branch: `reasoning_tokens: 0` — fix branch: `reasoning_tokens:
25`

### Automated Testing

- 3 new tests: explicit think tags, `starts_in_thinking=True`, and
gpt-oss Harmony analysis channel
2026-04-09 12:29:35 +01:00
ciaranbor b12cd1b186 Cancel SSE keep-alive when instance is deleted (#1828)
## Motivation

When a model instance is deleted (e.g. node disconnect, manual
teardown), any in-flight SSE streaming connections for that instance
hang indefinitely. The API never closes the response stream, so clients
block forever waiting for more chunks.

## Changes

- Listen for `InstanceDeleted` events in the API event loop
- Add `_close_streams_for_instance()` to find and close any active
text/image generation queues tied to tasks on the deleted instance
- Add unit tests covering text gen, image gen, and
unrelated-instance-not-closed scenarios

## Why It Works

When an instance is deleted, we iterate `state.tasks` to find commands
running on that instance, then close and remove their send-side queue
handles. This causes the SSE generator to terminate, unblocking the
client.

## Test Plan

### Manual Testing
- This was causing issues for me on another branch (integration tests).
Including this fix solved the issue

### Automated Testing
- `test_instance_deleted_stream_cleanup.py`: 3 tests covering text gen
cleanup, image gen cleanup, and ensuring unrelated streams are not
affected
2026-04-08 16:14:28 +01:00
mlpy0 62570227ff Catch ClosedResourceError when forwarding chunks to client queues (#1856)
While stress-testing inference with rapid client cancels mid-stream, I
hit a reproducible crash where the entire exo process exits.

When a client cancels a streaming chat completion partway through, its
receive stream gets closed cleanly via its context manager. The producer
in `API._apply_state` then calls `queue.send(event.chunk)`, which raises
`anyio.ClosedResourceError` rather than `BrokenResourceError`. The
existing handler only catches `BrokenResourceError`, so the exception
propagates through the API task group, kills the Node task group, and
the process exits with `EXO Shutdown complete`.

Trace from one of the crashes:

```
File "exo/api/main.py", line 1818, in _apply_state
    await queue.send(event.chunk)
File "anyio/streams/memory.py", line 212, in send_nowait
    raise ClosedResourceError
anyio.ClosedResourceError
```

The fix is to catch `ClosedResourceError` alongside
`BrokenResourceError` in both queue handlers (text and image), so the
dead queue gets dropped and `_apply_state` keeps running for other
in-flight requests.
2026-04-08 08:50:04 +00:00
Alex CheemaandClaude Opus 4.6 645bc20950 Add Fast Synch Enabled toggle to macOS app settings (#1852)
## Motivation

The exo backend already supports `--fast-synch` / `--no-fast-synch` CLI
flags and the `EXO_FAST_SYNCH` environment variable, but there was no
way to toggle this from the macOS app UI. Users who want fast CPU-to-GPU
synchronization for RDMA with Tensor Parallelism had to use CLI flags.

## Changes

- **ExoProcessController.swift**: Added `fastSynchEnabled`
UserDefaults-backed property and pass `EXO_FAST_SYNCH=on` to the exo
process environment when enabled.
- **SettingsView.swift**: Added a "Performance" section to the Advanced
tab with a "Fast Synch Enabled" toggle, an info icon (ⓘ) tooltip
explaining the feature and trade-offs, and a "Save & Restart" button.

## Why It Works

Follows the exact same pattern as the existing `offlineMode` and
`enableImageModels` settings — UserDefaults persistence, `@Published`
property with `didSet`, environment variable passthrough in
`makeEnvironment()`, and pending state with Save & Restart in the
settings UI. The `EXO_FAST_SYNCH=on` value matches what the Python
backend already reads in `main.py`.

## Test Plan

### Manual Testing
<!-- Hardware: macOS app -->
- Open Settings → Advanced tab → verify "Performance" section with "Fast
Synch Enabled" toggle appears
- Hover the ⓘ icon → verify tooltip explains the feature and GPU lock
trade-off
- Toggle on → click "Save & Restart" → verify process restarts with
`EXO_FAST_SYNCH=on` in env
- Close and reopen Settings → verify the toggle state persists
- Verify "Save & Restart" button is disabled when no changes are pending

### Automated Testing
- Existing settings patterns are well-established; no new automated
tests needed for this UI toggle

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-08 01:04:42 +00:00
rltakashige 5757c27dd5 Add download utility script (#1855)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-04-08 00:58:39 +00:00
Andrei Cravtov fd5b23281c Workspace tweaks (#1849)
## Changes

Mostly chore changes around vscode and jetbrains workspace settings, and
some basedpyright settings tweaks, to allow direnv to work and nixd
autocomplete with flake parts to work
2026-04-07 17:26:29 +00:00
rltakashige 43b3df45fb Fix BatchGenerator in line with upstream refactor (and prevent Qwen3.5 memory leak) (#1835)
## Motivation

MLX LM has had a massive refactor to their BatchGenerator recently.
Since we'd like new features from MLX LM such as Gemma 4, we need to
update the code to handle this.

Additionally this fixes a significant memory leak in GatedDeltaNet (the
difference is quite substantial, up to 1GB every 1000 tokens, explaining
several memory issues users were facing with Qwen3.5 models)

## Testing
Before
<img width="3146" height="884" alt="image"
src="https://github.com/user-attachments/assets/5af0f55a-393c-4a32-9eed-ae43f1611af4"
/>


After (no memory leak, as one of the changes upstream)
<img width="3190" height="892" alt="image"
src="https://github.com/user-attachments/assets/f0bd128d-fd48-40d4-9bbd-50a564beab14"
/>
2026-04-07 11:50:12 +00:00
mlpy0 24420eb10a Fix reasoning_tokens always reported as 0 for thinking models (#1836)
When `enable_thinking` is set, chat templates (Qwen3, DeepSeek, etc.)
append `<think>` to the prompt. The model starts generating thinking
content directly without emitting a `<think>` token in the output
stream.

Both generators initialized `in_thinking = False` and only set it to
`True` on seeing a `<think>` token in output. Since that token was part
of the prompt, the flag never flipped and `reasoning_tokens` stayed at 0
in the usage response.

Fix: initialize `in_thinking` from `detect_thinking_prompt_suffix()`,
which already exists and is used by `model_output_parsers` for routing
thinking content correctly.
2026-04-05 00:05:18 +00:00
rltakashige 59669c1168 Tighten EXO bench concurrency numbers and explain methodology (#1811)
## Motivation

The timings in the batch generator are a little optimistic; a minor
change is needed to make them more correct.

## Changes

Include the time spent in the API in the generation tps and make sure to
send all requests simultaneously
2026-04-05 00:57:52 +01:00
ciaranbor 1d2ce464dc Allow pausing and deleting active downloads (#1829)
## Motivation

No way to pause active downloads or delete partial/failed downloads from
the dashboard.

## Changes

- **Backend:** Added `POST /download/cancel` endpoint with
`CancelDownloadParams`/`CancelDownloadResponse` types. Wires into the
existing `CancelDownload` command + coordinator handler.
- **Dashboard store:** Added `cancelDownload(nodeId, modelId)` function.
- **Dashboard UI:**
  - Pause + delete buttons on active (downloading) cells
  - Delete button on paused/pending and failed cells
- Extracted duplicated SVG icons into `{#snippet}` blocks (`trashIcon`,
`downloadIcon`, `pauseIcon`, `deleteButton`)
- **Tests:** 3 coordinator-level tests for cancel: active download →
pending, nonexistent → no-op, cancel then resume.

## Why It Works

`CancelDownload` command and coordinator handler already existed — just
needed an HTTP endpoint and dashboard wiring. Delete endpoint already
supported all download states.

## Test Plan

### Manual Testing

Started a model download, paused it. Deleted some paused downloads.
Deleted some ongoing downloads.

### Automated Testing

- `test_cancel_active_download_transitions_to_pending` — cancels
in-progress download, asserts `DownloadPending` event and cleanup
- `test_cancel_nonexistent_download_is_noop` — no events emitted
- `test_cancel_then_resume_download` — restart after cancel works
2026-04-02 15:56:33 +01:00
ciaranborandEvan eb6ae9fd3c Prevent failed instance retries (#1763)
## Motivation

Currently, when a runner fails, the master retries the instance. Most of
the time, this causes a loop over failure. Retries need backoff and a
cap.

## Changes

- src/exo/worker/main.py: Before creating a runner, check an exponential
backoff timer per instance. After EXO_MAX_INSTANCE_RETRIES failures,
send DeleteInstance to permanently remove the instance. Record attempts
on Shutdown; reset on InstanceDeleted.
- src/exo/utils/keyed_backoff.py: Add attempts() method to query retry
count
- src/exo/shared/constants.py: Add EXO_MAX_INSTANCE_RETRIES = 3.

## Why It Works

The worker gates CreateRunner tasks behind a KeyedBackoff, adding
exponential delay (2s base, 30s cap) between retries. After 3 failures
the worker sends DeleteInstance, stopping retries entirely. The backoff
resets when the instance is deleted, so a fresh placement starts clean.

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-04-01 21:03:34 +01:00
rltakashige 4688adb5d2 Support PDFs in dashboard (#1822)
Like ChatGPT does, we now send both the extracted text and the image of
each PDF page.
2026-03-31 18:25:40 +01:00
rltakashige d9ed943034 Fix Nemotron cache leak upstream (#1819)
## Motivation
Nemotron Cascade and Nano failing at long decodes.

## Changes

Fixed upstream, just change pyproject and uv lock here.


## Test Plan
### Automated Testing
Tested with a reproduce script upstream
2026-03-30 16:53:21 +00:00
rltakashige c6815bfdce Only update KV prefix cache on a good cache hit (#1817)
## Motivation

Addresses #1816 

## Changes

Update on min prefix cache > min_prefix_hit_length **and** hit ratio >
_MIN_PREFIX_HIT_RATIO_TO_UPDATE
min_prefix_hit_length = max(1000, system prompt length) -> system
prompts must match exactly.

## Test Plan

### Manual Testing
Test on OpenCode and Claude Code
2026-03-30 15:04:38 +01:00
rltakashige 39c39e8199 Integrations helpers (#1810)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-03-30 14:28:41 +01:00
rltakashige e5cb7b80d0 Add SSE-keepalive to not time out on long prefill on clients (#1803)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-03-30 12:18:38 +01:00
rltakashigeandDavid Hind 635801d515 Add multimodality! (#1802)
## Motivation

Images!

TODO (in a future PR): Add audio and video support.

## Test Plan

### Manual Testing
<img width="2652" height="1900" alt="image"
src="https://github.com/user-attachments/assets/7d3a7137-542f-4f94-9193-2c73b7c4a5ec"
/>

<img width="2770" height="1956" alt="image"
src="https://github.com/user-attachments/assets/e3c3a096-8029-4409-97a6-aca31a9a3f24"
/>
<img width="2738" height="1768" alt="image"
src="https://github.com/user-attachments/assets/d70ea37f-cd1d-4a4c-ad08-3beb9fafa380"
/>

(And batching also works)

---------

Co-authored-by: David Hind <davehind@yahoo.co.uk>
2026-03-30 11:52:19 +01:00
rltakashige 2efbb8ab4f Improve exo harness with path state (#1815)
<img width="3224" height="1476" alt="image"
src="https://github.com/user-attachments/assets/d90a7d8a-9fe5-43a1-a715-1ef7ecc15422"
/>
2026-03-30 11:20:46 +01:00
Evan Quiney c6c5a3e73c feat: /state/paths (#1796)
adds a path option to the /state endpoint, allowing you to query
subfields of state without grabbing the whole blob

## test plan
poking around in the api
2026-03-30 10:10:00 +00:00
ArvidSU 10ef7ec9e8 feat: add Firefox AI sidebar (?q=) support to dashboard (#1814)
This PR builds on https://github.com/exo-explore/exo/pull/1677 to enable
custom prompts sent from Firefox `browser.ml.chat` to EXO dashboard
using URL parameters in sidebar for summary and other browser
interactions. See "Summarize page" example below.

## Summary
- Parse `?q=<encoded prompt>` URL parameter on page load and auto-submit
it as a chat message
- Clean up the URL with `history.replaceState` to prevent re-submission
on refresh
- Defer auto-send until both cluster state and model list are loaded so
model auto-selection works correctly

## Context
Firefox's built-in AI sidebar (`about:config: browser.ml.chat.enabled`)
integrates with chat providers by appending the user's prompt as
`?q=<URL-encoded prompt>`. Previously the exo dashboard ignored this
parameter. Users can now configure `http://localhost:52415` as a Firefox
AI chatbot provider.

See: https://support.mozilla.org/en-US/kb/ai-chatbot

## Technical notes
- Frontend-only change in `dashboard/src/routes/+page.svelte`
- Uses a Svelte `$effect` that reacts to `pendingFirefoxQuery`, `data`
(cluster state), and `models.length` — fires exactly once when all three
are ready
- If no model is selected, `handleAutoSend` auto-picks the best
available model; if no model fits memory, a toast is shown
- If a model is selected but not running, the message is queued until
the model loads

## Testing
```
http://localhost:52415/?q=Hello+world
http://localhost:52415/?q=Summarize+this+page%3A+%5Bpage+title%5D+%5Bpage+url%5D
```

<img width="2056" height="1329" alt="image"
src="https://github.com/user-attachments/assets/74463eb4-ca1a-400d-806a-c19ba93147b9"
/>
2026-03-30 11:02:35 +01:00
Evan Quiney 1e51dc89b0 chore: bump exo-version with release version (#1807)
our pyproject.toml version was 0.3.68 - update to .69 in line with
release!!
2026-03-27 11:47:13 +00:00
Alex CheemaandClaude Opus 4.6 5327bdde84 Fix custom model add requiring two attempts + enlarge sidebar buttons (#1805)
## Motivation

Adding a custom model from the Hub tab shows "Added" toast but the model
doesn't appear in the All tab. You have to add it a second time for it
to work. Also, the "All" button in the model picker sidebar is too small
to read comfortably.

## Changes

**Race condition fix (`src/exo/api/main.py`):**
- Call `add_to_card_cache(card)` directly in `add_custom_model()` after
sending the `ForwarderCommand`, before the API response returns

**Sidebar sizing
(`dashboard/src/lib/components/FamilySidebar.svelte`):**
- Increased sidebar min-width from 72/64px to 80/72px
- Increased "All" icon from `w-5 h-5` to `w-6 h-6`
- Increased all sidebar labels from 9px to 11px

## Why It Works

`POST /models/add` sends a `ForwarderCommand(AddCustomModelCard)` and
returns immediately. The frontend then calls `GET /models` which reads
from `_card_cache`. But the cache was only updated by the worker event
handler after the event round-trips through the master — a race the
frontend almost always loses. By updating the cache directly in the API
handler, `GET /models` immediately reflects the new model. The worker's
later `add_to_card_cache` call is idempotent (dict key assignment).

## Test Plan

### Manual Testing
<!-- Hardware: any Mac -->
- Open model picker → Hub tab → add a custom model → verify it appears
in All tab on the first attempt
- Verify sidebar "All" button and other labels are visually larger and
readable

### Automated Testing
- `uv run basedpyright` passes with 0 errors
- `uv run ruff check` passes

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-26 17:57:00 -07:00
ciaranbor 15f1b61f4c Rework model storage directory management (for external storage) (#1765)
## Motivation

Replace confusing EXO_MODELS_DIR/EXO_MODELS_PATH with clearer
multi-directory support, enabling automatic download spillover across
volumes.

## Changes

- EXO_MODELS_DIRS: colon-separated writable dirs (default always
prepended, first with enough space wins)
- EXO_MODELS_READ_ONLY_DIRS: colon-separated read-only dirs (protected
from deletion)
- select_download_dir(): picks writable dir by free space
- resolve_existing_model(): unified lookup across all dirs
- is_read_only_model_dir(): path-based read-only detection instead of
hardcoded flag
- Updated coordinator, worker, model cards, tests

## Why It Works

Default dir always included so zero-config behavior is unchanged. Disk
space checked at download time for automatic spillover. Read-only status
derived from path, not hardcoded.

## Test Plan

### Manual Testing

- No env vars set → identical behavior
- EXO_MODELS_DIRS=/Volumes/SSD/models → downloads to external storage
- EXO_MODELS_READ_ONLY_DIRS=/mnt/nfs → models found, deletion blocked

### Automated Testing

- 4 new tests in test_xdg_paths.py (prepend, default-only, overlap,
empty read-only)
- Existing tests updated to patch new constants
2026-03-26 17:46:46 +00:00
Michael HarriganandEvan 9034300163 [Fix] Node hang on reelection (#1801)
## Motivation

During master reelection, `_elect_loop` called `worker.shutdown()` (fire
& forget) then immediately created and started a new Worker.

This caused the old runner subprocess's Metal/GPU teardown to race with
the new worker's startup, resulting in `IOConnectUnmapMemory failed:
kr=0xe00002bc` errors and a full node hang requiring `^C`. Same issue
existed for `DownloadCoordinator`.

## Changes

- Added `anyio.Event`-based `_stopped` signal to `Worker` and
`DownloadCoordinator`, set at the end of their `run()` finally blocks
- Added `wait_stopped()` async method to both classes
- Updated `_elect_loop` to `await wait_stopped()` after calling
`shutdown()` on the old Worker and DownloadCoordinator before creating
replacements

## Why It Works

The old Worker's task group contains the RunnerSupervisor tasks, whose
finally blocks join the runner subprocess (with 5s timeout + SIGTERM +
SIGKILL escalation). By awaiting `wait_stopped()`, we guarantee the old
runner process has fully exited — including GPU memory cleanup — before
a new Worker can start and potentially access the GPU. This eliminates
the race without changing the shutdown mechanics themselves.

## Test Plan

### Manual Testing
Hardware: M4 Pro Mac Mini 24GB + M3 Ultra Mac Studio 96GB, connected via
Thunderbolt

**Repro steps:**
1. Start exo on two nodes with a model sharded across both (e.g.
`Josiefied-Qwen3-14B-abliterated-v3-4bit`)
2. Wait for "runner ready" on both
3. `kill -9` the master node
4. Observe the surviving node's re-election behavior

**Before fix (original crash):**
```
[ 11:02:39.0896AM ] Runner supervisor shutting down
[ 11:02:39.0905AM ] bye from the runner
[ 11:02:39.1052AM ] Stopping Worker
IOConnectUnmapMemory failed: kr=0xe00002bc
IOConnectUnmapMemory failed: kr=0xe00002bc
IOConnectUnmapMemory failed: kr=0xe00002bc
IOConnectUnmapMemory failed: kr=0xe00002bc
^C[ 11:03:45 ] ← hung for over a minute, required manual kill
```

**After fix (clean re-election):**
```
[ 12:15:22.4703PM ] runner loaded
[ 12:15:24.1672PM ] runner ready
[ 12:15:33.5393PM ] Waiting for other campaign to finish
[ 12:15:36.5409PM ] Node elected Master
[ 12:15:36.5413PM ] Unpausing API
```
No `IOConnectUnmapMemory` errors, no hang, no `^C` needed.

### Automated Testing
- No existing tests cover the `_elect_loop` re-election path; this is an
integration-level flow requiring a live router/election/worker stack
- All existing tests pass (307/308, 1 pre-existing Rust binding failure)
- basedpyright: 0 errors, ruff: all checks passed

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-03-26 17:28:47 +00:00
rltakashige 1d1dfaa1f3 Don't download original/ and metal/ folders from HF (#1800)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-03-26 13:36:07 +00:00
Evan Quiney 7625213df0 fix: enable macmon if preflight fails (#1799)
missed in #1747, issue #1798.

### the issue

we didn't set the memory poll rate after failling the macmon preflight,
only after failing the followups - as we never ran macmon if preflight
failed, we never hit the followup errors etc.

### testing

requires testing on an m5 pro, but the core issue is solved.
2026-03-26 11:35:22 +00:00
Alex CheemaandClaude Opus 4.6 f318f9ea14 Fix macOS build bundling wrong macmon binary (#1797)
## Motivation

PR #1747 fixed macmon support for M5 Pro/Max by pinning the
`swiftraccoon/macmon` fork in `flake.nix`. This works when running from
source (via Nix) but the distributed macOS `.app` build was still broken
on M5 Pro/Max because it was bundling the wrong macmon.

The error on M5 Pro/Max:
```
macmon preflight failed with return code -6: thread 'main' panicked at src/sources.rs:394:41
```

## Changes

- Removed `macmon` from `brew install` in `build-app.yml` — this was
installing the upstream `vladkens/macmon` which doesn't support M5
Pro/Max
- Added a new step that resolves the pinned macmon fork from the Nix dev
shell (same `swiftraccoon/macmon` at rev `9154d23` already defined in
`flake.nix`) and adds it to `$GITHUB_PATH`
- Added a safety `brew uninstall macmon` to ensure no Homebrew macmon
can shadow the pinned version

## Why It Works

PyInstaller bundles macmon via `shutil.which("macmon")`. Previously this
found the Homebrew (upstream) binary. Now it finds the Nix-overlayed
fork that has M5 Pro/Max support, because `$GITHUB_PATH` prepends the
Nix store path before the PyInstaller step runs.

## Test Plan

### Manual Testing
<!-- Hardware: M5 Pro -->
- Trigger a macOS build and verify the bundled macmon is the pinned fork
- Run the built `.app` on M5 Pro/Max and confirm macmon preflight
succeeds

### Automated Testing
- Existing CI build workflow will validate that the macmon binary is
found and bundled correctly

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-26 09:47:48 +00:00
ciaranbor 30fd5aa1cc Prefer higher % downloaded nodes for API placement previews (#1795)
Follow up to https://github.com/exo-explore/exo/pull/1767

Same thing for placement previews through API
2026-03-25 17:26:31 +00:00
ciaranbor 6de14cfedb Support image generation cancellation (#1774)
## Motivation

Support cancelling image generation, similar to existing support for
cancelling text generation

## Changes

- Dashboard (app.svelte.ts): Wire up AbortController for both
generateImage and editImage API calls. On abort, show "Cancelled"
instead of an error. Clean up the controller in finally.
- Pipeline runner (pipeline/runner.py): Introduce a cancel_checker
callback and NaN-sentinel cancellation protocol for distributed
diffusion:
  - _check_cancellation() - only rank 0 polls the cancel callback
- _send() - replaces data with NaN sentinels when cancelling, so
downstream ranks detect cancellation via _recv_and_check()
  - _recv() / _recv_like() wrappers that eval and check for NaN sentinel
  - After cancellation, drains any pending ring recv to prevent deadlock
  - Skips partial image yields and final decode when cancelled
- Image runner (runner/image_models/runner.py): Deduplicate the
ImageGeneration and ImageEdits match arms into a shared
_run_image_task() method. Thread a cancel_checker closure (backed by the
existing cancel_receiver + cancelled_tasks set) into generate_image().
- Plumbing (distributed_model.py, generate.py): Pass cancel_checker
through the call chain.

## Why It Works

- Rank 0 is the only node that knows about task-level cancellation. When
it detects cancellation, it sends NaN tensors instead of real data.
Higher-order ranks detect the NaN sentinel on recv, set their own
_cancelling flag, and propagate NaN forward
- A drain step after the loop prevents the deadlock case where the last
rank already sent patches that the first would never consume.
- For single-node mode, the loop simply breaks immediately on
cancellation.

## Test Plan

### Automated Testing

New tests in src/exo/worker/tests/unittests/test_image
2026-03-25 16:56:04 +00:00
fc1ae90111 fix: DeepSeek V3.2 warmup crash and tool calling + add catalog cards (#1769)
## Summary

DeepSeek V3.2 (`DeepseekV32ForCausalLM`) is already supported by exo's
inference engine (architecture whitelisted in `model_cards.py`, DSML
encoding added in #1548), but **doesn't work out of the box** due to two
bugs:

### Bug 1: `warmup_inference` passes empty model ID

`warmup_inference()` in `generate.py` accepts `model_id: ModelId` as a
parameter but creates `TextGenerationTaskParams(model=ModelId(""), ...)`
instead of using it. Since `_needs_dsml_encoding()` checks
`"deepseek-v3.2" in task_params.model.lower()`, the empty string never
matches → falls back to `tokenizer.apply_chat_template()` →
**ValueError** because V3.2 has no Jinja chat template.

**Fix:** `model=ModelId("")` → `model=model_id` (one line).

### Bug 2: `_needs_dsml_encoding` limited to tool calling

`_needs_dsml_encoding()` returns `True` only when `task_params.tools` is
present or tool messages exist in `chat_template_messages`. For warmup
and regular chat requests without tools → `return False` → Jinja
fallback → **ValueError**.

Unlike V3.1 (which has a `.jinja` chat template file that transformers
picks up automatically), V3.2 **has no Jinja template at all** — it uses
Python-based DSML encoding for all message types.

**Fix:** For V3.2, always return `True` — DSML encoding handles all
message types.

### Catalog cards

Added inference model cards for:
- `mlx-community/DeepSeek-V3.2-8bit`
- `mlx-community/DeepSeek-V3.2-4bit`

Parameters taken from model `config.json` on HuggingFace, storage sizes
from HF API. Capabilities include `thinking_toggle` (related: #1456).

## Notes

- The model ID string matching approach (`"deepseek-v3.2" in
model.lower()`) is acknowledged tech debt — see #1371 for the planned
architecture-based approach.

## Test plan

- [x] Start exo with DeepSeek V3.2 model → warmup should complete
without crash
- [x] Send a regular chat message (no tools) → should get a response
- [x] Send a chat message with tools → should work as before
- [x] V3.2 cards should appear in the dashboard model catalog

---------

Co-authored-by: user <user@m1.note>
Co-authored-by: Ryuichi Leo Takashige <leo@exolabs.net>
Co-authored-by: Evan <evanev7@gmail.com>
2026-03-25 16:20:35 +00:00
rltakashige 565ed41c13 Fix occasional warmup bugs by using mlx_generate (#1794)
## Motivation

Warmup occasionally had issues; e.g. #1748 and #1793 because we were
using a standard stream_generate, all of which are issues that are
resolved in the wrapper function mlx_generate.
2026-03-25 15:53:54 +00:00
DeepZimaandEvan 2da740c387 Feat/static peer discovery (#1690)
**Enabling peers to be discovered in environments where mDNS is
unavailable (SSH sessions, headless servers, Docker).**

## Motivation
Exo discovers peers exclusively via mDNS, which works great on a local
network but breaks once you move beyond a single L2 broadcast domain:

- SSH sessions on macOS — TCC blocks mDNS multicast from non-GUI
sessions (#1488)
- Headless servers/rack machines — #1682 ("DGX Spark does not find other
nodes")
- Docker Compose — mDNS is often unavailable across container networks;
e.g. #1462 (E2E test framework) needs an alternative

Related works: 
#1488 (working implementation made by @AlexCheema and closed because SSH
had a GUI workaround),
#1023 (Headscale WAN then closed due to merge conflicts), 
#1656 (discovery cleanup, open). 

This PR introduces an optional bootstrap mechanism for peer discovery
while leaving the existing mDNS behavior unchanged.

## Changes
Adds two new CLI flags:

- `--bootstrap-peers` (env: `EXO_BOOTSTRAP_PEERS`) — comma-separated
libp2p multiaddrs to dial on startup and retry periodically
- `--libp2p-port` — fixed TCP port for libp2p to listen on (default:
OS-assigned). Required when bootstrap peers, so other nodes know which
port to dial.

8 files: 
- `rust/networking/src/discovery.rs`: Store bootstrap addrs, dial in
existing retry loop
- `rust/networking/src/swarm.rs`: Thread `bootstrap_peers` parameter to
`Behaviour`
- `rust/networking/examples/chatroom.rs`: Updated call site for new
create_swarm signature
- `rust/networking/tests/bootstrap_peers.rs`: Integration tests
- `rust/exo_pyo3_bindings/src/networking.rs`: Accept optional
`bootstrap_peers` in PyO3 constructor
- `rust/exo_pyo3_bindings/exo_pyo3_bindings.pyi` : Update type stub 
- `src/exo/routing/router.py`: Pass peers to `NetworkingHandle` 
- `src/exo/main.py` : `--bootstrap-peers` CLI arg +
`EXO_BOOTSTRAP_PEERS` env var

## Why It Works

Bootstrap peers are dialed in the existing retry loop — the same path
taken by peers when mDNS-discovered. The swarm handles connection, Noise
handshake, and gossipsub mesh joining from there.

PeerId is intentionally not required in the multiaddr, the Noise
handshake discovers it.

Docker Compose example:

```yaml
services:
  exo-1:
    environment:
      EXO_BOOTSTRAP_PEERS: "/ip4/exo-2/tcp/30000"
  exo-2:
    environment:
      EXO_BOOTSTRAP_PEERS: "/ip4/exo-1/tcp/30000"
```

## Test Plan

### Manual Testing
<details>
<summary>Docker Compose config</summary>

```
services:
  exo-node1:
    build:
      context: .
      dockerfile: Dockerfile.bootstrap-test
    container_name: exo-bootstrap-node1
    hostname: exo-node1
    command: ["-q", "--libp2p-port", "30000", "--bootstrap-peers", "/ip4/172.30.20.3/tcp/30000"]
    environment:
      - EXO_LIBP2P_NAMESPACE=bootstrap-test
    ports:
      - "52415:52415"
    networks:
      bootstrap-net:
        ipv4_address: 172.30.20.2
    deploy:
      resources:
        limits:
          memory: 4g

  exo-node2:
    build:
      context: .
      dockerfile: Dockerfile.bootstrap-test
    container_name: exo-bootstrap-node2
    hostname: exo-node2
    command: ["-q", "--libp2p-port", "30000", "--bootstrap-peers", "/ip4/172.30.20.2/tcp/30000"]
    environment:
      - EXO_LIBP2P_NAMESPACE=bootstrap-test
    ports:
      - "52416:52415"
    networks:
      bootstrap-net:
        ipv4_address: 172.30.20.3
    deploy:
      resources:
        limits:
          memory: 4g

networks:
  bootstrap-net:
    driver: bridge
    ipam:
      config:
        - subnet: 172.30.20.0/24
```
</details> 

Two containers on a bridge network (`172.30.20.0/24`), fixed IPs,
`--libp2p-port 30000`, cross-referencing `--bootstrap-peers`.

Both nodes found each other and established a connection then ran the
election protocol.

### Automated Testing

4 Rust integration tests in `rust/networking/tests/bootstrap_peers.rs`
(`cargo test -p networking`):

| Test | What it verifies | Result |
|------|-----------------|--------|
| `two_nodes_connect_via_bootstrap_peers` | Node B discovers Node A via
bootstrap addr (real TCP connection) | PASS |
| `create_swarm_with_empty_bootstrap_peers` | Backward compatibility —
no bootstrap peers works | PASS |
| `create_swarm_ignores_invalid_bootstrap_addrs` | Invalid multiaddrs
silently filtered | PASS |
| `create_swarm_with_fixed_port` | `listen_port` parameter works | PASS
|

All 4 pass. The connection test takes ~6s

---------

Signed-off-by: DeepZima <deepzima@outlook.com>
Co-authored-by: Evan <evanev7@gmail.com>
2026-03-25 10:55:12 +00:00
rltakashige 7117d748ec Update dependencies including mlx 0.31.2 (#1789)
Update mlx fork to 0.31.2 and mflux to 0.17.2
2026-03-25 06:03:19 +00:00
Alex CheemaandClaude Opus 4.6 178c617bbb Rename Nemotron to NVIDIA in model picker with logo (#1790)
## Motivation

The Nemotron model family in the model picker sidebar was displaying as
"Nemotron" with a generic checkmark icon. Since these models are
NVIDIA's Nemotron models, the category should be branded as "NVIDIA"
with the official NVIDIA logo, consistent with how other families are
branded (e.g., "llama" → "Meta", "gpt-oss" → "OpenAI").

## Changes

- **FamilySidebar.svelte**: Added `nemotron: "NVIDIA"` to the
`familyNames` mapping so the sidebar displays "NVIDIA" instead of
"Nemotron"
- **FamilyLogos.svelte**: Added the NVIDIA "eye" logo as an inline SVG
for the `nemotron` family, matching the `viewBox="0 0 24 24"` /
`fill="currentColor"` pattern used by all other brand logos
- **ModelPickerModal.svelte**: Added `"nemotron"` to the `familyOrder`
array so NVIDIA appears in a consistent position in the sidebar

## Why It Works

The model picker derives categories from the `family` field in TOML
model cards. Nemotron models already have `family = "nemotron"`, but the
three UI components (display name, logo, sort order) lacked explicit
entries for it, causing fallback behavior (auto-capitalized name,
checkmark icon, alphabetical sorting). Adding explicit entries for all
three aligns NVIDIA with the existing brand pattern.

## Test Plan

### Manual Testing
<!-- Hardware: N/A - dashboard UI change only -->
- Built dashboard successfully (`npm run build`)
- Verified the NVIDIA logo renders in the sidebar alongside existing
brand logos

### Automated Testing
- No test changes needed — this is a purely cosmetic dashboard change

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-25 01:56:59 +00:00
rltakashige 7277c90389 Fix enable thinking (#1786)
## Motivation

Minor regression from #1746 

## Why It Works

Pass thinking properly

## Test Plan

### Manual Testing
Works, it thinks
2026-03-24 16:26:25 -07:00
Evan QuineyandAlex Cheema 7ee88c1f05 override macmon in flake (#1747)
updates macmon to an upstream fork that fixes m5 max issues.
might see if the upstream version gets merged before we release.

---------

Co-authored-by: Alex Cheema <alexcheema123@gmail.com>
2026-03-24 17:30:19 +00:00
Evan Quiney 509533d49e send error finish reason on failing to parse a tool call (#1785)
a simplification of #1757 which is now stale
2026-03-24 17:21:39 +00:00
rltakashige b6240a97e8 Prevent Qwen3.5 looping by using mlx lm fork (#1784)
## Motivation

Move back to an MLX LM to improve the Qwen 3.5 experience. 

## Test Plan

### Manual Testing
Seems to loop less from testing, no speed regressions.
2026-03-24 17:16:41 +00:00
rltakashige 6cdfbb7e8b Add HF_ENDPOINT in the app settings (#1783)
## Motivation
Some users (primarily in China) are unable to access HuggingFace.co. HF
supports the HF_ENDPOINT env variable, and we also support it, but there
is no way to easily do that from the app currently.

## Test Plan

### Manual Testing

hf-mirror works
empty field works
google.com endpoint fails
2026-03-24 17:05:49 +00:00
Evan Quiney fac6832e5f fix warmup consistency for slow machines (#1748)
a fix from pr #1643 which is now stale - should make prefill more
consistent on very slow machines

## testing
qwen-3.5-35b-a3b loads normally
gpt-oss-120b-mxfp4-q8 loads normally
2026-03-24 16:45:55 +00:00
rltakashige 7df3774ca2 Improve batch performance and stats reporting (#1777)
## Motivation

Batch generation reports incorrect statistics, as mlx lm never clears
the original stats, meaning they get polluted over time.
The dashboard also seems considerably slower than bench statistics.
We also have a large discrepancy between B=1 batch generating and
mlx_generate.
Extracting logprobs is massively expensive, causing up to a 25% slowdown
compared to pure batching.
```
[ 12:02:01.1240AM | INFO    ] step overhead: 3.49ms (next=12.49ms total=15.99ms)
[ 12:02:02.1600AM | INFO    ] step overhead: 3.23ms (next=13.01ms total=16.24ms)
[ 12:02:03.2228AM | INFO    ] step overhead: 3.28ms (next=13.38ms total=16.66ms)
[ 12:02:04.2798AM | INFO    ] step overhead: 3.25ms (next=12.84ms total=16.10ms)
[ 12:02:05.3152AM | INFO    ] step overhead: 3.18ms (next=12.61ms total=15.79ms)
[ 12:02:06.3522AM | INFO    ] step overhead: 3.41ms (next=12.83ms total=16.25ms)
[ 12:02:07.3987AM | INFO    ] step overhead: 3.38ms (next=13.14ms total=16.52ms)
[ 12:02:08.4537AM | INFO    ] step overhead: 1.84ms (next=19.44ms total=21.28ms)
```

## Changes

1. Report stats ourselves instead of using mlx lm's stats for batch
generation (they use perf_counter anyway).
2. Adjust exo bench to match
3. Improve logprobs extraction speed by 10x, improving tps for dashboard
& any requests for logprobs
4. Use an SSE comment to align the speed to the real numbers at the end
of generation
5. Patch mlx for several optimizations given our assumptions and use
cases (e.g. use vllm style RoPE).
6. Switch MLX LM version to latest main, including support for Nemotron
Super and some Qwen3.5 fixes.

## Why It Works
1. Exo bench no longer reports polluted stats
2. Exo bench now handles the reported per-request stats rather than the
aggregate stats
3. The decode speed now jumps back to a real number at the end of the
generation
4. Large batch speedup for rotating KV cache models + 1:1 matching cache
with vllm

## Test Plan

### Manual Testing
Needs testing on OpenCode and CC
Needs eval testing

### Automated Testing
Only going to show the performance optimization difference after the
accurate reporting:

**GPT OSS 20B MXFP4 Q8 (large change)**
Before:
<img width="2466" height="1534" alt="image"
src="https://github.com/user-attachments/assets/88b50637-fca2-4db4-9413-b9eee6e2057e"
/>
<img width="2410" height="1240" alt="image"
src="https://github.com/user-attachments/assets/21e5c76a-2f5f-44d2-8953-121b3ebdbd68"
/>


After:
<img width="2476" height="1472" alt="image"
src="https://github.com/user-attachments/assets/fec5cfbd-fff8-430a-b12e-a329410107a2"
/>
<img width="2454" height="1236" alt="image"
src="https://github.com/user-attachments/assets/0400344b-a4a6-42c0-a9dd-4ee91ade714a"
/>



**Qwen 3.5 35B A3B 8bit (No change)**
Before:
<img width="2414" height="1396" alt="image"
src="https://github.com/user-attachments/assets/e75f0b38-df5d-49fd-ab90-bc1667d981b3"
/>


After:
<img width="2346" height="1234" alt="image"
src="https://github.com/user-attachments/assets/eabfb59c-851f-4d88-b927-e1e699a75cc6"
/>


**Llama 3.2 1B Instruct 4bit (small change)**
Before:
<img width="2516" height="1220" alt="image"
src="https://github.com/user-attachments/assets/c2873655-acff-4536-8263-fb8aea33db80"
/>

After:
<img width="2566" height="1370" alt="image"
src="https://github.com/user-attachments/assets/15f95c75-1c2f-4474-85a2-88c4d0a32543"
/>
2026-03-24 14:03:03 +00:00
ciaranbor 248919c2a8 Fix first start in offline mode crash (#1782)
## Motivation

Running exo in offline mode on a machine where a model has never been
downloaded causes a crash

## Changes

- `download_shard` now catches `FileNotFoundError` from
`fetch_file_list_with_cache` and returns a `not_started` progress
instead of propagating the exception

## Why It Works

A status query should never crash its caller. By returning
`not_started`, the coordinator's existing offline guard (`if
self.offline:` at line 198) is reached and emits a graceful
`DownloadFailed` event. This also eliminates the warning spam on startup
where the status iterator catches the same exception for every
predefined model.

## Test Plan

### Manual Testing
- Start exo with `--offline` on a machine with no cached models, request
a model via the API — should get a graceful failure instead of a crash
2026-03-24 13:58:23 +00:00
ciaranbor 49951e1b1a Sync custom model cards across nodes (#1768)
## Motivation

Custom model cards were only saved locally on the node that handled the
API request.

## Changes

- Added AddCustomModelCard and DeleteCustomModelCard commands
- Added CustomModelCardAdded and CustomModelCardDeleted events
- Added custom_model_cards field to cluster State
- Master handles new commands by emitting corresponding events
- Workers persist model cards to disk and update the in-memory cache on
event receipt
- Separated fetch_from_hf (pure fetch) from disk persistence (now
handled by event-sourcing layer)
- Exposed add_to_card_cache() helper for the worker to update the cache

## Why It Works

- Follows the existing event-sourcing pattern: API → Command → Master →
Event → all Workers
- Every node applies the same events, so custom model cards are
consistent across the cluster

## Test Plan

### Manual Testing

Add/delete a custom model via the API on one node, verify it
appears/disappears on all nodes

### Automated Testing

Existing tests cover apply() logic; new event types follow the same
discriminated-union pattern
2026-03-24 12:51:36 +00:00
ciaranbor e06e70a835 Prefer higher model download % for placement (#1767)
## Motivation

When placing a model instance across the cluster, the master previously
only considered available RAM. This meant it could pick a node that
hasn't downloaded the model yet, even when another node already has it
(or is further along in downloading it).

## Changes

- Added download_status parameter to place_instance() in placement.py
- Added _get_node_download_fraction() to compute 0.0–1.0 download
progress per node/model
- Added _cycle_download_score() to sum download fractions across a
cycle's nodes
- Cycle selection now uses a (download_score, available_ram) tuple key —
download progress is the primary sort, RAM is the tiebreaker
- Passed self.state.downloads into place_instance() from master/main.py

## Why It Works

Python's tuple comparison gives download progress strict priority over
RAM, so a node with the model already downloaded will always be
preferred over one with more free RAM but no download.

## Test Plan

### Automated Testing

3 new tests cover: completed download preferred, higher partial progress
preferred, failed download not preferred over no-download node
2026-03-24 12:11:56 +00:00
vskiwianduser e9fdd8d4af improve logging: add dates to verbose stderr, match file log level to verbosity (#1772)
## Summary

- **Add ISO date to verbose stderr format**: when running with `-v`, the
stderr timestamp changes from `HH:mm:ss.SSS` to `YYYY-MM-DD
HH:mm:ss.SSS`, matching the file log format. This makes it possible to
correlate entries across days when stderr is captured by launchd,
systemd, Docker, or file redirection.
- **File log respects verbosity**: `exo.log` now uses DEBUG level when
`-v` is passed, so the persistent log has the same detail as stderr.
Previously it was always INFO regardless of verbosity.
- **Startup banner with PID**: adds a visual separator and process ID to
the startup message, making it easy to identify session boundaries in
long-running logs (e.g. `grep "Starting EXO"`).

The non-verbose (default) stderr format is unchanged — end-user terminal
experience is not affected.

## Motivation

When exo runs as a service (launchd, systemd, Docker), stderr is
typically captured to a file. Without calendar dates in the timestamp,
it is impossible to tell which day a log entry belongs to. This caused
misidentification of log entries during a multi-day RDMA debugging
session on a 4-node cluster.

The file log (`exo.log`) already had dates and rotation, but only
captured INFO level — missing the DEBUG output needed for postmortem
analysis.

## Test plan

- [x] `basedpyright` — 0 errors, 0 warnings, 0 notes
- [x] `ruff check` — all checks passed
- [x] `pytest` — 249 passed, 1 skipped

Co-authored-by: user <user@m1.note>
2026-03-23 16:06:26 +00:00
Evan Quiney 07598a3af1 teeny refactor (#1753)
api.py keeps growing. it's not tied to the master, so should have it's
own top level folder.

## testing
ci, pytest
2026-03-19 15:57:50 +00:00
ciaranbor 63f57fc193 Ciaran/dashboard download bug (#1755)
## Motivation

Download progress in the dashboard was broken: mainly treating all
download statuses as ongoing

## Changes

- Backend (apply.py): Deduplicate download progress events by model_id
instead of full shard_metadata, preventing duplicate entries per node
- Dashboard (+page.svelte): Extract shared collectDownloadStatus()
helper that both getModelDownloadStatus and getInstanceDownloadStatus
use, eliminating ~100 lines of duplicated logic. Adds proper handling
for
DownloadCompleted/DownloadFailed events, uses a Map to deduplicate
per-node entries, and introduces a typed NodeDownloadStatus with
explicit status states (downloading/completed/partial/pending)
- ModelCard: Replace single aggregate progress bar with per-node
download bars, each color-coded by status. Instance preview now scopes
download status to participating nodes only

## Why It Works

- Deduplicating by model_id in apply.py ensures each node has exactly
one download entry per model
- The perNodeMap in the frontend keeps only the latest event per node,
preventing duplicate bars
- Handling DownloadCompleted allows the UI to show finished downloads
instead of dropping them
- Scoping instance previews to assigned nodes avoids showing irrelevant
download progress
2026-03-19 14:47:45 +00:00
ciaranbor a6519ba006 Update mflux to 0.16.9 (#1751)
Prevents malformed output from Qwen-Image
2026-03-17 16:58:23 +00:00
rltakashige b713889f73 Fix exo bench again again (#1750)
Mb premature auto merge
2026-03-17 13:47:24 +00:00
rltakashige 6ee673147d Fix exo bench prefill and decode tps (#1749) 2026-03-17 13:36:44 +00:00
ciaranbor ff4d20eed9 Fix image models through dashboard (#1746)
## Motivation

Image generation/editing from the dashboard was broken. ChatForm
bypassed the parent's model-launch logic, so image requests fell through
to text chat.

## Changes

- Moved image routing logic from ChatForm.svelte to +page.svelte
(routeMessage())
- ChatForm now always delegates to parent via onAutoSend (made required)
- Fixed missing updateActiveConversation() call on message retry

## Why It Works

All sends now go through the parent's launch-then-route path, so image
models get launched before the request is dispatched to the correct
endpoint.
2026-03-17 11:22:01 +00:00
Evan Quiney 7ed4639540 use structured concurrency in download coordinator (#1722)
identical to #1721 but a little safer imo. 


## manual testing
ran a few downloads, cancelled non-master, cancelled master -- no errors
reported.
2026-03-13 14:55:37 +00:00
Mazin Sharaf 29d4165fe2 Add step logo condition to FamilyLogos component (#1676)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->
This was to fix a small issue which was that the StepFun logo was not
included in the sidebar, and I also noticed it from an open issue:
- #1662 

## Changes

<!-- Describe what you changed in detail -->
I added a condition to the FamilyLogos where if the family is "step"
then the logo will be included in the sidebar.

## Test Plan
Open exo, go choose a model, and scroll down the sidebar until you see
the step logo, which should be there.

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->
Check for the step logo in the sidebar.
2026-03-13 13:31:46 +00:00
ecohash-co 12af7c9586 fix: shield macmon cleanup from cancellation to prevent orphaned process (#1714)
## Summary

Fixes a bug where macmon metrics (GPU usage, temperature, power, RAM)
freeze permanently in the dashboard while non-macmon metrics (disk
usage) continue updating normally.

**Root cause: asyncio subprocess pipe transport flow control stall**

Observed on a 2-node Mac Studio M3 Ultra cluster (Python 3.13.12, anyio
4.11.0, macOS with kqueue) running EXO.app for ~36 hours. Diagnostics
confirmed:

1. **Only macmon monitoring is stuck** — disk metrics from the same
InfoGatherer continue updating, proving the Worker, EventRouter, and API
pipelines are healthy
2. **macmon IS producing data** — its stdout pipe is full at exactly
65536 bytes (64KB OS buffer), and macmon is blocked on write at 0% CPU
3. **The pipe read-end FD is still open** — the exo process holds it,
but asyncio isn't reading from it
4. **The stale GPU value (0.21) is wrong** — should be ~1.0 (matching
the other node under identical load)

This is consistent with asyncio's subprocess pipe transport getting
stuck in flow control: `pause_reading()` is called when the internal
buffer exceeds the high-water mark, but `resume_reading()` is never
called, permanently deregistering the FD from kqueue. The `receive()`
coroutine waits forever for data asyncio will never deliver.

```
$ lsof -p <macmon_pid>
macmon  74691  FD=1  PIPE  SIZE=65536  ->0x5ae78ecf376ccd0e  # full pipe

$ lsof -p <exo_pid> | grep <pipe_id>
exo     74689  FD=47  PIPE  SIZE=65536  ->0xd129da474c1340f7  # read end held open, not consumed
```

Note: anyio 4.11's `Process.aclose()` already uses
`CancelScope(shield=True)` for cleanup during cancellation — this is NOT
an election cleanup issue (confirmed by @ciaranbor's testing).

**Fix:** Replace the `async for` iteration with an explicit `receive()`
inside `fail_after()`. If macmon produces no output for 10× its
configured interval (minimum 30s), `TimeoutError` fires, `open_process`
cleanup kills macmon and tears down the stale transport, and the loop
restarts with a fresh subprocess and fresh asyncio transport.

## Test plan

- [x] All 246 existing tests pass
- [ ] Verify macmon restarts after simulated pipe stall (e.g., `SIGSTOP`
macmon, wait for timeout, confirm restart and metrics resume)
- [ ] Long-running soak test on multi-node cluster to confirm the fix
prevents recurrence
2026-03-13 12:54:30 +00:00
ciaranbor f28b2fd037 Extract mlx revision from uv lock (#1715)
## Motivation

The MLX version and git revision in nix/mlx.nix were hardcoded and had
to be manually kept in sync with uv.lock

## Changes

- flake.nix: Extract MLX git rev from uv.lock's source.git URL and pass
as uvLockMlxRev
- nix/mlx.nix: Use uvLockMlxVersion and uvLockMlxRev instead of
hardcoded values; remove version mismatch assertion

## Why It Works

uv.lock is already the source of truth — now Nix reads both version and
rev from it directly. The pinned fetchFromGitHub hash still guards
against unexpected changes.
2026-03-13 12:34:54 +00:00
Mustafa Alp Yılmaz ea18a62581 fix: guard against ZeroDivisionError in mlx_lm stats (#1707)
## Problem

Running short completions (like `max_tokens=1` health check probes) can
finish so fast that `mlx_lm`'s internal `generation_time` rounds to
zero. When that happens, `BatchGenerator.stats()` in
`mlx_lm/generate.py` divides `generation_tokens / generation_time` and
throws a `ZeroDivisionError`, which kills the runner process.

EXO already handles this on its side — lines 289-293 in
`batch_generate.py` guard the TPS calculation with `if
generation_elapsed > 0`. But the call to `self._exo_gen.stats()` on line
294 goes into mlx_lm's *separate* timing code, which doesn't have the
same guard. Two different timers, only one is protected.

In my case, this was triggered by health check probes (content: `"a"`,
`max_tokens=1`). The generation completed in sub-microsecond time,
`generation_time` was exactly `0`, and the runner crashed. Since the
health check command stays in the queue and retries after recovery, it
created an infinite crash loop — every ~15 seconds the runner would load
the model, get the same health check, and die again.

## Fix

Wrap `self._exo_gen.stats()` in a `try/except ZeroDivisionError`. If it
throws, set `mlx_stats` to `None` and fall back to `0.0` for
`prompt_tps`. The only fields EXO reads from `mlx_stats` are
`prompt_tps` and `prompt_time` — losing them on a sub-microsecond
generation has no practical impact.

## Traceback

```
File "batch_generate.py", line 294, in step
    mlx_stats = self._exo_gen.stats()
File "mlx_lm/generate.py", line 1224, in stats
    self._stats.generation_tokens / self._stats.generation_time
ZeroDivisionError: division by zero
```
2026-03-12 14:48:36 +00:00
Miguel CruzandEvan 0782d90ec5 fix: show partial download progress on initial dashboard load (#1706)
## Summary

- Dashboard now shows partial download progress for models that were
partially downloaded in a previous session, instead of showing 0%
- Both `getModelDownloadStatus()` and `getInstanceDownloadStatus()` now
handle `DownloadPending` entries that carry non-zero
`downloaded`/`total` bytes

Fixes #1042

## Root cause

When exo restarts with partially downloaded models, the
`DownloadCoordinator` emits `DownloadPending` events (because
`downloaded_this_session` is 0, even though real bytes exist on disk).
The main page dashboard only checked for `DownloadOngoing` entries, so
these partially downloaded models showed as 0%.

The dedicated `/downloads` page already handled this correctly — it
renders `DownloadPending` entries with a progress bar when `downloaded >
0`. This fix brings the same behavior to the main page.

## Changes

For both `getModelDownloadStatus()` and `getInstanceDownloadStatus()` in
`+page.svelte`:
- Accept `DownloadPending` in addition to `DownloadOngoing`
- For `DownloadPending` entries with `downloaded > 0` or `total > 0`,
synthesize a `DownloadProgress` object from the top-level fields (with
`speed: 0` and `etaMs: 0` since no active download is in progress)
- Skip `DownloadPending` entries where both `downloaded` and `total` are
0 (truly pending, not yet started)

## Test plan

- [ ] Partially download a model, quit exo, relaunch — dashboard should
show partial progress instead of 0%
- [ ] Fully downloaded models still show as complete
- [ ] Active downloads still show real-time progress with speed/ETA
- [ ] Models never downloaded show as not started (not falsely showing
progress)
- [ ] Dashboard builds without errors (`cd dashboard && npm run build`)

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-03-12 10:39:34 +00:00
rltakashige f221a6c85c Normalise Responses API tool call format (#1704)
## Motivation

The responses API often does not provide tool args nested under a
"function" field. Since we follow the chat completions format of tools
in the backend (for MLX chat templates), we need to normalise to this
format.

## Test Plan

### Manual Testing
Works on n8n!
<img width="3442" height="2076" alt="image"
src="https://github.com/user-attachments/assets/9e11d679-0102-4d83-9a8e-b0a7a5898708"
/>
2026-03-11 18:10:12 +00:00
Mustafa Alp Yılmaz 2994b41089 fix: validate num_key_value_heads in tensor sharding placement (#1669)
## Problem

Models with fewer KV heads than nodes crash during tensor parallelism.
For example, Qwen3.5 MoE models have only 2 KV heads — trying to shard
across 4 nodes produces empty tensors and a reshape error at runtime.

The placement system already validates `hidden_size % num_nodes == 0`
but doesn't check KV heads, so it creates configurations that look valid
but blow up when the worker tries to split the attention heads.

Affected models include Qwen3.5-35B-A3B, Qwen3.5-122B-A10B,
Qwen3.5-397B-A17B, Qwen3-Next-80B-A3B, and Qwen3-Coder-Next (all have 2
KV heads).

## Changes

**Placement validation** (`src/exo/master/placement.py`):
- Combined KV heads divisibility check with the existing hidden_size
filter in a single pass
- Cycles where `num_key_value_heads % len(cycle) != 0` are now excluded
for tensor sharding
- Error message includes both constraints when no valid cycle is found

**Model card schema** (`src/exo/shared/models/model_cards.py`):
- Added optional `num_key_value_heads` field to `ModelCard` and
`ConfigData`
- Extracted from HuggingFace `config.json` (handles both top-level and
`text_config` nesting)
- Passed through in `fetch_from_hf()` for dynamically fetched cards

**All 68 inference model cards**
(`resources/inference_model_cards/*.toml`):
- Populated `num_key_value_heads` from each model's HuggingFace config

**Utility script** (`scripts/fetch_kv_heads.py`):
- Fetches `num_key_value_heads` from HuggingFace and updates TOML cards
- `--missing`: only fills in cards that don't have the field yet
- `--all`: re-fetches and overwrites everything
- Uses tomlkit for safe TOML editing and ThreadPoolExecutor for parallel
fetches

## Behavior

- Instance previews no longer show tensor options for models that can't
split their KV heads across the cluster size
- `place_instance()` rejects with a clear error instead of crash-looping
- Pipeline parallelism is unaffected
- 2-node tensor still works for 2-KV-head models (2 ÷ 2 = 1)
- Field is optional — existing custom cards without it continue to work
(validation is skipped when `None`)
2026-03-11 13:46:33 +00:00
Mustafa Alp YılmazandRyuichi Leo Takashige 38f0c09175 fix: use StreamingDetokenizer in batch generator to fix emoji/UTF-8 corruption (#1691)
## Problem

Emojis and other multi-byte UTF-8 characters are rendered as `\ufffd`
(Unicode Replacement Character) in batch streaming responses.

Byte-level BPE tokenizers (like Qwen's) can split multi-byte UTF-8
characters (e.g. a 4-byte emoji) across multiple tokens. The batch
generator decodes each token independently with
`tokenizer.decode([token_id])`, which produces U+FFFD for partial byte
sequences.

The sequential generator doesn't have this problem — it uses
`stream_generate()` from mlx_lm, which internally uses
`StreamingDetokenizer` to buffer incomplete bytes.

## Fix

Use `StreamingDetokenizer` in the batch generator, matching the
sequential path:

- Each `_EngineTask` gets its own `StreamingDetokenizer` instance
- `add_token()` buffers tokens, holding back incomplete UTF-8 byte
sequences until they form valid characters
- `last_segment` returns only complete, valid text
- `finalize()` flushes any remaining buffered bytes when generation
completes

Empty text segments during buffering are harmless — they're already
handled correctly by the downstream streaming pipeline.

---------

Co-authored-by: Ryuichi Leo Takashige <leo@exolabs.net>
2026-03-10 16:10:22 +00:00
ciaranbor f36fd56c38 Include power usage in bench responses (#1692)
## Motivation

Add power/energy usage tracking to /bench API responses.

## Changes

- New PowerSampler class that periodically samples sys_power from each
node during inference
- New PowerUsage / NodePowerStats API types
- Integrated into both bench chat completion and bench image generation
endpoints

## Why It Works

Runs concurrently via anyio.create_task_group, reads from existing
state.node_system heartbeat data — no new plumbing needed. Energy =
avg_power × elapsed_time.

## Test Plan

### Manual Testing

Run a /bench request and verify power_usage appears in the response.

### Automated Testing

7 async tests covering single/multi-node, energy math, dynamic power
changes, empty state, and lifecycle.
2026-03-10 16:02:55 +00:00
rltakashige 82c54dd6d6 Add support for Nemotron sharding (#1693)
### Automated Testing
tested logits match
2026-03-10 15:51:07 +00:00
ciaranbor 3536161f15 Fix stale state.runners (#1684)
## Motivation

When a runner shuts down, there's no mechanism to remove it from
state.runners, causing stale state to accumulate.

## Changes

- apply.py: apply_runner_status_updated now handles RunnerShutdown
status by removing the runner from state instead of adding/updating it.
Removed the separate apply_runner_deleted function entirely.
- events.py: Removed the RunnerDeleted event type — runner cleanup is
now handled via RunnerStatusUpdated with a RunnerShutdown status.
- Tests: New test_apply_runner_deleted.py with 2 tests: verifying
RunnerShutdown removes the runner, and that a normal status update adds
it.

## Why It Works

Instead of a separate RunnerDeleted event and coordinating its emission
from master/placement, runner cleanup is handled naturally through the
existing RunnerStatusUpdated path — a RunnerShutdown status signals
removal. This is simpler and requires no changes to master or placement
logic.

## Test Plan

### Automated Testing

2 new unit tests covering RunnerShutdown removal and normal runner
status addition
2026-03-10 09:32:25 +00:00
ArvidSU a6aa07ed83 feat(dashboard): add mobile drawer support for sidebars (#1677)
## Summary

- Adds mobile-friendly drawer UI for the chat sidebar and model family
sidebar
- Introduces responsive header navigation with mobile-specific toggle
buttons
- Uses slide-in drawer overlay pattern on small screens instead of
collapsible sidebars
- Stores mobile drawer state in app store for consistent state
management

## Testing

Tested on desktop (responsive mode) and mobile viewport widths. Sidebars
now slide in as drawers on small screens with proper z-index layering
and close-on-outside-click behavior.
2026-03-10 04:18:11 +00:00
rltakashigeandEvan Quiney 131ad0ff36 Implement continuous batching (#1642)
## Motivation

Following the changes made in #1632 !
Closes #1020

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->

---------

Co-authored-by: Evan Quiney <evanev7@gmail.com>
2026-03-09 15:04:45 +00:00
ciaranbor d01636100a Ciaran/gpt oss tool call finish reason (#1673)
## Motivation

When gpt-oss truncates a response mid-tool-call (e.g. hitting max
tokens), the finish_reason was swallowed because the parser was in
tool-accumulation mode and never yielded it back to the caller. This
caused the API to hang or fail to signal completion.

## Changes

In parse_gpt_oss, when accumulating tool call arguments and a
finish_reason arrives, yield the response (with empty text) so the
finish reason propagates

## Why It Works

The parser now checks for finish_reason on every chunk while inside a
tool call. When the model stops generating (truncation), the finish
signal is forwarded immediately rather than being silently consumed.

## Test Plan

### Automated Testing

Added tests covering truncated tool calls (finish_reason="length") and
plain text truncation
2026-03-06 21:09:20 +00:00
ciaranbor 79c8dbeacd Ciaran/minor download bugs (#1674)
## Motivation

Two minor bugs in the download coordinator: cancelling a download didn't
reset its status to pending, and a single error in
_emit_existing_download_progress would kill the loop permanently.

## Changes

- Cancel emits pending status: When a download is cancelled, emit a
DownloadPending event so the UI/cluster state reflects that the model is
no longer actively downloading.
- Resilient progress emission loop: Move the try/except inside the while
loop so transient errors are logged but the periodic emission continues.

## Why It Works

- The cancel fix ensures the download status is consistent — without it,
a cancelled download would still appear as in-progress.
- The loop fix prevents a single exception from permanently stopping the
60-second periodic progress broadcast.
2026-03-06 17:46:18 +00:00
Evan Quiney 0096159728 up gossipsub limit (#1671)
bump gossipsub limit to 8MB + add warning
this is a bandaid solution for large messages while we figure out the
point to point protocol
2026-03-06 14:20:51 +00:00
7a36d3968d docs: Update documentation for v1.0.68 release (#1667)
## Motivation

Updated documentation for v1.0.68 release

## Changes

**docs/api.md:**
- Added documentation for new API endpoints: Claude Messages API
(`/v1/messages`), OpenAI Responses API (`/v1/responses`), and Ollama API
compatibility endpoints
- Documented custom model management endpoints (`POST /models/add`,
`DELETE /models/custom/{model_id}`)
- Added `enable_thinking` parameter documentation for thinking-capable
models (DeepSeek V3.1, Qwen3, GLM-4.7)
- Documented usage statistics in responses (prompt_tokens,
completion_tokens, total_tokens)
- Added streaming event format documentation for all API types
- Updated image generation section with FLUX.1-Kontext-dev support and
new dimensions (1024x1365, 1365x1024)
- Added request cancellation documentation
- Updated complete endpoint summary with all new endpoints
- Added security notes about trust_remote_code being opt-in

**README.md:**
- Updated Features section to highlight multiple API compatibility
options
- Added Environment Variables section documenting all configuration
options (EXO_MODELS_PATH, EXO_OFFLINE, EXO_ENABLE_IMAGE_MODELS,
EXO_LIBP2P_NAMESPACE, EXO_FAST_SYNCH, EXO_TRACING_ENABLED)
- Expanded "Using the API" section with examples for Claude Messages
API, OpenAI Responses API, and Ollama API
- Added custom model loading documentation with security notes
- Updated file locations to include log files and custom model cards
paths

**CONTRIBUTING.md:** 
- Added documentation for TOML model cards format and the API adapter
pattern

**docs/architecture.md:**
- Documented the adapter architecture introduced in PR #1167

Closes #1653

---------

Co-authored-by: askmanu[bot] <192355599+askmanu[bot]@users.noreply.github.com>
Co-authored-by: Evan Quiney <evanev7@gmail.com>
2026-03-06 11:32:46 +00:00
Mustafa Alp Yılmaz eee3432738 feat(mlx): add repetition_penalty and repetition_context_size to chat completions (#1665)
## Problem

Models running through exo can get stuck in repetition loops —
generating the same text over and over until hitting the token limit. It
happens more with quantized models where probability distributions can
become degenerate.

`mlx_lm` has `make_logits_processors(repetition_penalty,
repetition_context_size)` that handles this, but it is never called
anywhere in the pipeline.

## Solution

Wire `repetition_penalty` and `repetition_context_size` through the
stack:

- `ChatCompletionRequest` → accept the params from the client
- `TextGenerationTaskParams` → carry them through the pipeline
- `chat_completions.py` adapter → map to internal params
- `generate.py` → call `make_logits_processors()` and merge into the
`logits_processors` list

When `repetition_penalty` is `None` (default), `make_logits_processors`
returns `[]` so existing behavior is unchanged.

## Usage

```json
{
  "model": "mlx-community/...",
  "messages": [...],
  "repetition_penalty": 1.15,
  "repetition_context_size": 64
}
```
2026-03-06 10:51:25 +00:00
Alex CheemaandEvan e8c3a873a6 feat(dashboard): improve model picker feedback and HuggingFace search (#1661)
## Motivation

Fixes #1648. Users adding custom models from HuggingFace got no clear
feedback that the model was successfully added — the model didn't appear
prominently in the list. Additionally, searches were limited to
`mlx-community` models with no fallback.

## Changes

- **Success feedback**: After adding a custom model, a toast
notification appears, the view auto-switches to "All Models", and the
newly added model is scrolled into view with a green highlight that
fades over 4 seconds.
- **HuggingFace search fallback**: The `/models/search` endpoint now
searches `mlx-community` first; if no results are found, it falls back
to searching all of HuggingFace.
- **Inline HF results**: When the main search bar finds no local
matches, HuggingFace search results appear inline with "+ Add" buttons
and a "See all results on Hub" link.
- **Full repo ID for non-mlx models**: Non-mlx-community models now
display the full repo ID (e.g., `meta-llama/Llama-3.1-8B`) instead of
just the short name.

## Why It Works

The toast + scroll + highlight gives immediate, unambiguous feedback
that the model was added and where it lives in the list. The HF search
fallback broadens discoverability while still prioritising mlx-community
models. Inline results in the main search bar mean users don't need to
navigate to the HF tab to discover new models.

## Test Plan

### Manual Testing
- Open model picker → HuggingFace Hub tab → search for a model → click
"+ Add"
- Verify: toast appears, view switches to All Models, model highlighted
with green glow
- Search for a term with no mlx-community results → verify fallback to
full HF search
- Non-mlx-community results show full repo ID (e.g., `org/model-name`)
- On All Models tab, search for a model not in the local list → verify
"From HuggingFace" section appears

### Automated Testing
- Existing tests cover the model catalog and API; no new automated tests
needed for UI behaviour changes

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-03-06 10:23:08 +00:00
Michael Harrigan afab3095b0 fix: KVPrefixCache Regression (#1668) 2026-03-06 10:11:08 +00:00
ciaranbor b9d40e8e35 Ciaran/re download bug (#1658)
## Motivation

After deleting a model and re-downloading it, the CachedShardDownloader
returns the stale cached path, so ensure_shard short-circuits and no
download actually happens.

## Changes

- Added invalidate(model_id) method to the ShardDownloader ABC and all
implementations
- CachedShardDownloader.invalidate evicts cache entries matching the
model ID and delegates down
- DownloadCoordinator.delete_model calls invalidate after cancelling
active downloads, before deleting files
- Added end-to-end test that downloads, deletes, and re-downloads a
model through the coordinator

## Why It Works

The cache is cleared when a model is deleted, so the next ensure_shard
call performs a fresh download instead of returning the stale path.

## Test Plan

## Automated Testing

New test_re_download_after_delete_completes exercises the full download
→ delete → re-download flow through DownloadCoordinator with
CachedShardDownloader + SingletonShardDownloader wrappers matching
production.
2026-03-05 14:18:17 +00:00
wysieandWysie 3a4d635d0c Fix copy code button not working in dashboard (#1659)
Fixes #1657

## Motivation

The copy button on code blocks in the dashboard does nothing when
clicked. This affects all code blocks in assistant responses.

## Root Cause

In `MarkdownContent.svelte`, click event listeners are bound to
`.copy-code-btn` elements via `setupCopyButtons()` inside a Svelte 5
`$effect`. However, the effect fires before the DOM has been updated
with the new HTML, so `querySelectorAll(".copy-code-btn")` finds zero
buttons.

Additionally, during streaming, the `content` prop updates on every
token, causing the entire `{@html processedHtml}` to be re-rendered.
This destroys all previously bound event listeners, even if they were
successfully attached.

## Changes

Replaced the per-button `addEventListener` approach with **event
delegation** — a single click listener on the container element that
catches clicks bubbling up from any `.copy-code-btn` or
`.copy-math-btn`. This:

- Eliminates the timing issue (the listener exists before the buttons
are rendered)
- Survives HTML re-renders during streaming (no need to re-bind)
- Removes the need for `setupCopyButtons()` and the `data-listenerBound`
tracking

## Testing

1. Load any model
2. Prompt it to generate a code block (e.g. "write a hello world in
Python")
3. Click the copy button on the code block
4. Paste — the code is copied correctly
5. Verified the button also works during streaming (before generation
completes)

Co-authored-by: Wysie <wysie@users.noreply.github.com>
2026-03-05 12:41:17 +00:00
Miguel Miranda DiasandEvan 8485805042 fix(worker): emit error chunks when a runner dies mid-command (#1645)
Closes #1586

## Summary
- track in-flight tasks in `RunnerSupervisor` (not only unacknowledged
pending tasks)
- when `_check_runner()` detects a crashed runner, emit
`ChunkGenerated(ErrorChunk)` for each in-flight command task
(`TextGeneration`, `ImageGeneration`, `ImageEdits`)
- keep existing `RunnerStatusUpdated(RunnerFailed)` emission so
planner/state still transition correctly
- add a unit test for supervisor crash path to ensure an error chunk is
emitted before failed runner status

## Why
`#1586` reports streams that can hang forever when runners crash during
warmup/loading. This keeps failure signaling at the runner-supervisor
layer, matching maintainer guidance in the issue thread.

## Validation
- attempted: `uv run pytest
src/exo/worker/tests/unittests/test_runner/test_runner_supervisor.py`
- blocked locally by environment disk exhaustion while uv tried to
materialize heavy CUDA wheels (`No space left on device` during
`nvidia-cudnn-cu13` extraction)

I kept the change scoped and added a targeted unit test for the failure
path.

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-03-04 17:15:58 +00:00
ciaranbor 4de8f801c7 #Add reasoning parms to chat completion and responses APIs (#1654)
## Motivation

Adds reasoning_effort parameter support to both the Chat Completions and
Responses APIs, aligning with the OpenAI spec and enabling thinking
control for gpt-oss models

## Changes

- Added ReasoningEffort literal type ("none" | "minimal" | "low" |
"medium" | "high" | "xhigh") and a resolve_reasoning_params() helper
that cross-derives reasoning_effort ↔ enable_thinking when only one is
provided
- Added reasoning_effort field to ChatCompletionRequest and reasoning
(with Reasoning model) + enable_thinking to ResponsesRequest
- Both adapters now call resolve_reasoning_params() before building
TextGenerationTaskParams
- reasoning_effort is passed through to the MLX chat template as a
template variable

## Why It Works

resolve_reasoning_params is a pure function that normalises the two
overlapping knobs (reasoning_effort and enable_thinking) into a
consistent pair, so downstream code always has both values regardless of
which the caller supplied.

## Test Plan

### Automated Testing

Added test_resolve_reasoning_params.py with 10 test cases covering:
both-None, both-set passthrough, enable_thinking → effort derivation,
and effort → enable_thinking derivation for every ReasoningEffort
variant.
2026-03-04 14:27:49 +00:00
OwleksiyandEvan 5777bf3c39 fix: coerce tool-call argument types from tool schema (#1651)
Apply schema-aware coercion to parsed tool-call arguments so
Hermes-style toolcalls can still return typed JSON (e.g. integer ids).

 - pass request tools into parse_tool_calls
 - coerce parsed argument values by function parameters schema
 - add unit tests for coercion and unknown-tool passthrough

## Motivation

Models that use Hermes-based toolcall syntax (Qwen3.5) can't reliably
call tools with non-string parameters
Example tool:
```json
    {
      "type": "function",
      "function": {
        "name": "process",
        "description": "Manage background processes",
        "parameters": {
          "type": "object",
          "properties": {
            "action": {
              "type": "string",
              "enum": ["spawn", "output", "kill", "list"]
            },
            "id": {
              "type": "integer",
              "description": "Process id"
            },
            "command": {
              "type": "string",
              "description": "Command to run for spawn"
            }
          },
          "required": ["action"],
          "additionalProperties": false
        }
      }
    }
```
Model transcript:
```
<tool_call>
<function=process>
<parameter=action>
output
</parameter>
<parameter=id>
0
</parameter>
</function>
</tool_call>
```
And the API returns:

`{"id":"a8f11689-d840-4ca5-ab1d-ead3678a11a9","name":"process","arguments":"{\"action\":
\"output\", \"id\": \"0\"}"}}`

Tool definition declared `id` as `integer`, the model output is
type-agnostic, and the translation layer treats everything as a string.

The same Qwen3.5-27B on OpenRouter and GPT-4.1-mini on openai obey the
function signature and emit correct call:
```
{"name":"process","arguments":"{\"action\": \"output\", \"id\": 0}"}}
```

Steps to reproduce:
```
❯ curl -sS -v http://localhost:52415/v1/chat/completions \
  -H 'Content-Type: application/json' \  -d @- <<'JSON'
{
  "model": "mlx-community/Qwen3.5-27B-4bit",
  "stream": false,
  "temperature": 0,
  "messages": [
    {
      "role": "user",
      "content": "Call the process tool with action=output and id=0. Do not explain anything. Just make the tool call."
    }
  ],
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "process",
        "description": "Manage background processes",
        "parameters": {
          "type": "object",
          "properties": {
            "action": {
              "type": "string",
              "enum": ["spawn", "output", "kill", "list"]
            },
            "id": {
              "type": "integer",
              "description": "Process id"
            },
            "command": {
              "type": "string",
              "description": "Command to run for spawn"
            }
          },
          "required": ["action"],
          "additionalProperties": false
        }
      }
    }
  ],
  "tool_choice": {
    "type": "function",
    "function": {
      "name": "process"
    }
  }
}
JSON
```
Look for type of `id` function call

## Changes

Function call parameters are now converted to the types that the
function declaration has

## Why It Works

We now explicitly convert types where we know it (and skip if we don't)

## Test Plan

### Manual Testing
Create Qwen3.5-<ANY> instance, send the curl command above. Check that
'id' is now serialized as a number

### Automated Testing
Unit tests to cover basic type conversions

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-03-04 12:22:53 +00:00
Evan Quiney 886192f1e6 ignore closed resource errors when trying to cancel a task (#1652)
fixes an occasional crash during the shutdown of a failed instance.
2026-03-03 16:48:43 +00:00
Evan Quiney d914acd64e check if we have a task before we delete it (#1634)
caused a crash we should instead be logging
2026-03-03 15:32:12 +00:00
rltakashigeandEvan Quiney 37296c8249 Refactor runner for implementing batching (#1632)
## Motivation

Batching will require us to send tasks concurrently and queue them up.
Our current infrastructure cannot handle that all. This PR gets us
closer to this by allowing multiple tasks to be sent in parallel and
then queuing up tasks.

## Changes

Change Plan logic
Make runner main into a class
Add a "BatchGenerator" to which tasks can be submitted (although tasks
are handled sequentially) and sent back through an MpSender.
Refactor runner to accept tasks during generation
Keep the generator threading
Separate the runner into several files for better readability

## Test Plan

### Manual Testing
Tested manually, needs a lot more automated testing. Cancellation still
works on a single device. Needs checking on multiple devices.

### Automated Testing

---------

Co-authored-by: Evan Quiney <evanev7@gmail.com>
2026-03-03 14:38:55 +00:00
28817d3ee3 Add support for Qwen3.5 (#1644)
## Motivation

Qwen3.5 MoE models (e.g., `Qwen3.5-397B-A17B-6bit`) are now supported by
`mlx-lm` via `qwen3_5_moe` model type, but exo lacks tensor parallel
sharding support for this architecture. This prevents running large
Qwen3.5 models across multiple nodes.

Qwen3.5 uses a GatedDeltaNet hybrid attention mechanism similar to
Qwen3-Next, but with a different projection layout — separate
`in_proj_qkv`, `in_proj_z`, `in_proj_b`, `in_proj_a` instead of
Qwen3-Next's combined `in_proj_qkvz` and `in_proj_ba`. This requires
architecture-aware sharding logic.

## Changes (evan summary)

- enable qwen3_5 dense + moe tensor parallelism from config
- defensively skip evalling _cache.keys if it doesn't exist
- ignore kwargs in qwen35 pipeline masking and ensure pipeline segments match global model parameters for mask creation
- add sharding for qwen3_5 moe linear attention
- added another 6 million model cards

## Why It Works

Qwen3.5's GatedDeltaNet has an `in_proj_qkv` linear layer with three
concatenated sections: `[q(key_dim), k(key_dim), v(value_dim)]`. A naive
contiguous split (`segments=1`) would slice across section boundaries,
corrupting q/k/v values and producing garbled output.

By passing `segments=[key_dim, key_dim + key_dim]` to `shard_linear()`,
each section is split independently before distributing across devices.
This ensures every rank receives correctly aligned q, k, and v
components.

The remaining separate projections (`in_proj_z`, `in_proj_b`,
`in_proj_a`) and the MoE layers follow the same `all_to_sharded` /
`sharded_to_all` pattern already used for Qwen3-Next.

Some pipeline splits didn't include an ssm layer or a linear layer resulting in a subset of the model acting like it shouldn't create the appropriate masks for the next layer - we patch the model to manually create such masks.

## Test Plan

tensor sharded 2,3,4 models & pipeline sharded 2,3,4 with simple eval.

---------

Co-authored-by: hw <hw@hwStudio1.local>
Co-authored-by: Ryuichi Leo Takashige <leo@exolabs.net>
Co-authored-by: Evan <evanev7@gmail.com>
2026-03-03 14:31:57 +00:00
Alex Cheema 0e1b9501c3 fix: mini topology sidebar navigates home on click (#1616)
## Motivation

Clicking on the mini topology view in the right sidebar during chat was
selecting/filtering nodes instead of navigating back to the home view.
The entire mini topology section is wrapped in a button that calls
`handleGoHome()`, but individual node clicks were intercepting the event
via `stopPropagation()`.

## Changes

- Removed `onNodeClick={togglePreviewNodeFilter}` from the mini
sidebar's `TopologyGraph` component
- Added `pointer-events-none` to the topology graph container so all
clicks pass through to the parent button

## Why It Works

`TopologyGraph` only registers click/hover handlers when `onNodeClick`
is provided. Removing it disables node interaction entirely in the mini
view. The `pointer-events-none` class ensures no SVG element can
intercept clicks — they all bubble up to the parent `<button
onclick={handleGoHome}>`, which resets the chat state and returns to the
main topology view.

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
- Start exo and open the dashboard
- Start a chat to trigger the mini topology sidebar
- Click on the mini topology view → should navigate back to home
- Confirm nodes in the mini topology are not selectable or hoverable

### Automated Testing
- Dashboard builds successfully (`npm run build`)
2026-03-03 10:46:52 +00:00
Mustafa Alp YılmazandEvan f0d4ccbeb3 feat: add POST /v1/cancel/{command_id} endpoint (#1579)
## Summary

- Adds explicit `POST /v1/cancel/{command_id}` REST endpoint to cancel
in-flight text and image generation commands by ID
- Previously, cancellation only worked via HTTP disconnect (client
closes SSE connection, triggering `anyio.get_cancelled_exc_class()`).
Non-streaming clients and external consumers had no way to cleanly
cancel active generation
- The endpoint looks up the command in active generation queues, sends
`TaskCancelled` to notify workers, and closes the sender stream. Returns
404 (OpenAI error format) if the command is not found or already
completed

## Changes

| File | Change |
|------|--------|
| `src/exo/shared/types/api.py` | Add `CancelCommandResponse` model |
| `src/exo/master/api.py` | Import, route registration, `cancel_command`
handler |
| `src/exo/master/tests/test_cancel_command.py` | 3 test cases (404,
text cancel, image cancel) |
| `docs/api.md` | Document new endpoint + update summary table |

## Design Decisions

- Uses `self._send()` (not raw `command_sender.send()`) — respects API
pause state during elections
- Uses `raise HTTPException` — feeds into exo's centralized OpenAI-style
error handler
- Returns typed `CancelCommandResponse` — consistent with
`CreateInstanceResponse` / `DeleteInstanceResponse` patterns
- `sender.close()` is idempotent so concurrent cancel requests for the
same command are harmless

## Test Plan

- [x] `test_cancel_nonexistent_command_returns_404` — verifies 404 with
OpenAI error format
- [x] `test_cancel_active_text_generation` — verifies 200,
`sender.close()` called, `TaskCancelled` sent with correct
`cancelled_command_id`
- [x] `test_cancel_active_image_generation` — same verification for
image queue
- [x] `basedpyright` — 0 new errors
- [x] `ruff check` — all checks passed
- [x] `pytest` — 3/3 new tests pass, no regressions

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-03-03 10:38:52 +00:00
wysieandWysie 858dc808df fix: replace Master event_sender after EventRouter recreation (#1630) (#1637)
## Motivation
Fix for https://github.com/exo-explore/exo/issues/1630.
When loading a model that spans 2 nodes as the first action after
startup, Exo crashes with anyio.BrokenResourceError in
Master._command_processor. The receive end of the Master's event_sender
channel is already closed (open_receive_channels=0 ) when the Master
tries to send an InstanceCreated event.

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes
In _elect_loop, when is_new_master is True and the EventRouter is
replaced, the Master's event_sender is now swapped in-place with a fresh
sender from the new EventRouter. This mirrors the existing pattern where
the Worker and DownloadCoordinator are already recreated with new
channels from the new router.
<!-- Describe what you changed in detail -->

## Why It Works
When is_new_master fires (which happens on every startup when a second
node joins), the old EventRouter is shut down and a new one is created.
Shutting down the old router cancels its _ingest coroutine, which closes
the receive end of every channel created by event_router.sender(). The
Worker and DownloadCoordinator are already recreated with senders from
the new router, but the Master was not — its event_sender still pointed
to the dead channel from the old router.
This patch replaces self.master.event_sender with a new sender from the
new EventRouter, so events flow through a live channel. We swap in-place
rather than recreating the Master to preserve its DiskEventLog, internal
state, and running tasks.
The reason loading a single-node model first appeared to work around the
issue is a timing race: if the model loads fast enough, the Master sends
its events before the election fires and kills the old channel. With a
2-node model, placement and RDMA setup take longer, so the election
completes first and the channel is already dead by the time the Master
tries to send.
<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
2 x Mac Studio M4 Max 128GB, RDMA
<!-- What you did: -->
Loaded a model that requires 2 nodes and it works properly now.
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->

Co-authored-by: Wysie <wysie@users.noreply.github.com>
2026-03-02 10:05:47 +00:00
ciaranbor 635118ef24 Support trace deletion in dashboard (#1628)
## Motivation

Enable using the dashboard to delete traces

## Changes

- Backend (src/exo/master/api.py): Added POST /v1/traces/delete endpoint
that deletes trace files by task ID, with path traversal protection
- API types (src/exo/shared/types/api.py): Added DeleteTracesRequest and
DeleteTracesResponse models
  - Dashboard store (app.svelte.ts): Added deleteTraces() method
- Traces page (+page.svelte): Added multi-select UI (click to select,
select all/deselect all) with a bulk delete button and confirmation
dialog

## Why It Works

- Uses existing _get_trace_path helper (now hardened against path
traversal) to resolve and delete trace files
- Dashboard selection state is reactive via Svelte 5 runes; deleted
traces refresh the list automatically

## Test Plan

### Manual Testing

- Select individual traces, select all, delete with confirmation dialog
  - Verify traces are removed from disk and list refreshes
2026-02-27 17:29:57 +00:00
Jake Hillion dc0bb5e13b fmt: add taplo TOML formatter to treefmt configuration 2026-02-27 17:19:48 +00:00
rltakashige 152a27ea5d Fix pipeline mismatched send after 1587 (#1629)
## Motivation

Tests caught a bug. It was a real bug.
2026-02-26 16:48:34 +00:00
rltakashige db36bd5ac6 Add custom prefill for pipeline (#1587)
## Motivation

Since we need to do distributed communications between prefill step
sizes, the out-of-the-box stream_generate that we currently use prevents
pipeline parallel models from doing overlapped computation. While this
was technically a regression, this communication is necessary for
cancellation, and we will need various distributed communications in the
future (e.g. for coordinating batching).

500 lines are for one testing file, so the diffs aren't as bad as they
look!

## Changes

Added a special prefill function for pipeline parallel models
Edited the model to handle 
Added a test to verify this new prefill and the original prefill produce
identical results
Improved type stubs to remove some type: ignores 

## Why It Works
<img width="768" height="1246" alt="image"
src="https://github.com/user-attachments/assets/8986ff17-ac23-4a02-9bd7-e6253a0ca799"
/>

## Test Plan

### Manual Testing
Needs more testing, but seems good so far.

### Automated Testing
Passes CI, considerable speedup seen in benchmarks (up to 1.98x) on
prefill speed.

Before:
<img width="3280" height="1238" alt="image"
src="https://github.com/user-attachments/assets/9abc1cbc-ecdb-4e48-a675-2c4cb04a32a0"
/>


After:
<img width="3344" height="1236" alt="image"
src="https://github.com/user-attachments/assets/e03c7987-41b4-4950-9ac3-2840e774ce30"
/>
2026-02-26 16:00:38 +00:00
Evan Quiney 639243aa09 event router (#1572)
replace the nack & resend logic in the worker/download coordinator with
a dedicated subsystem in front of the topic router. this centralizes
that logic (and the concept of system ids) to reduce replication in the
codebase. each system reading or writing events now gets a clean stream
of events in and can trust written events will be retried until
acknowledged.
2026-02-26 14:17:02 +00:00
Evan Quiney db73c4fd5d move messaging into rust (#1549)
the main body of the rust refactor. fixes the tokio panic on shutdown.
simplifies the networking module significantly. doesn't touch lp2p
behaviour
2026-02-26 13:58:22 +00:00
ciaranbor eaed92952c Use tmpdir for coordination file (#1624)
## Motivation

Coordination files for MLX distributed init were written to the current
working directory (./hosts_*.json)

## Changes

- Move coordination file creation to a tempfile.TemporaryDirectory(),
which auto-cleans on context manager exit
2026-02-26 10:59:36 +00:00
Evan Quiney ba611f9cd0 Revert "report macmon failures more aggressively (#1618)" (#1625)
this pr broke macmon - revert it
2026-02-25 19:15:55 +00:00
Evan QuineyandAlex Cheema eab3e0b456 report macmon failures more aggressively (#1618)
macmon appears to be going silent. time it out and restart it after
3*interval (Default 3s) and report warning.

Co-authored-by: Alex Cheema <41707476+AlexCheema@users.noreply.github.com>
2026-02-25 17:49:10 +00:00
Evan Quiney c4e874e97d skip nan logprobs on tokens (#1622)
sometimes we generate NaN logprobs for tokens, this causes pydantic
validation errors on the receiving end. in this case we should just not
send the logprob items
2026-02-25 17:44:15 +00:00
rltakashige e23c3a3026 Address Mac Mini pipeline GPU timeouts (#1620)
## Motivation
Users were reporting GPU timeout errors on Mac Minis, which we never saw
on testing with Mac Studios. It also seems to only happen with large
models.

## Changes
Eval specific distributed operations.

## Why It Works

As I wrote in a Slack message:
Basically, prefill is too slow for pipeline communications. If there are
both communications and GPU operations as part of an mlx graph, the
communications become subject to the GPU's 5 second command buffer
timeout.

For normal generation, I added evals to the communications (only during
prefill, as it slows down decode) to do this, fixing GPU timeouts.

But we don't do this during warmup, as the prompt is absolutely tiny.
This is still too slow on an M4 Pro on some models that it causes a GPU
timeout during warmup...


----------------------
This was one of the issues. However, there is another issue:

mx.all_gather sometimes reads stale data with FAST_SYNCH enabled. I'm
still investigating the root cause, but the code as it is now works on
Mac Minis.



## Test Plan

### Manual Testing
<img width="2762" height="1808" alt="image"
src="https://github.com/user-attachments/assets/27c88542-606c-4551-8f7c-bd2c0471f54e"
/>

<img width="2820" height="1898" alt="image"
src="https://github.com/user-attachments/assets/0ba3478c-ee39-438d-902c-92893db23d05"
/>


### Automated Testing
Needs a bunch on mac minis
2026-02-25 17:37:32 +00:00
Alex CheemaandClaude Opus 4.6 190e63e56d fix: log exceptions causing silent node shutdown (#1621)
## Motivation

Nodes silently shut down mid-inference with no exception logged. The
logs show a clean-looking shutdown cascade (unsubscribe all topics →
stop worker → runner communication closed) but no error explaining
*why*. This makes debugging cluster issues extremely difficult.

## Changes

**`src/exo/routing/router.py`** — Added `try/except Exception` around
`_networking_recv()` and `_networking_recv_connection_messages()` loops.
Logs the root cause at ERROR level via
`logger.opt(exception=...).error(...)` before re-raising. Uses
`Exception` (not `BaseException`) so clean SIGTERM cancellation is
unaffected.

**`src/exo/main.py`** — Wrapped `anyio.run(node.run)` in
`try/except/finally`:
- `except BaseException`: logs the fatal exception at CRITICAL level
through loguru
- `finally`: ensures `logger.info("EXO Shutdown complete")` and
`logger_cleanup()` always run, guaranteeing the async log queue is
flushed before process exit

## Why It Works

The Router's recv loops (`_networking_recv`,
`_networking_recv_connection_messages`) are infinite `while True` loops
calling Rust bindings with **no exception handling**. When the Rust
networking layer raises `ConnectionError` (e.g. channel closed), the
exception cascades silently through anyio task groups: Router → Node →
all components shut down. The exception was never logged because (1) no
try-except anywhere in the cascade, and (2) `logger_cleanup()` was
skipped when `anyio.run()` raised, so loguru's async queue was never
flushed.

## Test Plan

### Manual Testing
- Run `uv run exo`, send SIGTERM → should see clean shutdown with "EXO
Shutdown complete", no error/critical logs
- Next time the silent shutdown reproduces, logs should now show the
actual exception with full traceback at ERROR level from the recv loop,
plus CRITICAL level from main()

### Automated Testing
- All 222 existing tests pass (1 pre-existing failure in
`test_python.py::test_sleep_on_multiple_items` unrelated to this change)
- `uv run basedpyright` — 0 errors
- `uv run ruff check` — all checks passed
- `nix fmt` — applied

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-25 08:24:18 -08:00
Alex CheemaandClaude Opus 4.6 7660893538 fix: replace flaky internet checks with explicit offline mode (#1615)
## Motivation

The socket-based internet connectivity probes (connecting to
1.1.1.1/8.8.8.8/1.0.0.1 every 10 seconds) are flaky — they produce false
negatives on some networks/ISPs, causing downloads to silently fail or
get stuck. Instead of dynamically detecting connectivity, this replaces
it with an explicit offline mode that the user opts into.

## Changes

- **Removed internet check infrastructure**: Deleted
`_test_internet_connection()` (socket probes) and
`_check_internet_connection()` (10s polling loop) from
`DownloadCoordinator`
- **Renamed `internet_connection` → `offline`** on `ShardDownloader`
base class and all subclasses (`SingletonShardDownloader`,
`CachedShardDownloader`, `ResumableShardDownloader`)
- **Removed `on_connection_lost` callbacks** from download calls — no
longer needed without dynamic connectivity detection
- **Added `EXO_OFFLINE` env var support** in `constants.py` and `Args`
default in `main.py`
- **Added Offline Mode toggle** to macOS app Settings → General tab,
which sets `EXO_OFFLINE=true` in the process environment and restarts

## Why It Works

The download behavior (`skip_internet` conditionals in
`download_utils.py`) is unchanged — we just changed what drives it.
Instead of a flaky socket probe setting
`internet_connection=True/False`, the user explicitly sets offline mode
via:
1. `--offline` CLI flag
2. `EXO_OFFLINE=true` environment variable
3. macOS app Settings → General → Offline Mode toggle

This is more reliable and predictable. Users on flaky networks no longer
get intermittent download failures.

## Test Plan

### Manual Testing
- `uv run exo --offline` starts without internet checks, rejects
downloads for unavailable models
- `EXO_OFFLINE=true uv run exo` behaves identically
- macOS app Settings toggle persists across restarts and passes env var
to the exo process

### Automated Testing
- All existing tests pass (222 passed), including 8 offline-mode
specific tests
- `uv run basedpyright` — 0 errors
- `uv run ruff check` — all checks passed
- `nix fmt` — 0 files changed

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-24 13:36:19 -08:00
Evan Quineyandrltakashige 9a2d2a4a7c bump (#1608)
should be release for 1.0.68 - let's synch our py version with our app
version - 0.3.68.
next minor release should be 1.4.0 and 0.4.0 respectively.

Co-authored-by: rltakashige <rl.takashige@gmail.com>
2026-02-24 19:14:15 +00:00
Alex CheemaandClaude Opus 4.6 bea64fe85e fix: prevent stale loading state and conversation loss when switching chats (#1613)
## Motivation

When navigating back to a previous conversation that used a different
model than the one currently loading, the UI incorrectly displayed the
other model's loading/download progress bar instead of the conversation
messages. Additionally, attempting to continue that old conversation by
sending a message would create an entirely new chat rather than
continuing the existing one.

## Changes

All changes in `dashboard/src/routes/+page.svelte`:

1. **Added fallthrough reset in the `chatLaunchState` restore
`$effect`**: When switching to a conversation whose model has no active
instance (not running, not downloading, not loading), the effect now
resets `chatLaunchState` to `"idle"` instead of leaving stale state from
a different model.

2. **Added `skipCreate` parameter to `launchModelForChat()`**: When
continuing an existing conversation (has messages),
`createConversation()` is skipped so the old conversation is preserved
rather than replaced with a new empty one.

3. **Reordered view conditional**: Progress views
(downloading/loading/launching) take priority, then conversation
messages (when idle with messages or model ready), then model selector
(idle with no messages). This ensures:
   - Old conversations display normally when their model isn't running
- Download progress shows when relaunching a model for any conversation
   - Model selector only appears when there are no messages

## Why It Works

- The `$effect` fallthrough prevents `chatLaunchState` from retaining a
stale value (e.g., `"downloading"`) from Model A when the user switches
to a conversation using Model B that has no active state.
- The `skipCreate` flag ensures `launchModelForChat` can be reused for
both new conversations (from model picker) and continuing existing ones
(from chat input) without always creating a new conversation.
- The reordered view logic ensures each state maps to the correct UI:
active launch → progress, existing messages → chat view, nothing → model
selector.

## Test Plan

### Manual Testing
- Load an old conversation while a different model is downloading →
should see conversation messages, not the other model's loading bar
- Send a message from that old conversation → model should
launch/download with progress shown → conversation continues with the
response appended
- Select a new model from the picker → should still create a new
conversation as before
- New conversation with model download → progress bar shows correctly

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-24 19:08:31 +00:00
rltakashige 14526d281a update mlx 2 (#1611)
## Motivation

GPU locks because of prompt progress callbacks taking time. Current
solution: Don't fix it, make the symptom better

## Changes
Shortened timeout by 2x
Get event leak fixes from latest upstream
2026-02-24 18:30:48 +00:00
Evan Quiney 73e50df827 fix glm5 tool calling (#1612)
glm5 is a deepseekv32 model, so was parsing dsml style tool calls
instead of glm style tool calls. fix it!!
2026-02-24 18:21:18 +00:00
Alex CheemaandClaude Opus 4.6 2b417f28ff fix: sync model selectors between sidebar and chat input (#1610)
## Motivation

The "Load Model" dropdown in the right sidebar and the model selector
above the chat input were operating on independent state
(`selectedPreviewModelId` vs `selectedChatModel`). This caused several
UX issues:
- Selecting a model in one selector didn't update the other
- After going home from a chat, the chat selector showed "SELECT MODEL"
while the sidebar still showed the model
- On page refresh, only the sidebar restored the saved model
- Sending a chat message with a running model briefly showed the
recommended models view instead of starting the chat

## Changes

- **`handleModelPickerSelect`**: Also sets `selectedChatModel` when a
model is picked from the sidebar dropdown
- **`handleChatPickerSelect`**: Also sets `selectedPreviewModelId` when
a model is picked from the chat selector
- **`handleGoHome`**: Restores `selectedChatModel` from the sidebar's
`selectedModelId` instead of clearing it
- **`applyLaunchDefaults`**: Syncs `selectedChatModel` when restoring
saved defaults on page load
- **`handleChatSend`**: Sets `chatLaunchState = "ready"` before calling
`sendMessage` when the model is already running, ensuring the chat view
renders correctly on view transition

## Why It Works

The two selectors were backed by different store properties that were
never kept in sync. By updating both properties in every selection path
(sidebar pick, chat pick, go-home, page restore), they always reflect
the same model. The `chatLaunchState = "ready"` fix mirrors what
`launchModelForChat` already does (line 2649) and prevents the chat
state from being "idle" when transitioning from the welcome view to the
chat view.

## Test Plan

### Manual Testing
<!-- Hardware: MacBook Pro M4 Max 48GB -->
- Select a model from sidebar "Load Model" dropdown → chat input
selector updates to match
- Select a model from chat input selector → sidebar dropdown updates to
match
- Launch a model, chat with it, click "Go Home" → both selectors show
the same model
- Refresh the page → both selectors show the previously selected model
- With a running model, type and send a message from the welcome view →
chat starts directly without flashing the recommended models view

### Automated Testing
No new automated tests — this is a UI state synchronization fix in the
Svelte dashboard with no backend changes.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-24 17:31:20 +00:00
Alex CheemaandClaude Opus 4.6 b65982ddd7 fix: improve text contrast on HOME and DOWNLOADS nav links (#1609)
## Motivation

Follow-up to #1601 (downloads page contrast fix). The HOME and DOWNLOADS
navigation links in the top-right header use `text-exo-light-gray`
(`oklch(0.6 0 0)`) which is too dim against the dark header background.

## Changes

Changed both nav links in `HeaderNav.svelte` from `text-exo-light-gray`
to `text-white/70` for better visibility. Hover state
(`text-exo-yellow`) is unchanged.

## Why It Works

`text-white/70` provides noticeably better contrast against
`bg-exo-dark-gray` while still looking subdued relative to the yellow
accent color on hover. This is consistent with the approach used in
#1601.

## Test Plan

### Manual Testing
- Verified both links are clearly readable on the home page and
downloads page
- Hover state still transitions to yellow as expected

### Automated Testing
- Dashboard builds successfully

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-24 13:48:40 +00:00
rltakashige 2fe689315b download .model files in exo bench (#1607)
## Motivation

failed again for kimi on a machine that had never downloaded it.

## Test Plan

### Manual Testing
it worked this time
2026-02-24 11:13:04 +00:00
Alex CheemaandClaude Opus 4.6 644c5573ce fix: improve text contrast on downloads page (#1601)
## Summary
- Bumps opacity on dark-grey text/icons on the downloads page that were
nearly invisible against the dark background
- Informational text (GB sizes, speeds, disk free, model IDs) → full
`text-exo-light-gray`
- Interactive icons (delete, resume, retry) → `/70` at rest
- Hover-only elements (download button) → `/60`

## Test plan
- [ ] Open http://localhost:52415/downloads with models downloaded
- [ ] Verify GB downloaded amounts are clearly visible
- [ ] Verify delete trash icons are visible (not just on hover)
- [ ] Verify download speed text is readable
- [ ] Verify "paused" labels and resume buttons are visible
- [ ] Verify "X GB free" disk labels in column headers are readable

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-24 10:42:49 +00:00
rltakashige 12c3015f52 fix qwen moe tensor sharding (#1604)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-02-23 21:23:27 +00:00
rltakashige 365dd68d9a Final fixes for release (#1603)
## Motivation

<!-- Why is this change needed? What problem does it solve? -->
<!-- If it fixes an open issue, please link to the issue here -->

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-02-23 21:10:15 +00:00
Alex CheemaandClaude Opus 4.6 d3d129581e test: verify instance deletion cancels ongoing tasks (#1508)
## Summary
- The cancellation logic for issue #1215 already exists in
`get_transition_events()` (`src/exo/master/placement.py:208-227`) — when
an instance is deleted, `TaskStatusUpdated(Cancelled)` events are
emitted for all Pending/Running tasks on that instance
- Combined with PR #1276's token-boundary cancellation in runners, the
full pipeline works end-to-end
- However, the existing test
`test_get_transition_events_delete_instance` passed `{}` for tasks, so
this path was never exercised
- This PR adds 4 tests covering the cancellation behavior:
  - Running tasks are cancelled on instance deletion
  - Pending tasks are cancelled on instance deletion
  - Completed/Failed/TimedOut/Cancelled tasks are left alone
  - Only tasks matching the deleted instance are cancelled

Closes #1215

## Test plan
- [x] `uv run pytest src/exo/master/tests/test_placement.py -v` — all 15
tests pass
- [x] `uv run basedpyright` — 0 errors
- [x] `uv run ruff check` — all checks passed

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-23 20:12:23 +00:00
Alex Cheema c90a0cec78 fix: suppress closure errors in runnersupervisor and force spawn start method (#1547)
some errors could be thrown during shutdown - we can dismiss these safely

co-authored by me :)
2026-02-23 18:30:41 +00:00
Alex CheemaandClaude Opus 4.6 e8c1337168 fix: add download/resume buttons to pending downloads (#1581)
## Summary
- Adds a **resume button** (download icon) to paused pending downloads
(those with partial progress)
- Adds a **download button** to not-started pending downloads
- Both buttons call the existing `startDownload()` function which
handles both new downloads and resuming partial ones
- Previously, paused downloads only showed a "paused" label with no
action, and not-started downloads showed "..." with no way to trigger
them

## Test plan
- [ ] Build dashboard (`cd dashboard && npm run build`)
- [ ] Start exo, navigate to Downloads tab
- [ ] Verify paused downloads show a clickable resume (download arrow)
icon below the progress bar
- [ ] Verify not-started pending downloads show a clickable download
icon
- [ ] Click both button types and confirm downloads start/resume

> Note: Screenshot could not be captured because the dashboard requires
the exo backend API to render, and exo has a pre-existing
`Keypair.generate()` startup bug on main.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-23 18:20:35 +00:00
Alex CheemaandClaude Opus 4.6 7024ddcf3e fix: detect completed downloads by checking final file exists (#1582)
## Summary

Split from #1547 per review feedback.

When scanning existing download status, `get_downloaded_size()` returns
bytes from either the final file or its `.partial` counterpart — so a
`.partial` file with all bytes downloaded (e.g. process killed before
hash verification and rename) could be falsely reported as complete.

The previous approach (commit 3b54e7df) added a byte-comparison fallback
in the coordinator (`downloaded >= total > 0`), but this suffered from
the same `.partial` conflation issue.

**Fix:** Check whether the final (non-`.partial`) file actually exists
on disk before marking status as `"complete"` in `download_utils.py`.
This is the only reliable signal that hash verification passed and the
rename from `.partial` succeeded. The coordinator-level byte comparison
is removed since the source now reports correctly.

### Changes
- `download_utils.py`: Add `final_file_exists` check — only report
`"complete"` when the renamed, hash-verified file exists with matching
size
- `coordinator.py`: Revert to simple `progress.status == "complete"`
check, removing the byte-comparison fallback

**Note:** The corresponding byte-comparison workaround in #1547 should
also be removed.

## Test plan
- [x] basedpyright — 0 errors
- [x] ruff check — passes
- [x] pytest — 218 passed, 1 skipped (1 pre-existing Rust bindings
failure)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-23 18:12:07 +00:00
Alex CheemaandClaude Opus 4.6 dc89ba662a feat: add info button to model picker variant rows (#1589)
## Motivation

When a model has multiple quantization variants (e.g., 4-bit, 8-bit),
expanding the group shows each variant's quantization and size — but
there's no way to see which specific nodes have each variant downloaded.
The top-level model group has an (i) info button, but the individual
variant rows don't. This makes it hard to tell which nodes have which
quantization.

## Changes

- Added an (i) info button to each expanded variant row in
`ModelPickerGroup.svelte`
- When clicked, opens the existing info modal scoped to that single
variant (showing its quantization, size, and which nodes have it
downloaded)
- Changed the variant row element from `<button>` to `<div
role="button">` to allow nesting the info `<button>` (matching the
pattern used by the top-level model row)

## Why It Works

Reuses the existing `onShowInfo` callback and info modal by constructing
a synthetic single-variant `ModelGroup` from the clicked variant. No
changes needed to `ModelPickerModal.svelte` — the info modal already
handles single-variant groups correctly.

## Test Plan

### Manual Testing
<!-- Hardware: MacBook Pro -->
- Open dashboard, open model picker
- Expand a model with multiple variants — each variant row now has an
(i) icon
- Click a variant's (i) — the info modal shows that single variant's
quantization/size and which nodes have it downloaded

### Automated Testing
- Dashboard builds successfully with `npm run build`
- `nix fmt` passes

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-23 09:53:36 -08:00
Alex CheemaandClaude Opus 4.6 5cd96b5060 feat: seamless chat UX with auto model selection and smart recommendations (#1590)
## Motivation

The current chat experience requires users to manually select a model
before they can start chatting. This creates unnecessary friction —
especially for new users who may not know which model to pick.
Additionally, models that are already running or downloading appear
greyed out in the picker if available memory is low, making them
impossible to select even though they're already loaded.

## Changes

### New: ChatModelSelector component
- Category-based model recommendations (Best for Coding, Writing,
Agentic, Biggest) shown on the idle chat screen
- Tiered model ranking system (`AUTO_TIERS`) that picks the best model
fitting available memory
- Fixed-position tooltips that escape overflow-hidden ancestors

### Auto model selection from chat
- Typing in an empty chat auto-picks the best model using tier rankings
- Prefers already-running models over launching smaller new ones (avoids
the "launched a big model but recommends a smaller one" bug)
- If a model is already downloading/loading, attaches to existing
progress instead of launching a duplicate

### Launch-from-chat state machine
- Full `idle → launching → downloading → loading → ready` flow with
inline progress display
- `pendingAutoMessage` queue: messages typed during model launch are
remembered and sent once model is ready
- Fixed `$effect` race condition where restore effect competed with
auto-advance effect, causing lost messages

### Model picker improvements
- Instance status badges: green dot (ready), blue pulsing (downloading),
yellow pulsing (loading) on both group and variant rows
- "Ready" availability filter in ModelFilterPopover
- **Models with active instances (ready, downloading, loading) are never
greyed out** regardless of memory — they're already loaded/in-progress
and must be selectable

### Home page model display
- Model picker button shows the best running model name instead of "—
SELECT MODEL —" when a model is already active
- Uses the same `bestRunningModelId` tier-based derived as the chat
auto-selection

### Chat sidebar & form
- ChatSidebar conversation management with rename, delete, "New Chat"
- `userForcedIdle` guard prevents reactive effects from overriding
explicit user actions (e.g., clicking "New Chat")
- ChatForm simplified: uses ModelPickerModal instead of inline dropdown,
`modelDisplayOverride` prop for showing auto-selected models

## Why It Works

The tier-based auto-selection (`getAutoTierIndex` + `pickAutoModel`)
ensures users always get the best model their cluster can run. By
checking running instances first and comparing tiers, we avoid the bug
where loading a large model reduces available memory and causes a
smaller model to be recommended. The `$effect` ordering fix (moving the
`chatLaunchState === "ready"` guard after the `hasRunningInstance`
check) ensures pending messages are always delivered. The instance
status override in ModelPickerGroup (`anyVariantHasInstance`) ensures
models with active instances bypass memory-based greying regardless of
available RAM.

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
- Launch exo, navigate to Chat view
- Verify idle chat shows category recommendations (Coding, Writing,
Agentic, Biggest) based on available memory
- Type a message without selecting a model → verify auto-selection picks
the best tier model
- Launch a model, go to New Chat → verify the input bar shows the
running model name (not "SELECT MODEL")
- Launch a model, go to Home → verify model picker button shows the
running model
- While a model is downloading, send a message → verify download
progress appears and message is queued
- Open model picker with a model running → verify it is NOT greyed out
and is selectable
- Click "New Chat" while in a conversation → verify it resets to idle
with model selector

### Automated Testing
- Dashboard builds successfully (`cd dashboard && npm run build`)
- No TypeScript errors in the build output


🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-23 09:32:55 -08:00
rltakashige 05986f77aa add exo bench protobuf dependency (#1596)
kimi k2.5 requires protobuf
2026-02-23 16:45:22 +00:00
dab7ed4821 fix: handle gossipsub MessageTooLarge error to prevent silent crash (#1583)
## Summary

Large prompts (70K+ tokens / ~500KB+ JSON) cause exo to silently crash.
The root cause is an unhandled `PublishError::MessageTooLarge` from
gossipsub when serialized `TextGeneration` commands exceed the 1MB
`max_transmit_size` limit.

The error propagates as a generic Python exception through the PyO3
bindings. Since `_networking_publish` in `router.py` only catches
`NoPeersSubscribedToTopicError` and `AllQueuesFullError`, the unhandled
exception crashes the networking async task, causing exo to shut down
silently — no error message, no API response.

## Changes

- **Rust (PyO3 bindings):** Add `MessageTooLargeError` exception class
and handle `PublishError::MessageTooLarge` explicitly in the gossipsub
publish path, matching the existing pattern for
`NoPeersSubscribedToTopicError` and `AllQueuesFullError`
- **Python (router):** Catch `MessageTooLargeError` in
`_networking_publish` and log a warning with the message size,
preventing the networking task from crashing

## Reproduction

On a multi-node cluster with a large model (e.g., GLM-5 754B tensor
parallel over JACCL RDMA):
1. Send a chat completion request with ~70K+ tokens
2. exo silently shuts down — no error logged, curl gets no response
3. With shorter prompts (< ~50K tokens): works fine

## Test plan

- Verified `cargo check` passes for `networking` and `exo_pyo3_bindings`
crates
- Verified `ruff check` passes for modified Python files
- Manual testing on 4× Mac Studio M3 Ultra cluster: 50K token requests
pass, 70K+ previously caused silent shutdown, now logs a warning and
drops the oversized message gracefully

Co-authored-by: vsm <vsm@nomail.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: rltakashige <rl.takashige@gmail.com>
2026-02-23 16:21:21 +00:00
Evan Quiney 2261014715 runner process checks (#1592)
partial solve to some of the more mysterious failures

notably, runner now switches to failed after EXO RUNNER MUST OOM
2026-02-23 16:11:18 +00:00
Evan Quiney 61d2a2b6cf add lazy task group (#1569)
in a few places we instantiate task groups early, in others we have an
optional task group.

this standardizes these patterns into a lazy task group, which queues
tasks until it is entered, at which point it enters its inner task group
and starts all its queued tasks. it should be noted that no queued tasks
will be started until the group itself is started.
2026-02-23 16:05:59 +00:00
Evan Quiney 0ff99a2c40 fix isinstance for qwen3Moe (#1595)
we were checking if qwen3 had a transformers Qwen3DecoderLayer rather
than an mlx Qwen3MoeDecoderLayer causing an assertion error on loading
qwen models - this corrects it to the actual layer type
2026-02-23 15:39:13 +00:00
rltakashige fbb80e1cc9 Address ring slowdown by turning on FAST SYNCH (#1594)
## Motivation

Large models + large prompts + pipeline RING = 0.2tps generation speed
Large models + large prompts + pipeline JACCL = 15 tps generation speed

Why? Well, MLX_METAL_FAST_SYNCH is on in pipeline JACCL.

## Changes

Just turn on fast synch everywhere, especially as GPU locks are old news

Also, changed to use mx.device_info as mx.metal.device_info is going to
be deprecated.

## Why It Works

Some magic thing that happens in the mlx backend. I really tried to find
a regression but couldn't. I will probably try again at some point.

## Test Plan

### Manual Testing
Did a bunch, no longer 0.2tps

### Automated Testing
We'll do that today.
2026-02-23 15:14:58 +00:00
Jake Hillion 8d94eab6c6 bench: fix KeyError on DownloadCompleted total field
The bench harness accessed the serialized DownloadCompleted field as
"totalBytes", but the Python field is `total: Memory` which serializes
to "total" (not snake_case, so the camelCase alias generator leaves it
unchanged). This caused a KeyError when --danger-delete-downloads
needed to free disk space by deleting existing models.

Changed the key from "totalBytes" to "total" to match the actual
serialized JSON structure.

Test plan:
- CI
- Reproduced KeyError on unfixed code by filling disk on a test node
  to 14GB free and running exo-bench with a 16GB model
  (Meta-Llama-3.1-8B-Instruct-bf16) with --danger-delete-downloads
- Verified fixed code successfully deletes smaller models to free
  space and completes the benchmark run
2026-02-23 14:17:41 +00:00
f370452d7e Better onboarding UX (#1533)
## Summary
- **Complete onboarding wizard**: 7-step flow guiding new users from
Welcome → Your Devices (topology) → Add More Devices (animation) →
Choose Model → Download → Load → Chat
- **Native macOS integration**: NSPopover welcome callout anchored to
menu bar icon on first launch, polished DMG installer with
drag-to-Applications arrow
- **Dashboard UX polish**: auto-download on model select, toast
notifications, connection banner, skeleton loading, download progress in
header, recommended model tags, sidebar hidden in home state for cleaner
first impression
- **Settings & menu bar overhaul**: native Settings window with Advanced
tab, onboarding reset, chat sidebar toggle

## Test plan
- [ ] Fresh install: verify onboarding wizard appears and flows Welcome
→ Topology → Animation → Model → Download → Load → Chat
- [ ] Verify topology shows real device data in onboarding step 2
- [ ] Verify selecting a model in the main dashboard picker
auto-triggers download
- [ ] Verify chat sidebar is hidden on home view, appears when chat is
active
- [ ] Verify DMG installer has white background with curved arrow
- [ ] Verify NSPopover appears anchored to menu bar icon on first launch

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Ryuichi Leo Takashige <leo@exolabs.net>
2026-02-23 11:27:28 +00:00
Alex CheemaandClaude Opus 4.6 a4c2aa2b87 fix: raise error when MlxJaccl requested without RDMA cycles (#1585)
## Summary
- When MlxJaccl (RDMA) placement is requested but no RDMA-connected
cycles exist, raise a clear ValueError instead of silently falling back
to non-RDMA cycles
- Split from #1519 for independent review

## Test plan
- [x] basedpyright — 0 errors
- [x] ruff check — passes

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-23 10:09:01 +00:00
Alex CheemaandClaude Opus 4.6 7312c535b4 feat: add user context prompt and GitHub issue option to macOS bug report (#1544)
## Summary

- When clicking "Send Bug Report" in the macOS app, users are now
prompted with "What's the issue? (optional)" before the diagnostic
upload begins
- The user's description is included in the uploaded report JSON
(`user_description` field)
- After successful upload, a "Create GitHub Issue" button opens the
browser to `github.com/exo-explore/exo/issues/new` pre-filled with the
user's description, macOS version, and EXO version

## Changes

- **`ContentView.swift`**: Replaced simple button with multi-phase
inline UI (idle → prompting → sending → success/failure). Added
`openGitHubIssue()` helper using `URLComponents` for pre-filled GitHub
issue URLs.
- **`BugReportService.swift`**: Added `userDescription` parameter to
`sendReport()` and `makeReportJson()`, included in the JSON payload when
non-empty.
- Removed the previous `BugReportModal.svelte` approach (the feature
belongs in the macOS app, not the dashboard).

## Test plan

- [ ] Build the Xcode project and run the macOS app
- [ ] Click "Send Bug Report" → verify text prompt appears
- [ ] Type a description, click Send → verify upload succeeds and
"Create GitHub Issue" button appears
- [ ] Click "Create GitHub Issue" → verify browser opens with pre-filled
template
- [ ] Test Cancel returns to idle, test empty description still works
- [ ] Test failure case (e.g. with exo stopped) shows error and dismiss

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-22 15:37:43 +00:00
Alex CheemaandClaude Opus 4.6 18717023ad chore: remove deprecated MlxIbv dashboard references (#1584)
## Summary
- Remove legacy MlxIbvInstance references from ChatSidebar and ModelCard
components
- MlxIbv was replaced by MlxJaccl; these are leftover type checks
- Split from #1519 for independent review

## Test plan
- [x] Visual inspection of dashboard components

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-22 06:56:12 -08:00
1780e4ade4 fix: change RDMA AVAILABLE to RDMA NOT ENABLED warning (#1580)
## Summary
- Changed blue info badge "RDMA AVAILABLE" to yellow warning badge "RDMA
NOT ENABLED" — more accurately describes the state
- Added hover tooltip with enable instructions to all views (was missing
in 2 of 4 instances)
- Warning icon instead of info icon, consistent with other cluster
warnings (TB cycle, macOS mismatch)

## Screenshots

**Badge (yellow warning):**
![RDMA warning
badge](https://raw.githubusercontent.com/exo-explore/exo/3f7bdb482c5011d60f140aa84ab21023032e4a57/rdma-warning.png)

**Hover tooltip with instructions:**
![RDMA warning
hover](https://raw.githubusercontent.com/exo-explore/exo/3f7bdb482c5011d60f140aa84ab21023032e4a57/rdma-warning-hover.png)

## Test plan
- [x] Dashboard builds successfully
- [ ] Verify badge appears when 2+ TB5 nodes have RDMA disabled
- [ ] Verify hover tooltip shows in normal layout
- [ ] Verify hover tooltip shows in topology-only mode
- [ ] Verify dismiss button works
- [ ] Verify compact badge in status bar shows yellow warning

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: rltakashige <rl.takashige@gmail.com>
2026-02-20 21:40:07 +00:00
Jake Hillion ab9273e723 downloads: add read_only flag to DownloadCompleted for EXO_MODELS_PATH
Models in EXO_MODELS_PATH are pre-downloaded into read-only directories
and must not be deleted. The DownloadCoordinator had no awareness of
these paths, so they never appeared as completed downloads in cluster
state, and the bench harness could attempt to delete them when freeing
disk space.

Added a `read_only: bool` field to `DownloadCompleted` (default False).
The DownloadCoordinator now checks `resolve_model_in_path` in
`_start_download`, proactively scans EXO_MODELS_PATH in
`_emit_existing_download_progress` to emit DownloadCompleted events for
all pre-downloaded models (overriding DownloadPending from the regular
scan), and refuses deletion of read-only models. The bench harness
filters out read-only models from deletion candidates.

Test plan:
- Ran with EXO_MODELS_PATH. Available models now show as downloaded in
  the UI. There isn't good UI for the fact they can't be deleted, but it
  should work with exo_bench.
2026-02-20 20:27:45 +00:00
Jake Hillion 71e48c0f62 model-cards: add missing metadata for Qwen3 Coder Next variants (#1576)
The Qwen3-Coder-Next model card TOML files were missing family,
quantization, base_model, and capabilities fields. This caused them not
to appear under the Qwen family filter in the dashboard's model picker.

Added the missing metadata to all five variants (4bit, 5bit, 6bit, 8bit,
bf16), matching the format used by the existing Qwen3-Coder-480B model
cards.

Test plan:
- Eyeballs
2026-02-20 18:25:49 +00:00
Jake Hillion 42da58c297 worker: add EXO_MODELS_PATH for pre-downloaded model directories
Users with pre-existing model files (e.g. on shared NFS mounts or from
prior downloads) had no way to point exo at those directories without
going through the download coordinator. EXO_MODELS_DIR only moves the
download target directory, it doesn't support read-only search paths.

Added EXO_MODELS_PATH environment variable as a colon-separated list of
directories to search for models. When the worker's plan loop encounters
a DownloadModel task, it checks these directories first and emits a
synthetic DownloadCompleted event if found, bypassing the download
coordinator entirely. The runner's build_model_path also checks these
directories first so the correct path is used during model loading.

This keeps the existing event sourcing state machine unchanged — the
DownloadCompleted event propagates naturally through the system, so
_load_model and all downstream logic work without modification.

Test plan:
- `s1@s1s-Mac-Studio ~ % EXO_LIBP2P_NAMESPACE=jake EXO_MODELS_PATH="/Volumes/Definitely Leo's SSD" nix --extra-experimental-features 'nix-command flakes' run github:exo-explore/exo/f2babbc2f742357d97dc177619fec062ef545be4`
- Started mlx-community/Qwen3-Coder-Next-4bit - it's present on the disk
  and it worked.
- Renamed one safetensor of mlx-community/Qwen3-Coder-Next-4bit on the
  disk. It then started the download locally, as expected.
2026-02-20 18:17:56 +00:00
Mustafa Alp Yılmazandrltakashige 6b5a705959 fix: immediate cancel check after prefill completes (#1575)
## Problem

When a request is cancelled during prefill, the cancellation is not
detected until `check_for_cancel_every` additional tokens have been
generated. This is because `tokens_since_last_cancel_check` is
initialized to `0`, meaning the first cancel check only happens after
generating `check_for_cancel_every` tokens post-prefill.

For long prefills (which are the most likely to be cancelled), this adds
unnecessary latency before the cancellation is actually honoured.

## Fix

Initialize `tokens_since_last_cancel_check` to `check_for_cancel_every`
instead of `0`, so the very first token generated after prefill triggers
an immediate cancel check.

```diff
- tokens_since_last_cancel_check = 0
+ tokens_since_last_cancel_check = check_for_cancel_every
```

## Impact

- Cancellations issued during prefill are detected immediately when
generation begins
- No change in behaviour for non-cancelled requests (the counter resets
to `0` after each check as before)
- 1 line changed

Co-authored-by: rltakashige <rl.takashige@gmail.com>
2026-02-20 18:00:59 +00:00
6b54a27019 fix: add downloaded_bytes to DownloadPending event (#1564)
## Summary
- Add downloaded_bytes field to existing DownloadPending event for
accurate resume progress
- Minimal change per maintainer directive — no new download states
introduced

## Test plan
- [x] 42 tests passed, 1 skipped
- [x] Verified downloaded_bytes populates correctly for partial
downloads

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: rltakashige <rl.takashige@gmail.com>
2026-02-20 17:54:18 +00:00
rltakashige e01f50a5cd Update mlx fork (#1565)
## Motivation

Some fixes upstream. This sort of commit will probably be quite common
until GPU locks are resolved.
2026-02-20 17:23:52 +00:00
Evan Quiney 1093080214 cancel active downloads on coordinator shutdown (#1567)
we were seeing some crashes as lost download tasks were trying to push
data toward a deleted coordinator. this cancels download tasks with the
coordinator's shutdown on master election
2026-02-20 17:17:43 +00:00
rltakashige 1a2b8b044a Refactor runner into separate runners (#1570)
## Motivation

We're going to be refactoring the llm inference code, so we should split
the runner up into parts while we can.

## Test Plan

### Manual Testing
Works on single node, at least.

### Automated Testing
Passes CI. Will be tested by our tests today.
2026-02-20 17:11:01 +00:00
Evan Quiney dc8d42b4dc add system ids (#1536)
addresses some election edge cases where a new worker with an old master
would get stuck on the old workers buffer index - we now use new system
ids each time we instantiate a node, and each event-producing system has
a unique system id for its lifespan (until the master moves).
2026-02-20 15:41:59 +00:00
Jake Hillion d484b062e8 bench: add download timing to bench output (#1566)
The bench script downloads models during the planning phase but doesn't
record how long the download took, making it difficult to track download
performance for a given model over time.

Modified `run_planning_phase` to return download metadata: whether a
fresh download occurred, the wall-clock duration, and the model size in
bytes. These fields are included in every JSON output row alongside the
existing per-run metrics, and a summary line is logged to the console.

This allows filtering bench results by `download_occurred` and grouping
by `model_id` to compute average download times across runs.

Test plan:

```
# existing model
jake@maverick:/data/users/jake/repos/exo/ > nix run .#exo-bench -- --host s1 --model mlx-community/gpt-oss-120b-MXFP4-Q8 --pp 128 --tg 128
...
2026-02-20 15:23:49.081 | INFO     | __main__:main:340 - Planning phase: checking downloads...
2026-02-20 15:23:49.152 | INFO     | harness:run_planning_phase:402 - Started download on 12D3KooWKx41iikn188ozrxSdoG26g88jFCfie9wEA1eQR8csbPm
2026-02-20 15:23:49.184 | INFO     | __main__:main:352 - Download: model already cached
...
Wrote results JSON: bench/results.json
jake@maverick:/data/users/jake/repos/exo/ > cat bench/results.json
[
  {
    "elapsed_s": 2.9446684420108795,
    "output_text_preview": "The user just typed a long series of \"a\". Possibly they are testing. There's no explicit question. Could be they want a response? Might be a test of handling long input. We can respond politely, ask i",
    "stats": {
      "prompt_tps": 117.7872141515621,
      "generation_tps": 85.49598231498028,
      "prompt_tokens": 129,
      "generation_tokens": 128,
      "peak_memory_usage": {
        "inBytes": 68215145744
      }
    },
    "model_short_id": "gpt-oss-120b-MXFP4-Q8",
    "model_id": "mlx-community/gpt-oss-120b-MXFP4-Q8",
    "placement_sharding": "Pipeline",
    "placement_instance_meta": "MlxRing",
    "placement_nodes": 1,
    "instance_id": "68babc2a-6e94-4c70-aa07-7ec681f7c856",
    "pp_tokens": 128,
    "tg": 128,
    "repeat_index": 0
  }
]%
# no change to output
```

```
# missing model
jake@maverick:/data/users/jake/repos/exo/ > nix run .#exo-bench -- --host s1 --model mlx-community/Meta-Llama-3.1-8B-Instruct-4bit --pp 128 --tg 128
...
2026-02-20 15:24:42.553 | INFO     | __main__:main:340 - Planning phase: checking downloads...
2026-02-20 15:24:42.625 | INFO     | harness:run_planning_phase:402 - Started download on 12D3KooWKx41iikn188ozrxSdoG26g88jFCfie9wEA1eQR8csbPm
2026-02-20 15:25:37.494 | INFO     | __main__:main:350 - Download: 54.9s (freshly downloaded)
...
Wrote results JSON: bench/results.json
jake@maverick:/data/users/jake/repos/exo/ > cat bench/results.json
[
  {
    "elapsed_s": 1.500349276990164,
    "output_text_preview": "It seems like you've entered a large number of 'a's. If you'd like to discuss something or ask a question, I'm here to help. If not, is there anything else I can assist you with? \n\nIf you're intereste",
    "stats": {
      "prompt_tps": 395.43264952543666,
      "generation_tps": 128.03520443181478,
      "prompt_tokens": 129,
      "generation_tokens": 128,
      "peak_memory_usage": {
        "inBytes": 5116952079
      }
    },
    "model_short_id": "Meta-Llama-3.1-8B-Instruct-4bit",
    "model_id": "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit",
    "placement_sharding": "Pipeline",
    "placement_instance_meta": "MlxRing",
    "placement_nodes": 1,
    "instance_id": "ccd9bd71-d4cc-4b75-a37f-98090544626a",
    "pp_tokens": 128,
    "tg": 128,
    "repeat_index": 0,
    "download_duration_s": 54.88322358299047
  }
]%
# one new field
```
2026-02-20 15:33:08 +00:00
e32b649d2f fix: enable psutil fallback for memory monitoring when macmon is missing (#1478)
## Summary
- On macOS, memory monitoring relied exclusively on `macmon` — the
psutil fallback was explicitly disabled (`memory_poll_rate = None`)
- When `macmon` is not installed (e.g., mac-mini-2 through mac-mini-4 in
our cluster), **no memory data was reported**, causing nodes to show 0GB
memory in the cluster state
- This blocked the scheduler from placing shards on those nodes since it
had no memory data to work with
- Fix: when `macmon` is not found on Darwin, fall back to psutil-based
memory polling (`memory_poll_rate = 1`)

## Root cause
`InfoGatherer` has two memory monitoring paths:
1. `macmon` (Darwin-only): provides memory + GPU/CPU/power stats
2. `psutil` (non-Darwin fallback): provides memory via
`MemoryUsage.from_psutil()`

Line 378 disabled psutil on Darwin: `memory_poll_rate = None if
IS_DARWIN else 1`
Line 389 only starts macmon if the binary exists: `if
shutil.which("macmon") is not None`

If macmon is missing on Darwin, **neither path runs** — zero memory
reported.

## Test plan
- [ ] Verify `uv run basedpyright` passes (0 errors confirmed)
- [ ] Verify `uv run ruff check` passes (confirmed)
- [ ] Verify `uv run pytest src/exo/utils/info_gatherer/` passes (2/2
confirmed)
- [ ] Deploy to cluster nodes without macmon and verify memory appears
in `/state`

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: rltakashige <rl.takashige@gmail.com>
2026-02-20 13:12:22 +00:00
bddad7e79c feat: show ETA on prefill progress bar (#1557)
## Summary
- Show estimated time remaining during prefill (prompt processing phase)
- Track prefill start time via performance.now() and extrapolate from
observed token throughput
- Display ~Xs remaining or ~Xm Ys remaining next to the percentage on
the progress bar
- Wait 200ms before showing ETA to ensure a stable sample window

## Changes
**PrefillProgressBar.svelte**: Add etaText derived computation that
calculates remaining time from (remainingTokens / tokensPerMs). Renders
in a new flex row below the progress bar alongside the percentage.

**app.svelte.ts**: Add startedAt: number field to PrefillProgress
interface. Set on first prefill_progress SSE event, preserved across
subsequent updates.

## Test plan
- [ ] Start inference with a long prompt (10k+ tokens) on a multi-node
cluster
- [ ] Verify the progress bar shows ~Xs remaining after ~200ms of
prefill
- [ ] Verify the ETA decreases as prefill progresses
- [ ] Verify short prefills (<200ms) dont flash a briefly-visible ETA
- [ ] Verify ETA disappears when prefill completes and token generation
begins

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: rltakashige <rl.takashige@gmail.com>
2026-02-20 12:37:56 +00:00
rltakashige addf73a144 Add support for Ollama API (#1560)
## Motivation

Ollama has a bunch of integrations, such as OpenWebUI, that are very
handy. Let's support it :)

## Test Plan

### Manual Testing
<img width="3426" height="1998" alt="image"
src="https://github.com/user-attachments/assets/44b07f1e-308e-4ff1-9a11-922d8279939f"
/>
2026-02-20 12:03:27 +00:00
Mustafa Alp Yılmazandrltakashige a16ff2c047 fix: correct misleading docstring in seed_models (#1561)
## Summary
- Fixed stale docstring in `seed_models()` that referenced
`.cache/huggingface/hub` when the function actually moves models to
`EXO_MODELS_DIR` (resolved via `ensure_models_dir()`)
- The old docstring was misleading for AI coding agents analyzing the
codebase, causing incorrect conclusions about model storage paths

## Changes
`src/exo/download/download_utils.py`: Updated docstring from `"Move
model in resources folder of app to .cache/huggingface/hub"` to `"Move
models from resources folder to EXO_MODELS_DIR."`

Co-authored-by: rltakashige <rl.takashige@gmail.com>
2026-02-20 11:57:55 +00:00
rltakashigeandEvan 3006c8ea4e Ensure coordinator is rank 0 (#1559)
## Motivation

Coordinator can be a random rank. Let's just fix this to rank 0 as
that's what we typically assume.

## Test Plan

### Manual Testing
Works as normal on 2 nodes.


Let's wait for a little more testing to merge this.

---------

Co-authored-by: Evan <evanev7@gmail.com>
2026-02-20 11:46:24 +00:00
rltakashige f662c129dd Prioritise tb for ring instances (#1556)
## Motivation

TB has better bandwidth and latency than ethernet. We should prioritise
TB5 where possible. This drastically improves distributed image
generation performance.

## Test Plan

### Manual Testing
Saw on the dashboard that TB (169.254) addresses were prioritised.

Tested that image models scale much better.

### Automated Testing
No regression on Kimi K2.5
2026-02-19 21:32:48 +00:00
Evan Quiney c45ff9ad43 memory tidy (#1558)
add some pythonic extensions to memory, did a bunch of cleanup.
2026-02-19 21:15:33 +00:00
rltakashige 7031901ae5 Prevent common fatal crashes (#1555)
## Motivation
Occasionally, memory does not get released when we shut down. There is
no reason to delay deleting the model.

Also handles can become None during shutdown, causing TypeErrors which
are not handled and bringing down exo.

Similarly, we were closing the event sender in the wrong place.

Also let's not verify the SSL certificate for http connections to local
peers, as this is failing sometimes and crashing.

## Test Plan

### Manual Testing
No more crashes as described.
2026-02-19 20:51:17 +00:00
rltakashige cf648a53b8 Add thinking in thinking blocks, and fix DeepSeek interleaved tool calls (#1548)
## Motivation

OpenCode shows <think> tags and not thinking blocks as we aren't
following the API specs properly.

Claude was also getting horrible prefix cache hits because it sends
headers.

## Changes

Handle thinking tokens properly by placing them in think tags for each
of the API endpoints.
Also support DeepSeekV3.2 tool calling properly as a minor feature.
Strips Claude headers at the API level.

## Test Plan

### Manual Testing
Tested OpenCode manually
Needs testing with Claude.

### Automated Testing
All CI and tests passing - added a new e2e test for DeepSeekV32 tool
parsing.
2026-02-19 18:44:49 +00:00
94b2ce6922 feat: Mac Studio en2 RDMA port warning v2 (#1551)
Rebuilt from scratch (replaces PR #1543). Detects when Mac Studio uses
RDMA over en2 (TB5 port next to Ethernet) which does not support RDMA.
Shows dismissible warning banner with hover tooltip showing affected
devices, SVG rear panel illustration, and fix instructions. 205 lines in
+page.svelte.

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: rltakashige <rl.takashige@gmail.com>
2026-02-19 18:39:17 +00:00
rltakashige 423ed0f07f Strip Claude headers to improve prefix cache hit rates (#1552)
## Motivation
Our hits are really bad at the moment (0.2%). This PR makes it 98.5% on
average.

## Changes

Also adds an example for how to run Claude using Exo.

## Why It Works
Claude sends some billing and session headers that change with each
message.

## Test Plan

### Manual Testing
Works in manual testing.
2026-02-19 18:29:34 +00:00
Evan Quiney ed001f2409 remove prefillprogress event (#1550)
this should never have been a separate event, but i didnt quite
communicate that well when this was merged. convert PrefillProgress to a
chunk like the rest of the runner responses.

tested with Llama-3.3-70B, prefill progress events still show up in the
dashboard as usual
2026-02-19 18:23:28 +00:00
Evan Quiney 4c4c6ce99f simplify rust ident module
this is partly dead code, partly narrowing the rust-python boundary in
prep for future rewrites. no testing as this is all type safe
refactoring.
2026-02-19 17:19:31 +00:00
Jake Hillion 42e1e7322b bench: restore --danger-delete-downloads planning phase (#1542)
c2f2111b extracted shared utilities from exo_bench.py into harness.py
but accidentally dropped the run_planning_phase function and
--danger-delete-downloads CLI argument in the process.

Restored run_planning_phase in harness.py (where its dependencies now
live) and re-added the --danger-delete-downloads argument to
add_common_instance_args. Re-wired the planning phase call in
exo_bench.py's main() before the benchmark loop.
2026-02-19 15:42:02 +00:00
Alex CheemaandClaude Opus 4.6 aa3f106fb9 fix: import ResponsesStreamEvent and DRY up SSE formatting (#1499)
## Summary
- `ResponsesStreamEvent` was defined in `openai_responses.py` as a union
of all 11 streaming event types but never imported or used anywhere in
the codebase
- Import it in the responses adapter and add a `_format_sse(event:
ResponsesStreamEvent) -> str` helper
- Replace 13 hardcoded `f"event: {type}\ndata:
{event.model_dump_json()}\n\n"` strings with `_format_sse()` calls

## Test plan
- [x] `uv run basedpyright` — 0 errors
- [x] `uv run ruff check` — all checks passed
- [x] `nix fmt` — 0 files changed
- [x] `uv run pytest` — 188 passed, 1 skipped

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-19 13:40:24 +00:00
Mustafa Alp Yılmaz 2e29605194 fix: finalize cancel tasks (#1498)
# Cancel task finalization (main.py)

After forwarding the cancel to the runner supervisor, emit TaskStatusUpdated(Complete) for the cancel task itself. This ensures the cancel task is properly removed from state.tasks.
2026-02-19 13:27:34 +00:00
Evan Quiney cacb456cb2 remove nightly (#1538)
we have no good need for rust nightly (nor futures, for that matter)
2026-02-19 12:55:31 +00:00
rltakashige 51021f6fc6 Add cancellation button and the ability to cancel during prefill (#1540)
## Motivation
There's no way to easily use the cancellation features we added! Also,
prefill can take ages so let's allow cancelling out of that.

## Changes

Wiring up our existing functionality to easily cancel during generation
(and adding stuff to do so during prefill)

## Test Plan

### Manual Testing
Tested it works during both prefill and decode.

### Automated testing
Needs testing to see if this causes a GPU timeout error on large prefill
on large models in pipeline parallel. However, from manually testing GLM
5 pipeline ring on 2 nodes, and from reading the code, it does not seem
like this will be the case.
2026-02-19 11:40:59 +00:00
025ed9fd82 feat: add prefill progress bar for long prompts (#1181)
## Motivation

Users processing long prompts have no visibility into when token
generation will start. This feature adds a progress bar showing prefill
progress, giving users real-time feedback during prompt processing.

## Changes

### Backend
- Added `PrefillProgress` event type with `command_id`,
`processed_tokens`, `total_tokens`
- Added `PrefillProgressResponse` type (though now using direct callback
approach)
- Wired `prompt_progress_callback` through MLX's `stream_generate()`
- Progress events sent directly from callback for real-time updates (not
batched)
- API generates SSE named events: `event: prefill_progress\ndata: {...}`
- Added `PrefillProgressData` dataclass and `StreamEvent` union type in
API

### Dashboard
- Added `PrefillProgress` interface to store
- Updated SSE parsing to handle `event:` lines (named events)
- Created `PrefillProgressBar.svelte` with animated progress bar
- Shows "Processing prompt: X/Y tokens" with percentage
- Progress bar disappears when first token arrives

## Why It Works

MLX's `stream_generate()` accepts a `prompt_progress_callback(processed,
total)` that's called after each prefill chunk. By sending events
directly from this callback (rather than yielding from the generator),
progress updates are sent in real-time during prefill.

Using SSE named events (`event: prefill_progress`) maintains full
OpenAI/Claude API compatibility - standard clients ignore named events
they don't recognize, while the exo dashboard explicitly listens for
them.

## Test Plan

### Manual Testing
- Hardware: MacBook Pro M3 Max
- Set `prefill_step_size=256` for more frequent updates
- Tested with long prompts (pasted large documents)
- Verified progress bar updates incrementally during prefill
- Confirmed progress bar disappears when generation starts
- Tested with curl - standard `data:` events still work normally

Here is it working:


https://github.com/user-attachments/assets/5cc6f075-c5b2-4a44-bb4d-9efb246bc5fe


### Automated Testing
- Type checker passes (0 errors)
- All 192 tests pass
- Dashboard builds successfully

### API Compatibility
- Named SSE events are ignored by OpenAI SDK clients
- Regular token data uses standard `data: {...}` format
- `[DONE]` sentinel works as expected

---

**Note:** `prefill_step_size` is temporarily set to 256 for testing.
Should be changed back to 2048 before merging for production
performance.

---------

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Evan <evanev7@gmail.com>
Co-authored-by: Ryuichi Leo Takashige <leo@exolabs.net>
2026-02-19 03:18:25 +00:00
rltakashige 19bc09550d Add status=downloaded filter for model endpoint (#1539)
## Motivation

https://github.com/exo-explore/exo/issues/1346#issuecomment-3831427905


## Test Plan

### Manual Testing
**Without filter**
<img width="1708" height="1010" alt="Screenshot 2026-02-18 at 22 26 22"
src="https://github.com/user-attachments/assets/f4bf7142-717d-4042-ac28-d8a55a8e45e7"
/>

**With filter**
<img width="1723" height="1021" alt="Screenshot 2026-02-18 at 22 26 45"
src="https://github.com/user-attachments/assets/40a522d5-c6e6-4148-b21a-02caa1221ebe"
/>
2026-02-18 22:34:11 +00:00
7cadca4f27 Try multiple endpoints for internet connectivity check (#1516)
## Summary
- `_test_internet_connection()` previously only tried `1.1.1.1:443`,
which some ISPs/networks block, causing exo to incorrectly report no
internet and fail downloads on startup
- Now tries `1.1.1.1`, `8.8.8.8`, and `1.0.0.1` in sequence, succeeding
if any endpoint responds
- Returns early on first success for minimal latency in the common case

Fixes #1425

## Test plan
- [ ] Verify downloads work on networks that block `1.1.1.1`
- [ ] Verify existing behavior unchanged on networks where `1.1.1.1`
works
- [ ] Verify `internet_connection` is set to `False` only when all three
endpoints fail

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: rltakashige <rl.takashige@gmail.com>
2026-02-18 22:10:07 +00:00
rltakashige 24e99ce197 Cleanup mistakes (#1537)
Oops
2026-02-18 22:05:26 +00:00
315992549b fix: unblock MpReceiver.close() to prevent shutdown hang (#1511)
## Summary

- `MpReceiver.close()` did not unblock threads stuck on `queue.get()` in
`receive_async()`, causing abandoned threads (via
`abandon_on_cancel=True`) to keep the Python process alive indefinitely
after tests pass
- This caused the `aarch64-darwin` CI jobs in PR #1462 to hang for ~6
hours until the GitHub Actions timeout killed them
- Sends an `_MpEndOfStream` sentinel before closing the buffer,
mirroring what `MpSender.close()` already does

## Test plan

- [x] `uv run basedpyright` — 0 errors
- [x] `uv run ruff check` — clean
- [x] `nix fmt` — 0 changed
- [x] `uv run pytest` — 188 passed, 1 skipped in 12s (no hang)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: rltakashige <rl.takashige@gmail.com>
Co-authored-by: Ryuichi Leo Takashige <leo@exolabs.net>
2026-02-18 21:59:02 +00:00
ce5a65d3b9 Add MiniMax M2.5 model cards (#1514)
## Summary
- Adds model cards for MiniMax M2.5 in three quantizations: 4bit (~129
GB), 6bit (~186 GB), 8bit (~243 GB)
- No code changes needed — `MiniMaxM2ForCausalLM` is already in the
tensor parallel whitelist and `MiniMaxShardingStrategy` is already
implemented in `auto_parallel.py`
- Credit to @vskiwi for confirming MiniMax M2.5 works out of the box
with existing code

Closes #1480

## Test plan
- [x] `basedpyright` passes with 0 errors
- [x] `ruff check` passes
- [x] `pytest` passes (260 passed, 1 skipped)
- [ ] Verify MiniMax M2.5 models appear in model selector on dashboard

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: rltakashige <rl.takashige@gmail.com>
2026-02-18 21:11:13 +00:00
rltakashige c2f2111b88 Fix tool calling (#1529)
## Motivation

GPT OSS tool calling issues.

## Changes

Fixes those and adds a bunch of evals for tool calling.
Fixes GLM5 prefix caching, where CacheList wasn't getting handled
properly.
Extracts a bunch of the setup functionality of exo bench to a harness
that can be reused elsewhere, such as in the tool calling eval.

## Test Plan
### Automated Testing
Let's run the evals for all models
2026-02-18 20:29:18 +00:00
Alex CheemaandClaude Opus 4.6 6c322ebb72 feat: only show thinking toggle for models that support it (#1497)
## Summary
- Adds `thinking_toggle` capability to 26 model cards that support
toggling thinking mode on/off
- GPT-OSS models (20b, 120b) excluded — they always think and don't
support toggling
- Dashboard UI updated to check for `thinking_toggle` capability before
showing the toggle button

## Test plan
- [x] `uv run basedpyright` — 0 errors
- [x] `uv run ruff check` — all checks passed
- [x] `nix fmt` — 0 files changed
- [x] `uv run pytest` — 188 passed, 0 failed
- [x] Security review passed (no secrets, eval/exec, innerHTML, or dep
changes)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-18 17:05:00 +00:00
vskiwi 2ebe6216b4 feat: add explicit --offline mode for air-gapped clusters (#1525)
## Motivation

Closes #1510

There is currently no reliable way to run exo on an air-gapped or offline cluster where models are pre-staged on local disks. The two existing mechanisms — `--no-downloads` and `HF_HUB_OFFLINE=1` — each cover only a subset of the problem:

1. **`--no-downloads` blocks model loading**: When passed, `DownloadCoordinator` is not created. No `NodeDownloadProgress` events are ever emitted, so `_model_needs_download()` in `plan.py` perpetually returns `DownloadModel`, short-circuiting `_load_model()` and preventing the model from ever being loaded.

2. **`HF_HUB_OFFLINE=1` doesn't cover exo's aiohttp code**: exo's download pipeline primarily uses raw `aiohttp` for HTTP operations (file list fetching, file downloads, HEAD verification), not the `huggingface_hub` library. These calls will attempt connections and time out on air-gapped networks.

3. **`skip_internet` is not propagated to `download_file_with_retry()`**: Even when `internet_connection = False`, the `_download_file()` function still makes HTTP HEAD calls via `file_meta()` to verify local files and unconditionally attempts downloads for missing files.

## Changes

### `src/exo/main.py`
- Add `--offline` flag to `Args` with env var detection (`EXO_OFFLINE=1`, `HF_HUB_OFFLINE=1`)
- Pass `offline` to `DownloadCoordinator` at creation and re-creation (election loop)

### `src/exo/download/coordinator.py`
- Add `offline: bool = False` field
- In offline mode: set `internet_connection = False` immediately in `__post_init__`, skip `_test_internet_connection()` ping (avoids 3s timeout), skip `_check_internet_connection` periodic loop
- In `_start_download()`: if model is not fully available locally, emit `DownloadFailed` with clear message instead of starting a download task

### `src/exo/download/download_utils.py`
- Add `skip_internet: bool` parameter to `download_file_with_retry()` and `_download_file()`
- When `skip_internet=True` in `_download_file()`: return local file immediately without HTTP HEAD verification; raise `FileNotFoundError` for missing files
- Propagate `skip_internet` from `download_shard()` to `download_file_with_retry()`

### `src/exo/download/tests/test_offline_mode.py` (new)
- 8 tests covering `_download_file`, `download_file_with_retry`, and `fetch_file_list_with_cache` in offline mode

## Why It Works

Unlike `--no-downloads` which disables `DownloadCoordinator` entirely, `--offline` keeps the coordinator running in a restricted mode. The existing `_emit_existing_download_progress()` disk scanner still runs every 60 seconds, emitting `DownloadCompleted` events for pre-staged models. These events flow through the event-sourcing pipeline and populate `state.downloads`, which unblocks `_model_needs_download()` in `plan.py` — no changes to the planning logic required.

```
--offline flag
  → DownloadCoordinator (offline mode)
    → Skip 1.1.1.1 ping, internet_connection = False
    → _emit_existing_download_progress scans disk
      → Emits DownloadCompleted for pre-staged models
        → _model_needs_download sees DownloadCompleted
          → _load_model proceeds normally
```

## Test Plan

### Automated Testing
- `ruff check` — passes
- 8 new tests in `test_offline_mode.py` — all pass
- 11 existing download tests in `test_download_verification.py` — all pass (no regressions)

### Manual Testing
1. Pre-stage a model on disk (e.g., `~/.exo/models/mlx-community--Qwen3-0.6B-4bit/`)
2. Start exo with `--offline` (or `EXO_OFFLINE=1`)
3. Place an instance via API or dashboard
4. Verify: model loads into memory and inference works without any network calls

### Environment
- macOS (Apple Silicon), multi-node cluster with Thunderbolt interconnect
- Models pre-staged via rsync / NFS mount
2026-02-18 16:18:09 +00:00
ciaranbor f54c80b121 Ciaran/image edit api (#1500)
## Motivation

- Image editing previously ignored input image dimensions, always
defaulting to 1024x1024
- Size dropdown was hidden in edit mode, giving users no control over
output dimensions
- Portrait/landscape presets used non-standard aspect ratios (1024x1365
/ 1365x1024)

## Changes

- Added "auto" size option that uses input image dimensions for edits,
defaults to 1024x1024 for generation
- Introduced ImageSize Literal type and normalize_image_size() validator
(replaces raw str size fields)
  - Updated portrait/landscape presets to standard 1024x1536 / 1536x1024
  - Made size selector visible in edit mode (previously hidden)
  - Default size changed from "1024x1024" to "auto"

## Why It Works

- "auto" reads actual input image dimensions via PIL at generation time,
so edits preserve the original aspect ratio
- Pydantic field_validator on both ImageGenerationTaskParams and
ImageEditsTaskParams normalizes None → "auto", keeping the API
backward-compatible

## Test Plan

### Manual Testing

- Verify image edits output at the input image's native resolution when
size is "auto"
- Verify size dropdown appears and works in both generate and edit modes
2026-02-18 16:05:39 +00:00
rltakashige 48b8f86395 Add support for GLM 5 (#1526)
## Motivation

Add GLM 5 support in favor of #1513 

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
<!-- What you did: -->
<!-- - -->

### Automated Testing
<!-- Describe changes to automated tests, or how existing tests cover
this change -->
<!-- - -->
2026-02-18 14:04:06 +00:00
Evan 5cbd6377a2 prioritize official model cards over custom model cards
our old model card search path would override official model cards with
custom model cards - our packaged model cards should always be the
default here
2026-02-18 13:20:05 +00:00
Evan Quiney 8f01523ddb remove dead code (#1496) 2026-02-18 11:43:27 +00:00
Alex CheemaandClaude Opus 4.6 3addeadea8 Update mlx-lm to 0.30.7 (#1520)
## Summary
- Bumps `mlx-lm` from 0.30.6 to 0.30.7 in `pyproject.toml` and `uv.lock`

## Test plan
- [x] `uv lock` resolves successfully
- [x] `basedpyright` — no new errors (63 pre-existing in unrelated
`test_tool_call_tracker.py`)
- [x] `ruff check` — all checks passed
- [x] `nix fmt` — no formatting changes
- [x] `pytest` — 188 passed, 1 skipped

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-18 11:14:23 +00:00
f2be929211 Leo/address rdma gpu locks 2 (#1515)
Same as #1489 . Had to revert and redo thanks to Claude.

---------

Co-authored-by: Jake Hillion <jake@hillion.co.uk>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-17 14:00:52 -08:00
rltakashige 83af8c63fa Revert "Use custom fork that resolves GPU locks" (#1502)
Reverts exo-explore/exo#1489

Goddammit Claude...
2026-02-17 18:18:54 +00:00
Evan Quiney eccc6298d1 Revert "Add MetaInstance declarative layer (#1447)"
This reverts commit a962a28afc.
2026-02-17 18:11:47 +00:00
Evan Quiney c8997217cf Revert "feat: better onboarding UX for new users (#1479)"
This reverts commit 490d2e46ba.
2026-02-17 18:02:32 +00:00
Alex CheemaandClaude Opus 4.6 490d2e46ba feat: better onboarding UX for new users (#1479)
## Summary

- **Auto-open dashboard** in browser on first launch (uses
`~/.exo/.dashboard_opened` marker)
- **Welcome overlay** with "Choose a Model" CTA button when no model
instance is running
- **Tutorial progress messages** during model download → loading → ready
lifecycle stages
- **Fix conversation sidebar** text contrast — bumped to white text,
added active state background
- **Simplify technical jargon** — sharding/instance type/min nodes
hidden behind collapsible "Advanced Options" toggle; strategy display
hidden behind debug mode
- **Polished DMG installer** with drag-to-Applications layout, custom
branded background, and AppleScript-configured window positioning

## Test plan

- [ ] Launch exo for the first time (delete `~/.exo/.dashboard_opened`
to simulate) — browser should auto-open
- [ ] Verify welcome overlay appears on topology when no model is loaded
- [ ] Launch a model and verify download/loading/ready messages appear
in instance cards
- [ ] Check conversation sidebar text is readable (white on dark, yellow
when active)
- [ ] Verify "Advanced Options" toggle hides/shows sharding controls
- [ ] Build DMG with `packaging/dmg/create-dmg.sh` and verify
drag-to-Applications layout

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-17 17:52:49 +00:00
facf2d4d03 Use custom fork that resolves GPU locks (#1489)
## Motivation

There is an issue on Macs that means that an explicit synchronization is
necessary for memory to be updated from L1 cache. This means that GPU
locks can occur when a spin wait does not see the updated timestamp.

## Changes

Updated in my own personal fork.

## Why It Works

https://github.com/ARM-software/acle/releases

## Test Plan

### Manual Testing
Tested manually that no GPU locks occur (even with multiple simultaneous
instances running) and that the performance differential is negligible
(267 vs 269 tps on Llama 3.2 1B at an approx 10k context.)


------------------------------------------------------
I have seen a GPU lock, specifically when sending a particularly large
chat completion while the model was loading. However, I have since been
unable to reproduce and this may be something I did wrong. Please do
create an issue and tag me if any GPU locks do occur.

---------

Co-authored-by: Jake Hillion <jake@hillion.co.uk>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-17 17:48:43 +00:00
Alex CheemaandClaude Opus 4.6 a962a28afc Add MetaInstance declarative layer (#1447)
## Motivation

Users currently manage instances directly, which means if a node
disconnects or connections break, the instance dies and nothing
recreates it. MetaInstance is a declarative primitive: "ensure an
instance matching these parameters always exists." The reconciler
watches for unhealthy or missing backing instances and re-places them
automatically.

## Changes

- **MetaInstance type** (`meta_instance.py`): declarative constraint
with `model_id`, `min_nodes`, optional `node_ids`, and `sharding`
- **Reconciler** (`reconcile.py`): `find_unsatisfied_meta_instances`
checks which MetaInstances lack a healthy backing instance,
`try_place_for_meta_instance` creates one
- **Master loop** (`main.py`): periodically reconciles unsatisfied
MetaInstances; immediate placement on `CreateMetaInstance` command
- **API** (`api.py`): `create_meta_instance` / `delete_meta_instance` /
`GET /meta_instances` endpoints; delete cascades to backing instances
with task cancellation
- **Binding via `meta_instance_id` on Instance** (`instances.py`): no
separate binding event or backing map — the instance carries its parent
MetaInstance ID directly, eliminating race conditions in the reconciler
- **Dashboard**: sidebar shows MetaInstances with their backing instance
status; orphan instances (created directly) still shown separately
- **Tests**: constraint matching, connection health, unsatisfied
detection, exclusive binding, cascade delete with task cancellation

### Recent improvements

- **fix: cancel active tasks on cascade delete** — `DeleteMetaInstance`
now emits `TaskStatusUpdated(Cancelled)` for any Pending/Running tasks
on backing instances before emitting `InstanceDeleted`. Previously,
cascade-deleting backing instances left orphaned task references in
state.
- **Lifecycle logging** — added `logger.info`/`logger.warning` for:
`CreateMetaInstance` (model, min_nodes, sharding), `DeleteMetaInstance`
(with cascade count), reconciler placement success/failure, and retry
decisions with attempt counts in `InstanceHealthReconciler`.
- **GET `/meta_instances` endpoint** — lists all meta-instances without
needing to fetch full state.
- **2 regression tests** — `test_cascade_delete_cancels_active_tasks`
and `test_cascade_delete_skips_completed_tasks` verify the
cascade-delete event sequence.

## Why It Works

Putting `meta_instance_id` on `BaseInstance` makes binding inherent to
instance creation. When the reconciler creates an instance for a
MetaInstance, it tags it via `model_copy`. When the instance is deleted,
the binding disappears with it. This avoids the two bugs that a separate
binding mechanism would introduce:
1. Stale exclusion sets — the reconciler loop can't accidentally bind
two MetaInstances to the same instance
2. Delete ordering race — no window between deleting an instance and its
binding where the reconciler could re-place

## Test Plan

### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
- Created MetaInstance via dashboard, verified instance placed
- Verified delete cascades (deleting MetaInstance removes backing
instance)
- Verified orphan instances still work independently

### Automated Testing
- 30 tests in `test_meta_instance_edge_cases.py`: lifecycle, retry
logic, error handling, concurrent operations, cascade delete with task
cancellation
- 24 tests in `test_reconcile.py`: constraint matching, connection
health (single/multi-node, edge removal, IP changes), unsatisfied
detection, exclusive binding, idempotency
- All 261 tests pass
- basedpyright 0 errors, ruff clean, dashboard builds

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-17 09:48:19 -08:00
db79c350c1 Fix graceful process shutdown in macOS app (#1372)
## Motivation

Fixes #1370

When the macOS app stops exo, GPU/system memory isn't released. This
happens because:

1. The macOS app calls `process.terminate()` (SIGTERM) but the Python
process only registers a graceful shutdown handler for SIGINT, not
SIGTERM. SIGTERM's default Python behavior raises `SystemExit` which
bypasses the cleanup cascade (runner subprocess MLX cleanup via
`mx.clear_cache()`, channel closing, etc.).
2. The app doesn't wait for the process to actually finish cleanup — it
immediately nils out the process reference.

## Changes

**`src/exo/main.py`**: Register SIGTERM handler alongside SIGINT so the
graceful shutdown cascade (`Node.shutdown()` → cancel task group →
worker/runner cleanup → `mx.clear_cache()` + `gc.collect()`) runs
regardless of which signal is received.

**`app/EXO/EXO/ExoProcessController.swift`**: Replace immediate
`process.terminate()` with escalating shutdown per @Evanev7's
suggestion:
1. Send SIGINT via `process.interrupt()` — triggers the registered
Python handler for graceful cleanup
2. Wait up to 5 seconds for the process to exit
3. If still running, escalate to SIGTERM via `process.terminate()`
4. Wait up to 3 seconds
5. If still running, force kill via SIGKILL

The escalation runs in a detached `Task` so the UI updates immediately
(status → stopped) without blocking.

## Why It Works

The root cause is that SIGTERM wasn't triggering the graceful shutdown
path. By registering a SIGTERM handler in Python and sending SIGINT
first from the macOS app, the process gets a chance to run the full
cleanup cascade: cancelling the task group, shutting down runners (which
call `del model; mx.clear_cache(); gc.collect()`), closing channels, and
flushing logs. The escalation to SIGTERM and SIGKILL ensures the process
always terminates even if graceful shutdown hangs.

## Test Plan

### Manual Testing
<!-- Hardware: Mac Studio M4 Max 128GB -->
- Start exo via macOS app, load a model, run inference
- Stop via the toggle switch, verify memory is released without
requiring a system restart
- Test rapid stop/start (restart) to ensure no race conditions

### Automated Testing
- `uv run basedpyright` — 0 errors
- `uv run ruff check` — passes
- `nix fmt` — no changes

---------

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Evan Quiney <evanev7@gmail.com>
2026-02-17 09:03:54 -08:00
Alex CheemaandClaude Opus 4.6 d6301ed593 dashboard: redesign downloads page as model×node table (#1465)
## Motivation

The current downloads page uses a node-centric card grid layout that is
messy and hard to read — the same model across different nodes appears
in separate cards, and deep nesting wastes space. This makes it
difficult to quickly see which models are on which nodes.

## Changes

Rewrote the downloads page
(`dashboard/src/routes/downloads/+page.svelte`) from a card grid to a
clean table layout:

- **Rows** = models (unique across all nodes)
- **Columns** = nodes (with disk free shown in header)
- **Cells** show status at a glance:
  -  Green checkmark + size for completed downloads
  - 🟡 Yellow percentage + mini progress bar + speed for active downloads
  - `...` for pending downloads
  -  Red X for failed downloads
  - `--` for models not present on a node
- Delete/download action buttons appear on row hover
- Model name column is sticky on horizontal scroll (for many-node
clusters)
- Models sorted by number of nodes with completed downloads
- Imported shared utilities from `$lib/utils/downloads` instead of
inline re-implementations

### Backend: model directory in download events

- Added `model_directory` field to `BaseDownloadProgress` so all
download status events include the on-disk path
- Added `_model_dir()` helper to `DownloadCoordinator` to compute the
path from `EXO_MODELS_DIR`
- Dashboard uses this to show file location and enable "open in Finder"
for completed downloads

### Info modal

- Clicking a model name opens an info modal showing card details
(family, quantization, capabilities, storage size, layer count, tensor
parallelism support)

### Other fixes

- Fixed model name truncation in the table
- Excluded `tests/start_distributed_test.py` from pytest collection (CLI
script that calls `sys.exit()` at import time)

## Test Plan

- [x] `uv run basedpyright` — 0 errors
- [x] `uv run ruff check` — all passed
- [x] `nix fmt` — clean
- [x] `uv run pytest` — 188 passed, 1 skipped

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-17 14:31:47 +00:00
Evan Quiney 6d1ca6689b don't time out node identities (#1493)
currently nodes leaving and rejoining the cluster can lose their identity. We have no need to delete this data on node timing out, so let's just persist it.
2026-02-17 11:48:28 +00:00
Evan c01b6fff21 eprint banner
our banner was being printed to stdout but should be printed to stderr
as its essentially a log message
2026-02-17 11:43:06 +00:00
Jake Hillion 8392e78afe bench: add spec for automatic canary benchmarks (#1483)
Adds all the models that can fit onto a single M3 Ultra for single
machine benchmarks. Fixes the macOS version, GPU spec, and chip type for
maximum reproducibility. Specifies the minimum memory accordingly for
each type of model, using the smallest machine available (the smallest
M3 Ultra is 96GiB).

Test plan:
- Running this with some code that makes machines of this spec available
and stores the results. It works.

This will become part of a larger testing/stability strategy once we've
collected more of the data.
2026-02-17 10:52:05 +00:00
Evan 86735ece78 begins
begins
2026-02-16 19:26:19 +00:00
459 changed files with 52525 additions and 13739 deletions

No files matched your search

+7
View File
@@ -1 +1,8 @@
use flake
# creates .venv if doesn't exist and loads its environment
export VIRTUAL_ENV=".venv"
if ! [ -d "./$VIRTUAL_ENV" ]; then
uv venv
fi
layout python
+124 -4
View File
@@ -32,7 +32,6 @@ jobs:
SPARKLE_ED25519_PRIVATE: ${{ secrets.SPARKLE_ED25519_PRIVATE }}
SPARKLE_S3_BUCKET: ${{ secrets.SPARKLE_S3_BUCKET }}
SPARKLE_S3_PREFIX: ${{ secrets.SPARKLE_S3_PREFIX }}
EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT: ${{ secrets.EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT }}
AWS_REGION: ${{ secrets.AWS_REGION }}
EXO_BUILD_NUMBER: ${{ github.run_number }}
EXO_LIBP2P_NAMESPACE: ${{ github.ref_name }}
@@ -159,7 +158,7 @@ jobs:
fi
- name: Install Homebrew packages
run: brew install just awscli macmon
run: brew install just awscli
- name: Install UV
uses: astral-sh/setup-uv@v6
@@ -239,10 +238,92 @@ jobs:
# Export keychain path for other steps
echo "BUILD_KEYCHAIN_PATH=$KEYCHAIN_PATH" >> $GITHUB_ENV
# ============================================================
# Pre-flight credential / profile validation
# Runs BEFORE the ~16 min build so auth/expiry failures surface in <1 min.
# ============================================================
- name: Validate Apple notarization credentials
env:
APPLE_NOTARIZATION_USERNAME: ${{ secrets.APPLE_NOTARIZATION_USERNAME }}
APPLE_NOTARIZATION_PASSWORD: ${{ secrets.APPLE_NOTARIZATION_PASSWORD }}
APPLE_NOTARIZATION_TEAM: ${{ secrets.APPLE_NOTARIZATION_TEAM }}
run: |
# All-or-nothing: either all three creds are set, or none are.
CRED_COUNT=0
for v in "$APPLE_NOTARIZATION_USERNAME" "$APPLE_NOTARIZATION_PASSWORD" "$APPLE_NOTARIZATION_TEAM"; do
[[ -n "$v" ]] && CRED_COUNT=$((CRED_COUNT + 1))
done
if [[ "$CRED_COUNT" -eq 0 ]]; then
echo "No notarization credentials configured — skipping notarization for this build."
exit 0
fi
if [[ "$CRED_COUNT" -ne 3 ]]; then
echo "ERROR: partial notarization credentials set ($CRED_COUNT/3). Aborting before build."
exit 1
fi
# Cheap, ~5s, auth-only call. Fails instantly with a clear message if
# the app-specific password is stale, wrong team-id, etc.
echo "Verifying Apple notarization credentials via notarytool history..."
if ! xcrun notarytool history \
--apple-id "$APPLE_NOTARIZATION_USERNAME" \
--password "$APPLE_NOTARIZATION_PASSWORD" \
--team-id "$APPLE_NOTARIZATION_TEAM" >/dev/null; then
echo "ERROR: notarytool rejected the provided credentials. Fix before rerunning."
echo "Common causes: app-specific password expired/revoked, wrong team-id,"
echo "Apple ID not on the team, or 2FA not configured for this Apple ID."
exit 1
fi
echo "Apple notarization credentials OK."
- name: Validate provisioning profile expiry
run: |
PROFILE="$HOME/Library/Developer/Xcode/UserData/Provisioning Profiles/EXO.provisionprofile"
if [[ ! -f "$PROFILE" ]]; then
echo "ERROR: provisioning profile not found at $PROFILE"
exit 1
fi
EXPIRY=$(security cms -D -i "$PROFILE" | plutil -extract ExpirationDate raw -o - - 2>/dev/null || true)
if [[ -z "$EXPIRY" ]]; then
echo "WARNING: could not read ExpirationDate from provisioning profile; skipping expiry check."
exit 0
fi
# Try a couple of known plutil date formats. If none parse, skip the check rather
# than risk a false-positive "expired" block on a format we didn't anticipate.
EXPIRY_EPOCH=""
for fmt in "%Y-%m-%dT%H:%M:%SZ" "%Y-%m-%d %H:%M:%S %z" "%Y-%m-%d %H:%M:%S +0000"; do
if parsed=$(date -j -f "$fmt" "$EXPIRY" +%s 2>/dev/null); then
EXPIRY_EPOCH="$parsed"
break
fi
done
if [[ -z "$EXPIRY_EPOCH" ]]; then
echo "WARNING: could not parse ExpirationDate '$EXPIRY'; skipping expiry check."
exit 0
fi
NOW_EPOCH=$(date +%s)
if [[ "$EXPIRY_EPOCH" -le "$NOW_EPOCH" ]]; then
echo "ERROR: provisioning profile expired on $EXPIRY. Regenerate it before rerunning."
exit 1
fi
DAYS_LEFT=$(( (EXPIRY_EPOCH - NOW_EPOCH) / 86400 ))
echo "Provisioning profile valid until $EXPIRY ($DAYS_LEFT days remaining)."
if [[ "$DAYS_LEFT" -lt 14 ]]; then
echo "WARNING: profile expires in under 14 days — regenerate soon."
fi
# ============================================================
# Build the bundle
# ============================================================
- name: Add pinned macmon to PATH
run: |
MACMON_DIR=$(nix develop --command sh -c 'dirname $(which macmon)')
echo "Using macmon from: $MACMON_DIR"
echo "$MACMON_DIR" >> $GITHUB_PATH
# Remove any Homebrew macmon so PyInstaller can't accidentally pick it up
brew uninstall macmon 2>/dev/null || true
- name: Build PyInstaller bundle
run: uv run pyinstaller packaging/pyinstaller/exo.spec
@@ -265,7 +346,6 @@ jobs:
EXO_BUILD_COMMIT="$GITHUB_SHA" \
SPARKLE_FEED_URL="$SPARKLE_FEED_URL" \
SPARKLE_ED25519_PUBLIC="$SPARKLE_ED25519_PUBLIC" \
EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT="$EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT" \
CODE_SIGNING_IDENTITY="$SIGNING_IDENTITY" \
CODE_SIGN_INJECT_BASE_ENTITLEMENTS=YES
mkdir -p ../../output
@@ -298,11 +378,41 @@ jobs:
APPLE_NOTARIZATION_PASSWORD: ${{ secrets.APPLE_NOTARIZATION_PASSWORD }}
APPLE_NOTARIZATION_TEAM: ${{ secrets.APPLE_NOTARIZATION_TEAM }}
run: |
set -o pipefail
cd output
security unlock-keychain -p "$MACOS_CERTIFICATE_PASSWORD" "$BUILD_KEYCHAIN_PATH"
SIGNING_IDENTITY=$(security find-identity -v -p codesigning "$BUILD_KEYCHAIN_PATH" | awk -F '"' '{print $2}')
# Fail fast if notarization creds are partial. All-or-nothing.
CRED_COUNT=0
for v in "$APPLE_NOTARIZATION_USERNAME" "$APPLE_NOTARIZATION_PASSWORD" "$APPLE_NOTARIZATION_TEAM"; do
[[ -n "$v" ]] && CRED_COUNT=$((CRED_COUNT + 1))
done
if [[ "$CRED_COUNT" -ne 0 && "$CRED_COUNT" -ne 3 ]]; then
echo "ERROR: partial Apple notarization credentials set ($CRED_COUNT/3). Aborting."
exit 1
fi
/usr/bin/codesign --deep --force --timestamp --options runtime \
--sign "$SIGNING_IDENTITY" EXO.app
# Pre-flight: verify the signed app BEFORE building DMG and submitting to Apple.
# If this fails, notarization will fail too — cheap way to fail in seconds, not 15 minutes.
echo "===== codesign --verify EXO.app ====="
if ! /usr/bin/codesign --verify --deep --strict --verbose=2 EXO.app; then
echo "ERROR: EXO.app failed codesign verification. Dumping signing status of every executable:"
find EXO.app -type f \( -perm -111 -o -name "*.dylib" -o -name "*.so" -o -name "*.framework" \) -print0 |
while IFS= read -r -d '' f; do
printf -- '--- %s\n' "$f"
/usr/bin/codesign -dv --verbose=2 "$f" 2>&1 | sed 's/^/ /' || true
done
exit 1
fi
# Gatekeeper assessment. A failure here strongly predicts notarization rejection.
echo "===== spctl assessment (predicts notarization outcome) ====="
/usr/bin/spctl -a -vvv -t install EXO.app || echo "WARNING: spctl assessment failed — notarization is likely to fail too."
mkdir -p dmg-root
cp -R EXO.app dmg-root/
ln -s /Applications dmg-root/Applications
@@ -310,12 +420,22 @@ jobs:
hdiutil create -volname "EXO" -srcfolder dmg-root -ov -format UDZO "$DMG_NAME"
/usr/bin/codesign --force --timestamp --options runtime \
--sign "$SIGNING_IDENTITY" "$DMG_NAME"
echo "===== codesign --verify DMG ====="
if ! /usr/bin/codesign --verify --verbose=2 "$DMG_NAME"; then
echo "ERROR: DMG failed codesign verification."
exit 1
fi
if [[ -n "$APPLE_NOTARIZATION_USERNAME" ]]; then
echo "===== notarytool submit ====="
# `|| true` so set -e doesn't abort before we can echo output / fetch the log.
# We rely on the parsed STATUS below to decide pass/fail.
SUBMISSION_OUTPUT=$(xcrun notarytool submit "$DMG_NAME" \
--apple-id "$APPLE_NOTARIZATION_USERNAME" \
--password "$APPLE_NOTARIZATION_PASSWORD" \
--team-id "$APPLE_NOTARIZATION_TEAM" \
--wait --timeout 15m 2>&1)
--wait --timeout 15m 2>&1) || true
echo "$SUBMISSION_OUTPUT"
SUBMISSION_ID=$(echo "$SUBMISSION_OUTPUT" | awk 'tolower($1)=="id:" && $2 ~ /^[0-9a-fA-F-]+$/ {print $2; exit}')
-3
View File
@@ -91,9 +91,6 @@ jobs:
nix build .#metal-toolchain
fi
# Build mlx (depends on metal-toolchain)
nix build .#mlx
- name: Build all Nix outputs
run: |
nix flake show --json | jq -r '
+2
View File
@@ -38,3 +38,5 @@ bench/**/*.json
# tmp
tmp/models
/build/exo
/.claude/skills
-31
View File
@@ -1,31 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<module type="EMPTY_MODULE" version="4">
<component name="FacetManager">
<facet type="Python" name="Python facet">
<configuration sdkName="Python 3.13 virtualenv at ~/Desktop/exo/.venv" />
</facet>
</component>
<component name="Go" enabled="true" />
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$">
<sourceFolder url="file://$MODULE_DIR$/scripts/src" isTestSource="false" />
<sourceFolder url="file://$MODULE_DIR$/src" isTestSource="false" />
<sourceFolder url="file://$MODULE_DIR$/rust/exo_pyo3_bindings/src" isTestSource="false" />
<sourceFolder url="file://$MODULE_DIR$/rust/exo_pyo3_bindings/tests" isTestSource="true" />
<sourceFolder url="file://$MODULE_DIR$/rust/util/src" isTestSource="false" />
<sourceFolder url="file://$MODULE_DIR$/rust/networking/examples" isTestSource="false" />
<sourceFolder url="file://$MODULE_DIR$/rust/networking/src" isTestSource="false" />
<sourceFolder url="file://$MODULE_DIR$/rust/networking/tests" isTestSource="true" />
<sourceFolder url="file://$MODULE_DIR$/rust/system_custodian/src" isTestSource="false" />
<excludeFolder url="file://$MODULE_DIR$/.venv" />
<excludeFolder url="file://$MODULE_DIR$/.direnv" />
<excludeFolder url="file://$MODULE_DIR$/build" />
<excludeFolder url="file://$MODULE_DIR$/dist" />
<excludeFolder url="file://$MODULE_DIR$/.go_cache" />
<excludeFolder url="file://$MODULE_DIR$/rust/target" />
</content>
<orderEntry type="jdk" jdkName="Python 3.13 (exo)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
<orderEntry type="library" name="Python 3.13 virtualenv at ~/Desktop/exo/.venv interpreter library" level="application" />
</component>
</module>
+1 -1
View File
@@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ExternalDependencies">
<plugin id="systems.fehn.intellijdirenv" />
<plugin id="al.aoli.intellijdirenv" />
</component>
</project>
-14
View File
@@ -1,14 +0,0 @@
<component name="InspectionProjectProfileManager">
<profile version="1.0">
<option name="myName" value="Project Default" />
<inspection_tool class="PyCompatibilityInspection" enabled="true" level="WARNING" enabled_by_default="true">
<option name="ourVersions">
<value>
<list size="1">
<item index="0" class="java.lang.String" itemvalue="3.14" />
</list>
</value>
</option>
</inspection_tool>
</profile>
</component>
-3
View File
@@ -4,7 +4,4 @@
<option name="sdkName" value="Python 3.13 (exo)" />
</component>
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.13 (exo)" project-jdk-type="Python SDK" />
<component name="PythonCompatibilityInspectionAdvertiser">
<option name="version" value="3" />
</component>
</project>
+23 -6
View File
@@ -215,6 +215,22 @@ class StreamContext:
traceback: object | None = ...,
) -> None: ...
def device_info() -> dict[str, str | int]:
"""
Get information about the GPU device and system settings.
Currently returns:
* ``architecture``
* ``max_buffer_size``
* ``max_recommended_working_set_size``
* ``memory_size``
* ``resource_limit``
Returns:
dict: A dictionary with string keys and string or integer values.
"""
def abs(a: array, /, *, stream: Stream | Device | None = ...) -> array:
"""
Element-wise absolute value.
@@ -1751,12 +1767,12 @@ def clip(
array: The clipped array.
"""
def compile(
fun: Callable,
def compile[F: Callable[..., object]](
fun: F,
inputs: object | None = ...,
outputs: object | None = ...,
shapeless: bool = ...,
) -> Callable:
) -> F:
"""
Returns a compiled function which produces the same output as ``fun``.
@@ -2380,7 +2396,7 @@ def degrees(a: array, /, *, stream: Stream | Device | None = ...) -> array:
array: The angles in degrees.
"""
def depends(inputs: array | Sequence[array], dependencies: array | Sequence[array]):
def depends[T](inputs: T, dependencies: array | Sequence[array]) -> T:
"""
Insert dependencies between arrays in the graph. The outputs are
identical to ``inputs`` but with dependencies on ``dependencies``.
@@ -2899,8 +2915,8 @@ def gather_mm(
a: array,
b: array,
/,
lhs_indices: array,
rhs_indices: array,
lhs_indices: array | None = ...,
rhs_indices: array | None = ...,
*,
sorted_indices: bool = ...,
stream: Stream | Device | None = ...,
@@ -4691,6 +4707,7 @@ def softmax(
/,
axis: int | Sequence[int] | None = ...,
*,
precise: bool = ...,
stream: Stream | Device | None = ...,
) -> array:
"""
+2 -6
View File
@@ -1,9 +1,5 @@
"""
This type stub file was generated by pyright.
"""
from layers import *
from utils import *
from .layers import *
from .utils import *
from . import init as init
from . import losses as losses
+16 -20
View File
@@ -1,20 +1,16 @@
"""
This type stub file was generated by pyright.
"""
from activations import *
from base import *
from containers import *
from convolution import *
from convolution_transpose import *
from distributed import *
from dropout import *
from embedding import *
from linear import *
from normalization import *
from pooling import *
from positional_encoding import *
from quantized import *
from recurrent import *
from transformer import *
from upsample import *
from .activations import *
from .base import *
from .containers import *
from .convolution import *
from .convolution_transpose import *
from .distributed import *
from .dropout import *
from .embedding import *
from .linear import *
from .normalization import *
from .pooling import *
from .positional_encoding import *
from .quantized import *
from .recurrent import *
from .transformer import *
from .upsample import *
+6 -2
View File
@@ -53,10 +53,14 @@ class Module(dict):
mx.eval(model.parameters())
"""
__call__: Callable
def __call__(self, *args: Any, **kwargs: Any) -> mx.array: ...
def __init__(self) -> None:
"""Should be called by the subclasses of ``Module``."""
def __getitem__(self, key: str) -> mx.array | Module: ...
def get(
self, key: str, default: mx.array | Module | None = ...
) -> mx.array | Module | None: ...
@property
def training(self): # -> bool:
"""Boolean indicating if the model is in training mode."""
@@ -200,7 +204,7 @@ class Module(dict):
) -> mx.MX_ARRAY_TREE: # -> dict[Any, Any | dict[Any, Any | dict[Any, Any] | list[Any]] | dict[Any, Any] | list[Any]]:
"""Return the submodules that do not contain other modules."""
def update(self, parameters: dict, strict: bool = ...) -> Module:
def update(self, parameters: dict[str, Any], strict: bool = ...) -> Module:
"""Replace the parameters of this Module with the provided ones in the
dict of dicts and lists.
@@ -32,6 +32,7 @@ class Conv1d(Module):
"""
weight: mx.array
bias: mx.array | None
groups: int
def __init__(
self,
+4
View File
@@ -40,6 +40,10 @@ class Linear(Module):
bias (bool, optional): If set to ``False`` then the layer will
not use a bias. Default is ``True``.
"""
weight: mx.array
bias: mx.array | None
def __init__(self, input_dims: int, output_dims: int, bias: bool = ...) -> None: ...
def __call__(self, x: mx.array) -> mx.array: ...
def to_quantized(
@@ -88,6 +88,9 @@ class RMSNorm(Module):
dims (int): The feature dimension of the input to normalize over
eps (float): A small additive constant for numerical stability
"""
weight: mx.array
def __init__(self, dims: int, eps: float = ...) -> None: ...
def __call__(self, x) -> mx.array: ...
+5 -3
View File
@@ -2,7 +2,7 @@
This type stub file was generated by pyright.
"""
from typing import Callable, Optional, Union
from typing import Any, Callable, Optional, Union
import mlx.core as mx
from base import Module
@@ -13,8 +13,10 @@ def quantize(
bits: int = ...,
*,
mode: str = ...,
class_predicate: Optional[Callable[[str, Module], Union[bool, dict]]] = ...,
): # -> None:
class_predicate: Optional[
Callable[[str, Module], Union[bool, dict[str, Any]]]
] = ...,
) -> None:
"""Quantize the sub-modules of a module according to a predicate.
By default all layers that define a ``to_quantized(group_size, bits)``
+11 -8
View File
@@ -7,7 +7,10 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from mlx.core import MX_ARRAY_TREE
def tree_map(
fn: Callable, tree: Any, *rest: Any, is_leaf: Optional[Callable] = ...
fn: Callable[..., Any],
tree: Any,
*rest: Any,
is_leaf: Callable[..., bool] | None = ...,
) -> Any:
"""Applies ``fn`` to the leaves of the Python tree ``tree`` and
returns a new collection with the results.
@@ -44,11 +47,11 @@ def tree_map(
"""
def tree_map_with_path(
fn: Callable,
fn: Callable[..., Any],
tree: Any,
*rest: Any,
is_leaf: Optional[Callable] = ...,
path: Optional[Any] = ...,
is_leaf: Callable[..., bool] | None = ...,
path: str | None = ...,
) -> Any:
"""Applies ``fn`` to the path and leaves of the Python tree ``tree`` and
returns a new collection with the results.
@@ -80,9 +83,9 @@ def tree_map_with_path(
def tree_flatten(
tree: Any,
prefix: str = ...,
is_leaf: Optional[Callable] = ...,
destination: Optional[Union[List[Tuple[str, Any]], Dict[str, Any]]] = ...,
) -> Union[List[Tuple[str, Any]], Dict[str, Any]]:
is_leaf: Callable[..., bool] | None = ...,
destination: list[tuple[str, Any]] | dict[str, Any] | None = ...,
) -> list[tuple[str, Any]] | dict[str, Any]:
"""Flattens a Python tree to a list of key, value tuples.
The keys are using the dot notation to define trees of arbitrary depth and
@@ -118,7 +121,7 @@ def tree_flatten(
the Python tree.
"""
def tree_unflatten(tree: Union[List[Tuple[str, Any]], Dict[str, Any]]) -> Any:
def tree_unflatten(tree: list[tuple[str, Any]] | dict[str, Any]) -> Any:
"""Recreate a Python tree from its flat representation.
.. code-block:: python
+5
View File
@@ -0,0 +1,5 @@
"""
This type stub file was generated by pyright.
"""
__version__ = ...
+289 -56
View File
@@ -3,13 +3,12 @@ This type stub file was generated by pyright.
"""
import contextlib
from dataclasses import dataclass
from typing import Any, Callable, Generator, List, Optional, Tuple, Union
import mlx.core as mx
import mlx.nn as nn
from dataclasses import dataclass
from collections import deque
from typing import Any, Callable, Generator, List, Optional, Sequence, Tuple, Union
from transformers import PreTrainedTokenizer
from .tokenizer_utils import TokenizerWrapper
DEFAULT_PROMPT = ...
@@ -29,8 +28,9 @@ def str2bool(string): # -> bool:
...
def setup_arg_parser(): # -> ArgumentParser:
"""Set up and return the argument parser."""
...
generation_stream = ...
generation_stream: mx.Stream
@contextlib.contextmanager
def wired_limit(
@@ -43,6 +43,7 @@ def wired_limit(
async eval could be running pass in the streams to synchronize with prior
to exiting the context manager.
"""
...
@dataclass
class GenerationResponse:
"""
@@ -73,9 +74,11 @@ class GenerationResponse:
finish_reason: Optional[str] = ...
def maybe_quantize_kv_cache(
prompt_cache, quantized_kv_start, kv_group_size, kv_bits
): # -> None:
...
prompt_cache: Any,
quantized_kv_start: int | None,
kv_group_size: int | None,
kv_bits: int | None,
) -> None: ...
def generate_step(
prompt: mx.array,
model: nn.Module,
@@ -89,7 +92,7 @@ def generate_step(
kv_bits: Optional[int] = ...,
kv_group_size: int = ...,
quantized_kv_start: int = ...,
prompt_progress_callback: Optional[Callable[[int], int]] = ...,
prompt_progress_callback: Optional[Callable[[int, int], None]] = ...,
input_embeddings: Optional[mx.array] = ...,
) -> Generator[Tuple[mx.array, mx.array], None, None]:
"""
@@ -115,7 +118,7 @@ def generate_step(
kv_group_size (int): Group size for KV cache quantization. Default: ``64``.
quantized_kv_start (int): Step to begin using a quantized KV cache.
when ``kv_bits`` is non-None. Default: ``0``.
prompt_progress_callback (Callable[[int], int]): A call-back which takes the
prompt_progress_callback (Callable[[int, int], None]): A call-back which takes the
prompt tokens processed so far and the total number of prompt tokens.
input_embeddings (mx.array, optional): Input embeddings to use instead of or in
conjunction with prompt tokens. Default: ``None``.
@@ -123,6 +126,7 @@ def generate_step(
Yields:
Tuple[mx.array, mx.array]: One token and a vector of log probabilities.
"""
...
def speculative_generate_step(
prompt: mx.array,
@@ -168,6 +172,7 @@ def speculative_generate_step(
Tuple[mx.array, mx.array, bool]: One token, a vector of log probabilities,
and a bool indicating if the token was generated by the draft model
"""
...
def stream_generate(
model: nn.Module,
@@ -175,7 +180,7 @@ def stream_generate(
prompt: Union[str, mx.array, List[int]],
max_tokens: int = ...,
draft_model: Optional[nn.Module] = ...,
**kwargs: object,
**kwargs: Any,
) -> Generator[GenerationResponse, None, None]:
"""
A generator producing text based on the given prompt from the model.
@@ -197,6 +202,7 @@ def stream_generate(
GenerationResponse: An instance containing the generated text segment and
associated metadata. See :class:`GenerationResponse` for details.
"""
...
def generate(
model: nn.Module,
@@ -217,6 +223,9 @@ def generate(
kwargs: The remaining options get passed to :func:`stream_generate`.
See :func:`stream_generate` for more details.
"""
...
def _merge_caches(caches: List[List[Any]]) -> List[Any]: ...
@dataclass
class BatchStats:
"""
@@ -240,10 +249,263 @@ class BatchStats:
generation_time: float = ...
peak_memory: float = ...
class SequenceStateMachine:
"""A state machine that uses one Aho-Corasick trie per state to efficiently
track state across a generated sequence.
The transitions are provided as state -> [(sequence, new_state)].
Example:
sm = SequenceStateMachine(
transitions={
"normal": [
(think_start_tokens, "reasoning"),
(tool_start_tokens, "tool"),
(eos, None),
],
"reasoning": [
(think_end_tokens, "normal"),
(eos, None),
],
"tool": [
(tool_end_tokens, None),
(eos, None)
],
},
initial="normal"
)
"""
def __init__(self, transitions=..., initial=...) -> None: ...
def __deepcopy__(self, memo): # -> SequenceStateMachine:
...
def make_state(self): # -> tuple[str, Any, dict[Any, Any]]:
...
@staticmethod
def match(state, x): # -> tuple[tuple[Any, Any | None, Any], Any | None, Any]:
...
class PromptProcessingBatch:
"""
A batch processor for prompt tokens with support for incremental processing.
This class handles batched prompt processing, managing KV caches and preparing
tokens for generation. It supports extending, filtering, and splitting batches.
"""
@dataclass
class Response:
uid: int
progress: tuple
end_of_segment: bool
end_of_prompt: bool
...
def __init__(
self,
model: nn.Module,
uids: List[int],
caches: List[List[Any]],
tokens: Optional[List[List[int]]] = ...,
prefill_step_size: int = ...,
samplers: Optional[List[Callable[[mx.array], mx.array]]] = ...,
fallback_sampler: Optional[Callable[[mx.array], mx.array]] = ...,
logits_processors: Optional[
List[List[Callable[[mx.array, mx.array], mx.array]]]
] = ...,
state_machines: Optional[List[SequenceStateMachine]] = ...,
max_tokens: Optional[List[int]] = ...,
) -> None: ...
def __len__(self): # -> int:
...
def extract_cache(self, idx: int) -> List[Any]: ...
def extend(self, batch): # -> None:
...
def split(self, indices: List[int]): # -> Self:
...
def filter(self, keep: List[int]): # -> None:
...
def prompt(self, tokens: List[List[int]]): # -> None:
"""
Process prompt tokens through the model.
Args:
tokens: List of token sequences to process.
"""
...
def generate(self, tokens: List[List[int]]): # -> GenerationBatch:
"""
Transition from prompt processing to generation.
Args:
tokens: Final tokens for each sequence to start generation.
Returns:
A GenerationBatch ready for token generation.
"""
...
@classmethod
def empty(
cls,
model: nn.Module,
fallback_sampler: Callable[[mx.array], mx.array],
prefill_step_size: int = ...,
): # -> Self:
...
class GenerationBatch:
"""
A batched token generator that manages multiple sequences in parallel.
This class handles the generation phase after prompt processing, managing
KV caches, sampling, and stop sequence detection for multiple sequences.
"""
@dataclass
class Response:
uid: int
token: int
logprobs: mx.array
finish_reason: Optional[str]
current_state: Optional[str]
match_sequence: Optional[List[int]]
prompt_cache: Optional[List[Any]]
all_tokens: Optional[List[int]]
...
model: nn.Module
uids: List[int]
prompt_cache: List[Any]
tokens: List[List[int]]
samplers: Optional[List[Callable[[mx.array], mx.array]]]
fallback_sampler: Callable[[mx.array], mx.array]
logits_processors: Optional[List[List[Callable[[mx.array, mx.array], mx.array]]]]
state_machines: List[SequenceStateMachine]
max_tokens: List[int]
_current_tokens: Optional[mx.array]
_current_logprobs: mx.array | List[mx.array]
_next_tokens: Optional[mx.array]
_next_logprobs: mx.array | List[mx.array]
_token_context: List[Any]
_num_tokens: List[int]
_matcher_states: List[Any]
def __init__(
self,
model: nn.Module,
uids: List[int],
inputs: mx.array,
prompt_cache: List[Any],
tokens: List[List[int]],
samplers: Optional[List[Callable[[mx.array], mx.array]]],
fallback_sampler: Callable[[mx.array], mx.array],
logits_processors: Optional[
List[List[Callable[[mx.array, mx.array], mx.array]]]
],
state_machines: List[SequenceStateMachine],
max_tokens: List[int],
) -> None: ...
def __len__(self) -> int: ...
def extend(self, batch: GenerationBatch) -> None: ...
def extract_cache(self, idx: int) -> List[Any]: ...
def filter(self, keep: List[int]) -> None: ...
def _step(self) -> Tuple[List[int], List[mx.array]]: ...
def next(self) -> List[Response]:
"""
Generate the next batch of tokens.
Returns:
List of Response objects for each sequence in the batch.
"""
...
@classmethod
def empty(
cls, model: nn.Module, fallback_sampler: Callable[[mx.array], mx.array]
): # -> Self:
...
class BatchGenerator:
"""
A batch generator implements continuous batching.
This class provides automatic management of prompt processing and generation
batches, handling the transition between the two.
It also allows for segmented prompt processing which guarantees that the
generator will stop at these boundaries when processing an input.
"""
def __init__(
self,
model: nn.Module,
max_tokens: int = ...,
stop_tokens: Optional[Sequence[Sequence[int]]] = ...,
sampler: Optional[Callable[[mx.array], mx.array]] = ...,
logits_processors: Optional[
List[Callable[[mx.array, mx.array], mx.array]]
] = ...,
completion_batch_size: int = ...,
prefill_batch_size: int = ...,
prefill_step_size: int = ...,
) -> None: ...
def close(self) -> None: ...
def __del__(self): # -> None:
...
@contextlib.contextmanager
def stats(self, stats=...): # -> Generator[Any | BatchStats, Any, None]:
...
_unprocessed_sequences: deque[tuple[Any, ...]]
_prompt_batch: PromptProcessingBatch
_generation_batch: GenerationBatch
_currently_processing: list[Any]
_gen_tokens_counter: int
_steps_counter: int
def _next(
self,
) -> tuple[
List[PromptProcessingBatch.Response], List[GenerationBatch.Response]
]: ...
def insert(
self,
prompts: List[List[int]],
max_tokens: Optional[List[int]] = ...,
caches: Optional[List[List[Any]]] = ...,
all_tokens: Optional[List[List[int]]] = ...,
samplers: Optional[List[Callable[[mx.array], mx.array]]] = ...,
logits_processors: Optional[
List[List[Callable[[mx.array, mx.array], mx.array]]]
] = ...,
state_machines: Optional[List[SequenceStateMachine]] = ...,
) -> List[int]: ...
def insert_segments(
self,
segments: List[List[List[int]]],
max_tokens: Optional[List[int]] = ...,
caches: Optional[List[List[Any]]] = ...,
all_tokens: Optional[List[List[int]]] = ...,
samplers: Optional[List[Callable[[mx.array], mx.array]]] = ...,
logits_processors: Optional[
List[List[Callable[[mx.array, mx.array], mx.array]]]
] = ...,
state_machines: Optional[List[SequenceStateMachine]] = ...,
) -> List[int]: ...
def extract_cache(self, uids: List[int]) -> dict[int, Any]: ...
def remove(
self, uids: List[int], return_prompt_caches: bool = ...
) -> dict[int, Any]: ...
@property
def prompt_cache_nbytes(self) -> int: ...
def next(
self,
) -> tuple[
List[PromptProcessingBatch.Response], List[GenerationBatch.Response]
]: ...
def next_generated(self) -> List[GenerationBatch.Response]: ...
@dataclass
class BatchResponse:
"""
An data object to hold a batch generation response.
A data object to hold a batch generation response.
Args:
texts: (List[str]): The generated text for each prompt.
@@ -252,55 +514,18 @@ class BatchResponse:
texts: List[str]
stats: BatchStats
@dataclass
class Batch:
uids: List[int]
y: mx.array
logprobs: mx.array
max_tokens: List[int]
num_tokens: List[int]
cache: List[Any]
def __len__(self): # -> int:
...
def filter(self, keep_idx: List[int]): # -> None:
...
def extend(self, other): # -> None:
...
class BatchGenerator:
@dataclass
class Response:
uid: int
token: int
logprobs: mx.array
finish_reason: Optional[str]
def __init__(
self,
model,
max_tokens: int = ...,
stop_tokens: Optional[set] = ...,
sampler: Optional[Callable[[mx.array], mx.array]] = ...,
completion_batch_size: int = ...,
prefill_batch_size: int = ...,
prefill_step_size: int = ...,
) -> None: ...
def insert(
self, prompts, max_tokens: Union[List[int], int, None] = ...
): # -> list[Any]:
...
def stats(self): # -> BatchStats:
...
def next(self): # -> list[Any]:
...
caches: Optional[List[List[Any]]]
...
def batch_generate(
model,
tokenizer,
prompts: List[int],
prompts: List[List[int]],
prompt_caches: Optional[List[List[Any]]] = ...,
max_tokens: Union[int, List[int]] = ...,
verbose: bool = ...,
return_prompt_caches: bool = ...,
logits_processors: Optional[List[Callable[[mx.array, mx.array], mx.array]]] = ...,
**kwargs,
) -> BatchResponse:
"""
@@ -309,14 +534,22 @@ def batch_generate(
Args:
model (nn.Module): The language model.
tokenizer (PreTrainedTokenizer): The tokenizer.
prompt (List[List[int]]): The input prompts.
prompts (List[List[int]]): The input prompts.
prompt_caches (List[List[Any]], optional): Pre-computed prompt-caches
for each input prompt. Note, unlike ``generate_step``, the caches
won't be updated in-place.
verbose (bool): If ``True``, print tokens and timing information.
Default: ``False``.
max_tokens (Union[int, List[int]): Maximum number of output tokens. This
can be per prompt if a list is provided.
return_prompt_caches (bool): Return the prompt caches in the batch
responses. Default: ``False``.
logits_processors (List[Callable[[mx.array, mx.array], mx.array]], optional):
A list of functions that take tokens and logits and return the processed logits. Default: ``None``.
kwargs: The remaining options get passed to :obj:`BatchGenerator`.
See :obj:`BatchGenerator` for more details.
"""
...
def main(): # -> None:
...
@@ -0,0 +1,19 @@
"""
This type stub file was generated by pyright.
"""
import mlx.core as mx
import mlx.nn as nn
from functools import partial
@partial(mx.compile, shapeless=True)
def swiglu(gate, x): ...
@partial(mx.compile, shapeless=True)
def xielu(x, alpha_p, alpha_n, beta, eps): # -> array:
...
class XieLU(nn.Module):
def __init__(
self, alpha_p_init=..., alpha_n_init=..., beta=..., eps=...
) -> None: ...
def __call__(self, x: mx.array) -> mx.array: ...
+5 -5
View File
@@ -3,7 +3,7 @@ This type stub file was generated by pyright.
"""
from dataclasses import dataclass
from typing import Optional
from typing import Any, Optional
import mlx.core as mx
@@ -37,10 +37,10 @@ def quantized_scaled_dot_product_attention(
bits: int = ...,
) -> mx.array: ...
def scaled_dot_product_attention(
queries,
keys,
values,
cache,
queries: mx.array,
keys: mx.array,
values: mx.array,
cache: Optional[Any],
scale: float,
mask: Optional[mx.array],
sinks: Optional[mx.array] = ...,
+52 -61
View File
@@ -16,7 +16,7 @@ class Cache(Protocol):
self, keys: mx.array, values: mx.array
) -> tuple[mx.array, mx.array]: ...
@property
def state(self) -> tuple[mx.array, mx.array]: ...
def state(self) -> tuple[mx.array | None, mx.array | None]: ...
@state.setter
def state(self, v) -> None: ...
@@ -88,17 +88,18 @@ def create_attention_mask(
) -> array | Literal["causal"] | None: ...
class _BaseCache(Cache):
keys: mx.array
values: mx.array
keys: mx.array | None
values: mx.array | None
offset: int
@property
def state(self) -> tuple[mx.array, mx.array]: ...
def state(self) -> tuple[mx.array | None, mx.array | None]: ...
@state.setter
def state(self, v) -> None: ...
@property
def meta_state(self) -> Literal[""]: ...
@meta_state.setter
def meta_state(self, v) -> None: ...
def trim(self, n: int) -> int: ...
def is_trimmable(self) -> Literal[False]: ...
@classmethod
def from_state(cls, state, meta_state) -> Self: ...
@@ -114,15 +115,13 @@ class ConcatenateKVCache(_BaseCache):
def update_and_fetch(self, keys, values): # -> tuple[Any | array, Any | array]:
...
@property
def state(self): # -> tuple[Any | array | None, Any | array | None]:
...
def state(self) -> tuple[mx.array | None, mx.array | None]: ...
@state.setter
def state(self, v): # -> None:
...
def is_trimmable(self): # -> Literal[True]:
...
def trim(self, n): # -> int:
...
def trim(self, n: int) -> int: ...
def make_mask(self, *args, **kwargs): # -> array | Literal['causal'] | None:
...
@@ -132,10 +131,7 @@ class QuantizedKVCache(_BaseCache):
def update_and_fetch(self, keys, values): # -> Any:
...
@property
def state(
self,
): # -> tuple[Any | tuple[array, array, array] | None, Any | tuple[array, array, array] | None] | Any:
...
def state(self) -> tuple[mx.array | None, mx.array | None]: ...
@state.setter
def state(self, v): # -> None:
...
@@ -147,8 +143,7 @@ class QuantizedKVCache(_BaseCache):
...
def is_trimmable(self): # -> Literal[True]:
...
def trim(self, n): # -> int:
...
def trim(self, n: int) -> int: ...
def make_mask(self, *args, **kwargs): # -> array | Literal['causal'] | None:
...
@@ -160,22 +155,30 @@ class KVCache(_BaseCache):
@property
def state(
self,
) -> tuple[array, array]: ...
) -> tuple[mx.array | None, mx.array | None]: ...
@state.setter
def state(self, v) -> None: ...
def is_trimmable(self): # -> Literal[True]:
...
def trim(self, n): # -> int:
...
def trim(self, n: int) -> int: ...
def to_quantized(
self, group_size: int = ..., bits: int = ...
) -> QuantizedKVCache: ...
def make_mask(self, *args, **kwargs): # -> array | Literal['causal'] | None:
...
def make_mask(
self, *args: Any, **kwargs: Any
) -> mx.array | Literal["causal"] | None: ...
class RotatingKVCache(_BaseCache):
step = ...
keys: mx.array | None
values: mx.array | None
keep: int
max_size: int
_idx: int
def __init__(self, max_size, keep=...) -> None: ...
def _trim(
self, trim_size: int, v: mx.array, append: mx.array | None = ...
) -> mx.array: ...
def update_and_fetch(
self, keys, values
): # -> tuple[array | Any, array | Any] | tuple[array | Any, array | Any | None]:
@@ -183,8 +186,7 @@ class RotatingKVCache(_BaseCache):
@property
def state(
self,
): # -> tuple[Any | array, Any | array] | tuple[Any | array | None, Any | array | None]:
...
) -> tuple[mx.array | None, mx.array | None]: ...
@state.setter
def state(self, v): # -> None:
...
@@ -196,8 +198,7 @@ class RotatingKVCache(_BaseCache):
...
def is_trimmable(self): # -> bool:
...
def trim(self, n): # -> int:
...
def trim(self, n: int) -> int: ...
def to_quantized(
self, group_size: int = ..., bits: int = ...
) -> QuantizedKVCache: ...
@@ -212,8 +213,7 @@ class ArraysCache(_BaseCache):
...
def __getitem__(self, idx): ...
@property
def state(self): # -> list[Any | array] | list[array]:
...
def state(self) -> tuple[mx.array | None, mx.array | None]: ...
@state.setter
def state(self, v): # -> None:
...
@@ -227,8 +227,7 @@ class ArraysCache(_BaseCache):
In-place extend this cache with the other cache.
"""
def make_mask(self, N: int): # -> array | None:
...
def make_mask(self, N: int) -> mx.array | None: ...
class MambaCache(ArraysCache):
def __init__(self, left_padding: Optional[List[int]] = ...) -> None: ...
@@ -239,8 +238,7 @@ class ChunkedKVCache(KVCache):
...
def update_and_fetch(self, keys, values): # -> tuple[array, array]:
...
def trim(self, n): # -> int:
...
def trim(self, n: int) -> int: ...
@property
def meta_state(self): # -> tuple[str, ...]:
...
@@ -253,10 +251,9 @@ class CacheList(_BaseCache):
def __getitem__(self, idx): ...
def is_trimmable(self): # -> bool:
...
def trim(self, n): ...
def trim(self, n: int) -> int: ...
@property
def state(self): # -> list[Any]:
...
def state(self) -> list[tuple[mx.array | None, mx.array | None]]: ...
@state.setter
def state(self, v): # -> None:
...
@@ -271,29 +268,14 @@ class CacheList(_BaseCache):
"""
class BatchKVCache(_BaseCache):
step = ...
def __init__(self, left_padding: List[int]) -> None:
"""
The BatchKV cache expects inputs to be left-padded.
E.g. the following prompts:
[1, 3, 5]
[7]
[2, 6, 8, 9]
Should be padded like so:
[0, 1, 3, 5]
[0, 0, 0, 7]
[2, 6, 8, 9]
And ``left_padding`` specifies the amount of padding for each.
In this case, ``left_padding = [1, 3, 0]``.
"""
def update_and_fetch(self, keys, values): # -> tuple[array | Any, array | Any]:
...
step: int
keys: array | None
values: array | None
offset: array
left_padding: array
_idx: int
def __init__(self, left_padding: List[int]) -> None: ...
def update_and_fetch(self, keys: array, values: array) -> tuple[array, array]: ...
@property
def state(
self,
@@ -319,12 +301,21 @@ class BatchKVCache(_BaseCache):
"""
class BatchRotatingKVCache(_BaseCache):
step = ...
def __init__(self, max_size, left_padding: List[int]) -> None: ...
def update_and_fetch(
self, keys, values
): # -> tuple[array | Any, array | Any] | tuple[array | Any, array | Any | None]:
...
step: int
keys: array | None
values: array | None
offset: array
left_padding: array
max_size: int
_idx: int
_offset: int
rotated: bool
_lengths: array | None
def __init__(self, max_size: int, left_padding: List[int]) -> None: ...
def _trim(self, trim_size: int, v: array, append: array | None = ...) -> array: ...
def _update_in_place(self, keys: array, values: array) -> tuple[array, array]: ...
def _update_concat(self, keys: array, values: array) -> tuple[array, array]: ...
def update_and_fetch(self, keys: array, values: array) -> tuple[array, array]: ...
@property
def state(
self,
+280
View File
@@ -0,0 +1,280 @@
"""Type stubs for mlx_lm.models.deepseek_v4"""
from dataclasses import dataclass
from typing import Any, Dict, List, Optional
import mlx.core as mx
import mlx.nn as nn
from .base import BaseModelArgs
from .cache import ArraysCache, RotatingKVCache
from .switch_layers import SwitchGLU
@dataclass
class ModelArgs(BaseModelArgs):
model_type: str
vocab_size: int
hidden_size: int
intermediate_size: int
moe_intermediate_size: int
num_hidden_layers: int
num_attention_heads: int
num_key_value_heads: int
n_shared_experts: Optional[int]
n_routed_experts: int
num_experts_per_tok: int
head_dim: int
qk_rope_head_dim: int
q_lora_rank: int
o_lora_rank: int
o_groups: int
sliding_window: int
hc_mult: int
hc_sinkhorn_iters: int
hc_eps: float
compress_ratios: Optional[List[int]]
compress_rope_theta: float
rope_theta: float
rope_scaling: Optional[Dict[str, Any]]
rms_norm_eps: float
swiglu_limit: float
attention_bias: bool
max_position_embeddings: int
class DeepseekV4RoPE(nn.Module):
dims: int
freqs: mx.array
def __init__(
self,
dims: int,
base: float,
scaling_config: Optional[Dict[str, Any]] = None,
) -> None: ...
def __call__(
self,
x: mx.array,
offset: int = 0,
inverse: bool = False,
) -> mx.array: ...
class HyperConnection(nn.Module):
dim: int
hc_mult: int
norm_eps: float
def __init__(
self,
dim: int,
hc_mult: int,
norm_eps: float,
sinkhorn_iters: int,
hc_eps: float,
) -> None: ...
class HyperHead(nn.Module):
dim: int
hc_mult: int
def __init__(
self,
dim: int,
hc_mult: int,
norm_eps: float,
hc_eps: float,
) -> None: ...
def __call__(self, x: mx.array) -> mx.array: ...
class Compressor(nn.Module):
dim: int
head_dim: int
rope_head_dim: int
compress_ratio: int
overlap: bool
wkv_gate: nn.Linear
ape: mx.array
norm: nn.RMSNorm
rope: DeepseekV4RoPE
def __init__(
self,
dim: int,
compress_ratio: int,
head_dim: int,
rope_head_dim: int,
rms_norm_eps: float,
rope: DeepseekV4RoPE,
) -> None: ...
def __call__(
self,
x: mx.array,
cache: "DeepseekV4Cache",
offset: Any,
key: str = ...,
) -> mx.array: ...
class Indexer(nn.Module):
def __init__(
self,
args: ModelArgs,
compress_ratio: int,
rope: DeepseekV4RoPE,
) -> None: ...
class _CompressorBranch:
buffer_kv: Optional[mx.array]
buffer_gate: Optional[mx.array]
prev_kv: Optional[mx.array]
prev_gate: Optional[mx.array]
pool: Optional[mx.array]
buffer_lengths: Optional[List[int]]
pool_lengths: Optional[List[int]]
buffer_count: int
_new_pool_lengths: Optional[List[int]]
def __init__(self) -> None: ...
class DeepseekV4Cache:
local: RotatingKVCache
offset: int
keys: Optional[mx.array]
values: Optional[mx.array]
state: Any
meta_state: Any
nbytes: int
_branches: Dict[str, _CompressorBranch]
_pending_lengths: Optional[List[int]]
def __init__(self, sliding_window: int) -> None: ...
def update_and_fetch(
self, keys: mx.array, values: mx.array
) -> tuple[mx.array, mx.array]: ...
def is_trimmable(self) -> bool: ...
def trim(self, n: int) -> int: ...
def empty(self) -> bool: ...
def size(self) -> int: ...
def prepare(
self,
*,
left_padding: Optional[List[int]] = None,
lengths: Optional[List[int]] = None,
right_padding: Optional[List[int]] = None,
) -> None: ...
def finalize(self) -> None: ...
def filter(self, batch_indices: mx.array) -> None: ...
def extend(self, other: "DeepseekV4Cache") -> None: ...
def extract(self, idx: int) -> "DeepseekV4Cache": ...
@classmethod
def merge(cls, caches: List["DeepseekV4Cache"]) -> "DeepseekV4Cache": ...
class V4Attention(nn.Module):
args: ModelArgs
layer_id: int
dim: int
n_heads: int
head_dim: int
rope_head_dim: int
nope_head_dim: int
n_groups: int
q_lora_rank: int
o_lora_rank: int
window: int
eps: float
scale: float
compress_ratio: int
wqkv_a: nn.Linear
q_norm: nn.RMSNorm
wq_b: nn.Linear
kv_norm: nn.RMSNorm
attn_sink: mx.array
wo_a: nn.Linear
wo_b: nn.Linear
rope: DeepseekV4RoPE
compressor: Compressor
indexer: Indexer
def __init__(self, args: ModelArgs, layer_id: int) -> None: ...
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[Any] = None,
) -> mx.array: ...
class DeepseekV4MLP(nn.Module):
gate_proj: nn.Linear
up_proj: nn.Linear
down_proj: nn.Linear
def __init__(
self,
hidden_size: int,
intermediate_size: int,
swiglu_limit: float = 0.0,
) -> None: ...
def __call__(self, x: mx.array) -> mx.array: ...
class MoEGate(nn.Module):
weight: mx.array
def __init__(self, args: ModelArgs, layer_id: int) -> None: ...
def __call__(
self, x: mx.array, input_ids: mx.array
) -> tuple[mx.array, mx.array]: ...
class DeepseekV4MoE(nn.Module):
num_experts_per_tok: int
switch_mlp: SwitchGLU
gate: MoEGate
shared_experts: DeepseekV4MLP
def __init__(self, args: ModelArgs, layer_id: int) -> None: ...
def __call__(self, x: mx.array, input_ids: mx.array) -> mx.array: ...
class DeepseekV4Block(nn.Module):
attn_norm: nn.RMSNorm
attn: V4Attention
hc_attn: HyperConnection
ffn_norm: nn.RMSNorm
ffn: DeepseekV4MoE
hc_ffn: HyperConnection
def __init__(self, args: ModelArgs, layer_id: int) -> None: ...
def __call__(
self,
h: mx.array,
cache: Optional[Any],
input_ids: mx.array,
) -> mx.array: ...
class DeepseekV4Model(nn.Module):
args: ModelArgs
vocab_size: int
embed_tokens: nn.Embedding
layers: list[DeepseekV4Block]
norm: nn.RMSNorm
hc_head: HyperHead
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[List[Any]] = None,
) -> mx.array: ...
class Model(nn.Module):
args: ModelArgs
model_type: str
model: DeepseekV4Model
lm_head: nn.Linear
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[List[Any]] = None,
) -> mx.array: ...
def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
def make_cache(self) -> list[RotatingKVCache | DeepseekV4Cache]: ...
@property
def layers(self) -> list[DeepseekV4Block]: ...
@@ -0,0 +1,35 @@
from typing import Optional
import mlx.core as mx
def compute_g(A_log: mx.array, a: mx.array, dt_bias: mx.array) -> mx.array: ...
def gated_delta_update(
q: mx.array,
k: mx.array,
v: mx.array,
a: mx.array,
b: mx.array,
A_log: mx.array,
dt_bias: mx.array,
state: Optional[mx.array] = ...,
mask: Optional[mx.array] = ...,
use_kernel: bool = ...,
) -> tuple[mx.array, mx.array]: ...
def gated_delta_ops(
q: mx.array,
k: mx.array,
v: mx.array,
g: mx.array,
beta: mx.array,
state: Optional[mx.array] = ...,
mask: Optional[mx.array] = ...,
) -> tuple[mx.array, mx.array]: ...
def gated_delta_kernel(
q: mx.array,
k: mx.array,
v: mx.array,
g: mx.array,
beta: mx.array,
state: mx.array,
mask: Optional[mx.array] = ...,
) -> tuple[mx.array, mx.array]: ...
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@@ -0,0 +1,31 @@
from dataclasses import dataclass
from typing import Any, Optional
import mlx.core as mx
import mlx.nn as nn
from . import gemma4_text
from .base import BaseModelArgs
from .cache import KVCache, RotatingKVCache
@dataclass
class ModelArgs(BaseModelArgs):
model_type: str
text_config: Optional[dict[str, Any]]
vocab_size: int
def __post_init__(self) -> None: ...
class Model(nn.Module):
args: ModelArgs
model_type: str
language_model: gemma4_text.Model
def __init__(self, args: ModelArgs) -> None: ...
def __call__(self, *args: Any, **kwargs: Any) -> mx.array: ...
def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
@property
def layers(self) -> list[gemma4_text.DecoderLayer]: ...
@property
def quant_predicate(self) -> Any: ...
def make_cache(self) -> list[KVCache | RotatingKVCache]: ...
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@@ -0,0 +1,179 @@
from dataclasses import dataclass
from typing import Any, Dict, List, Optional
import mlx.core as mx
import mlx.nn as nn
from .base import BaseModelArgs
from .cache import KVCache, RotatingKVCache
from .switch_layers import SwitchGLU
@dataclass
class ModelArgs(BaseModelArgs):
model_type: str
hidden_size: int
num_hidden_layers: int
intermediate_size: int
num_attention_heads: int
head_dim: int
global_head_dim: int
global_partial_rotary_factor: float
rms_norm_eps: float
vocab_size: int
vocab_size_per_layer_input: int
num_key_value_heads: int
num_global_key_value_heads: Optional[int]
num_kv_shared_layers: int
pad_token_id: int
hidden_size_per_layer_input: int
rope_traditional: bool
partial_rotary_factor: float
rope_parameters: Optional[Dict[str, Any]]
sliding_window: int
sliding_window_pattern: int
max_position_embeddings: int
attention_k_eq_v: bool
final_logit_softcapping: float
use_double_wide_mlp: bool
enable_moe_block: bool
num_experts: Optional[int]
top_k_experts: Optional[int]
moe_intermediate_size: Optional[int]
layer_types: Optional[List[str]]
tie_word_embeddings: bool
def __post_init__(self) -> None: ...
class MLP(nn.Module):
gate_proj: nn.Linear
down_proj: nn.Linear
up_proj: nn.Linear
def __init__(self, config: ModelArgs, layer_idx: int = 0) -> None: ...
def __call__(self, x: mx.array) -> mx.array: ...
class Router(nn.Module):
proj: nn.Linear
scale: mx.array
per_expert_scale: mx.array
def __init__(self, config: ModelArgs) -> None: ...
def __call__(self, x: mx.array) -> tuple[mx.array, mx.array]: ...
class Experts(nn.Module):
switch_glu: SwitchGLU
def __init__(self, config: ModelArgs) -> None: ...
def __call__(
self, x: mx.array, top_k_indices: mx.array, top_k_weights: mx.array
) -> mx.array: ...
class Attention(nn.Module):
layer_idx: int
layer_type: str
is_sliding: bool
head_dim: int
n_heads: int
n_kv_heads: int
use_k_eq_v: bool
scale: float
q_proj: nn.Linear
k_proj: nn.Linear
v_proj: nn.Linear
o_proj: nn.Linear
q_norm: nn.Module
k_norm: nn.Module
v_norm: nn.Module
rope: nn.Module
def __init__(self, config: ModelArgs, layer_idx: int) -> None: ...
def __call__(self, *args: Any, **kwargs: Any) -> Any: ...
class DecoderLayer(nn.Module):
layer_idx: int
layer_type: str
self_attn: Attention
mlp: MLP
enable_moe: bool
router: Router
experts: Experts
input_layernorm: nn.Module
post_attention_layernorm: nn.Module
pre_feedforward_layernorm: nn.Module
post_feedforward_layernorm: nn.Module
post_feedforward_layernorm_1: nn.Module
post_feedforward_layernorm_2: nn.Module
pre_feedforward_layernorm_2: nn.Module
hidden_size_per_layer_input: int
per_layer_input_gate: Optional[nn.Linear]
per_layer_projection: Optional[nn.Linear]
post_per_layer_input_norm: Optional[nn.Module]
layer_scalar: mx.array
def __init__(self, config: ModelArgs, layer_idx: int) -> None: ...
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = ...,
cache: Optional[Any] = ...,
per_layer_input: Optional[mx.array] = ...,
shared_kv: Optional[tuple[mx.array, mx.array]] = ...,
offset: Optional[mx.array] = ...,
) -> tuple[mx.array, tuple[mx.array, mx.array], mx.array]: ...
class Gemma4TextModel(nn.Module):
config: ModelArgs
vocab_size: int
window_size: int
sliding_window_pattern: int
num_hidden_layers: int
embed_tokens: nn.Embedding
embed_scale: float
layers: list[DecoderLayer]
norm: nn.Module
hidden_size_per_layer_input: int
embed_tokens_per_layer: Optional[nn.Embedding]
per_layer_model_projection: Optional[nn.Linear]
per_layer_projection_norm: Optional[nn.Module]
previous_kvs: list[int]
def __init__(self, config: ModelArgs) -> None: ...
def __call__(
self,
inputs: Optional[mx.array] = ...,
cache: Optional[list[Any]] = ...,
input_embeddings: Optional[mx.array] = ...,
per_layer_inputs: Optional[mx.array] = ...,
) -> mx.array: ...
def _get_per_layer_inputs(
self,
input_ids: Optional[mx.array],
input_embeddings: Optional[mx.array] = ...,
) -> mx.array: ...
def _project_per_layer_inputs(
self,
input_embeddings: mx.array,
per_layer_inputs: Optional[mx.array] = ...,
) -> mx.array: ...
def _make_masks(self, h: mx.array, cache: list[Any]) -> list[Any]: ...
class Model(nn.Module):
args: ModelArgs
model_type: str
model: Gemma4TextModel
final_logit_softcapping: float
tie_word_embeddings: bool
lm_head: nn.Linear
def __init__(self, args: ModelArgs) -> None: ...
def __call__(self, *args: Any, **kwargs: Any) -> mx.array: ...
def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
@property
def layers(self) -> list[DecoderLayer]: ...
@property
def head_dim(self) -> int: ...
@property
def n_kv_heads(self) -> int: ...
@property
def quant_predicate(self) -> Any: ...
def make_cache(self) -> list[KVCache | RotatingKVCache]: ...
@@ -0,0 +1,46 @@
"""Type stubs for mlx_lm.models.glm_moe_dsa"""
from dataclasses import dataclass
from typing import Any, Dict, Optional
from .base import BaseModelArgs
from .deepseek_v32 import Model as DSV32Model
@dataclass
class ModelArgs(BaseModelArgs):
model_type: str
vocab_size: int
hidden_size: int
index_head_dim: int
index_n_heads: int
index_topk: int
intermediate_size: int
moe_intermediate_size: int
num_hidden_layers: int
num_attention_heads: int
num_key_value_heads: int
n_shared_experts: Optional[int]
n_routed_experts: Optional[int]
routed_scaling_factor: float
kv_lora_rank: int
q_lora_rank: int
qk_rope_head_dim: int
v_head_dim: int
qk_nope_head_dim: int
topk_method: str
scoring_func: str
norm_topk_prob: bool
n_group: int
topk_group: int
num_experts_per_tok: int
moe_layer_freq: int
first_k_dense_replace: int
max_position_embeddings: int
rms_norm_eps: float
rope_parameters: Dict[str, Any]
attention_bias: bool
rope_scaling: Dict[str, Any] | None
rope_theta: float | None
class Model(DSV32Model):
def __init__(self, config: ModelArgs) -> None: ...
+103
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@@ -0,0 +1,103 @@
"""Type stubs for mlx_lm.models.gpt_oss"""
from dataclasses import dataclass
from typing import Any, List, Optional
import mlx.core as mx
import mlx.nn as nn
from .base import BaseModelArgs
from .cache import KVCache
from .switch_layers import SwitchGLU
@dataclass
class ModelArgs(BaseModelArgs):
model_type: str
hidden_size: int
intermediate_size: int
num_hidden_layers: int
num_attention_heads: int
num_key_value_heads: int
num_local_experts: int
num_experts_per_tok: int
vocab_size: int
rms_norm_eps: float
sliding_window: int
layer_types: Optional[List[str]]
def mlx_topk(a: mx.array, k: int, axis: int = -1) -> tuple[mx.array, mx.array]: ...
class AttentionBlock(nn.Module):
head_dim: int
num_attention_heads: int
num_key_value_heads: int
num_key_value_groups: int
sinks: mx.array
q_proj: nn.Linear
k_proj: nn.Linear
v_proj: nn.Linear
o_proj: nn.Linear
sm_scale: float
rope: nn.Module
def __init__(self, config: ModelArgs) -> None: ...
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[Any] = None,
) -> mx.array: ...
class TransformerBlock(nn.Module):
self_attn: AttentionBlock
mlp: MLPBlock
def __init__(self, config: ModelArgs) -> None: ...
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[Any] = None,
) -> mx.array: ...
class MLPBlock(nn.Module):
hidden_size: int
num_local_experts: int
num_experts_per_tok: int
experts: SwitchGLU
router: nn.Linear
sharding_group: Optional[mx.distributed.Group]
def __init__(self, config: ModelArgs) -> None: ...
def __call__(self, x: mx.array) -> mx.array: ...
class GptOssMoeModel(nn.Module):
embed_tokens: nn.Embedding
norm: nn.RMSNorm
layer_types: List[str]
layers: list[TransformerBlock]
window_size: int
swa_idx: int
ga_idx: int
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[Any] = None,
) -> mx.array: ...
class Model(nn.Module):
model_type: str
model: GptOssMoeModel
lm_head: nn.Linear
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[Any] = None,
) -> mx.array: ...
@property
def layers(self) -> list[nn.Module]: ...
def make_cache(self) -> list[KVCache]: ...
+94
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@@ -0,0 +1,94 @@
"""Type stubs for mlx_lm.models.minimax"""
from dataclasses import dataclass
from typing import Any, Optional
import mlx.core as mx
import mlx.nn as nn
from .base import BaseModelArgs
from .switch_layers import SwitchGLU
@dataclass
class ModelArgs(BaseModelArgs):
model_type: str
hidden_size: int
intermediate_size: int
num_hidden_layers: int
num_attention_heads: int
num_key_value_heads: int
num_local_experts: int
num_experts_per_tok: int
max_position_embeddings: int
class MiniMaxAttention(nn.Module):
num_heads: int
num_attention_heads: int
num_key_value_heads: int
head_dim: int
scale: float
q_proj: nn.Linear
k_proj: nn.Linear
v_proj: nn.Linear
o_proj: nn.Linear
q_norm: nn.Module
k_norm: nn.Module
rope: nn.Module
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[Any] = None,
) -> mx.array: ...
class MiniMaxSparseMoeBlock(nn.Module):
num_experts_per_tok: int
gate: nn.Linear
switch_mlp: SwitchGLU
e_score_correction_bias: mx.array
sharding_group: Optional[mx.distributed.Group]
def __init__(self, args: ModelArgs) -> None: ...
def __call__(self, x: mx.array) -> mx.array: ...
class MiniMaxDecoderLayer(nn.Module):
self_attn: MiniMaxAttention
block_sparse_moe: MiniMaxSparseMoeBlock
input_layernorm: nn.RMSNorm
post_attention_layernorm: nn.RMSNorm
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[Any] = None,
) -> mx.array: ...
class MiniMaxModel(nn.Module):
embed_tokens: nn.Embedding
layers: list[MiniMaxDecoderLayer]
norm: nn.RMSNorm
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[Any] = None,
) -> mx.array: ...
class Model(nn.Module):
model_type: str
model: MiniMaxModel
lm_head: nn.Linear
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[Any] = None,
) -> mx.array: ...
@property
def layers(self) -> list[MiniMaxDecoderLayer]: ...
+168
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@@ -0,0 +1,168 @@
from dataclasses import dataclass
from typing import Any, List, Optional, Tuple
import mlx.core as mx
import mlx.nn as nn
from .cache import ArraysCache, KVCache
from .switch_layers import SwitchMLP
@dataclass
class ModelArgs:
model_type: str
vocab_size: int
hidden_size: int
intermediate_size: int
num_hidden_layers: int
max_position_embeddings: int
num_attention_heads: int
num_key_value_heads: int
attention_bias: bool
mamba_num_heads: int
mamba_head_dim: int
mamba_proj_bias: bool
ssm_state_size: int
conv_kernel: int
n_groups: int
mlp_bias: bool
layer_norm_epsilon: float
use_bias: bool
use_conv_bias: bool
hybrid_override_pattern: List[str]
head_dim: Optional[int]
moe_intermediate_size: Optional[int]
moe_shared_expert_intermediate_size: Optional[int]
n_group: Optional[int]
n_routed_experts: Optional[int]
n_shared_experts: Optional[int]
topk_group: Optional[int]
num_experts_per_tok: Optional[int]
norm_topk_prob: Optional[bool]
routed_scaling_factor: Optional[float]
time_step_limit: Optional[Tuple[float, float]]
time_step_min: Optional[float]
time_step_max: Optional[float]
@classmethod
def from_dict(cls, params: dict[str, Any]) -> ModelArgs: ...
def __post_init__(self) -> None: ...
class NemotronHMamba2Mixer(nn.Module):
num_heads: int
hidden_size: int
ssm_state_size: int
conv_kernel_size: int
intermediate_size: int
n_groups: int
head_dim: int
conv_dim: int
conv1d: nn.Conv1d
in_proj: nn.Linear
dt_bias: mx.array
A_log: mx.array
D: mx.array
norm: nn.RMSNorm
heads_per_group: int
out_proj: nn.Linear
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
hidden_states: mx.array,
mask: Optional[mx.array],
cache: Optional[ArraysCache] = None,
) -> mx.array: ...
class NemotronHAttention(nn.Module):
hidden_size: int
num_heads: int
head_dim: int
num_key_value_heads: int
scale: float
q_proj: nn.Linear
k_proj: nn.Linear
v_proj: nn.Linear
o_proj: nn.Linear
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[KVCache] = None,
) -> mx.array: ...
class MoEGate(nn.Module):
config: ModelArgs
top_k: int
norm_topk_prob: bool
weight: mx.array
def __init__(self, config: ModelArgs) -> None: ...
def __call__(self, x: mx.array) -> tuple[mx.array, mx.array]: ...
class NemotronHMLP(nn.Module):
up_proj: nn.Linear
down_proj: nn.Linear
def __init__(
self, args: ModelArgs, intermediate_size: Optional[int] = None
) -> None: ...
def __call__(self, x: mx.array) -> mx.array: ...
class NemotronHMoE(nn.Module):
config: ModelArgs
num_experts_per_tok: int
moe_latent_size: Optional[int]
switch_mlp: SwitchMLP
gate: MoEGate
shared_experts: NemotronHMLP
fc1_latent_proj: nn.Linear
fc2_latent_proj: nn.Linear
def __init__(self, config: ModelArgs) -> None: ...
def __call__(self, x: mx.array) -> mx.array: ...
class NemotronHBlock(nn.Module):
block_type: str
norm: nn.RMSNorm
mixer: NemotronHMamba2Mixer | NemotronHAttention | NemotronHMLP | NemotronHMoE
def __init__(self, args: ModelArgs, block_type: str) -> None: ...
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[Any] = None,
) -> mx.array: ...
class NemotronHModel(nn.Module):
embeddings: nn.Embedding
layers: list[NemotronHBlock]
norm_f: nn.RMSNorm
fa_idx: int
ssm_idx: int
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[Any] = None,
) -> mx.array: ...
class Model(nn.Module):
args: ModelArgs
backbone: NemotronHModel
lm_head: nn.Linear
model_type: str
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[Any] = None,
) -> mx.array: ...
@property
def layers(self) -> list[NemotronHBlock]: ...
def make_cache(self) -> list[ArraysCache | KVCache]: ...
def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
+153
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@@ -0,0 +1,153 @@
from dataclasses import dataclass
from typing import Any, Optional
import mlx.core as mx
import mlx.nn as nn
from .cache import ArraysCache, KVCache
from .qwen3_next import (
Qwen3NextAttention as Attention,
Qwen3NextMLP as MLP,
Qwen3NextRMSNormGated as RMSNormGated,
Qwen3NextSparseMoeBlock,
)
SparseMoeBlock = Qwen3NextSparseMoeBlock
from .switch_layers import SwitchGLU
@dataclass
class TextModelArgs:
model_type: str
hidden_size: int
intermediate_size: int
num_hidden_layers: int
num_attention_heads: int
rms_norm_eps: float
vocab_size: int
num_key_value_heads: int
max_position_embeddings: int
linear_num_value_heads: int
linear_num_key_heads: int
linear_key_head_dim: int
linear_value_head_dim: int
linear_conv_kernel_dim: int
tie_word_embeddings: bool
attention_bias: bool
head_dim: Optional[int]
full_attention_interval: int
num_experts: int
num_experts_per_tok: int
decoder_sparse_step: int
shared_expert_intermediate_size: int
moe_intermediate_size: int
norm_topk_prob: bool
rope_parameters: Optional[dict[str, Any]]
partial_rotary_factor: float
rope_theta: float
rope_scaling: Optional[dict[str, Any]]
@classmethod
def from_dict(cls, params: dict[str, Any]) -> TextModelArgs: ...
def __post_init__(self) -> None: ...
class GatedDeltaNet(nn.Module):
hidden_size: int
num_v_heads: int
num_k_heads: int
head_k_dim: int
head_v_dim: int
key_dim: int
value_dim: int
conv_kernel_size: int
conv_dim: int
conv1d: nn.Conv1d
in_proj_qkv: nn.Linear
in_proj_z: nn.Linear
in_proj_b: nn.Linear
in_proj_a: nn.Linear
dt_bias: mx.array
A_log: mx.array
norm: RMSNormGated
out_proj: nn.Linear
def __init__(self, config: TextModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[Any] = None,
) -> mx.array: ...
class DecoderLayer(nn.Module):
is_linear: bool
linear_attn: GatedDeltaNet
self_attn: Attention
input_layernorm: nn.RMSNorm
post_attention_layernorm: nn.RMSNorm
mlp: MLP | SparseMoeBlock
def __init__(self, args: TextModelArgs, layer_idx: int) -> None: ...
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[Any] = None,
) -> mx.array: ...
class Qwen3_5TextModel(nn.Module):
embed_tokens: nn.Embedding
layers: list[DecoderLayer]
norm: nn.RMSNorm
ssm_idx: int
fa_idx: int
def __init__(self, args: TextModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[Any] = None,
input_embeddings: Optional[mx.array] = None,
) -> mx.array: ...
class TextModel(nn.Module):
args: TextModelArgs
model_type: str
model: Qwen3_5TextModel
lm_head: nn.Linear
def __init__(self, args: TextModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[Any] = None,
input_embeddings: Optional[mx.array] = None,
) -> mx.array: ...
@property
def layers(self) -> list[DecoderLayer]: ...
def make_cache(self) -> list[ArraysCache | KVCache]: ...
def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
@dataclass
class ModelArgs:
model_type: str
text_config: dict[str, Any]
@classmethod
def from_dict(cls, params: dict[str, Any]) -> ModelArgs: ...
class Model(nn.Module):
args: ModelArgs
model_type: str
language_model: TextModel
def __init__(self, args: ModelArgs) -> None: ...
def __call__(
self,
inputs: mx.array,
cache: Optional[Any] = None,
input_embeddings: Optional[mx.array] = None,
) -> mx.array: ...
def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
@property
def layers(self) -> list[DecoderLayer]: ...
def make_cache(self) -> list[ArraysCache | KVCache]: ...
@@ -0,0 +1,19 @@
from dataclasses import dataclass
from typing import Any, Optional
import mlx.core as mx
import mlx.nn as nn
from .cache import ArraysCache, KVCache
from .qwen3_5 import DecoderLayer, Model as Qwen3_5Model, TextModel
@dataclass
class ModelArgs:
model_type: str
text_config: dict[str, Any]
@classmethod
def from_dict(cls, params: dict[str, Any]) -> ModelArgs: ...
class Model(Qwen3_5Model):
def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
+14
View File
@@ -5,8 +5,18 @@ from typing import Any, Optional
import mlx.core as mx
import mlx.nn as nn
from .cache import ArraysCache, KVCache
from .switch_layers import SwitchGLU
class Qwen3NextRMSNormGated(nn.Module):
eps: float
weight: mx.array
def __init__(self, hidden_size: int, eps: float = ...) -> None: ...
def __call__(
self, hidden_states: mx.array, gate: mx.array | None = None
) -> mx.array: ...
class Qwen3NextMLP(nn.Module):
gate_proj: nn.Linear
down_proj: nn.Linear
@@ -61,6 +71,7 @@ class Qwen3NextAttention(nn.Module):
class Qwen3NextSparseMoeBlock(nn.Module):
norm_topk_prob: bool
num_experts: int
num_experts_per_tok: int
top_k: int
gate: nn.Linear
switch_mlp: SwitchGLU
@@ -90,6 +101,8 @@ class Qwen3NextModel(nn.Module):
embed_tokens: nn.Embedding
layers: list[Qwen3NextDecoderLayer]
norm: nn.RMSNorm
ssm_idx: int
fa_idx: int
def __init__(self, args: Any) -> None: ...
def __call__(
@@ -112,3 +125,4 @@ class Model(nn.Module):
def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
@property
def layers(self) -> list[Qwen3NextDecoderLayer]: ...
def make_cache(self) -> list[ArraysCache | KVCache]: ...
+51
View File
@@ -0,0 +1,51 @@
from typing import Any, Optional
import mlx.nn as nn
class YarnRoPE(nn.Module):
def __init__(
self,
dims: int,
traditional: bool = ...,
max_position_embeddings: int = ...,
base: float = ...,
scaling_factor: float = ...,
original_max_position_embeddings: int = ...,
beta_fast: float = ...,
beta_slow: float = ...,
mscale: float = ...,
mscale_all_dim: float = ...,
) -> None: ...
class Llama3RoPE(nn.Module):
def __init__(
self,
dims: int,
traditional: bool = ...,
max_position_embeddings: int = ...,
base: float = ...,
scaling_factor: float = ...,
original_max_position_embeddings: int = ...,
low_freq_factor: float = ...,
high_freq_factor: float = ...,
) -> None: ...
class SuScaledRoPE(nn.Module):
def __init__(
self,
dims: int,
traditional: bool = ...,
max_position_embeddings: int = ...,
base: float = ...,
short_factor: Any = ...,
long_factor: Any = ...,
original_max_position_embeddings: int = ...,
) -> None: ...
def initialize_rope(
dims: int,
base: float = ...,
traditional: bool = ...,
scaling_config: Optional[dict[str, Any]] = ...,
max_position_embeddings: Optional[int] = ...,
) -> nn.Module: ...
@@ -73,6 +73,9 @@ class SwitchGLU(nn.Module):
def __call__(self, x, indices) -> mx.array: ...
class SwitchMLP(nn.Module):
fc1: SwitchLinear
fc2: SwitchLinear
def __init__(
self,
input_dims: int,
+5 -1
View File
@@ -48,7 +48,11 @@ def make_logits_processors(
logit_bias: Optional[Dict[int, float]] = ...,
repetition_penalty: Optional[float] = ...,
repetition_context_size: Optional[int] = ...,
): # -> list[Any]:
presence_penalty: Optional[float] = ...,
presence_context_size: Optional[int] = ...,
frequency_penalty: Optional[float] = ...,
frequency_context_size: Optional[int] = ...,
) -> list[Callable[[mx.array, mx.array], mx.array]]:
"""
Make logits processors for use with ``generate_step``.
+6 -4
View File
@@ -39,11 +39,11 @@ class StreamingDetokenizer:
"""
__slots__ = ...
def reset(self): ...
def add_token(self, token): ...
def finalize(self): ...
def reset(self) -> None: ...
def add_token(self, token: int) -> None: ...
def finalize(self) -> None: ...
@property
def last_segment(self):
def last_segment(self) -> str:
"""Return the last segment of readable text since last time this property was accessed."""
class NaiveStreamingDetokenizer(StreamingDetokenizer):
@@ -117,6 +117,8 @@ class TokenizerWrapper:
think_end: str | None
think_start_id: int | None
think_end_id: int | None
think_start_tokens: list[int] | None
think_end_tokens: list[int] | None
def __init__(
self,
+50
View File
@@ -0,0 +1,50 @@
"""
This type stub file was generated by pyright.
"""
import mlx.nn as nn
class DoRALinear(nn.Module):
@staticmethod
def from_base(
linear: nn.Linear, r: int = ..., dropout: float = ..., scale: float = ...
): # -> DoRALinear:
...
def fuse(self, dequantize: bool = ...): # -> QuantizedLinear | Linear:
...
def __init__(
self,
input_dims: int,
output_dims: int,
r: int = ...,
dropout: float = ...,
scale: float = ...,
bias: bool = ...,
) -> None: ...
def set_linear(self, linear): # -> None:
"""
Set the self.linear layer and recompute self.m.
"""
...
def __call__(self, x): ...
class DoRAEmbedding(nn.Module):
def from_base(
embedding: nn.Embedding, r: int = ..., dropout: float = ..., scale: float = ...
): # -> DoRAEmbedding:
...
def fuse(self, dequantize: bool = ...): # -> Embedding:
...
def __init__(
self,
num_embeddings: int,
dims: int,
r: int = ...,
dropout: float = ...,
scale: float = ...,
) -> None: ...
def set_embedding(self, embedding: nn.Module): # -> None:
...
def __call__(self, x): ...
def as_linear(self, x): ...
+66
View File
@@ -0,0 +1,66 @@
"""
This type stub file was generated by pyright.
"""
import mlx.nn as nn
class LoRALinear(nn.Module):
@staticmethod
def from_base(
linear: nn.Linear, r: int = ..., dropout: float = ..., scale: float = ...
): # -> LoRALinear:
...
def fuse(self, dequantize: bool = ...): # -> QuantizedLinear | Linear:
...
def __init__(
self,
input_dims: int,
output_dims: int,
r: int = ...,
dropout: float = ...,
scale: float = ...,
bias: bool = ...,
) -> None: ...
def __call__(self, x): # -> array:
...
class LoRASwitchLinear(nn.Module):
@staticmethod
def from_base(
linear: nn.Module, r: int = ..., dropout: float = ..., scale: float = ...
): # -> LoRASwitchLinear:
...
def fuse(self, dequantize: bool = ...): # -> QuantizedSwitchLinear | SwitchLinear:
...
def __init__(
self,
input_dims: int,
output_dims: int,
num_experts: int,
r: int = ...,
dropout: float = ...,
scale: float = ...,
bias: bool = ...,
) -> None: ...
def __call__(self, x, indices, sorted_indices=...): ...
class LoRAEmbedding(nn.Module):
@staticmethod
def from_base(
embedding: nn.Embedding, r: int = ..., dropout: float = ..., scale: float = ...
): # -> LoRAEmbedding:
...
def fuse(self, dequantize: bool = ...): # -> QuantizedEmbedding | Embedding:
...
def __init__(
self,
num_embeddings: int,
dims: int,
r: int = ...,
dropout: float = ...,
scale: float = ...,
) -> None: ...
def __call__(self, x): # -> array:
...
def as_linear(self, x): # -> array:
...
+57
View File
@@ -0,0 +1,57 @@
"""
This type stub file was generated by pyright.
"""
import mlx.nn as nn
from typing import Dict
def build_schedule(schedule_config: Dict): # -> Any:
"""
Build a learning rate schedule from the given config.
"""
...
def linear_to_lora_layers(
model: nn.Module, num_layers: int, config: Dict, use_dora: bool = ...
): # -> None:
"""
Convert some of the models linear layers to lora layers.
Args:
model (nn.Module): The neural network model.
num_layers (int): The number of blocks to convert to lora layers
starting from the last layer.
config (dict): More configuration parameters for LoRA, including the
rank, scale, and optional layer keys.
use_dora (bool): If True, uses DoRA instead of LoRA.
Default: ``False``
"""
...
def load_adapters(model: nn.Module, adapter_path: str) -> nn.Module:
"""
Load any fine-tuned adapters / layers.
Args:
model (nn.Module): The neural network model.
adapter_path (str): Path to the adapter configuration file.
Returns:
nn.Module: The updated model with LoRA layers applied.
"""
...
def remove_lora_layers(model: nn.Module) -> nn.Module:
"""
Remove the LoRA layers from the model.
Args:
model (nn.Module): The model with LoRA layers.
Returns:
nn.Module: The model without LoRA layers.
"""
...
def print_trainable_parameters(model): # -> None:
...
View File
Whitespace-only changes.
+12
View File
@@ -0,0 +1,12 @@
from typing import Any
def get_message_json(
model_name: str,
prompt: str,
role: str = "user",
skip_image_token: bool = False,
skip_audio_token: bool = False,
num_images: int = 0,
num_audios: int = 0,
**kwargs: Any,
) -> dict[str, Any]: ...
+15
View File
@@ -0,0 +1,15 @@
from pathlib import Path
from typing import Any
class ImageProcessor:
def preprocess(
self, images: list[dict[str, Any]], **kwargs: Any
) -> dict[str, Any]: ...
def __call__(self, **kwargs: Any) -> dict[str, Any]: ...
def load_image_processor(
model_path: str | Path, **kwargs: Any
) -> ImageProcessor | None: ...
def load_processor(
model_path: str | Path, add_detokenizer: bool = ..., **kwargs: Any
) -> ImageProcessor: ...
+8
View File
@@ -0,0 +1,8 @@
from typing import Any, Self
class safe_open:
def __init__(self, filename: str, framework: str = "pt") -> None: ...
def __enter__(self) -> Self: ...
def __exit__(self, *args: Any) -> None: ...
def keys(self) -> list[str]: ...
def get_tensor(self, name: str) -> Any: ...
+2 -1
View File
@@ -1,7 +1,8 @@
{
"recommendations": [
"detachhead.basedpyright",
"ms-python.python"
"ms-python.python",
"jnoortheen.nix-ide"
],
"unwantedRecommendations": [
"ms-python.vscode-pylance",
+30 -1
View File
@@ -1,3 +1,32 @@
{
"basedpyright.importStrategy": "fromEnvironment"
"files.associations": {
"*.nix": "nix",
},
"nix.enableLanguageServer": true,
"nix.serverPath": "nixd",
"nix.serverSettings": {
"nixd": {
"formatting": {
"command": ["nixpkgs-fmt"]
},
"nixpkgs": {
"expr": "(builtins.getFlake \"path:${workspaceFolder}\").currentSystem.config._module.args.pkgs"
},
"options": {
"flake-parts": {
"expr": "(builtins.getFlake \"path:${workspaceFolder}\").debug.options"
},
"flake-parts-perSystem": {
"expr": "(builtins.getFlake \"path:${workspaceFolder}\").currentSystem.options"
}
}
}
},
"[nix]": {
"editor.defaultFormatter": "jnoortheen.nix-ide"
},
"python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python",
"basedpyright.analysis.configFilePath": "${workspaceFolder}/pyproject.toml",
"basedpyright.importStrategy": "fromEnvironment",
}
+124 -2
View File
@@ -11,9 +11,18 @@ To run EXO from source:
```bash
brew install uv
```
- [macmon](https://github.com/vladkens/macmon) (for hardware monitoring on Apple Silicon)
- [rust](https://github.com/rust-lang/rustup) (to build Rust bindings, nightly for now)
```bash
brew install macmon
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
rustup toolchain install nightly
```
- [macmon](https://github.com/vladkens/macmon) (for hardware monitoring on Apple Silicon)
Use the pinned fork revision used by this repo instead of Homebrew `macmon`.
```bash
cargo install --git https://github.com/vladkens/macmon \
--rev a1cd06b6cc0d5e61db24fd8832e74cd992097a7d \
macmon \
--force
```
```bash
@@ -39,6 +48,119 @@ Write pure functions where possible. When adding new code, prefer Rust unless th
Run `nix fmt` to auto-format your code before submitting.
## Model Cards
EXO uses TOML-based model cards to define model metadata and capabilities. Model cards are stored in:
- `resources/inference_model_cards/` for text generation models
- `resources/image_model_cards/` for image generation models
- `~/.exo/custom_model_cards/` for user-added custom models
### Adding a Model Card
To add a new model, create a TOML file with the following structure:
```toml
model_id = "mlx-community/Llama-3.2-1B-Instruct-4bit"
n_layers = 16
hidden_size = 2048
supports_tensor = true
tasks = ["TextGeneration"]
family = "llama"
quantization = "4bit"
base_model = "Llama 3.2 1B"
capabilities = ["text"]
[storage_size]
in_bytes = 729808896
```
### Required Fields
- `model_id`: Hugging Face model identifier
- `n_layers`: Number of transformer layers
- `hidden_size`: Hidden dimension size
- `supports_tensor`: Whether the model supports tensor parallelism
- `tasks`: List of supported tasks (`TextGeneration`, `TextToImage`, `ImageToImage`)
- `family`: Model family (e.g., "llama", "deepseek", "qwen")
- `quantization`: Quantization level (e.g., "4bit", "8bit", "bf16")
- `base_model`: Human-readable base model name
- `capabilities`: List of capabilities (e.g., `["text"]`, `["text", "thinking"]`)
### Optional Fields
- `components`: For multi-component models (like image models with separate text encoders and transformers)
- `uses_cfg`: Whether the model uses classifier-free guidance (for image models)
- `trust_remote_code`: Whether to allow remote code execution (defaults to `false` for security)
### Capabilities
The `capabilities` field defines what the model can do:
- `text`: Standard text generation
- `thinking`: Model supports chain-of-thought reasoning
- `thinking_toggle`: Thinking can be enabled/disabled via `enable_thinking` parameter
- `image_edit`: Model supports image-to-image editing (FLUX.1-Kontext)
### Security Note
By default, `trust_remote_code` is set to `false` for security. Only enable it if the model explicitly requires remote code execution from the Hugging Face hub.
## API Adapters
EXO supports multiple API formats through an adapter pattern. Adapters convert API-specific request formats to the internal `TextGenerationTaskParams` format and convert internal token chunks back to API-specific responses.
### Adapter Architecture
All adapters live in `src/exo/master/adapters/` and follow the same pattern:
1. Convert API-specific requests to `TextGenerationTaskParams`
2. Handle both streaming and non-streaming response generation
3. Convert internal `TokenChunk` objects to API-specific formats
4. Manage error handling and edge cases
### Existing Adapters
- `chat_completions.py`: OpenAI Chat Completions API
- `claude.py`: Anthropic Claude Messages API
- `responses.py`: OpenAI Responses API
- `ollama.py`: Ollama API (for OpenWebUI compatibility)
### Adding a New API Adapter
To add support for a new API format:
1. Create a new adapter file in `src/exo/master/adapters/`
2. Implement a request conversion function:
```python
def your_api_request_to_text_generation(
request: YourAPIRequest,
) -> TextGenerationTaskParams:
# Convert API request to internal format
pass
```
3. Implement streaming response generation:
```python
async def generate_your_api_stream(
command_id: CommandId,
chunk_stream: AsyncGenerator[TokenChunk | ErrorChunk | ToolCallChunk, None],
) -> AsyncGenerator[str, None]:
# Convert internal chunks to API-specific streaming format
pass
```
4. Implement non-streaming response collection:
```python
async def collect_your_api_response(
command_id: CommandId,
chunk_stream: AsyncGenerator[TokenChunk | ErrorChunk | ToolCallChunk, None],
) -> AsyncGenerator[str]:
# Collect all chunks and return single response
pass
```
5. Register the adapter endpoints in `src/exo/master/api.py`
The adapter pattern keeps API-specific logic isolated from core inference systems. Internal systems (worker, runner, event sourcing) only see `TextGenerationTaskParams` and `TokenChunk` objects - no API-specific types cross the adapter boundary.
For detailed API documentation, see [docs/api.md](docs/api.md).
## Testing
EXO relies heavily on manual testing at this point in the project, but this is evolving. Before submitting a change, test both before and after to demonstrate how your change improves behavior. Do the best you can with the hardware you have available - if you need help testing, ask and we'll do our best to assist. Add automated tests where possible - we're actively working to substantially improve our automated testing story.
Generated
+2306 -2312
View File
File diff suppressed because it is too large. Load diff
+17 -48
View File
@@ -1,10 +1,6 @@
[workspace]
resolver = "3"
members = [
"rust/networking",
"rust/exo_pyo3_bindings",
"rust/util",
]
members = ["rust/exo_net", "rust/networking"]
[workspace.package]
version = "0.0.1"
@@ -24,58 +20,31 @@ opt-level = 3
[workspace.dependencies]
## Crate members as common dependencies
networking = { path = "rust/networking" }
util = { path = "rust/util" }
# Proc-macro authoring tools
syn = "2.0"
quote = "1.0"
proc-macro2 = "1.0"
darling = "0.20"
# Macro dependecies
extend = "1.2"
delegate = "0.13"
impl-trait-for-tuples = "0.2"
clap = "4.5"
derive_more = { version = "2.0.1", features = ["display"] }
pin-project = "1"
# Utility dependencies
itertools = "0.14"
thiserror = "2"
internment = "0.8"
recursion = "0.5"
regex = "1.11"
once_cell = "1.21"
thread_local = "1.1"
bon = "3.4"
generativity = "1.1"
anyhow = "1.0"
keccak-const = "0.2"
# Functional generics/lenses frameworks
frunk_core = "0.4"
frunk = "0.4"
frunk_utils = "0.2"
frunk-enum-core = "0.3"
# Async dependencies
tokio = "1.46"
futures = "0.3"
futures-util = "0.3"
futures-timer = "3.0"
# Data structures
either = "1.15"
ordered-float = "5.0"
ahash = "0.8"
futures-lite = "2.6.1"
# Tracing/logging
log = "0.4"
env_logger = "0.11.10"
# networking
libp2p = "0.56"
libp2p-tcp = "0.44"
zenoh = "=1.9.0"
async-stream = "0.3.6"
netwatcher = "0.6.0"
parking_lot = "0.12.5"
pin-project = "1.1.10"
pyo3 = "0.27.2"
pyo3-async-runtimes = "0.27.0"
pyo3-log = "0.13.2"
pyo3-stub-gen = "0.22.2"
rand = "0.10.1"
serde_json = "1.0.149"
tracing = "0.1.44"
zenoh-plugin-storage-manager = { version = "=1.9.0", default-features = false }
zenoh-plugin-trait = "=1.9.0"
[workspace.lints.rust]
static_mut_refs = "warn" # Or use "warn" instead of deny
+148 -6
View File
@@ -26,6 +26,8 @@ exo connects all your devices into an AI cluster. Not only does exo enable runni
- **Topology-Aware Auto Parallel**: exo figures out the best way to split your model across all available devices based on a realtime view of your device topology. It takes into account device resources and network latency/bandwidth between each link.
- **Tensor Parallelism**: exo supports sharding models, for up to 1.8x speedup on 2 devices and 3.2x speedup on 4 devices.
- **MLX Support**: exo uses [MLX](https://github.com/ml-explore/mlx) as an inference backend and [MLX distributed](https://ml-explore.github.io/mlx/build/html/usage/distributed.html) for distributed communication.
- **Multiple API Compatibility**: Compatible with OpenAI Chat Completions API, Claude Messages API, OpenAI Responses API, and Ollama API - use your existing tools and clients.
- **Custom Model Support**: Load custom models from HuggingFace hub to expand the range of available models.
## Dashboard
@@ -72,18 +74,31 @@ There are two ways to run exo:
### Run from Source (macOS)
If you have [Nix](https://nixos.org/) installed, you can skip most of the steps below and run exo directly:
```bash
nix run .#exo
```
**Note:** To accept the Cachix binary cache (and avoid the Xcode Metal ToolChain), add to `/etc/nix/nix.conf`:
```
trusted-users = root (or your username)
experimental-features = nix-command flakes
```
Then restart the Nix daemon: `sudo launchctl kickstart -k system/org.nixos.nix-daemon`
**Prerequisites:**
- [Xcode](https://developer.apple.com/xcode/) (provides the Metal ToolChain required for MLX compilation)
- [brew](https://github.com/Homebrew/brew) (for simple package management on macOS)
```bash
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
```
- [uv](https://github.com/astral-sh/uv) (for Python dependency management)
- [macmon](https://github.com/vladkens/macmon) (for hardware monitoring on Apple Silicon)
- [node](https://github.com/nodejs/node) (for building the dashboard)
```bash
brew install uv macmon node
brew install uv node
```
- [rust](https://github.com/rust-lang/rustup) (to build Rust bindings, nightly for now)
@@ -91,6 +106,17 @@ There are two ways to run exo:
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
rustup toolchain install nightly
```
- [macmon](https://github.com/vladkens/macmon) (for hardware monitoring on Apple Silicon)
Install the pinned fork revision used by this repo instead of Homebrew `macmon`.
Homebrew `macmon 0.6.1` still crashes on Apple M5.
```bash
cargo install --git https://github.com/vladkens/macmon \
--rev a1cd06b6cc0d5e61db24fd8832e74cd992097a7d \
macmon \
--force
```
Clone the repo, build the dashboard, and run exo:
@@ -182,6 +208,8 @@ exo follows the [XDG Base Directory Specification](https://specifications.freede
- **Configuration files**: `~/.config/exo/` (or `$XDG_CONFIG_HOME/exo/`)
- **Data files**: `~/.local/share/exo/` (or `$XDG_DATA_HOME/exo/`)
- **Cache files**: `~/.cache/exo/` (or `$XDG_CACHE_HOME/exo/`)
- **Log files**: `~/.cache/exo/exo_log/` (with automatic log rotation)
- **Custom model cards**: `~/.local/share/exo/custom_model_cards/`
You can override these locations by setting the corresponding XDG environment variables.
@@ -261,8 +289,51 @@ After that, RDMA will be enabled in macOS and exo will take care of the rest.
---
## Environment Variables
exo supports several environment variables for configuration:
| Variable | Description | Default |
|----------|-------------|---------|
| `EXO_DEFAULT_MODELS_DIR` | Default directory for model downloads and caches. Always first in the writable dirs list. | `~/.local/share/exo/models` (Linux) or `~/.exo/models` (macOS) |
| `EXO_MODELS_DIRS` | Colon-separated additional writable directories for model downloads. Checked in order after the default; first with enough free space is used. | None |
| `EXO_MODELS_READ_ONLY_DIRS` | Colon-separated read-only directories to search for pre-downloaded models (e.g., NFS mounts, shared storage). Models here cannot be deleted. | None |
| `EXO_OFFLINE` | Run without internet connection (uses only local models) | `false` |
| `EXO_ENABLE_IMAGE_MODELS` | Enable image model support | `false` |
| `EXO_LIBP2P_NAMESPACE` | Custom namespace for cluster isolation | None |
| `EXO_FAST_SYNCH` | Control MLX_METAL_FAST_SYNCH behavior (for JACCL backend) | Auto |
| `EXO_TRACING_ENABLED` | Enable distributed tracing for performance analysis | `false` |
**Example usage:**
```bash
# Use pre-downloaded models from NFS mount (read-only)
EXO_MODELS_READ_ONLY_DIRS=/mnt/nfs/models:/opt/ai-models uv run exo
# Download models to an external SSD (falls back to default dir if full)
EXO_MODELS_DIRS=/Volumes/ExternalSSD/exo-models uv run exo
# Run in offline mode
EXO_OFFLINE=true uv run exo
# Enable image models
EXO_ENABLE_IMAGE_MODELS=true uv run exo
# Use custom namespace for cluster isolation
EXO_LIBP2P_NAMESPACE=my-dev-cluster uv run exo
```
---
### Using the API
exo provides multiple API-compatible interfaces for maximum compatibility with existing tools:
- **OpenAI Chat Completions API** - Compatible with OpenAI clients
- **Claude Messages API** - Compatible with Anthropic's Claude format
- **OpenAI Responses API** - Compatible with OpenAI's Responses format
- **Ollama API** - Compatible with Ollama and tools like OpenWebUI
If you prefer to interact with exo via the API, here is an example creating an instance of a small model (`mlx-community/Llama-3.2-1B-Instruct-4bit`), sending a chat completions request and deleting the instance.
---
@@ -352,14 +423,85 @@ When you're done, delete the instance by its ID (find it via `/state` or `/insta
curl -X DELETE http://localhost:52415/instance/YOUR_INSTANCE_ID
```
### Claude Messages API Compatibility
Use the Claude Messages API format with the `/v1/messages` endpoint:
```bash
curl -N -X POST http://localhost:52415/v1/messages \
-H 'Content-Type: application/json' \
-d '{
"model": "mlx-community/Llama-3.2-1B-Instruct-4bit",
"messages": [
{"role": "user", "content": "Hello"}
],
"max_tokens": 1024,
"stream": true
}'
```
### OpenAI Responses API Compatibility
Use the OpenAI Responses API format with the `/v1/responses` endpoint:
```bash
curl -N -X POST http://localhost:52415/v1/responses \
-H 'Content-Type: application/json' \
-d '{
"model": "mlx-community/Llama-3.2-1B-Instruct-4bit",
"messages": [
{"role": "user", "content": "Hello"}
],
"stream": true
}'
```
### Ollama API Compatibility
exo supports Ollama API endpoints for compatibility with tools like OpenWebUI:
```bash
# Ollama chat
curl -X POST http://localhost:52415/ollama/api/chat \
-H 'Content-Type: application/json' \
-d '{
"model": "mlx-community/Llama-3.2-1B-Instruct-4bit",
"messages": [
{"role": "user", "content": "Hello"}
],
"stream": false
}'
# List models (Ollama format)
curl http://localhost:52415/ollama/api/tags
```
### Custom Model Loading from HuggingFace
You can add custom models from the HuggingFace hub:
```bash
curl -X POST http://localhost:52415/models/add \
-H 'Content-Type: application/json' \
-d '{
"model_id": "mlx-community/my-custom-model"
}'
```
**Security Note:**
Custom models requiring `trust_remote_code` in their configuration must be explicitly enabled (default is false) for security. Only enable this if you trust the model's remote code execution. Models are fetched from HuggingFace and stored locally as custom model cards.
**Other useful API endpoints*:**
- List all models: `curl http://localhost:52415/models`
- List downloaded models only: `curl http://localhost:52415/models?status=downloaded`
- Search HuggingFace: `curl "http://localhost:52415/models/search?query=llama&limit=10"`
- Inspect instance IDs and deployment state: `curl http://localhost:52415/state`
For further details, see:
- API basic documentation in [docs/api.md](docs/api.md).
- API documentation in [docs/api.md](docs/api.md).
- API types and endpoints in [src/exo/master/api.py](src/exo/master/api.py).
---
@@ -418,4 +560,4 @@ On macOS, exo uses the GPU. On Linux, exo currently runs on CPU. We are working
## Contributing
See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on how to contribute to exo.
See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on how to contribute to exo.
+67 -210
View File
@@ -15,14 +15,14 @@ struct ContentView: View {
@EnvironmentObject private var localNetworkChecker: LocalNetworkChecker
@EnvironmentObject private var updater: SparkleUpdater
@EnvironmentObject private var thunderboltBridgeService: ThunderboltBridgeService
@EnvironmentObject private var settingsWindowController: SettingsWindowController
@EnvironmentObject private var bugReportWindowController: BugReportWindowController
@State private var focusedNode: NodeViewModel?
@State private var deletingInstanceIDs: Set<String> = []
@State private var showAllNodes = false
@State private var showAllInstances = false
@State private var baseURLCopied = false
@State private var showAdvanced = false
@State private var showDebugInfo = false
@State private var bugReportInFlight = false
@State private var bugReportMessage: String?
@State private var uninstallInProgress = false
@State private var pendingNamespace: String = ""
@State private var pendingHFToken: String = ""
@@ -258,139 +258,86 @@ struct ContentView: View {
VStack(alignment: .leading, spacing: 0) {
if controller.status != .stopped {
dashboardButton
baseURLRow
Divider()
.padding(.vertical, 8)
} else {
Divider()
.padding(.vertical, 4)
}
advancedSection
.padding(.bottom, 8)
controlButton(title: "Quit", tint: .secondary) {
HoverButton(
title: "Settings",
tint: .primary,
trailingSystemImage: "gear"
) {
settingsWindowController.open(
controller: controller,
updater: updater,
networkStatusService: networkStatusService,
thunderboltBridgeService: thunderboltBridgeService,
stateService: stateService
)
}
HoverButton(
title: "Check for Updates",
tint: .primary,
trailingSystemImage: "arrow.triangle.2.circlepath"
) {
updater.checkForUpdates()
}
HoverButton(
title: "Share Bug Report…",
tint: .primary,
trailingSystemImage: "ladybug"
) {
bugReportWindowController.open()
}
.padding(.bottom, 8)
HoverButton(title: "Quit", tint: .secondary) {
controller.stop()
NSApplication.shared.terminate(nil)
}
}
}
private var advancedSection: some View {
VStack(alignment: .leading, spacing: 6) {
HStack {
Text("Advanced")
.font(.caption)
.foregroundColor(.secondary)
Spacer()
collapseButton(isExpanded: $showAdvanced)
}
.animation(nil, value: showAdvanced)
if showAdvanced {
VStack(alignment: .leading, spacing: 8) {
VStack(alignment: .leading, spacing: 4) {
Text("Cluster Namespace")
.font(.caption2)
.foregroundColor(.secondary)
HStack {
TextField("optional", text: $pendingNamespace)
.textFieldStyle(.roundedBorder)
.font(.caption2)
.onAppear {
pendingNamespace = controller.customNamespace
}
Button("Save & Restart") {
controller.customNamespace = pendingNamespace
if controller.status == .running || controller.status == .starting {
controller.restart()
}
}
.font(.caption2)
.disabled(pendingNamespace == controller.customNamespace)
}
}
VStack(alignment: .leading, spacing: 4) {
Text("HuggingFace Token")
.font(.caption2)
.foregroundColor(.secondary)
HStack {
SecureField("optional", text: $pendingHFToken)
.textFieldStyle(.roundedBorder)
.font(.caption2)
.onAppear {
pendingHFToken = controller.hfToken
}
Button("Save & Restart") {
controller.hfToken = pendingHFToken
if controller.status == .running || controller.status == .starting {
controller.restart()
}
}
.font(.caption2)
.disabled(pendingHFToken == controller.hfToken)
}
}
Divider()
HStack {
Toggle(
"Enable Image Models (experimental)", isOn: $pendingEnableImageModels
)
.toggleStyle(.switch)
.font(.caption2)
.onAppear {
pendingEnableImageModels = controller.enableImageModels
}
Spacer()
Button("Save & Restart") {
controller.enableImageModels = pendingEnableImageModels
if controller.status == .running || controller.status == .starting {
controller.restart()
}
}
.font(.caption2)
.disabled(pendingEnableImageModels == controller.enableImageModels)
}
HoverButton(title: "Check for Updates", small: true) {
updater.checkForUpdates()
}
debugSection
HoverButton(title: "Uninstall", tint: .red, small: true) {
showUninstallConfirmationAlert()
}
.disabled(uninstallInProgress)
}
.transition(.opacity)
}
}
.animation(.easeInOut(duration: 0.25), value: showAdvanced)
}
private func controlButton(title: String, tint: Color = .primary, action: @escaping () -> Void)
-> some View
{
HoverButton(title: title, tint: tint, trailingSystemImage: nil, action: action)
}
private var dashboardButton: some View {
Button {
HoverButton(
title: "Web Dashboard",
tint: .primary,
trailingSystemImage: "arrow.up.right"
) {
guard let url = URL(string: "http://localhost:52415/") else { return }
NSWorkspace.shared.open(url)
} label: {
HStack {
Image(systemName: "arrow.up.right.square")
.imageScale(.small)
Text("Dashboard")
.fontWeight(.medium)
Spacer()
}
.padding(.vertical, 8)
.padding(.horizontal, 10)
.background(
RoundedRectangle(cornerRadius: 8, style: .continuous)
.fill(Color(red: 1.0, green: 0.87, blue: 0.0).opacity(0.2))
)
}
.buttonStyle(.plain)
.padding(.bottom, 4)
}
private var baseURLRow: some View {
HStack(spacing: 6) {
Image(systemName: "link")
.imageScale(.small)
.foregroundColor(.secondary)
Text("localhost:52415/v1")
.font(.system(.caption, design: .monospaced))
.foregroundColor(.primary)
Spacer()
Button {
NSPasteboard.general.clearContents()
NSPasteboard.general.setString("http://localhost:52415/v1", forType: .string)
baseURLCopied = true
DispatchQueue.main.asyncAfter(deadline: .now() + 2) {
baseURLCopied = false
}
} label: {
Image(systemName: baseURLCopied ? "checkmark" : "doc.on.doc")
.imageScale(.small)
.foregroundColor(baseURLCopied ? .green : .secondary)
.contentTransition(.symbolEffect(.replace))
}
.buttonStyle(.plain)
.help("Copy API base URL")
}
.padding(.vertical, 4)
.padding(.horizontal, 8)
}
private func collapseButton(isExpanded: Binding<Bool>) -> some View {
@@ -528,40 +475,6 @@ struct ContentView: View {
}
}
private var debugSection: some View {
VStack(alignment: .leading, spacing: 4) {
HoverButton(
title: "Debug Info",
tint: .primary,
trailingSystemImage: showDebugInfo ? "chevron.up" : "chevron.down",
small: true
) {
showDebugInfo.toggle()
}
if showDebugInfo {
VStack(alignment: .leading, spacing: 4) {
Text("Version: \(buildTag)")
.font(.caption2)
.foregroundColor(.secondary)
Text("Commit: \(buildCommit)")
.font(.caption2)
.foregroundColor(.secondary)
Text(thunderboltStatusText)
.font(.caption2)
.foregroundColor(thunderboltStatusColor)
clusterThunderboltBridgeView
interfaceIpList
rdmaStatusView
sendBugReportButton
.padding(.top, 6)
}
.padding(.leading, 8)
.transition(.opacity)
}
}
.animation(.easeInOut(duration: 0.25), value: showDebugInfo)
}
private var rdmaStatusView: some View {
let rdmaStatuses = stateService.latestSnapshot?.nodeRdmaCtl ?? [:]
let localNodeId = stateService.localNodeId
@@ -610,42 +523,6 @@ struct ContentView: View {
}
}
private var sendBugReportButton: some View {
VStack(alignment: .leading, spacing: 4) {
Button {
Task {
await sendBugReport()
}
} label: {
HStack {
if bugReportInFlight {
ProgressView()
.scaleEffect(0.6)
}
Text("Send Bug Report")
.font(.caption)
.fontWeight(.semibold)
Spacer()
}
.padding(.vertical, 6)
.padding(.horizontal, 8)
.background(
RoundedRectangle(cornerRadius: 6)
.fill(Color.accentColor.opacity(0.12))
)
}
.buttonStyle(.plain)
.disabled(bugReportInFlight)
if let message = bugReportMessage {
Text(message)
.font(.caption2)
.foregroundColor(.secondary)
.fixedSize(horizontal: false, vertical: true)
}
}
}
private var processToggleBinding: Binding<Bool> {
Binding(
get: {
@@ -686,19 +563,6 @@ struct ContentView: View {
)
}
private func sendBugReport() async {
bugReportInFlight = true
bugReportMessage = "Collecting logs..."
let service = BugReportService()
do {
let outcome = try await service.sendReport(isManual: true)
bugReportMessage = outcome.message
} catch {
bugReportMessage = error.localizedDescription
}
bugReportInFlight = false
}
private func showUninstallConfirmationAlert() {
let alert = NSAlert()
alert.messageText = "Uninstall EXO"
@@ -781,13 +645,6 @@ struct ContentView: View {
}
}
private var buildTag: String {
Bundle.main.infoDictionary?["EXOBuildTag"] as? String ?? "unknown"
}
private var buildCommit: String {
Bundle.main.infoDictionary?["EXOBuildCommit"] as? String ?? "unknown"
}
}
private struct HoverButton: View {
+18 -1
View File
@@ -21,7 +21,10 @@ struct EXOApp: App {
@StateObject private var localNetworkChecker: LocalNetworkChecker
@StateObject private var updater: SparkleUpdater
@StateObject private var thunderboltBridgeService: ThunderboltBridgeService
@StateObject private var settingsWindowController: SettingsWindowController
@StateObject private var bugReportWindowController: BugReportWindowController
private let terminationObserver: TerminationObserver
private let firstLaunchPopout = FirstLaunchPopout()
private let ciContext = CIContext(options: nil)
init() {
@@ -43,12 +46,14 @@ struct EXOApp: App {
_updater = StateObject(wrappedValue: updater)
let thunderboltBridge = ThunderboltBridgeService(clusterStateService: service)
_thunderboltBridgeService = StateObject(wrappedValue: thunderboltBridge)
_settingsWindowController = StateObject(wrappedValue: SettingsWindowController())
_bugReportWindowController = StateObject(wrappedValue: BugReportWindowController())
enableLaunchAtLoginIfNeeded()
// Install LaunchDaemon to disable Thunderbolt Bridge on startup (prevents network loops)
NetworkSetupHelper.promptAndInstallIfNeeded()
// Check local network access periodically (warning disappears when user grants permission)
localNetwork.startPeriodicChecking(interval: 10)
controller.scheduleLaunch(after: 15)
controller.scheduleLaunch(after: 5)
service.startPolling()
networkStatus.startPolling()
}
@@ -62,8 +67,20 @@ struct EXOApp: App {
.environmentObject(localNetworkChecker)
.environmentObject(updater)
.environmentObject(thunderboltBridgeService)
.environmentObject(settingsWindowController)
.environmentObject(bugReportWindowController)
} label: {
menuBarIcon
.onReceive(controller.$isFirstLaunchReady) { ready in
if ready {
DispatchQueue.main.asyncAfter(deadline: .now() + 3.0) {
self.firstLaunchPopout.onComplete = { [weak controller] in
controller?.markOnboardingCompleted()
}
self.firstLaunchPopout.show()
}
}
}
}
.menuBarExtraStyle(.window)
}
+170 -4
View File
@@ -4,7 +4,30 @@ import Foundation
private let customNamespaceKey = "EXOCustomNamespace"
private let hfTokenKey = "EXOHFToken"
private let hfEndpointKey = "EXOHFEndpoint"
private let enableImageModelsKey = "EXOEnableImageModels"
private let offlineModeKey = "EXOOfflineMode"
private let fastSynchEnabledKey = "EXOFastSynchEnabled"
private let onboardingCompletedKey = "EXOOnboardingCompleted"
private let defaultModelsDirKey = "EXODefaultModelsDir"
private let additionalModelsDirsKey = "EXOAdditionalModelsDirs"
private let readOnlyModelsDirsKey = "EXOReadOnlyModelsDirs"
private let customEnvironmentVariablesKey = "EXOCustomEnvironmentVariables"
/// A user-defined environment variable that is injected into the exo child
/// process at launch. Used as an escape hatch for env vars that don't have
/// first-class typed UI in Settings.
struct CustomEnvironmentVariable: Codable, Identifiable, Equatable {
var id: UUID
var key: String
var value: String
init(id: UUID = UUID(), key: String = "", value: String = "") {
self.id = id
self.key = key
self.value = value
}
}
@MainActor
final class ExoProcessController: ObservableObject {
@@ -51,6 +74,14 @@ final class ExoProcessController: ObservableObject {
UserDefaults.standard.set(hfToken, forKey: hfTokenKey)
}
}
@Published var hfEndpoint: String = {
return UserDefaults.standard.string(forKey: hfEndpointKey) ?? ""
}()
{
didSet {
UserDefaults.standard.set(hfEndpoint, forKey: hfEndpointKey)
}
}
@Published var enableImageModels: Bool = {
return UserDefaults.standard.bool(forKey: enableImageModelsKey)
}()
@@ -59,6 +90,71 @@ final class ExoProcessController: ObservableObject {
UserDefaults.standard.set(enableImageModels, forKey: enableImageModelsKey)
}
}
@Published var offlineMode: Bool = {
return UserDefaults.standard.bool(forKey: offlineModeKey)
}()
{
didSet {
UserDefaults.standard.set(offlineMode, forKey: offlineModeKey)
}
}
@Published var fastSynchEnabled: Bool = {
if UserDefaults.standard.object(forKey: fastSynchEnabledKey) == nil {
return true
}
return UserDefaults.standard.bool(forKey: fastSynchEnabledKey)
}()
{
didSet {
UserDefaults.standard.set(fastSynchEnabled, forKey: fastSynchEnabledKey)
}
}
@Published var defaultModelsDir: String = {
return UserDefaults.standard.string(forKey: defaultModelsDirKey) ?? ""
}()
{
didSet {
UserDefaults.standard.set(defaultModelsDir, forKey: defaultModelsDirKey)
}
}
@Published var additionalModelsDirs: String = {
return UserDefaults.standard.string(forKey: additionalModelsDirsKey) ?? ""
}()
{
didSet {
UserDefaults.standard.set(additionalModelsDirs, forKey: additionalModelsDirsKey)
}
}
@Published var readOnlyModelsDirs: String = {
return UserDefaults.standard.string(forKey: readOnlyModelsDirsKey) ?? ""
}()
{
didSet {
UserDefaults.standard.set(readOnlyModelsDirs, forKey: readOnlyModelsDirsKey)
}
}
@Published var customEnvironmentVariables: [CustomEnvironmentVariable] = {
guard
let data = UserDefaults.standard.data(forKey: customEnvironmentVariablesKey),
let decoded = try? JSONDecoder().decode(
[CustomEnvironmentVariable].self, from: data
)
else {
return []
}
return decoded
}()
{
didSet {
guard let data = try? JSONEncoder().encode(customEnvironmentVariables) else {
return
}
UserDefaults.standard.set(data, forKey: customEnvironmentVariablesKey)
}
}
/// Fires once when EXO transitions to `.running` for the very first time (fresh install).
@Published private(set) var isFirstLaunchReady = false
private var process: Process?
private var runtimeDirectoryURL: URL?
@@ -113,6 +209,9 @@ final class ExoProcessController: ObservableObject {
try child.run()
process = child
status = .running
// Show welcome popout on every launch
isFirstLaunchReady = true
} catch {
process = nil
status = .failed(message: "Launch error")
@@ -126,11 +225,37 @@ final class ExoProcessController: ObservableObject {
return
}
process.terminationHandler = nil
if process.isRunning {
process.terminate()
}
self.process = nil
status = .stopped
guard process.isRunning else {
self.process = nil
return
}
let proc = process
self.process = nil
Task.detached {
proc.interrupt()
for _ in 0..<50 {
if !proc.isRunning { return }
try? await Task.sleep(nanoseconds: 100_000_000)
}
if proc.isRunning {
proc.terminate()
}
for _ in 0..<30 {
if !proc.isRunning { return }
try? await Task.sleep(nanoseconds: 100_000_000)
}
if proc.isRunning {
kill(proc.processIdentifier, SIGKILL)
}
}
}
func restart() {
@@ -138,6 +263,17 @@ final class ExoProcessController: ObservableObject {
launch()
}
/// Mark onboarding as completed (user interacted with the welcome popout).
func markOnboardingCompleted() {
UserDefaults.standard.set(true, forKey: onboardingCompletedKey)
}
/// Reset onboarding so the welcome popout appears on next launch.
func resetOnboarding() {
UserDefaults.standard.removeObject(forKey: onboardingCompletedKey)
isFirstLaunchReady = false
}
func scheduleLaunch(after seconds: TimeInterval) {
cancelPendingLaunch()
let start = max(1, Int(ceil(seconds)))
@@ -220,9 +356,16 @@ final class ExoProcessController: ObservableObject {
if !hfToken.isEmpty {
environment["HF_TOKEN"] = hfToken
}
if !hfEndpoint.isEmpty {
environment["HF_ENDPOINT"] = hfEndpoint
}
if enableImageModels {
environment["EXO_ENABLE_IMAGE_MODELS"] = "true"
}
if offlineMode {
environment["EXO_OFFLINE"] = "true"
}
environment["EXO_FAST_SYNCH"] = fastSynchEnabled ? "true" : "false"
var paths: [String] = []
if let existing = environment["PATH"], !existing.isEmpty {
@@ -247,6 +390,29 @@ final class ExoProcessController: ObservableObject {
}
environment["PATH"] = paths.joined(separator: ":")
let trimmedDefaultModelsDir = defaultModelsDir.trimmingCharacters(in: .whitespaces)
if !trimmedDefaultModelsDir.isEmpty {
environment["EXO_DEFAULT_MODELS_DIR"] = trimmedDefaultModelsDir
}
let trimmedAdditionalModelsDirs = additionalModelsDirs.trimmingCharacters(in: .whitespaces)
if !trimmedAdditionalModelsDirs.isEmpty {
environment["EXO_MODELS_DIRS"] = trimmedAdditionalModelsDirs
}
let trimmedReadOnlyModelsDirs = readOnlyModelsDirs.trimmingCharacters(in: .whitespaces)
if !trimmedReadOnlyModelsDirs.isEmpty {
environment["EXO_MODELS_READ_ONLY_DIRS"] = trimmedReadOnlyModelsDirs
}
// Apply user-defined arbitrary environment variables last so that
// power users can override any of the typed fields above when
// necessary. Empty keys are ignored.
for variable in customEnvironmentVariables {
let trimmedKey = variable.key.trimmingCharacters(in: .whitespaces)
guard !trimmedKey.isEmpty else { continue }
environment[trimmedKey] = variable.value
}
return environment
}
+1 -1
View File
@@ -9,7 +9,7 @@
<key>EXOBuildCommit</key>
<string>$(EXO_BUILD_COMMIT)</string>
<key>EXOBugReportPresignedUrlEndpoint</key>
<string>$(EXO_BUG_REPORT_PRESIGNED_URL_ENDPOINT)</string>
<string>https://reports.exolabs.net/presigned-urls</string>
<key>NSLocalNetworkUsageDescription</key>
<string>EXO needs local network access to discover and connect to other devices in your cluster for distributed AI inference.</string>
<key>NSBonjourServices</key>
+9 -3
View File
@@ -38,7 +38,8 @@ struct BugReportService {
func sendReport(
baseURL: URL = URL(string: "http://127.0.0.1:52415")!,
now: Date = Date(),
isManual: Bool = false
isManual: Bool = false,
userDescription: String? = nil
) async throws -> BugReportOutcome {
let timestamp = Self.runTimestampString(now)
let dayPrefix = Self.dayPrefixString(now)
@@ -60,7 +61,8 @@ struct BugReportService {
ifconfig: ifconfigText,
debugInfo: debugInfo,
isManual: isManual,
clusterTbBridgeStatus: clusterTbBridgeStatus
clusterTbBridgeStatus: clusterTbBridgeStatus,
userDescription: userDescription
)
let eventLogFiles = readAllEventLogs()
@@ -306,7 +308,8 @@ struct BugReportService {
ifconfig: String,
debugInfo: DebugInfo,
isManual: Bool,
clusterTbBridgeStatus: [[String: Any]]?
clusterTbBridgeStatus: [[String: Any]]?,
userDescription: String? = nil
) -> Data? {
let system = readSystemMetadata()
let exo = readExoMetadata()
@@ -323,6 +326,9 @@ struct BugReportService {
if let tbStatus = clusterTbBridgeStatus {
payload["cluster_thunderbolt_bridge"] = tbStatus
}
if let desc = userDescription, !desc.isEmpty {
payload["user_description"] = desc
}
return try? JSONSerialization.data(withJSONObject: payload, options: [.prettyPrinted])
}
+18 -1
View File
@@ -17,7 +17,7 @@ final class ClusterStateService: ObservableObject {
init(
baseURL: URL = URL(string: "http://127.0.0.1:52415")!,
session: URLSession = .shared
session: URLSession = ClusterStateService.makeNonCachingSession()
) {
self.baseURL = baseURL
self.endpoint = baseURL.appendingPathComponent("state")
@@ -27,6 +27,23 @@ final class ClusterStateService: ObservableObject {
self.decoder = decoder
}
/// `URLSession.shared` carries an on-disk `URLCache` that persists every
/// response body under `~/Library/Caches/exolabs.EXO/`. We poll `/state`
/// at 2 Hz from `startPolling`, so leaving the shared cache attached
/// dirties ~500620 KB/sec of file-backed memory and trips macOS's
/// per-process `disk writes` resource limit (microstackshot reports
/// observed on M3 Ultra producing GBs of cached responses per hour).
/// Cluster-state polling responses are time-sensitive and small; they
/// gain nothing from being cached on disk. Use an ephemeral session
/// with `urlCache = nil` so neither response bodies nor metadata
/// touch disk.
private static func makeNonCachingSession() -> URLSession {
let config = URLSessionConfiguration.ephemeral
config.urlCache = nil
config.requestCachePolicy = .reloadIgnoringLocalCacheData
return URLSession(configuration: config)
}
func startPolling(interval: TimeInterval = 0.5) {
stopPolling()
Task {
@@ -0,0 +1,242 @@
import AppKit
import SwiftUI
/// Manages a standalone window for the bug-report flow.
/// Ensures only one instance exists and brings it to front on repeated opens.
@MainActor
final class BugReportWindowController: ObservableObject {
private var window: NSWindow?
func open() {
if let existing = window, existing.isVisible {
existing.makeKeyAndOrderFront(nil)
NSApp.activate()
return
}
let view = BugReportView(onDismiss: { [weak self] in
self?.window?.close()
})
let hostingController = NSHostingController(rootView: view)
hostingController.sizingOptions = [.preferredContentSize, .minSize]
let newWindow = NSWindow(contentViewController: hostingController)
newWindow.styleMask = [.titled, .closable, .resizable]
newWindow.title = "Send a Bug Report"
newWindow.center()
newWindow.setFrameAutosaveName("ExoBugReportWindow")
newWindow.isReleasedWhenClosed = false
newWindow.makeKeyAndOrderFront(nil)
NSApp.activate()
window = newWindow
}
}
private struct BugReportView: View {
fileprivate enum Phase: Equatable {
case prompting
case sending(String)
case success(String)
case failure(String)
}
let onDismiss: () -> Void
@State private var phase: Phase = .prompting
@State private var userDescription: String = ""
@FocusState private var descriptionFocused: Bool
var body: some View {
VStack(alignment: .leading, spacing: 12) {
switch phase {
case .prompting:
promptingView
case .sending(let message):
sendingView(message: message)
case .success(let message):
successView(message: message)
case .failure(let message):
failureView(message: message)
}
}
.padding(16)
.frame(minWidth: 380)
.animation(.easeInOut(duration: 0.2), value: phase)
.onAppear { descriptionFocused = true }
}
private var promptingView: some View {
VStack(alignment: .leading, spacing: 8) {
Text("Description (optional)")
.font(.subheadline)
.foregroundColor(.secondary)
ZStack(alignment: .topLeading) {
if userDescription.isEmpty {
Text("What were you doing when it broke?")
.font(.body)
.foregroundColor(Color(nsColor: .placeholderTextColor))
.padding(.horizontal, 10)
.padding(.vertical, 8)
.allowsHitTesting(false)
}
TextEditor(text: $userDescription)
.font(.body)
.scrollContentBackground(.hidden)
.padding(4)
.frame(height: 72)
.focused($descriptionFocused)
}
.background(
RoundedRectangle(cornerRadius: 6)
.fill(Color(nsColor: .textBackgroundColor))
)
.overlay(
RoundedRectangle(cornerRadius: 6)
.strokeBorder(Color(nsColor: .separatorColor), lineWidth: 1)
)
Text("Diagnostic logs will be uploaded with your report.")
.font(.caption)
.foregroundColor(.secondary)
HStack {
Spacer()
Button("Cancel") { onDismiss() }
.keyboardShortcut(.cancelAction)
Button("Send") {
Task { await send() }
}
.keyboardShortcut(.defaultAction)
}
.padding(.top, 4)
}
}
private func sendingView(message: String) -> some View {
VStack(alignment: .leading, spacing: 12) {
HStack(spacing: 10) {
ProgressView().controlSize(.small)
Text(message)
.foregroundColor(.secondary)
}
HStack {
Spacer()
Button("Cancel") { onDismiss() }
.keyboardShortcut(.cancelAction)
.disabled(true)
Button("Send") {}
.disabled(true)
}
}
}
private func successView(message: String) -> some View {
VStack(alignment: .leading, spacing: 12) {
HStack(alignment: .top, spacing: 10) {
Image(systemName: "checkmark.circle.fill")
.foregroundColor(.green)
.font(.title2)
Text(message)
.fixedSize(horizontal: false, vertical: true)
}
HStack {
Button {
openGitHubIssue()
} label: {
HStack(spacing: 4) {
Image(systemName: "arrow.up.right.square")
Text("Open GitHub Issue")
}
}
Spacer()
Button("Done") { onDismiss() }
.keyboardShortcut(.defaultAction)
}
}
}
private func failureView(message: String) -> some View {
VStack(alignment: .leading, spacing: 12) {
HStack(alignment: .top, spacing: 10) {
Image(systemName: "exclamationmark.triangle.fill")
.foregroundColor(.orange)
.font(.title2)
Text(message)
.fixedSize(horizontal: false, vertical: true)
}
HStack {
Spacer()
Button("Try Again") {
phase = .prompting
}
Button("Close") { onDismiss() }
.keyboardShortcut(.defaultAction)
}
}
}
private func send() async {
phase = .sending("Collecting logs and uploading…")
let service = BugReportService()
let description = userDescription.trimmingCharacters(in: .whitespacesAndNewlines)
do {
let outcome = try await service.sendReport(
isManual: true,
userDescription: description.isEmpty ? nil : description
)
if outcome.success {
phase = .success(outcome.message)
} else {
phase = .failure(outcome.message)
}
} catch {
phase = .failure(error.localizedDescription)
}
}
private func openGitHubIssue() {
let description = userDescription.trimmingCharacters(in: .whitespacesAndNewlines)
var bodyParts: [String] = []
bodyParts.append("## Describe the bug")
bodyParts.append("")
if !description.isEmpty {
bodyParts.append(description)
} else {
bodyParts.append("A clear and concise description of what the bug is.")
}
bodyParts.append("")
bodyParts.append("## Environment")
bodyParts.append("")
bodyParts.append("- macOS Version: \(ProcessInfo.processInfo.operatingSystemVersionString)")
bodyParts.append("- EXO Version: \(buildTag) (\(buildCommit))")
bodyParts.append("")
bodyParts.append("## Additional context")
bodyParts.append("")
bodyParts.append("A bug report with diagnostic logs was submitted via the app.")
let body = bodyParts.joined(separator: "\n")
var components = URLComponents(string: "https://github.com/exo-explore/exo/issues/new")!
components.queryItems = [
URLQueryItem(name: "template", value: "bug_report.md"),
URLQueryItem(name: "title", value: "[BUG] "),
URLQueryItem(name: "body", value: body),
URLQueryItem(name: "labels", value: "bug"),
]
if let url = components.url {
NSWorkspace.shared.open(url)
}
}
private var buildTag: String {
Bundle.main.infoDictionary?["EXOBuildTag"] as? String ?? "unknown"
}
private var buildCommit: String {
Bundle.main.infoDictionary?["EXOBuildCommit"] as? String ?? "unknown"
}
}
+200
View File
@@ -0,0 +1,200 @@
import AppKit
import SwiftUI
/// A popover callout anchored to the menu bar icon on every launch,
/// pointing the user to the web dashboard with an arrow connecting to the icon.
@MainActor
final class FirstLaunchPopout {
private var popover: NSPopover?
private var countdownTask: Task<Void, Never>?
private static let dashboardURL = "http://localhost:52415/"
/// Called when the user completes onboarding (clicks Open Dashboard or dismisses).
var onComplete: (() -> Void)?
func show() {
guard popover == nil else { return }
// The status bar button may not exist yet on first launch; retry generously.
showWithRetry(attemptsRemaining: 15)
}
private func showWithRetry(attemptsRemaining: Int) {
guard attemptsRemaining > 0 else {
// Exhausted retries fall back to just opening the dashboard directly.
openDashboard()
onComplete?()
return
}
guard let button = Self.findStatusItemButton() else {
DispatchQueue.main.asyncAfter(deadline: .now() + 1.0) { [weak self] in
self?.showWithRetry(attemptsRemaining: attemptsRemaining - 1)
}
return
}
let pop = NSPopover()
pop.behavior = .applicationDefined
pop.animates = true
pop.contentSize = NSSize(width: 280, height: 120)
pop.contentViewController = NSHostingController(
rootView: WelcomeCalloutView(
countdownDuration: 10,
onDismiss: { [weak self] in
self?.onComplete?()
self?.dismiss()
},
onOpen: { [weak self] in
self?.openDashboard()
self?.onComplete?()
self?.dismiss()
}
)
)
self.popover = pop
pop.show(relativeTo: button.bounds, of: button, preferredEdge: .minY)
// Auto-open dashboard after 10s then dismiss
countdownTask = Task {
try? await Task.sleep(nanoseconds: 10_000_000_000)
if !Task.isCancelled {
openDashboard()
onComplete?()
dismiss()
}
}
}
func dismiss() {
countdownTask?.cancel()
countdownTask = nil
guard let pop = popover else { return }
popover = nil
pop.performClose(nil)
}
private func openDashboard() {
guard let url = URL(string: Self.dashboardURL) else { return }
NSWorkspace.shared.open(url)
}
/// Finds the NSStatusBarButton created by SwiftUI's MenuBarExtra.
/// Walks the view hierarchy to find the actual button rather than the content view.
private static func findStatusItemButton() -> NSView? {
for window in NSApp.windows {
let className = NSStringFromClass(type(of: window))
// Match NSStatusBarWindow or any internal SwiftUI status bar window
guard className.contains("StatusBar") || className.contains("MenuBarExtra") else {
continue
}
if let content = window.contentView {
if let button = findButton(in: content) {
return button
}
// Fall back to the content view itself if it has a non-zero frame
if content.frame.width > 0 {
return content
}
}
}
return nil
}
/// Recursively searches the view hierarchy for an NSStatusBarButton.
private static func findButton(in view: NSView) -> NSView? {
let className = NSStringFromClass(type(of: view))
if className.contains("StatusBarButton") || className.contains("StatusItem") {
return view
}
for subview in view.subviews {
if let found = findButton(in: subview) {
return found
}
}
return nil
}
}
/// Minimal welcome callout friendly pointer, not a wall of text.
/// Rendered inside the NSPopover which provides its own chrome and arrow.
private struct WelcomeCalloutView: View {
let countdownDuration: Int
let onDismiss: () -> Void
let onOpen: () -> Void
@State private var countdown: Int
@State private var timerTask: Task<Void, Never>?
init(countdownDuration: Int, onDismiss: @escaping () -> Void, onOpen: @escaping () -> Void) {
self.countdownDuration = countdownDuration
self.onDismiss = onDismiss
self.onOpen = onOpen
self._countdown = State(initialValue: countdownDuration)
}
var body: some View {
VStack(alignment: .leading, spacing: 10) {
HStack(alignment: .top) {
Text("EXO is running")
.font(.system(.headline, design: .rounded))
.fontWeight(.semibold)
.foregroundColor(.primary)
Spacer()
Button {
onDismiss()
} label: {
Image(systemName: "xmark.circle.fill")
.font(.system(size: 14))
.foregroundStyle(.tertiary)
}
.buttonStyle(.plain)
}
Text("Run your first model here:")
.font(.system(.subheadline, design: .default))
.foregroundColor(.secondary)
HStack {
Button {
onOpen()
} label: {
Label("Open Dashboard", systemImage: "arrow.up.right.square")
.font(.system(.caption, design: .default))
.fontWeight(.medium)
}
.buttonStyle(.borderedProminent)
.tint(.accentColor)
.controlSize(.small)
Spacer()
if countdown > 0 {
Text("Launching in \(countdown) secs...")
.font(.system(.caption2, design: .default))
.foregroundColor(.secondary.opacity(0.6))
.monospacedDigit()
}
}
}
.padding(14)
.onAppear {
startCountdown()
}
.onDisappear {
timerTask?.cancel()
timerTask = nil
}
}
private func startCountdown() {
timerTask = Task {
while countdown > 0 {
try? await Task.sleep(nanoseconds: 1_000_000_000)
if !Task.isCancelled {
countdown -= 1
}
}
}
}
}
+718
View File
@@ -0,0 +1,718 @@
import AppKit
import SwiftUI
/// Native macOS Settings window following Apple HIG.
/// Organized into General, Model, Advanced, and About sections.
struct SettingsView: View {
@EnvironmentObject private var controller: ExoProcessController
@EnvironmentObject private var updater: SparkleUpdater
@EnvironmentObject private var networkStatusService: NetworkStatusService
@EnvironmentObject private var thunderboltBridgeService: ThunderboltBridgeService
@EnvironmentObject private var stateService: ClusterStateService
@State private var pendingNamespace: String = ""
@State private var pendingHFToken: String = ""
@State private var pendingHFEndpoint: String = ""
@State private var pendingEnableImageModels = false
@State private var pendingOfflineMode = false
@State private var pendingFastSynchEnabled = false
@State private var pendingDefaultModelsDir: String = ""
@State private var pendingAdditionalModelsDirs: String = ""
@State private var pendingReadOnlyModelsDirs: String = ""
@State private var pendingCustomEnvironmentVariables: [CustomEnvironmentVariable] = []
@State private var needsRestart = false
@State private var uninstallInProgress = false
var body: some View {
TabView {
generalTab
.tabItem {
Label("General", systemImage: "gear")
}
modelTab
.tabItem {
Label("Model", systemImage: "cube")
}
advancedTab
.tabItem {
Label("Advanced", systemImage: "wrench.and.screwdriver")
}
environmentTab
.tabItem {
Label("Environment", systemImage: "terminal")
}
aboutTab
.tabItem {
Label("About", systemImage: "info.circle")
}
}
.frame(width: 640, height: 560)
.onAppear {
pendingNamespace = controller.customNamespace
pendingHFToken = controller.hfToken
pendingHFEndpoint = controller.hfEndpoint
pendingEnableImageModels = controller.enableImageModels
pendingOfflineMode = controller.offlineMode
pendingFastSynchEnabled = controller.fastSynchEnabled
pendingDefaultModelsDir = controller.defaultModelsDir
pendingAdditionalModelsDirs = controller.additionalModelsDirs
pendingReadOnlyModelsDirs = controller.readOnlyModelsDirs
pendingCustomEnvironmentVariables = controller.customEnvironmentVariables
needsRestart = false
}
}
// MARK: - General Tab
private var generalTab: some View {
Form {
Section {
LabeledContent("Cluster Namespace") {
TextField("", text: $pendingNamespace, prompt: Text("default"))
.textFieldStyle(.roundedBorder)
.frame(width: 260)
}
Text("Nodes with the same namespace form a cluster. Leave empty for default.")
.font(.caption)
.foregroundColor(.secondary)
}
Section {
LabeledContent("HuggingFace Token") {
SecureField("", text: $pendingHFToken, prompt: Text("optional"))
.textFieldStyle(.roundedBorder)
.frame(width: 260)
}
Text("Required for gated models. Get yours at huggingface.co/settings/tokens")
.font(.caption)
.foregroundColor(.secondary)
}
Section {
LabeledContent("HuggingFace Endpoint") {
TextField("", text: $pendingHFEndpoint, prompt: Text("default"))
.textFieldStyle(.roundedBorder)
.frame(width: 260)
}
Text("Defaults to huggingface.co. Use a mirror (e.g. hf-mirror.com) for China.")
.font(.caption)
.foregroundColor(.secondary)
}
Section {
Toggle("Offline Mode", isOn: $pendingOfflineMode)
Text("Skip internet checks and use only locally available models.")
.font(.caption)
.foregroundColor(.secondary)
}
Section {
HStack {
Spacer()
Button("Save & Restart") {
applyGeneralSettings()
}
.disabled(!hasGeneralChanges)
}
}
}
.formStyle(.grouped)
.padding()
}
// MARK: - Model Tab
private var modelTab: some View {
Form {
Section {
Toggle("Enable Image Models (experimental)", isOn: $pendingEnableImageModels)
Text("Allow text-to-image and image-to-image models in the model picker.")
.font(.caption)
.foregroundColor(.secondary)
}
Section {
HStack {
Spacer()
Button("Save & Restart") {
applyModelSettings()
}
.disabled(!hasModelChanges)
}
}
}
.formStyle(.grouped)
.padding()
}
// MARK: - Advanced Tab
private var advancedTab: some View {
Form {
Section("Performance") {
Toggle("Fast Synch Enabled", isOn: $pendingFastSynchEnabled)
Text(
"Experimental: enables fast CPU to GPU synchronization. Can sometimes cause a \"GPU lock\" where inference hangs for ~10 seconds before starting. Necessary for low latency with RDMA and Tensor Parallelism."
)
.font(.caption)
.foregroundColor(.secondary)
HStack {
Spacer()
Button("Save & Restart") {
applyAdvancedSettings()
}
.disabled(!hasAdvancedChanges)
}
}
Section("Onboarding") {
HStack {
VStack(alignment: .leading) {
Text("Reset Onboarding")
Text("Opens the dashboard and resets the onboarding wizard.")
.font(.caption)
.foregroundColor(.secondary)
}
Spacer()
Button("Reset") {
guard let url = URL(string: "http://localhost:52415/?reset-onboarding")
else { return }
NSWorkspace.shared.open(url)
}
}
}
Section("Debug Info") {
LabeledContent("Thunderbolt Bridge") {
Text(thunderboltStatusText)
.foregroundColor(thunderboltStatusColor)
}
VStack(alignment: .leading, spacing: 2) {
clusterThunderboltBridgeView
}
VStack(alignment: .leading, spacing: 2) {
interfaceIpList
}
VStack(alignment: .leading, spacing: 2) {
rdmaStatusView
}
}
Section("Danger Zone") {
Button(role: .destructive) {
showUninstallConfirmationAlert()
} label: {
HStack {
Text("Uninstall EXO")
Spacer()
Image(systemName: "trash")
.imageScale(.small)
}
}
.disabled(uninstallInProgress)
}
}
.formStyle(.grouped)
.padding()
}
// MARK: - Environment Tab
private var environmentTab: some View {
Form {
Section("Models Directories") {
LabeledContent("Default Models Directory") {
TextField(
"",
text: $pendingDefaultModelsDir,
prompt: Text("~/.exo/models")
)
.textFieldStyle(.roundedBorder)
.font(.system(.body, design: .monospaced))
.frame(width: 260)
}
Text("Sets EXO_DEFAULT_MODELS_DIR. Where models are downloaded.")
.font(.caption)
.foregroundColor(.secondary)
LabeledContent("Additional Directories") {
TextField(
"",
text: $pendingAdditionalModelsDirs,
prompt: Text("optional, colon-separated")
)
.textFieldStyle(.roundedBorder)
.font(.system(.body, design: .monospaced))
.frame(width: 260)
}
Text("Sets EXO_MODELS_DIRS. Extra writable model directories.")
.font(.caption)
.foregroundColor(.secondary)
LabeledContent("Read-Only Directories") {
TextField(
"",
text: $pendingReadOnlyModelsDirs,
prompt: Text("optional, colon-separated")
)
.textFieldStyle(.roundedBorder)
.font(.system(.body, design: .monospaced))
.frame(width: 260)
}
Text("Sets EXO_MODELS_READ_ONLY_DIRS. Never written to.")
.font(.caption)
.foregroundColor(.secondary)
}
Section("Custom Environment Variables") {
Text(
"Escape hatch for env vars that don't have typed fields above. "
+ "Values here override the typed fields on conflict."
)
.font(.caption)
.foregroundColor(.secondary)
if pendingCustomEnvironmentVariables.isEmpty {
Text("No custom variables.")
.font(.caption)
.foregroundColor(.secondary)
} else {
ForEach($pendingCustomEnvironmentVariables) { $variable in
HStack(alignment: .center, spacing: 8) {
VStack(spacing: 4) {
TextField("key", text: $variable.key)
.labelsHidden()
.textFieldStyle(.roundedBorder)
.font(.system(.body, design: .monospaced))
TextField("value", text: $variable.value)
.labelsHidden()
.textFieldStyle(.roundedBorder)
.font(.system(.body, design: .monospaced))
}
VStack(spacing: 4) {
Button {
pendingCustomEnvironmentVariables.removeAll {
$0.id == variable.id
}
} label: {
Image(systemName: "minus.circle")
}
.buttonStyle(.borderless)
.help("Remove variable")
if !isValidEnvironmentVariableName(variable.key) {
Image(systemName: "exclamationmark.triangle.fill")
.foregroundColor(.orange)
.help(
"Invalid environment variable name. "
+ "Must match [A-Za-z_][A-Za-z0-9_]*."
)
}
}
}
}
}
HStack {
Button {
pendingCustomEnvironmentVariables.append(
CustomEnvironmentVariable()
)
} label: {
Label("Add Variable", systemImage: "plus")
}
Spacer()
}
}
Section {
HStack {
Spacer()
Button("Save & Restart") {
applyEnvironmentSettings()
}
.disabled(!hasEnvironmentChanges)
}
}
}
.formStyle(.grouped)
.padding()
}
// MARK: - About Tab
private var aboutTab: some View {
Form {
Section {
LabeledContent("Version") {
Text(buildTag)
.textSelection(.enabled)
}
LabeledContent("Commit") {
Text(buildCommit)
.font(.system(.body, design: .monospaced))
.textSelection(.enabled)
}
}
Section {
Button("Check for Updates") {
updater.checkForUpdates()
}
}
}
.formStyle(.grouped)
.padding()
}
// MARK: - Debug Info Views (moved from ContentView)
private var thunderboltStatusText: String {
switch networkStatusService.status.thunderboltBridgeState {
case .some(.disabled):
return "Disabled"
case .some(.deleted):
return "Deleted"
case .some(.enabled):
return "Enabled"
case nil:
return "Unknown"
}
}
private var thunderboltStatusColor: Color {
switch networkStatusService.status.thunderboltBridgeState {
case .some(.disabled), .some(.deleted):
return .green
case .some(.enabled):
return .red
case nil:
return .secondary
}
}
private var clusterThunderboltBridgeView: some View {
let bridgeStatuses = stateService.latestSnapshot?.nodeThunderboltBridge ?? [:]
let localNodeId = stateService.localNodeId
let nodeProfiles = stateService.latestSnapshot?.nodeProfiles ?? [:]
return VStack(alignment: .leading, spacing: 1) {
if bridgeStatuses.isEmpty {
Text("Cluster TB Bridge: No data")
.font(.caption2)
.foregroundColor(.secondary)
} else {
Text("Cluster TB Bridge Status:")
.font(.caption2)
.foregroundColor(.secondary)
ForEach(Array(bridgeStatuses.keys.sorted()), id: \.self) { nodeId in
if let status = bridgeStatuses[nodeId] {
let nodeName =
nodeProfiles[nodeId]?.friendlyName ?? String(nodeId.prefix(8))
let isLocal = nodeId == localNodeId
let prefix = isLocal ? " \(nodeName) (local):" : " \(nodeName):"
let statusText =
!status.exists
? "N/A"
: (status.enabled ? "Enabled" : "Disabled")
let color: Color =
!status.exists
? .secondary
: (status.enabled ? .red : .green)
Text("\(prefix) \(statusText)")
.font(.caption2)
.foregroundColor(color)
}
}
}
}
}
private var interfaceIpList: some View {
let statuses = networkStatusService.status.interfaceStatuses
return VStack(alignment: .leading, spacing: 1) {
Text("Interfaces (en0en7):")
.font(.caption2)
.foregroundColor(.secondary)
if statuses.isEmpty {
Text(" Unknown")
.font(.caption2)
.foregroundColor(.secondary)
} else {
ForEach(statuses, id: \.interfaceName) { status in
let ipText = status.ipAddress ?? "No IP"
Text(" \(status.interfaceName): \(ipText)")
.font(.caption2)
.foregroundColor(status.ipAddress == nil ? .red : .green)
}
}
}
}
private var rdmaStatusView: some View {
let rdmaStatuses = stateService.latestSnapshot?.nodeRdmaCtl ?? [:]
let localNodeId = stateService.localNodeId
let nodeProfiles = stateService.latestSnapshot?.nodeProfiles ?? [:]
let localDevices = networkStatusService.status.localRdmaDevices
let localPorts = networkStatusService.status.localRdmaActivePorts
return VStack(alignment: .leading, spacing: 1) {
if rdmaStatuses.isEmpty {
Text("Cluster RDMA: No data")
.font(.caption2)
.foregroundColor(.secondary)
} else {
Text("Cluster RDMA Status:")
.font(.caption2)
.foregroundColor(.secondary)
ForEach(Array(rdmaStatuses.keys.sorted()), id: \.self) { nodeId in
if let status = rdmaStatuses[nodeId] {
let nodeName =
nodeProfiles[nodeId]?.friendlyName ?? String(nodeId.prefix(8))
let isLocal = nodeId == localNodeId
let prefix = isLocal ? " \(nodeName) (local):" : " \(nodeName):"
let statusText = status.enabled ? "Enabled" : "Disabled"
let color: Color = status.enabled ? .green : .orange
Text("\(prefix) \(statusText)")
.font(.caption2)
.foregroundColor(color)
}
}
}
if !localDevices.isEmpty {
Text(" Local Devices: \(localDevices.joined(separator: ", "))")
.font(.caption2)
.foregroundColor(.secondary)
}
if !localPorts.isEmpty {
Text(" Local Active Ports:")
.font(.caption2)
.foregroundColor(.secondary)
ForEach(localPorts, id: \.device) { port in
Text(" \(port.device) port \(port.port): \(port.state)")
.font(.caption2)
.foregroundColor(.green)
}
}
}
}
// MARK: - Actions
private func showUninstallConfirmationAlert() {
let alert = NSAlert()
alert.messageText = "Uninstall EXO"
alert.informativeText = """
This will remove EXO and all its components:
• Network configuration daemon
• Launch at login registration
• EXO network location
• EXO data directory (~/.exo)
The app will be moved to Trash.
"""
alert.alertStyle = .warning
let checkbox = NSButton(
checkboxWithTitle: "Keep downloaded models (~/.exo/models)",
target: nil, action: nil)
checkbox.state = .off
checkbox.sizeToFit()
alert.accessoryView = checkbox
alert.addButton(withTitle: "Uninstall")
alert.addButton(withTitle: "Cancel")
if let uninstallButton = alert.buttons.first {
uninstallButton.hasDestructiveAction = true
}
let response = alert.runModal()
if response == .alertFirstButtonReturn {
performUninstall(keepModels: checkbox.state == .on)
}
}
private func performUninstall(keepModels: Bool) {
uninstallInProgress = true
controller.cancelPendingLaunch()
controller.stop()
stateService.stopPolling()
DispatchQueue.global(qos: .utility).async {
do {
try NetworkSetupHelper.uninstall()
try Self.removeExoDirectory(keepModels: keepModels)
DispatchQueue.main.async {
LaunchAtLoginHelper.disable()
self.moveAppToTrash()
DispatchQueue.main.asyncAfter(deadline: .now() + 0.5) {
NSApplication.shared.terminate(nil)
}
}
} catch {
DispatchQueue.main.async {
let errorAlert = NSAlert()
errorAlert.messageText = "Uninstall Failed"
errorAlert.informativeText = error.localizedDescription
errorAlert.alertStyle = .critical
errorAlert.addButton(withTitle: "OK")
errorAlert.runModal()
self.uninstallInProgress = false
}
}
}
}
private static func removeExoDirectory(keepModels: Bool) throws {
let fm = FileManager.default
let exoDir = ExoProcessController.exoDirectoryURL
guard fm.fileExists(atPath: exoDir.path) else { return }
if !keepModels {
try fm.removeItem(at: exoDir)
return
}
let contents = try fm.contentsOfDirectory(
at: exoDir, includingPropertiesForKeys: nil, options: [])
for entry in contents where entry.lastPathComponent != "models" {
try? fm.removeItem(at: entry)
}
}
private func moveAppToTrash() {
guard let appURL = Bundle.main.bundleURL as URL? else { return }
do {
try FileManager.default.trashItem(at: appURL, resultingItemURL: nil)
} catch {
// If we can't trash the app, that's OK - user can do it manually
}
}
// MARK: - Helpers
private var hasGeneralChanges: Bool {
pendingNamespace != controller.customNamespace || pendingHFToken != controller.hfToken
|| pendingHFEndpoint != controller.hfEndpoint
|| pendingOfflineMode != controller.offlineMode
}
private var hasModelChanges: Bool {
pendingEnableImageModels != controller.enableImageModels
}
private var hasAdvancedChanges: Bool {
pendingFastSynchEnabled != controller.fastSynchEnabled
}
private var hasEnvironmentChanges: Bool {
pendingDefaultModelsDir != controller.defaultModelsDir
|| pendingAdditionalModelsDirs != controller.additionalModelsDirs
|| pendingReadOnlyModelsDirs != controller.readOnlyModelsDirs
|| pendingCustomEnvironmentVariables != controller.customEnvironmentVariables
}
private func applyGeneralSettings() {
controller.customNamespace = pendingNamespace
controller.hfToken = pendingHFToken
controller.hfEndpoint = pendingHFEndpoint
controller.offlineMode = pendingOfflineMode
restartIfRunning()
}
private func applyModelSettings() {
controller.enableImageModels = pendingEnableImageModels
restartIfRunning()
}
private func applyAdvancedSettings() {
controller.fastSynchEnabled = pendingFastSynchEnabled
restartIfRunning()
}
private func applyEnvironmentSettings() {
controller.defaultModelsDir = pendingDefaultModelsDir.trimmingCharacters(
in: .whitespaces)
controller.additionalModelsDirs = pendingAdditionalModelsDirs.trimmingCharacters(
in: .whitespaces)
controller.readOnlyModelsDirs = pendingReadOnlyModelsDirs.trimmingCharacters(
in: .whitespaces)
pendingDefaultModelsDir = controller.defaultModelsDir
pendingAdditionalModelsDirs = controller.additionalModelsDirs
pendingReadOnlyModelsDirs = controller.readOnlyModelsDirs
// Trim whitespace from keys and drop empty ones so that the stored
// form matches what is actually injected into the child process and
// hasEnvironmentChanges doesn't show a stale diff after save.
let trimmed: [CustomEnvironmentVariable] =
pendingCustomEnvironmentVariables.compactMap { variable in
let key = variable.key.trimmingCharacters(in: .whitespaces)
guard !key.isEmpty else { return nil }
return CustomEnvironmentVariable(
id: variable.id, key: key, value: variable.value
)
}
// De-duplicate keys, keeping the last occurrence. This matches the
// effective semantics of the dictionary assignment in
// ExoProcessController.makeEnvironment and avoids silently losing
// visible rows after save.
var seenKeys = Set<String>()
var deduplicatedReversed: [CustomEnvironmentVariable] = []
for variable in trimmed.reversed() {
if seenKeys.insert(variable.key).inserted {
deduplicatedReversed.append(variable)
}
}
let sanitized = Array(deduplicatedReversed.reversed())
pendingCustomEnvironmentVariables = sanitized
controller.customEnvironmentVariables = sanitized
restartIfRunning()
}
/// Validates a POSIX-style environment variable name:
/// `[A-Za-z_][A-Za-z0-9_]*`. Uses an ASCII-only charset so that
/// Unicode letters (e.g. `ñ`, Cyrillic) are rejected in line with what
/// the help tooltip advertises. Empty strings are treated as valid
/// here so that a freshly added blank row does not immediately look
/// broken; the save step filters empty keys out instead.
private func isValidEnvironmentVariableName(_ key: String) -> Bool {
if key.isEmpty { return true }
let headAllowed = CharacterSet(
charactersIn: "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz_"
)
let tailAllowed = headAllowed.union(CharacterSet(charactersIn: "0123456789"))
guard let first = key.unicodeScalars.first, headAllowed.contains(first) else {
return false
}
for scalar in key.unicodeScalars.dropFirst() {
if !tailAllowed.contains(scalar) { return false }
}
return true
}
private func restartIfRunning() {
if controller.status == .running || controller.status == .starting {
controller.restart()
}
}
private var buildTag: String {
Bundle.main.infoDictionary?["EXOBuildTag"] as? String ?? "unknown"
}
private var buildCommit: String {
Bundle.main.infoDictionary?["EXOBuildCommit"] as? String ?? "unknown"
}
}
@@ -0,0 +1,47 @@
import AppKit
import SwiftUI
/// Manages a standalone native macOS Settings window.
/// Ensures only one instance exists and brings it to front on repeated opens.
@MainActor
final class SettingsWindowController: ObservableObject {
private var window: NSWindow?
func open(
controller: ExoProcessController,
updater: SparkleUpdater,
networkStatusService: NetworkStatusService,
thunderboltBridgeService: ThunderboltBridgeService,
stateService: ClusterStateService
) {
if let existing = window, existing.isVisible {
existing.makeKeyAndOrderFront(nil)
NSApp.activate(ignoringOtherApps: true)
return
}
let settingsView = SettingsView()
.environmentObject(controller)
.environmentObject(updater)
.environmentObject(networkStatusService)
.environmentObject(thunderboltBridgeService)
.environmentObject(stateService)
let hostingView = NSHostingView(rootView: settingsView)
let newWindow = NSWindow(
contentRect: NSRect(x: 0, y: 0, width: 640, height: 560),
styleMask: [.titled, .closable],
backing: .buffered,
defer: false
)
newWindow.title = "EXO Settings"
newWindow.contentView = hostingView
newWindow.center()
newWindow.isReleasedWhenClosed = false
newWindow.makeKeyAndOrderFront(nil)
NSApp.activate(ignoringOtherApps: true)
window = newWindow
}
}
+63 -7
View File
@@ -3,25 +3,55 @@
# EXO Uninstaller Script
#
# This script removes all EXO system components that persist after deleting the app.
# Run with: sudo ./uninstall-exo.sh
# Run with: sudo ./uninstall-exo.sh [--keep-models]
#
# Options:
# --keep-models Preserve ~/.exo/models when removing the EXO data directory.
#
# Components removed:
# - LaunchDaemon: /Library/LaunchDaemons/io.exo.networksetup.plist
# - Network script: /Library/Application Support/EXO/
# - Log files: /var/log/io.exo.networksetup.*
# - Network location: "exo"
# - EXO data directory: ~/.exo (or all of ~/.exo except models/ when --keep-models is set)
# - Launch at login registration
#
set -euo pipefail
KEEP_MODELS=0
for arg in "$@"; do
case "$arg" in
--keep-models)
KEEP_MODELS=1
;;
-h | --help)
echo "Usage: sudo ./uninstall-exo.sh [--keep-models]"
echo " --keep-models Preserve ~/.exo/models when removing the EXO data directory."
exit 0
;;
*)
echo "Unknown argument: $arg" >&2
echo "Usage: sudo ./uninstall-exo.sh [--keep-models]" >&2
exit 2
;;
esac
done
LABEL="io.exo.networksetup"
SCRIPT_DEST="/Library/Application Support/EXO/disable_bridge_enable_dhcp.sh"
# Current script path. Older installs used a different filename; keep the
# legacy path here so a fresh uninstall still cleans up upgraded machines.
CURRENT_SCRIPT_DEST="/Library/Application Support/EXO/disable_bridge.sh"
LEGACY_SCRIPT_DEST="/Library/Application Support/EXO/disable_bridge_enable_dhcp.sh"
PLIST_DEST="/Library/LaunchDaemons/io.exo.networksetup.plist"
LOG_OUT="/var/log/${LABEL}.log"
LOG_ERR="/var/log/${LABEL}.err.log"
APP_BUNDLE_ID="io.exo.EXO"
# Resolve the invoking user's home, even when run via sudo.
USER_HOME="$(eval echo "~${SUDO_USER:-$USER}")"
EXO_DIR="$USER_HOME/.exo"
# Colors for output
RED='\033[0;31m'
GREEN='\033[0;32m'
@@ -69,11 +99,17 @@ else
echo_warn "LaunchDaemon plist not found (already removed?)"
fi
# Remove the script and parent directory
if [[ -f $SCRIPT_DEST ]]; then
rm -f "$SCRIPT_DEST"
echo_info "Removed network setup script"
else
# Remove the script (current and legacy filenames) — backwards-compatible:
# tolerate either, both, or neither being present.
removed_any_script=0
for script in "$CURRENT_SCRIPT_DEST" "$LEGACY_SCRIPT_DEST"; do
if [[ -f $script ]]; then
rm -f "$script"
echo_info "Removed network setup script: $script"
removed_any_script=1
fi
done
if [[ $removed_any_script -eq 0 ]]; then
echo_warn "Network setup script not found (already removed?)"
fi
@@ -115,6 +151,22 @@ if networksetup -listnetworkservices 2>/dev/null | grep -q "Thunderbolt Bridge";
echo_info "Re-enabled Thunderbolt Bridge"
fi
# Remove EXO data directory (~/.exo)
EXO_DIR_REMOVED=""
if [[ -d $EXO_DIR ]]; then
if [[ $KEEP_MODELS == "1" && -d "$EXO_DIR/models" ]]; then
find "$EXO_DIR" -mindepth 1 -maxdepth 1 ! -name models -exec rm -rf {} +
EXO_DIR_REMOVED="kept_models"
echo_info "Removed ~/.exo (preserved models/)"
else
rm -rf "$EXO_DIR"
EXO_DIR_REMOVED="full"
echo_info "Removed ~/.exo"
fi
else
echo_warn "~/.exo not found (already removed?)"
fi
# Note about launch at login registration
# SMAppService-based login items cannot be removed from a shell script.
# They can only be unregistered from within the app itself or manually via System Settings.
@@ -144,6 +196,10 @@ echo " • Network setup LaunchDaemon"
echo " • Network configuration script"
echo " • Log files"
echo " • 'exo' network location"
case "$EXO_DIR_REMOVED" in
full) echo " • EXO data directory (~/.exo)" ;;
kept_models) echo " • EXO data directory (~/.exo, models preserved)" ;;
esac
echo ""
echo "Your network has been restored to use the 'Automatic' location."
echo "Thunderbolt Bridge has been re-enabled (if present)."
+160
View File
@@ -0,0 +1,160 @@
# Exo-Bench — Methodology
exo bench measures inference throughput and resource consumption of an exo cluster under controlled conditions. It sends prompts to the `/bench/chat/completions` endpoint, collects server-reported timing statistics, and records system-level metrics (power, GPU utilisation, temperature) throughout each run.
The goal is to have accurate, transparent and reproducible numbers to compare speed and scaling across different models and different setups, and to be able to track these results as optimizations and features are added to EXO.
Below is a technical summary of how Exo-Bench works. While the methodology and benchmark may change over time, this document will be kept up to date whenever this happens. If you find an issue with the methodology, or would like a feature to be added, please open a GitHub issue!
---
## Prompt Construction
Benchmarks need prompts of an exact token length. Unfortunately, we do not have direct access to the model but just the chat completion endpoint. To get around this fact, we create a request that will tokenise to a certain prompt length.
This is achieved by:
1. Tokenising a sample message through the model's `apply_chat_template()` to measure overhead (system tokens, special tokens, chat formatting).
2. Binary-searching over a repeated atom string (default `"a "`) to find the content length that produces exactly the target number of tokens after template expansion.
3. Returning both the content string and the verified token count.
The actual token count is recorded in every result row as `pp_tokens`, so downstream analysis can confirm the prompt hit its target.
Chat template formatting means that it may be impossible to attain very small pp benchmarks. e.g. pp=32 may not work. This tradeoff was made because the result of such a small prompt does not seem very interesting or useful for any real-world use cases.
---
## Bench Endpoint
When a request reaches the server via the `/bench/chat/completions` endpoint, three things change compared to a normal chat completion:
- **KV prefix cache is disabled by default**. Every request starts from a cold cache, ensuring prefill timing is not affected by prior requests. See [Prefix Cache Mode](#prefix-cache-mode) for the `--use-prefix-cache` option.
- **EOS tokens are banned**. A logits processor suppresses all end-of-sequence tokens, forcing the model to generate exactly `max_tokens` tokens. This guarantees consistent generation length for fair TPS comparison — the model cannot short-circuit a run by stopping early.
- **No model output parsing**. The bench collection path concatenates raw token text without any model-specific post-processing (thinking tag extraction, structured output handling, etc.). This is to avoid model outputs such as tool parsing or any structural mistakes from breaking the benchmark - we are testing for speed; see Exo-Eval for performance metrics.
---
## Timing
### Prefill TPS
Measured server-side per task.
```
prefill_tps = num_prompt_tokens / prefill_wall_seconds
```
### Generation TPS
Measured server-side per task. Each task records wall-clock timestamps as tokens arrive:
- First generated token: timestamp recorded
- Every subsequent token: timestamp updated
When generation completes:
```
gen_span = last_token_time - first_token_time
generation_tps = (completion_tokens - 1) / gen_span
```
The first token is excluded from the numerator because the rate measures inter-token throughput — the time between the first and last token divided by the number of intervals.
This does mean that tg=1 will not work.
---
## Concurrency
### Single Request
The client records wall-clock `elapsed_s` around the HTTP round-trip (network latency + server prefill + generation + response serialisation). This is a convenience metric for end-to-end latency. The authoritative TPS numbers come from the server-side per-task timing in the `generation_stats` response.
### Concurrent Requests
When `--concurrency N` is set with N > 1, all N requests must hit the server at the same instant. The mechanism:
1. The prompt is built once and shared across all threads.
2. Each thread gets its own HTTP connection.
3. A thread barrier blocks all threads until every thread is ready.
4. The first thread past the barrier records the batch start time and signals the others.
5. All threads use the same start time as their reference, then fire their HTTP request.
6. Each thread's `elapsed_s` is measured from the shared start time to its own response completion.
**Batch wall time** is the maximum `elapsed_s` across all N requests — the time until the last request finishes.
### Aggregate TPS
```
per_req_tps = max(generation_tps across N concurrent requests)
agg_gen_tps = per_req_tps * concurrency
```
`max` is used instead of `mean` because all requests run in parallel against the same model. The fastest request's generation rate represents the system's per-stream throughput capacity; multiplying by concurrency gives aggregate throughput.
---
## Prefix Cache Mode
When `--use-prefix-cache` is passed, the KV prefix cache remains active during benchmarking. This speeds up repeated runs by skipping redundant prefill work, which is useful when prompt processing is not the focus of the benchmark (e.g. when measuring generation throughput or power consumption across many configurations).
Each response includes a `prefix_cache_hit` field (`"none"`, `"partial"`, or `"exact"`):
- **none**: Cold prefill — no cached KV state was available. The reported `prompt_tps` is the real prefill throughput.
- **partial**: A prefix of the prompt was found in cache. Only the remaining tokens were prefilled. The reported `prompt_tps` reflects the real throughput on the uncached portion. This occurs when multiple ascending `--pp` values share a common prefix (e.g. `--pp 1000,5000` — the 5000-token prompt reuses the 1000-token cache entry and prefills the remaining 4000 tokens).
- **exact**: The entire prompt was found in cache (e.g. same `--pp` value on a `--repeat`). No prefill work was done. The reported `prompt_tps` is the TPS from when the cache entry was originally created, not a new measurement.
**Prompt TPS is approximate in this mode.** Exact-hit runs report the stored TPS from the original cold/partial prefill rather than a freshly measured value. For accurate cold prefill numbers, run without `--use-prefix-cache`.
Ascending `--pp` order (e.g. `--pp 1000,5000,10000`) gives the most useful data: each size gets a meaningful partial hit except the first which is cold. Descending order produces exact hits with approximate TPS from a longer prompt's original run.
---
## Warmup
Before measurement begins, `--warmup N` (default: 0) discarded requests are sent using the first pp/tg pair. Warmup results are not included in the output.
---
## System Metrics
A background thread polls each node at 1 Hz, collecting:
- GPU utilisation (%)
- Temperature (C)
- System power draw (W)
- CPU cluster usage (performance and efficiency cores)
**Energy** is computed via trapezoidal integration of the power samples over each inference window (the wall-clock span of each benchmark request or concurrent batch). Average power is `total_joules / total_inference_seconds`.
---
## Output Format
Results are written as JSON with three top-level keys:
- **`runs`**: Array of per-request result objects, each containing:
- `elapsed_s`, `output_text_preview` (first 200 chars)
- `stats`: `{ prompt_tps, generation_tps, prompt_tokens, generation_tokens, peak_memory_usage }`
- Placement metadata: `model_id`, `placement_sharding`, `placement_instance_meta`, `placement_nodes`
- Run metadata: `pp_tokens`, `tg`, `repeat_index`, `concurrency`, `concurrent_index`
- `download_duration_s` (if model was freshly downloaded)
- **`cluster`**: Cluster state snapshot at time of benchmarking.
- **`system_metrics`**: Per-node time-series samples (GPU, power, temperature).
---
## Reproducing Results
```bash
cd bench && uv run python exo_bench.py \
--model "mlx-community/Qwen3.5-27B-4bit" \
--instance-meta jaccl \
--sharding tensor \
--min-nodes 2 --max-nodes 2 \
--pp 512 4096 --tg 128 \
--repeat 3 \
--warmup 1
```
Run --help for all the available flags.
+5
View File
@@ -0,0 +1,5 @@
# Canary benchmark manifest
#
# Lists the suite files to include. Each file defines benchmarks
# with shared constraints, topology, and default args.
include = ["single-m3-ultra.toml"]
+347
View File
@@ -0,0 +1,347 @@
# Model evaluation configurations for exo_eval.
#
# Each [[model]] entry uses `patterns` — a list of substrings matched
# against the model_id. First matching entry wins.
#
# Required fields:
# name, patterns, reasoning
#
# Optional per-model overrides (CLI flags take priority over these):
# temperature, top_p, max_tokens, reasoning_effort, enable_thinking
#
# Fallback defaults (when no per-model config):
# reasoning: temperature=1.0, max_tokens=131072, reasoning_effort="high"
# non-reasoning: temperature=0.0, max_tokens=16384
#
# All per-model values below are sourced from official model cards,
# generation_config.json files, and vendor documentation.
# ─── Qwen3.5 (Feb 2026) ─────────────────────────────────────────────
# Source: HuggingFace model cards (Qwen/Qwen3.5-*)
# Model card recommends: temp=0.6, top_p=0.95, top_k=20
# We omit top_k to match vllm eval (which doesn't set it).
# max_tokens=121072 to match vllm eval (131072 context - 10000 safety margin).
[[model]]
name = "Qwen3.5 2B"
patterns = ["Qwen3.5-2B"]
reasoning = true
temperature = 0.6
top_p = 0.95
enable_thinking = true
max_tokens = 121072
[[model]]
name = "Qwen3.5 9B"
patterns = ["Qwen3.5-9B"]
reasoning = true
temperature = 0.6
top_p = 0.95
enable_thinking = true
max_tokens = 121072
[[model]]
name = "Qwen3.5 27B"
patterns = ["Qwen3.5-27B"]
reasoning = true
temperature = 0.6
top_p = 0.95
enable_thinking = true
max_tokens = 121072
[[model]]
name = "Qwen3.5 35B A3B"
patterns = ["Qwen3.5-35B-A3B"]
reasoning = true
temperature = 0.6
top_p = 0.95
enable_thinking = true
max_tokens = 121072
[[model]]
name = "Qwen3.5 122B A10B"
patterns = ["Qwen3.5-122B-A10B"]
reasoning = true
temperature = 0.6
top_p = 0.95
enable_thinking = true
max_tokens = 121072
[[model]]
name = "Qwen3.5 397B A17B"
patterns = ["Qwen3.5-397B-A17B"]
reasoning = true
temperature = 0.6
top_p = 0.95
enable_thinking = true
max_tokens = 121072
# ─── Qwen3 (Apr 2025) ───────────────────────────────────────────────
# Source: HuggingFace model cards (Qwen/Qwen3-*)
# Model card recommends: temp=0.6, top_p=0.95, top_k=20
# We omit top_k to match vllm eval (which doesn't set it).
# Non-thinking: temp=0.7, top_p=0.8
# max_tokens: 32768 general, 38912 for complex math/code
[[model]]
name = "Qwen3 0.6B"
patterns = ["Qwen3-0.6B"]
reasoning = true
temperature = 0.6
top_p = 0.95
enable_thinking = true
max_tokens = 38912
[[model]]
name = "Qwen3 30B A3B"
patterns = ["Qwen3-30B-A3B"]
reasoning = true
temperature = 0.6
top_p = 0.95
enable_thinking = true
max_tokens = 38912
[[model]]
name = "Qwen3 235B A22B"
patterns = ["Qwen3-235B-A22B"]
reasoning = true
temperature = 0.6
top_p = 0.95
enable_thinking = true
max_tokens = 38912
[[model]]
name = "Qwen3 Next 80B Thinking"
patterns = ["Qwen3-Next-80B-A3B-Thinking"]
reasoning = true
temperature = 0.6
top_p = 0.95
enable_thinking = true
max_tokens = 38912
[[model]]
name = "Qwen3 Next 80B Instruct"
patterns = ["Qwen3-Next-80B-A3B-Instruct"]
reasoning = false
temperature = 0.7
top_p = 0.8
max_tokens = 16384
[[model]]
name = "Qwen3 Coder 480B"
patterns = ["Qwen3-Coder-480B"]
reasoning = false
temperature = 0.7
top_p = 0.8
max_tokens = 16384
[[model]]
name = "Qwen3 Coder Next"
patterns = ["Qwen3-Coder-Next"]
reasoning = false
temperature = 1.0
top_p = 0.95
max_tokens = 121072
# ─── GPT-OSS (OpenAI) ───────────────────────────────────────────────
# Source: OpenAI GitHub README + HuggingFace discussion #21
# temp=1.0, top_p=1.0, NO top_k, NO repetition_penalty
# reasoning_effort supported: low/medium/high
[[model]]
name = "GPT-OSS 20B"
patterns = ["gpt-oss-20b"]
reasoning = true
temperature = 1.0
top_p = 1.0
[[model]]
name = "GPT-OSS 120B"
patterns = ["gpt-oss-120b"]
reasoning = true
temperature = 1.0
top_p = 1.0
# ─── DeepSeek ────────────────────────────────────────────────────────
# Source: https://api-docs.deepseek.com/quick_start/parameter_settings
# Coding/Math: temp=0.0, General: temp=1.3, Creative: temp=1.5
# NOTE: DeepSeek API applies nonlinear temp mapping. These are API values.
# When running model directly: API temp 1.0 = model temp 0.3
# We use temp=0.0 for eval (coding/math focus).
[[model]]
name = "DeepSeek V3.1"
patterns = ["DeepSeek-V3.1"]
reasoning = true
temperature = 0.0
[[model]]
name = "DeepSeek V3.2"
patterns = ["DeepSeek-V3.2"]
reasoning = true
temperature = 1.0
top_p = 0.95
enable_thinking = true
# ─── NVIDIA Nemotron ───────────────────────────────────────────────────
# Source: HuggingFace model cards
# All variants: temp=1.0, top_p=0.95, enable_thinking=true
[[model]]
name = "Nemotron Cascade 2 30B A3B"
patterns = ["Nemotron-Cascade-2-30B-A3B"]
reasoning = true
temperature = 1.0
top_p = 0.95
enable_thinking = true
[[model]]
name = "Nemotron 3 Super 120B A12B"
patterns = ["Nemotron-3-Super-120B-A12B", "NVIDIA-Nemotron-3-Super-120B-A12B"]
reasoning = true
temperature = 1.0
top_p = 0.95
enable_thinking = true
# ─── GLM (ZhipuAI / THUDM) ──────────────────────────────────────────
# Source: HuggingFace model cards + generation_config.json + docs.z.ai
# GLM 4.5+: temp=1.0, top_p=0.95
# max_tokens=121072 to match vllm eval (131072 context - 10000 safety margin)
[[model]]
name = "GLM-5"
patterns = ["GLM-5"]
reasoning = true
temperature = 1.0
top_p = 0.95
enable_thinking = true
max_tokens = 121072
[[model]]
name = "GLM 4.5 Air"
patterns = ["GLM-4.5-Air"]
reasoning = true
temperature = 1.0
top_p = 0.95
[[model]]
name = "GLM 4.7"
patterns = ["GLM-4.7-"]
reasoning = true
temperature = 1.0
top_p = 0.95
enable_thinking = true
max_tokens = 121072
# Note: matches both GLM-4.7 and GLM-4.7-Flash
# ─── Kimi (Moonshot AI) ─────────────────────────────────────────────
# Source: HuggingFace model cards (moonshotai/Kimi-K2-*)
# K2-Instruct: temp=0.6
# K2-Thinking: temp=1.0, max_length=262144
# K2.5: thinking temp=1.0, top_p=0.95; instant temp=0.6, top_p=0.95
[[model]]
name = "Kimi K2 Thinking"
patterns = ["Kimi-K2-Thinking"]
reasoning = true
temperature = 1.0
max_tokens = 131072
[[model]]
name = "Kimi K2.5"
patterns = ["Kimi-K2.5"]
reasoning = true
temperature = 1.0
top_p = 0.95
enable_thinking = true
max_tokens = 121072
[[model]]
name = "Kimi K2 Instruct"
patterns = ["Kimi-K2-Instruct"]
reasoning = false
temperature = 0.6
# ─── MiniMax ─────────────────────────────────────────────────────────
# Source: HuggingFace model cards + generation_config.json
# All models: temp=1.0, top_p=0.95
# max_tokens=90000 to match vllm eval (100000 context - 10000 safety margin)
[[model]]
name = "MiniMax M2.7"
patterns = ["MiniMax-M2.7"]
reasoning = true
temperature = 1.0
top_p = 0.95
enable_thinking = true
max_tokens = 90000
[[model]]
name = "MiniMax M2.5"
patterns = ["MiniMax-M2.5"]
reasoning = true
temperature = 1.0
top_p = 0.95
enable_thinking = true
max_tokens = 90000
[[model]]
name = "MiniMax M2.1"
patterns = ["MiniMax-M2.1"]
reasoning = true
temperature = 1.0
top_p = 0.95
# ─── Step (StepFun) ─────────────────────────────────────────────────
# Source: HuggingFace model card (stepfun-ai/Step-3.5-Flash)
# Reasoning: temp=1.0, top_p=0.95
# General chat: temp=0.6, top_p=0.95
# We use reasoning settings for eval.
[[model]]
name = "Step 3.5 Flash"
patterns = ["Step-3.5-Flash"]
reasoning = true
temperature = 1.0
top_p = 0.95
enable_thinking = true
max_tokens = 121072
# ─── Llama (Meta) ───────────────────────────────────────────────────
# Source: generation_config.json + meta-llama/llama-models generation.py
# All variants: temp=0.6, top_p=0.9
[[model]]
name = "Llama 3.2 1B"
patterns = ["Llama-3.2-1B"]
reasoning = false
temperature = 0.6
top_p = 0.9
[[model]]
name = "Llama 3.2 3B"
patterns = ["Llama-3.2-3B"]
reasoning = false
temperature = 0.6
top_p = 0.9
[[model]]
name = "Llama 3.1 8B"
patterns = ["Llama-3.1-8B", "Meta-Llama-3.1-8B"]
reasoning = false
temperature = 0.6
top_p = 0.9
[[model]]
name = "Llama 3.1 70B"
patterns = ["Llama-3.1-70B", "Meta-Llama-3.1-70B"]
reasoning = false
temperature = 0.6
top_p = 0.9
[[model]]
name = "Llama 3.3 70B"
patterns = ["Llama-3.3-70B", "llama-3.3-70b"]
reasoning = false
temperature = 0.6
top_p = 0.9
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# type: ignore
from __future__ import annotations
import argparse
import http.client
import json
import os
import time
from collections.abc import Iterator
from typing import Any
from urllib.parse import urlencode
from loguru import logger
_SETTLE_INITIAL_BACKOFF_S = 1.0
_SETTLE_MAX_BACKOFF_S = 60.0
_SETTLE_BACKOFF_MULTIPLIER = 2.0
class ExoHttpError(RuntimeError):
def __init__(self, status: int, reason: str, body_preview: str):
super().__init__(f"HTTP {status} {reason}: {body_preview}")
self.status = status
class ExoClient:
def __init__(self, host: str, port: int, timeout_s: float = 7200.0):
self.host = host
self.port = port
self.timeout_s = timeout_s
def request_json(
self,
method: str,
path: str,
params: dict[str, Any] | None = None,
body: dict[str, Any] | None = None,
headers: dict[str, str] | None = None,
) -> Any:
if not path.startswith("/"):
path = "/" + path
if params:
path = path + "?" + urlencode(params)
conn = http.client.HTTPConnection(self.host, self.port, timeout=self.timeout_s)
try:
payload: bytes | None = None
hdrs: dict[str, str] = {"Accept": "application/json"}
if body is not None:
payload = json.dumps(body).encode("utf-8")
hdrs["Content-Type"] = "application/json"
if headers:
hdrs.update(headers)
conn.request(method.upper(), path, body=payload, headers=hdrs)
resp = conn.getresponse()
raw = resp.read()
text = raw.decode("utf-8", errors="replace") if raw else ""
if resp.status >= 400:
raise ExoHttpError(resp.status, resp.reason, text[:300])
if not text:
return None
return json.loads(text)
finally:
conn.close()
def post_bench_chat_completions(self, payload: dict[str, Any]) -> dict[str, Any]:
return self.request_json("POST", "/bench/chat/completions", body=payload)
def stream_bench_chat_completions(self, payload: dict[str, Any]) -> Iterator[str]:
"""POST /bench/chat/completions with stream=True, yielding raw SSE lines."""
payload = {**payload, "stream": True}
data = json.dumps(payload).encode("utf-8")
conn = http.client.HTTPConnection(self.host, self.port, timeout=self.timeout_s)
try:
conn.request(
"POST",
"/bench/chat/completions",
body=data,
headers={
"Content-Type": "application/json",
"Accept": "text/event-stream",
},
)
resp = conn.getresponse()
if resp.status >= 400:
raw = resp.read().decode("utf-8", errors="replace")
raise ExoHttpError(resp.status, resp.reason, raw[:300])
for line in resp:
yield line.decode("utf-8", errors="replace")
finally:
conn.close()
def get_state_path(self, path: str) -> Any:
try:
return self.request_json("GET", f"/state/{path}")
except ExoHttpError as e:
if e.status == 404:
return None
raise
def get_instance(self, instance_id: str) -> dict[str, Any] | None:
return self.get_state_path(f"instances/{instance_id}")
def get_runner(self, runner_id: str) -> dict[str, Any] | None:
return self.get_state_path(f"runners/{runner_id}")
def get_node_downloads(self, node_id: str) -> list[dict[str, Any]] | None:
return self.get_state_path(f"downloads/{node_id}")
def get_node_disk(self, node_id: str) -> dict[str, Any] | None:
return self.get_state_path(f"nodeDisk/{node_id}")
def get_node_system(self, node_id: str) -> dict[str, Any] | None:
return self.get_state_path(f"nodeSystem/{node_id}")
def get_node_identities(self) -> dict[str, Any] | None:
return self.get_state_path("nodeIdentities")
def get_topology(self) -> dict[str, Any] | None:
return self.get_state_path("topology")
def unwrap_instance(instance: dict[str, Any]) -> dict[str, Any]:
if len(instance) != 1:
raise KeyError(f"Expected 1 key, got keys={list(instance.keys())}")
tag = next(iter(instance))
inner = instance[tag]
if not isinstance(inner, dict):
raise TypeError(f"payload for {tag} must be dict, got {type(inner)}")
return inner
def instance_id_from_instance(instance: dict[str, Any]) -> str:
inner = unwrap_instance(instance)
return str(inner["instanceId"])
def nodes_used_in_instance(instance: dict[str, Any]) -> int:
inner = unwrap_instance(instance)
return len(inner["shardAssignments"]["nodeToRunner"])
def runner_ids_from_instance(instance: dict[str, Any]) -> list[str]:
inner = unwrap_instance(instance)
runner_to_shard = inner["shardAssignments"]["runnerToShard"]
return list(runner_to_shard.keys())
def node_ids_from_instance(instance: dict[str, Any]) -> list[str]:
inner = unwrap_instance(instance)
return list(inner["shardAssignments"]["nodeToRunner"].keys())
def runner_ready(runner: dict[str, Any]) -> bool:
return "RunnerReady" in runner
def runner_failed(runner: dict[str, Any]) -> bool:
return "RunnerFailed" in runner
def get_runner_failed_message(runner: dict[str, Any]) -> str | None:
if "RunnerFailed" in runner:
return runner["RunnerFailed"].get("errorMessage")
return None
def wait_for_instance_ready(
client: ExoClient, instance_id: str, timeout: float = 24000.0
) -> None:
start_time = time.time()
instance_existed = False
last_loaded: dict[str, int] = {}
while time.time() - start_time < timeout:
instance = client.get_instance(instance_id)
if instance is None:
if instance_existed:
raise RuntimeError(
f"Instance {instance_id} was deleted (runner may have failed)"
)
time.sleep(0.1)
continue
instance_existed = True
rids = runner_ids_from_instance(instance)
all_ready = True
for rid in rids:
runner = client.get_runner(rid) or {}
if runner_failed(runner):
error_msg = get_runner_failed_message(runner) or "Unknown error"
raise RuntimeError(f"Runner {rid} failed: {error_msg}")
if "RunnerLoading" in runner:
loading = runner["RunnerLoading"]
loaded = loading.get("layersLoaded", 0)
total = loading.get("totalLayers", 0)
if total > 0 and last_loaded.get(rid) != loaded:
last_loaded[rid] = loaded
logger.debug(f"Runner {rid}: loading layers {loaded}/{total}")
if not runner_ready(runner):
all_ready = False
if all_ready:
return
time.sleep(0.1)
raise TimeoutError(f"Instance {instance_id} did not become ready within {timeout=}")
def wait_for_instance_gone(
client: ExoClient, instance_id: str, timeout: float = 3.0
) -> None:
start_time = time.time()
while time.time() - start_time < timeout:
try:
client.request_json("GET", f"/instance/{instance_id}")
time.sleep(0.4)
except ExoHttpError as e:
if e.status == 404:
return
raise
raise TimeoutError(f"Instance {instance_id} did not get deleted within {timeout=}")
def capture_cluster_snapshot(client: ExoClient) -> dict[str, Any]:
snapshot: dict[str, Any] = {}
identities = client.get_node_identities()
if identities:
snapshot["nodeIdentities"] = identities
topology = client.get_topology()
if topology:
snapshot["topology"] = topology
node_memory = client.get_state_path("nodeMemory")
if node_memory:
snapshot["nodeMemory"] = node_memory
node_system = client.get_state_path("nodeSystem")
if node_system:
snapshot["nodeSystem"] = node_system
return snapshot
def resolve_model_short_id(
client: ExoClient, model_arg: str, *, force_download: bool = False
) -> tuple[str, str]:
models = client.request_json("GET", "/models") or {}
data = models.get("data") or []
for m in data:
if (m.get("name") or "").lower() == model_arg.lower():
short_id = str(m["name"])
full_id = str(m.get("hugging_face_id") or m["name"])
return short_id, full_id
for m in data:
if m.get("hugging_face_id") == model_arg:
short_id = str(m["name"])
full_id = str(m["hugging_face_id"])
return short_id, full_id
if force_download and "/" in model_arg:
logger.info(f"Model not in /models, adding from HuggingFace: {model_arg}")
result = client.request_json(
"POST", "/models/add", body={"model_id": model_arg}
)
if result:
short_id = str(result.get("name") or model_arg.rsplit("/", 1)[-1])
full_id = str(result.get("hugging_face_id") or model_arg)
return short_id, full_id
raise ValueError(f"Model not found in /models: {model_arg}")
def placement_filter(instance_meta: str, wanted: str) -> bool:
s = (instance_meta or "").lower()
if wanted == "both":
return ("ring" in s) or ("jaccl" in s)
return wanted in s
def sharding_filter(sharding: str, wanted: str) -> bool:
s = (sharding or "").lower()
if wanted == "both":
return ("pipeline" in s) or ("tensor" in s)
return wanted in s
def fetch_and_filter_placements(
client: ExoClient,
full_model_id: str,
args: argparse.Namespace,
node_id: str | None = None,
) -> list[dict[str, Any]]:
params: dict[str, str] = {"model_id": full_model_id}
if node_id is not None:
params["node_ids"] = node_id
previews_resp = client.request_json("GET", "/instance/previews", params=params)
previews = previews_resp.get("previews") or []
selected: list[dict[str, Any]] = []
for p in previews:
if p.get("error") is not None:
continue
if not placement_filter(str(p.get("instance_meta", "")), args.instance_meta):
continue
if not sharding_filter(str(p.get("sharding", "")), args.sharding):
continue
instance = p.get("instance")
if not isinstance(instance, dict):
continue
n = nodes_used_in_instance(instance)
# Skip tensor ring single node as it is pointless when pipeline ring
if n == 1 and (
(args.sharding == "both" and "tensor" in p.get("sharding", "").lower())
or (
args.instance_meta == "both"
and "jaccl" in p.get("instance_meta", "").lower()
)
):
continue
if (
args.skip_pipeline_jaccl
and (
args.instance_meta == "both"
and "jaccl" in p.get("instance_meta", "").lower()
)
and (
args.sharding == "both" and "pipeline" in p.get("sharding", "").lower()
)
):
continue
if (
args.skip_tensor_ring
and (
args.instance_meta == "both"
and "ring" in p.get("instance_meta", "").lower()
)
and (args.sharding == "both" and "tensor" in p.get("sharding", "").lower())
):
continue
if args.min_nodes <= n <= args.max_nodes:
selected.append(p)
return selected
def settle_and_fetch_placements(
client: ExoClient,
full_model_id: str,
args: argparse.Namespace,
settle_timeout: float = 0,
node_id: str | None = None,
) -> list[dict[str, Any]]:
selected = fetch_and_filter_placements(client, full_model_id, args, node_id=node_id)
if not selected and settle_timeout > 0:
backoff = _SETTLE_INITIAL_BACKOFF_S
deadline = time.monotonic() + settle_timeout
while not selected and time.monotonic() < deadline:
remaining = deadline - time.monotonic()
logger.warning(
f"No valid placements yet (cluster may still be settling). "
f"Retrying in {backoff:.1f}s ({remaining:.0f}s remaining)..."
)
time.sleep(min(backoff, remaining))
backoff = min(backoff * _SETTLE_BACKOFF_MULTIPLIER, _SETTLE_MAX_BACKOFF_S)
selected = fetch_and_filter_placements(
client, full_model_id, args, node_id=node_id
)
return selected
def run_planning_phase(
client: ExoClient,
full_model_id: str,
preview: dict[str, Any],
danger_delete: bool,
timeout: float,
settle_deadline: float | None,
) -> float | None:
"""Check disk space and ensure model is downloaded before benchmarking.
Returns the wall-clock download duration in seconds if a fresh download
was needed, or None if the model was already cached on all nodes.
"""
# Get model size from /models
models = client.request_json("GET", "/models") or {}
model_bytes = 0
for m in models.get("data", []):
if m.get("hugging_face_id") == full_model_id:
model_bytes = m.get("storage_size_megabytes", 0) * 1024 * 1024
break
if not model_bytes:
logger.warning(
f"Could not determine size for {full_model_id}, skipping disk check"
)
return None
# Get nodes from preview
inner = unwrap_instance(preview["instance"])
node_ids = list(inner["shardAssignments"]["nodeToRunner"].keys())
runner_to_shard = inner["shardAssignments"]["runnerToShard"]
needs_download = False
for node_id in node_ids:
node_downloads = client.get_node_downloads(node_id) or []
already_downloaded = any(
"DownloadCompleted" in p
and unwrap_instance(p["DownloadCompleted"]["shardMetadata"])["modelCard"][
"modelId"
]
== full_model_id
for p in node_downloads
)
if already_downloaded:
continue
needs_download = True
disk_info = client.get_node_disk(node_id) or {}
backoff = _SETTLE_INITIAL_BACKOFF_S
while not disk_info and settle_deadline and time.monotonic() < settle_deadline:
remaining = settle_deadline - time.monotonic()
logger.info(
f"Waiting for disk info on {node_id} ({remaining:.0f}s remaining)..."
)
time.sleep(min(backoff, remaining))
backoff = min(backoff * _SETTLE_BACKOFF_MULTIPLIER, _SETTLE_MAX_BACKOFF_S)
disk_info = client.get_node_disk(node_id) or {}
if not disk_info:
logger.warning(f"No disk info for {node_id}, skipping space check")
continue
avail = disk_info.get("available", {}).get("inBytes", 0)
if avail >= model_bytes:
continue
if not danger_delete:
raise RuntimeError(
f"Insufficient disk on {node_id}: need {model_bytes // (1024**3)}GB, "
f"have {avail // (1024**3)}GB. Use --danger-delete-downloads to free space."
)
completed = [
(
unwrap_instance(p["DownloadCompleted"]["shardMetadata"])["modelCard"][
"modelId"
],
p["DownloadCompleted"]["total"]["inBytes"],
)
for p in node_downloads
if "DownloadCompleted" in p
and not p["DownloadCompleted"].get("readOnly", False)
]
for del_model, size in sorted(completed, key=lambda x: x[1]):
logger.info(f"Deleting {del_model} from {node_id} ({size // (1024**2)}MB)")
client.request_json("DELETE", f"/download/{node_id}/{del_model}")
avail += size
if avail >= model_bytes:
break
if avail < model_bytes:
raise RuntimeError(f"Could not free enough space on {node_id}")
# Start downloads (idempotent)
download_t0 = time.perf_counter() if needs_download else None
for node_id in node_ids:
runner_id = inner["shardAssignments"]["nodeToRunner"][node_id]
shard = runner_to_shard[runner_id]
client.request_json(
"POST",
"/download/start",
body={
"targetNodeId": node_id,
"shardMetadata": shard,
},
)
logger.info(f"Started download on {node_id}")
# Wait for downloads (no timeout — poll until complete or failed)
while True:
all_done = True
for node_id in node_ids:
node_downloads = client.get_node_downloads(node_id) or []
done = any(
"DownloadCompleted" in p
and unwrap_instance(p["DownloadCompleted"]["shardMetadata"])[
"modelCard"
]["modelId"]
== full_model_id
for p in node_downloads
)
failed = [
p["DownloadFailed"]["errorMessage"]
for p in node_downloads
if "DownloadFailed" in p
and unwrap_instance(p["DownloadFailed"]["shardMetadata"])["modelCard"][
"modelId"
]
== full_model_id
]
if failed:
raise RuntimeError(f"Download failed on {node_id}: {failed[0]}")
if not done:
all_done = False
ongoing = [
p
for p in node_downloads
if "DownloadOngoing" in p
and unwrap_instance(p["DownloadOngoing"]["shardMetadata"])[
"modelCard"
]["modelId"]
== full_model_id
]
if ongoing:
prog = ongoing[0]["DownloadOngoing"]["downloadProgress"]
speed_mb = prog.get("speed", 0) / (1024 * 1024)
eta_s = prog.get("etaMs", 0) / 1000
dl_bytes = prog.get("downloaded", {}).get("inBytes", 0)
total_bytes = prog.get("total", {}).get("inBytes", 0)
pct = (dl_bytes / total_bytes * 100) if total_bytes else 0
logger.info(
f"Downloading on {node_id}: {pct:.1f}% @ {speed_mb:.1f} MB/s, "
f"ETA {eta_s:.0f}s "
f"({prog.get('completedFiles', 0)}/{prog.get('totalFiles', 0)} files)"
)
if all_done:
if download_t0 is not None:
return time.perf_counter() - download_t0
return None
time.sleep(10)
def find_existing_instance(client: ExoClient, model_id: str) -> str | None:
"""Find an existing running instance for the given model."""
try:
state = client.request_json("GET", "/state")
except Exception:
return None
for inst_id, inst in state.get("instances", {}).items():
# Instance structure is nested: {"MlxJacclInstance": {"shardAssignments": {"modelId": ...}}}
for _inst_type, inner in inst.items():
if not isinstance(inner, dict):
continue
sa = inner.get("shardAssignments", {})
if sa.get("modelId") == model_id:
return inst_id
return None
def add_common_instance_args(ap: argparse.ArgumentParser) -> None:
ap.add_argument("--host", default=os.environ.get("EXO_HOST", "localhost"))
ap.add_argument(
"--port", type=int, default=int(os.environ.get("EXO_PORT", "52415"))
)
ap.add_argument("--model", required=True, help="Model short id or huggingface id")
ap.add_argument(
"--force-download",
action="store_true",
help="If model not in /models, add it from HuggingFace via exo and download.",
)
ap.add_argument(
"--max-nodes",
type=int,
default=4,
help="Only consider placements using <= this many nodes.",
)
ap.add_argument(
"--min-nodes",
type=int,
default=1,
help="Only consider placements using >= this many nodes.",
)
ap.add_argument(
"--instance-meta", choices=["ring", "jaccl", "both"], default="both"
)
ap.add_argument(
"--sharding", choices=["pipeline", "tensor", "both"], default="both"
)
ap.add_argument(
"--skip-pipeline-jaccl",
action="store_true",
help="Skip pipeline+jaccl placements, as it's often pointless.",
)
ap.add_argument(
"--skip-tensor-ring",
action="store_true",
help="Skip tensor+ring placements, as it's so slow.",
)
ap.add_argument(
"--timeout", type=float, default=7200.0, help="HTTP timeout (seconds)."
)
ap.add_argument(
"--settle-timeout",
type=float,
default=60.0,
help="Max seconds to wait for the cluster to produce valid placements (0 = try once).",
)
ap.add_argument(
"--danger-delete-downloads",
action="store_true",
help="Delete existing models from smallest to largest to make room for benchmark model.",
)
ap.add_argument(
"--reuse-instance",
action="store_true",
help="Reuse an existing running instance for this model instead of creating a new one.",
)
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# type: ignore
import argparse
import asyncio
import sys
import termios
import time
import tty
import aiohttp
NUM_REQUESTS = 10
BASE_URL = ""
QUESTIONS = [
"What is the capital of Australia?",
"How many bones are in the human body?",
"What year did World War II end?",
"What is the speed of light in meters per second?",
"Who wrote Romeo and Juliet?",
"What is the chemical formula for water?",
"How many planets are in our solar system?",
"What is the largest ocean on Earth?",
"Who painted the Mona Lisa?",
"What is the boiling point of water in Celsius?",
]
def write(s: str) -> None:
sys.stdout.write(s)
# ---------------------------------------------------------------------------
# Model picker (same style as exo_eval)
# ---------------------------------------------------------------------------
def fetch_models() -> list[str]:
import json
import urllib.request
with urllib.request.urlopen(f"{BASE_URL}/state") as resp:
data = json.loads(resp.read())
model_ids: set[str] = set()
for instance in data.get("instances", {}).values():
for variant in instance.values():
sa = variant.get("shardAssignments", {})
model_id = sa.get("modelId")
if model_id:
model_ids.add(model_id)
return sorted(model_ids)
def pick_model() -> str | None:
models = fetch_models()
if not models:
print("No models found.")
return None
cursor = 0
total_lines = len(models) + 4
def render(first: bool = False) -> None:
if not first:
write(f"\033[{total_lines}A")
write("\033[J")
write("\033[1mSelect model\033[0m (up/down, enter confirm, q quit)\r\n\r\n")
for i, model in enumerate(models):
line = f" {'>' if i == cursor else ' '} {model}"
write(f"\033[7m{line}\033[0m\r\n" if i == cursor else f"{line}\r\n")
write("\r\n")
sys.stdout.flush()
fd = sys.stdin.fileno()
old = termios.tcgetattr(fd)
try:
tty.setraw(fd)
write("\033[?25l")
render(first=True)
while True:
ch = sys.stdin.read(1)
if ch in ("q", "\x03"):
write("\033[?25h\033[0m\r\n")
return None
elif ch in ("\r", "\n"):
break
elif ch == "\x1b":
seq = sys.stdin.read(2)
if seq == "[A":
cursor = (cursor - 1) % len(models)
elif seq == "[B":
cursor = (cursor + 1) % len(models)
render()
finally:
termios.tcsetattr(fd, termios.TCSADRAIN, old)
write(f"\033[{total_lines}A\033[J") # clear picker UI
write("\033[?25h\033[0m")
sys.stdout.flush()
return models[cursor]
# ---------------------------------------------------------------------------
# Parallel requests
# ---------------------------------------------------------------------------
statuses: list[str] = []
times: list[str] = []
previews: list[str] = []
tokens: list[str] = []
full_responses: list[dict | None] = []
total_lines = 0
start_time: float = 0
selected_model: str = ""
def render_progress(first: bool = False) -> None:
if not first:
write(f"\033[{total_lines}A")
write("\033[J")
elapsed = time.monotonic() - start_time if start_time else 0
done = sum(1 for s in statuses if s == "done")
write(
f"\033[1m{selected_model}\033[0m [{done}/{NUM_REQUESTS}] {elapsed:.1f}s\r\n\r\n"
)
for i in range(NUM_REQUESTS):
q = QUESTIONS[i % len(QUESTIONS)]
status = statuses[i]
if status == "pending":
color = "\033[33m" # yellow
elif status == "running":
color = "\033[36m" # cyan
elif status == "done":
color = "\033[32m" # green
else:
color = "\033[31m" # red
write(
f" {i:>2} {color}{status:<8}\033[0m {times[i]:>6} {tokens[i]:>5}tok {q[:40]:<40} {previews[i][:50]}\r\n"
)
write("\r\n")
sys.stdout.flush()
async def send_request(
session: aiohttp.ClientSession, i: int, lock: asyncio.Lock
) -> None:
payload = {
"model": selected_model,
"messages": [{"role": "user", "content": QUESTIONS[i % len(QUESTIONS)]}],
"max_tokens": 1024,
}
statuses[i] = "running"
async with lock:
render_progress()
t0 = time.monotonic()
try:
async with session.post(
f"{BASE_URL}/v1/chat/completions", json=payload
) as resp:
data = await resp.json()
elapsed = time.monotonic() - t0
full_responses[i] = data
times[i] = f"{elapsed:.1f}s"
if resp.status == 200:
choice = data["choices"][0]
msg = choice["message"]
content = msg.get("content", "")
previews[i] = content[:50].replace("\n", " ") or "(empty)"
if "usage" in data:
tokens[i] = str(data["usage"].get("total_tokens", ""))
statuses[i] = "done"
else:
statuses[i] = f"err:{resp.status}"
previews[i] = str(data.get("error", {}).get("message", ""))[:50]
except Exception as e:
elapsed = time.monotonic() - t0
times[i] = f"{elapsed:.1f}s"
statuses[i] = "error"
previews[i] = str(e)[:50]
async with lock:
render_progress()
async def run_requests(print_stdout: bool = False) -> None:
global start_time, total_lines, statuses, times, previews, tokens, full_responses
statuses = ["pending"] * NUM_REQUESTS
times = ["-"] * NUM_REQUESTS
previews = ["-"] * NUM_REQUESTS
tokens = ["-"] * NUM_REQUESTS
full_responses = [None] * NUM_REQUESTS
total_lines = NUM_REQUESTS + 4
write("\033[?25l") # hide cursor
start_time = time.monotonic()
render_progress(first=True)
lock = asyncio.Lock()
try:
async with aiohttp.ClientSession() as session:
tasks = [send_request(session, i, lock) for i in range(NUM_REQUESTS)]
await asyncio.gather(*tasks)
total = time.monotonic() - start_time
write(
f"\033[1m=== All {NUM_REQUESTS} requests done in {total:.1f}s ===\033[0m\r\n\r\n"
)
if print_stdout:
for i in range(NUM_REQUESTS):
data = full_responses[i]
if not data or "choices" not in data:
continue
choice = data["choices"][0]
msg = choice["message"]
q = QUESTIONS[i % len(QUESTIONS)]
write(f"\033[1m--- #{i}: {q} ---\033[0m\r\n")
if msg.get("reasoning_content"):
write(f"\033[2m[Thinking]: {msg['reasoning_content']}\033[0m\r\n")
write(f"{msg.get('content', '')}\r\n")
if "usage" in data:
u = data["usage"]
write(
f"\033[2m[Usage: prompt={u.get('prompt_tokens')}, "
f"completion={u.get('completion_tokens')}, "
f"total={u.get('total_tokens')}]\033[0m\r\n"
)
write("\r\n")
finally:
write("\033[?25h") # show cursor
sys.stdout.flush()
def main() -> None:
global selected_model, BASE_URL
parser = argparse.ArgumentParser(
description="Send parallel requests to an exo cluster"
)
parser.add_argument(
"--host", required=True, help="Hostname of the exo node (e.g. s1)"
)
parser.add_argument("--port", type=int, default=52415, help="Port (default: 52415)")
parser.add_argument(
"--stdout", action="store_true", help="Print full responses after completion"
)
args = parser.parse_args()
BASE_URL = f"http://{args.host}:{args.port}"
model = pick_model()
if not model:
return
selected_model = model
asyncio.run(run_requests(print_stdout=args.stdout))
if __name__ == "__main__":
main()
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# Prefill/Decode disaggregation benchmark config.
#
# Top-level keys are bench-wide. [prefill] and [decode] sections set per-side
# placement filters and (optionally) per-side model.
#
# Example:
# uv run python bench/prefill_decode_bench.py --config bench/prefill-decode.toml
host = "james"
port = 52415
timeout = 7200.0
settle_timeout = 60.0
# Workload
pp = [4096]
tg = [512]
repeat = 1
warmup = 0
json_out = "bench/prefill_decode_results.json"
[prefill]
model = "mlx-community/gpt-oss-20b-MXFP4-Q8"
node = "mike"
instance_meta = "ring"
sharding = "pipeline"
min_nodes = 1
max_nodes = 1
[decode]
model = "mlx-community/gpt-oss-20b-MXFP4-Q8"
node = "james"
instance_meta = "ring"
sharding = "pipeline"
min_nodes = 1
max_nodes = 1
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# type: ignore
#!/usr/bin/env python3
"""Disaggregated prefill-decode benchmark for exo (MLX → MLX).
Spins up two MLX instances on the cluster, marks one as Prefill source and
the other as Decode target via /v1/instance-links, then sends chat
completions to the API. The master routes the request to the decode
instance and stamps `prefill_endpoint` pointing at the prefill instance —
the worker decides per-request whether to ship prefill remotely
(uncached_count > REMOTE_PREFILL_MIN_TOKENS).
Usage:
uv run python bench/prefill_decode_bench.py --model <id> --pp 2048,8192 --tg 128
uv run python bench/prefill_decode_bench.py --model <id> --pp 4096 --tg 128 --repeat 3
uv run python bench/prefill_decode_bench.py --model <id> --pp 2048 --tg 128 --dry-run
"""
from __future__ import annotations
import argparse
import contextlib
import copy
import itertools
import json
import sys
import time
import tomllib
from pathlib import Path
from statistics import mean
from typing import Any
from exo_bench import (
PromptSizer,
format_peak_memory,
load_tokenizer_for_bench,
parse_int_list,
)
from harness import (
ExoClient,
ExoHttpError,
add_common_instance_args,
instance_id_from_instance,
node_ids_from_instance,
nodes_used_in_instance,
resolve_model_short_id,
run_planning_phase,
settle_and_fetch_placements,
unwrap_instance,
wait_for_instance_gone,
wait_for_instance_ready,
)
from loguru import logger
def _node_id_to_friendly(client: ExoClient) -> dict[str, str]:
identities = client.get_node_identities() or {}
out: dict[str, str] = {}
for node_id, identity in identities.items():
if isinstance(identity, dict):
name = identity.get("friendlyName") or identity.get("friendly_name")
if isinstance(name, str):
out[str(node_id)] = name
return out
def _placement_node_friendly_names(
placement: dict[str, Any], id_to_friendly: dict[str, str]
) -> list[str]:
instance = placement["instance"]
return [id_to_friendly.get(nid, nid) for nid in node_ids_from_instance(instance)]
def _filter_by_node(
placements: list[dict[str, Any]],
friendly_name: str,
id_to_friendly: dict[str, str],
) -> list[dict[str, Any]]:
target = friendly_name.lower()
matched: list[dict[str, Any]] = []
for p in placements:
names = [n.lower() for n in _placement_node_friendly_names(p, id_to_friendly)]
if any(target == n or target in n for n in names):
matched.append(p)
return matched
def _node_id_by_friendly(id_to_friendly: dict[str, str], target: str) -> str | None:
target_lc = target.lower()
for nid, name in id_to_friendly.items():
if target_lc == name.lower() or target_lc in name.lower():
return nid
return None
def _load_toml(path: str) -> dict[str, Any]:
with Path(path).open("rb") as f:
return tomllib.load(f)
_TOP_LEVEL_TOML_KEYS = {
"host",
"port",
"timeout",
"settle_timeout",
"model",
"pp",
"tg",
"repeat",
"warmup",
"json_out",
"instance_meta",
"sharding",
"min_nodes",
"max_nodes",
"force_download",
"danger_delete_downloads",
"all_combinations",
}
def _inject_toml_into_argv() -> None:
"""If --config X is in sys.argv, pre-load it and inject required CLI args
(--model, --pp, --tg) so argparse's required=True checks pass."""
argv = sys.argv
if "--config" not in argv:
return
idx = argv.index("--config")
if idx + 1 >= len(argv):
return
cfg_path = argv[idx + 1]
cfg = _load_toml(cfg_path)
decode = cfg.get("decode", {})
def _has(flag: str) -> bool:
return any(a == flag or a.startswith(flag + "=") for a in argv)
# --model: prefer top-level, then [decode].model
if not _has("--model"):
model = cfg.get("model") or decode.get("model")
if model:
argv += ["--model", str(model)]
if not _has("--pp"):
pp = cfg.get("pp")
if pp:
argv += (
["--pp", *(str(x) for x in pp)]
if isinstance(pp, list)
else [
"--pp",
str(pp),
]
)
if not _has("--tg"):
tg = cfg.get("tg")
if tg:
argv += (
["--tg", *(str(x) for x in tg)]
if isinstance(tg, list)
else [
"--tg",
str(tg),
]
)
def _merge_toml_into_args(args: argparse.Namespace, cfg: dict[str, Any]) -> None:
"""Apply top-level toml keys onto args namespace where args has a default."""
for key, value in cfg.items():
if key in {"prefill", "decode"}:
continue
if key not in _TOP_LEVEL_TOML_KEYS:
continue
attr = key
current = getattr(args, attr, None)
if current in (None, [], False):
setattr(args, attr, value)
def _side_args(
base: argparse.Namespace, overrides: dict[str, Any]
) -> argparse.Namespace:
out = copy.copy(base)
for k in (
"instance_meta",
"sharding",
"min_nodes",
"max_nodes",
"skip_pipeline_jaccl",
"skip_tensor_ring",
):
if k in overrides:
setattr(out, k, overrides[k])
return out
def _pick_two_distinct_placements(
placements: list[dict[str, Any]],
) -> tuple[dict[str, Any], dict[str, Any]] | None:
if len(placements) < 2:
return None
seen_nodes: set[tuple[str, ...]] = set()
chosen: list[dict[str, Any]] = []
for p in placements:
nodes = tuple(sorted(str(n) for n in p.get("nodes", [])))
if nodes in seen_nodes:
continue
seen_nodes.add(nodes)
chosen.append(p)
if len(chosen) == 2:
return chosen[0], chosen[1]
return None
def _create_instance_link(
client: ExoClient,
prefill_instance_id: str,
decode_instance_id: str,
) -> str:
out = client.request_json(
"POST",
"/v1/instance-links",
body={
"prefill_instances": [prefill_instance_id],
"decode_instances": [decode_instance_id],
},
)
return str(out.get("commandId", ""))
def _list_instance_links(client: ExoClient) -> list[dict[str, Any]]:
out = client.request_json("GET", "/v1/instance-links")
return out if isinstance(out, list) else []
def _delete_instance_link(client: ExoClient, link_id: str) -> None:
client.request_json("DELETE", f"/v1/instance-links/{link_id}")
def run_one(
client: ExoClient,
model_id: str,
pp_hint: int,
tg: int,
prompt_sizer: PromptSizer,
) -> tuple[dict[str, Any], int]:
content, pp_tokens = prompt_sizer.build(pp_hint)
payload: dict[str, Any] = {
"model": model_id,
"messages": [{"role": "user", "content": content}],
"stream": False,
"max_tokens": tg,
}
t0 = time.perf_counter()
out = client.post_bench_chat_completions(payload)
elapsed = time.perf_counter() - t0
stats = out.get("generation_stats")
choices = out.get("choices") or [{}]
message = choices[0].get("message", {}) if choices else {}
text = message.get("content") or ""
preview = text[:200] if text else ""
return {
"elapsed_s": elapsed,
"output_text_preview": preview,
"stats": stats,
}, pp_tokens
def _run_phase(
*,
client: ExoClient,
label: str,
pp_tg_pairs: list[tuple[int, int]],
model_id: str,
prompt_sizer: PromptSizer,
warmup: int,
repeat: int,
common_meta: dict[str, Any],
) -> list[dict[str, Any]]:
logger.info(f"=== phase: {label} (model={model_id}) ===")
rows: list[dict[str, Any]] = []
for i in range(warmup):
run_one(client, model_id, pp_tg_pairs[0][0], pp_tg_pairs[0][1], prompt_sizer)
logger.debug(f" warmup {i + 1}/{warmup} done")
for pp, tg in pp_tg_pairs:
logger.info(f"--- {label}: pp={pp} tg={tg} ---")
runs: list[dict[str, Any]] = []
for r in range(repeat):
time.sleep(2)
try:
row, actual_pp_tokens = run_one(client, model_id, pp, tg, prompt_sizer)
except Exception as e:
logger.error(e)
continue
row.update(common_meta)
row.update(
{
"phase": label,
"phase_model_id": model_id,
"pp_tokens": actual_pp_tokens,
"tg": tg,
"repeat_index": r,
}
)
runs.append(row)
rows.append(row)
if runs:
prompt_tps = mean(x["stats"]["prompt_tps"] for x in runs)
gen_tps = mean(x["stats"]["generation_tps"] for x in runs)
ptok = mean(x["stats"]["prompt_tokens"] for x in runs)
gtok = mean(x["stats"]["generation_tokens"] for x in runs)
peak = mean(x["stats"]["peak_memory_usage"]["inBytes"] for x in runs)
avg_elapsed = mean(x["elapsed_s"] for x in runs)
logger.info(
f"[{label}] prompt_tps={prompt_tps:.2f} gen_tps={gen_tps:.2f} "
f"prompt_tokens={ptok} gen_tokens={gtok} "
f"peak_memory={format_peak_memory(peak)} "
f"avg_elapsed={avg_elapsed:.2f}s"
)
time.sleep(2)
return rows
def _summarise(rows: list[dict[str, Any]]) -> dict[tuple[int, int], dict[str, float]]:
grouped: dict[tuple[int, int], list[dict[str, Any]]] = {}
for r in rows:
key = (int(r["pp_tokens"]), int(r["tg"]))
grouped.setdefault(key, []).append(r)
out: dict[tuple[int, int], dict[str, float]] = {}
for key, runs in grouped.items():
out[key] = {
"prompt_tps": mean(x["stats"]["prompt_tps"] for x in runs),
"gen_tps": mean(x["stats"]["generation_tps"] for x in runs),
"elapsed_s": mean(x["elapsed_s"] for x in runs),
}
return out
def _print_diff(
disagg_rows: list[dict[str, Any]],
decode_alone_rows: list[dict[str, Any]],
prefill_alone_rows: list[dict[str, Any]],
) -> None:
disagg = _summarise(disagg_rows)
decode_alone = _summarise(decode_alone_rows)
prefill_alone = _summarise(prefill_alone_rows)
keys = set(disagg.keys()) | set(decode_alone.keys()) | set(prefill_alone.keys())
width = 64
for key in sorted(keys):
pp, tg = key
logger.info("" * width)
logger.info(f" pp={pp} tg={tg}")
logger.info("" * width)
logger.info(
f" {'phase':<16} {'elapsed':>10} {'prompt_tps':>11} {'gen_tps':>9}"
)
for label, summary in (
("disaggregated", disagg.get(key)),
("decode_alone", decode_alone.get(key)),
("prefill_alone", prefill_alone.get(key)),
):
if summary is None:
logger.info(f" {label:<16} {'':>10} {'':>11} {'':>9}")
continue
logger.info(
f" {label:<16} "
f"{summary['elapsed_s']:>9.2f}s "
f"{summary['prompt_tps']:>11.1f} "
f"{summary['gen_tps']:>9.2f}"
)
d = disagg.get(key)
da = decode_alone.get(key)
pa = prefill_alone.get(key)
if d and da and d["elapsed_s"] > 0:
logger.info(
f" speedup vs decode_alone: {da['elapsed_s'] / d['elapsed_s']:.2f}x"
)
if d and pa and d["elapsed_s"] > 0:
logger.info(
f" speedup vs prefill_alone: {pa['elapsed_s'] / d['elapsed_s']:.2f}x"
)
logger.info("" * width)
def main() -> int:
_inject_toml_into_argv()
ap = argparse.ArgumentParser(
prog="prefill-decode-bench",
description="Benchmark MLX-MLX disaggregated prefill/decode via instance links.",
)
add_common_instance_args(ap)
ap.add_argument(
"--pp",
nargs="+",
required=True,
help="Prompt-size hints (ints, must be >1000). Accepts commas.",
)
ap.add_argument(
"--tg",
nargs="+",
required=True,
help="Generation lengths (ints). Accepts commas.",
)
ap.add_argument(
"--repeat", type=int, default=1, help="Repetitions per (pp,tg) pair."
)
ap.add_argument(
"--warmup",
type=int,
default=0,
help="Warmup runs (uses first pp/tg).",
)
ap.add_argument(
"--json-out",
default="bench/prefill_decode_results.json",
help="Write raw per-run results JSON to this path.",
)
ap.add_argument("--stdout", action="store_true", help="Write results to stdout")
ap.add_argument(
"--dry-run", action="store_true", help="List selected placements and exit."
)
ap.add_argument(
"--all-combinations",
action="store_true",
help="Force all pp×tg combinations even when lists have equal length.",
)
ap.add_argument(
"--prefill-model",
default=None,
help="Model id for the prefill instance. Defaults to --model.",
)
ap.add_argument(
"--prefill-node",
default=None,
help="friendly_name of the node hosting the prefill instance.",
)
ap.add_argument(
"--decode-node",
default=None,
help="friendly_name of the node hosting the decode instance.",
)
ap.add_argument(
"--config",
default=None,
help="TOML config file. CLI flags override toml values.",
)
ap.add_argument(
"--compare-baseline",
action="store_true",
help="Also run each (pp,tg) pair without the prefill/decode link "
"(decode instance does its own prefill) and report the diff.",
)
args = ap.parse_args()
cfg = _load_toml(args.config) if args.config else {}
_merge_toml_into_args(args, cfg)
prefill_overrides = cfg.get("prefill", {}) if cfg else {}
decode_overrides = cfg.get("decode", {}) if cfg else {}
if args.prefill_model is None and "model" in prefill_overrides:
args.prefill_model = prefill_overrides["model"]
if args.prefill_node is None and "node" in prefill_overrides:
args.prefill_node = prefill_overrides["node"]
if args.decode_node is None and "node" in decode_overrides:
args.decode_node = decode_overrides["node"]
if "model" in decode_overrides and not args.model:
args.model = decode_overrides["model"]
pp_list = parse_int_list(args.pp)
tg_list = parse_int_list(args.tg)
if not pp_list or not tg_list:
logger.error("pp and tg lists must be non-empty")
return 2
for pp in pp_list:
if pp <= 1000:
logger.error(
f"pp={pp} must be >1000 (remote prefill triggers when uncached >1000)"
)
return 2
if args.repeat <= 0:
logger.error("--repeat must be >= 1")
return 2
use_combinations = args.all_combinations or len(pp_list) != len(tg_list)
if use_combinations:
logger.info(
f"pp/tg mode: combinations (product) — {len(pp_list) * len(tg_list)} pairs"
)
else:
logger.info(f"pp/tg mode: tandem (zip) — {len(pp_list)} pairs")
client = ExoClient(args.host, args.port, timeout_s=args.timeout)
decode_short_id, decode_full_id = resolve_model_short_id(
client, args.model, force_download=args.force_download
)
if args.prefill_model:
prefill_short_id, prefill_full_id = resolve_model_short_id(
client, args.prefill_model, force_download=args.force_download
)
else:
prefill_short_id, prefill_full_id = decode_short_id, decode_full_id
tokenizer = load_tokenizer_for_bench(decode_full_id)
if tokenizer is None:
raise RuntimeError("[prefill-decode-bench] decode tokenizer load failed")
try:
decode_prompt_sizer = PromptSizer(tokenizer)
except Exception:
logger.error("[prefill-decode-bench] decode prompt sizing failed")
raise
if prefill_full_id == decode_full_id:
prefill_prompt_sizer = decode_prompt_sizer
else:
prefill_tokenizer = load_tokenizer_for_bench(prefill_full_id)
if prefill_tokenizer is None:
raise RuntimeError("[prefill-decode-bench] prefill tokenizer load failed")
prefill_prompt_sizer = PromptSizer(prefill_tokenizer)
id_to_friendly = _node_id_to_friendly(client)
prefill_args = _side_args(args, prefill_overrides)
decode_args = _side_args(args, decode_overrides)
if prefill_full_id == decode_full_id and prefill_overrides == decode_overrides:
placements = settle_and_fetch_placements(
client, decode_full_id, args, settle_timeout=args.settle_timeout
)
prefill_candidates = (
_filter_by_node(placements, args.prefill_node, id_to_friendly)
if args.prefill_node
else placements
)
decode_candidates = (
_filter_by_node(placements, args.decode_node, id_to_friendly)
if args.decode_node
else placements
)
if args.prefill_node and not prefill_candidates:
logger.error(f"No placement on prefill node {args.prefill_node!r}.")
return 1
if args.decode_node and not decode_candidates:
logger.error(f"No placement on decode node {args.decode_node!r}.")
return 1
if args.prefill_node and args.decode_node:
prefill_p = prefill_candidates[0]
decode_p = decode_candidates[0]
else:
pair = _pick_two_distinct_placements(placements)
if pair is None:
logger.error(
"Need at least two distinct-node MLX placements for the same model."
)
return 1
prefill_p, decode_p = pair
if args.prefill_node:
prefill_p = prefill_candidates[0]
if args.decode_node:
decode_p = decode_candidates[0]
else:
prefill_node_id = (
_node_id_by_friendly(id_to_friendly, args.prefill_node)
if args.prefill_node
else None
)
decode_node_id = (
_node_id_by_friendly(id_to_friendly, args.decode_node)
if args.decode_node
else None
)
if args.prefill_node and prefill_node_id is None:
logger.error(f"Unknown node {args.prefill_node!r}.")
return 1
if args.decode_node and decode_node_id is None:
logger.error(f"Unknown node {args.decode_node!r}.")
return 1
prefill_placements = settle_and_fetch_placements(
client,
prefill_full_id,
prefill_args,
settle_timeout=args.settle_timeout,
node_id=prefill_node_id,
)
decode_placements = settle_and_fetch_placements(
client,
decode_full_id,
decode_args,
settle_timeout=args.settle_timeout,
node_id=decode_node_id,
)
if not prefill_placements:
logger.error(
f"No placement found for prefill model {prefill_full_id}"
f"{f' on node {args.prefill_node!r}' if args.prefill_node else ''}."
)
return 1
if not decode_placements:
logger.error(
f"No placement found for decode model {decode_full_id}"
f"{f' on node {args.decode_node!r}' if args.decode_node else ''}."
)
return 1
prefill_p = prefill_placements[0]
decode_p = decode_placements[0]
prefill_node_names = _placement_node_friendly_names(prefill_p, id_to_friendly)
decode_node_names = _placement_node_friendly_names(decode_p, id_to_friendly)
_ = unwrap_instance
prefill_instance = prefill_p["instance"]
decode_instance = decode_p["instance"]
prefill_id = instance_id_from_instance(prefill_instance)
decode_id = instance_id_from_instance(decode_instance)
prefill_meta = str(prefill_p.get("instance_meta", ""))
decode_meta = str(decode_p.get("instance_meta", ""))
prefill_nodes = nodes_used_in_instance(prefill_instance)
decode_nodes = nodes_used_in_instance(decode_instance)
logger.info("=" * 80)
logger.info(
f"PREFILL: {prefill_meta} / nodes={prefill_nodes} ({','.join(prefill_node_names)}) "
f"/ {prefill_short_id} ({prefill_full_id}) / instance_id={prefill_id}"
)
logger.info(
f"DECODE: {decode_meta} / nodes={decode_nodes} ({','.join(decode_node_names)}) "
f"/ {decode_short_id} ({decode_full_id}) / instance_id={decode_id}"
)
if args.dry_run:
return 0
settle_deadline = (
time.monotonic() + args.settle_timeout if args.settle_timeout > 0 else None
)
logger.info("Planning phase: prefill...")
run_planning_phase(
client,
prefill_full_id,
prefill_p,
args.danger_delete_downloads,
args.timeout,
settle_deadline,
)
logger.info("Planning phase: decode...")
run_planning_phase(
client,
decode_full_id,
decode_p,
args.danger_delete_downloads,
args.timeout,
settle_deadline,
)
if use_combinations:
pp_tg_pairs = list(itertools.product(pp_list, tg_list))
else:
pp_tg_pairs = list(zip(pp_list, tg_list, strict=True))
common_meta = {
"decode_model_short_id": decode_short_id,
"decode_model_id": decode_full_id,
"prefill_model_short_id": prefill_short_id,
"prefill_model_id": prefill_full_id,
"prefill_instance_id": prefill_id,
"prefill_instance_meta": prefill_meta,
"prefill_nodes": prefill_nodes,
"decode_instance_id": decode_id,
"decode_instance_meta": decode_meta,
"decode_nodes": decode_nodes,
}
all_rows: list[dict[str, Any]] = []
disagg_rows: list[dict[str, Any]] = []
decode_alone_rows: list[dict[str, Any]] = []
prefill_alone_rows: list[dict[str, Any]] = []
link_id = ""
prefill_alive = False
decode_alive = False
try:
logger.info("Creating prefill instance...")
client.request_json("POST", "/instance", body={"instance": prefill_instance})
wait_for_instance_ready(client, prefill_id)
prefill_alive = True
logger.info("Prefill instance ready")
if args.compare_baseline:
time.sleep(2)
prefill_alone_rows = _run_phase(
client=client,
label="prefill_alone",
pp_tg_pairs=pp_tg_pairs,
model_id=prefill_full_id,
prompt_sizer=prefill_prompt_sizer,
warmup=args.warmup,
repeat=args.repeat,
common_meta=common_meta,
)
all_rows.extend(prefill_alone_rows)
logger.info("Creating decode instance...")
client.request_json("POST", "/instance", body={"instance": decode_instance})
wait_for_instance_ready(client, decode_id)
decode_alive = True
logger.info("Decode instance ready")
logger.info("Linking instances (prefill → decode)...")
_create_instance_link(client, prefill_id, decode_id)
time.sleep(1)
links = _list_instance_links(client)
if not links:
logger.error("Link did not appear in state.")
return 1
link_id = str(links[-1].get("linkId") or links[-1].get("link_id") or "")
logger.info(f"Link created: {link_id}")
time.sleep(2)
disagg_rows = _run_phase(
client=client,
label="disaggregated",
pp_tg_pairs=pp_tg_pairs,
model_id=decode_full_id,
prompt_sizer=decode_prompt_sizer,
warmup=args.warmup,
repeat=args.repeat,
common_meta=common_meta,
)
all_rows.extend(disagg_rows)
if args.compare_baseline:
logger.info("Removing link and prefill instance to isolate decode_alone.")
with contextlib.suppress(ExoHttpError):
if link_id:
_delete_instance_link(client, link_id)
link_id = ""
with contextlib.suppress(ExoHttpError):
client.request_json("DELETE", f"/instance/{prefill_id}")
wait_for_instance_gone(client, prefill_id)
prefill_alive = False
time.sleep(2)
decode_alone_rows = _run_phase(
client=client,
label="decode_alone",
pp_tg_pairs=pp_tg_pairs,
model_id=decode_full_id,
prompt_sizer=decode_prompt_sizer,
warmup=args.warmup,
repeat=args.repeat,
common_meta=common_meta,
)
all_rows.extend(decode_alone_rows)
_print_diff(disagg_rows, decode_alone_rows, prefill_alone_rows)
finally:
with contextlib.suppress(ExoHttpError):
if link_id:
_delete_instance_link(client, link_id)
if decode_alive:
with contextlib.suppress(ExoHttpError):
client.request_json("DELETE", f"/instance/{decode_id}")
wait_for_instance_gone(client, decode_id)
if prefill_alive:
with contextlib.suppress(ExoHttpError):
client.request_json("DELETE", f"/instance/{prefill_id}")
wait_for_instance_gone(client, prefill_id)
logger.debug("Deleted both instances")
if args.stdout:
json.dump(all_rows, sys.stdout, indent=2, ensure_ascii=False)
elif args.json_out:
with open(args.json_out, "w", encoding="utf-8") as f:
json.dump(all_rows, f, indent=2, ensure_ascii=False)
logger.debug(f"\nWrote results JSON: {args.json_out}")
return 0
if __name__ == "__main__":
sys.exit(main())
+12 -5
View File
@@ -4,11 +4,18 @@ version = "0.1.0"
description = "Benchmarking tool for exo distributed inference"
requires-python = ">=3.13"
dependencies = [
"loguru>=0.7.3",
"transformers>=5.0.0",
"huggingface-hub>=0.33.4",
"tiktoken>=0.12.0",
"jinja2>=3.1.0",
"httpx>=0.27.0",
"loguru>=0.7.3",
"transformers>=5.0.0",
"huggingface-hub>=0.33.4",
"tiktoken>=0.12.0",
"jinja2>=3.1.0",
"protobuf>=5.29.0",
"datasets>=2.0.0",
"math-verify>=0.7.0",
"lm-eval[api,math]>=0.4.0",
"human-eval>=1.0.3",
"numpy>=1.24.0",
]
[build-system]
+306
View File
@@ -0,0 +1,306 @@
# Tool definitions — each becomes an OpenAI function tool.
# All scenarios get all tools unless they specify a `tools` list.
[tools.get_current_weather]
description = "Get the current weather in a given location"
required = ["location"]
[tools.get_current_weather.properties.location]
type = "string"
description = "City and state, e.g. San Francisco, CA"
[tools.get_current_weather.properties.unit]
type = "string"
enum = ["celsius", "fahrenheit"]
description = "Temperature unit"
[tools.calculate]
description = "Evaluate a mathematical expression and return the numeric result"
required = ["expression"]
[tools.calculate.properties.expression]
type = "string"
description = "The math expression to evaluate, e.g. '2 + 3 * 4'"
[tools.search_products]
description = "Search for products in a catalog by query, category, and price"
required = ["query"]
[tools.search_products.properties.query]
type = "string"
description = "Search query string"
[tools.search_products.properties.category]
type = "string"
enum = ["electronics", "clothing", "food", "books"]
description = "Product category to filter by"
[tools.search_products.properties.max_price]
type = "number"
description = "Maximum price in USD"
[tools.create_todos]
description = "Create a structured todo list"
required = ["todos"]
[tools.create_todos.properties.todos]
type = "array"
description = "List of todo items"
[tools.create_todos.properties.todos.items]
type = "object"
required = ["content", "status", "priority"]
[tools.create_todos.properties.todos.items.properties.content]
type = "string"
description = "The todo item text"
[tools.create_todos.properties.todos.items.properties.status]
type = "string"
description = "Status: pending, in_progress, or completed"
[tools.create_todos.properties.todos.items.properties.priority]
type = "string"
description = "Priority: low, normal, or high"
# -- Should call a tool --
[[scenarios]]
name = "weather_simple"
description = "Basic weather query -> get_current_weather"
expect_tool_call = true
expected_function = "get_current_weather"
required_arg_keys = ["location"]
[[scenarios.messages]]
role = "user"
content = "What's the weather like in Tokyo right now?"
[[scenarios]]
name = "calculator_simple"
description = "Math question -> calculate"
expect_tool_call = true
expected_function = "calculate"
required_arg_keys = ["expression"]
[[scenarios.messages]]
role = "user"
content = "Use the calculator to compute 3847 * 926 + 17293"
[[scenarios]]
name = "search_with_filters"
description = "Product search with category and price filter"
expect_tool_call = true
expected_function = "search_products"
required_arg_keys = ["query"]
[[scenarios.messages]]
role = "user"
content = "Find me electronics under $50"
# -- Multi-turn: tool call then follow-up --
[[scenarios]]
name = "weather_multi_turn"
description = "Weather query -> tool result -> natural language summary"
expect_tool_call = true
expected_function = "get_current_weather"
required_arg_keys = ["location"]
[scenarios.tool_result]
temperature = "18C"
condition = "partly cloudy"
humidity = "65%"
wind = "12 km/h NW"
[[scenarios.messages]]
role = "user"
content = "What's the weather in Paris?"
[[scenarios]]
name = "calculator_multi_turn"
description = "Math query -> tool result -> model reports the answer"
expect_tool_call = true
expected_function = "calculate"
required_arg_keys = ["expression"]
[scenarios.tool_result]
result = 491682
[[scenarios.messages]]
role = "user"
content = "Use the calculator to compute 1847 * 263 + 5921"
[[scenarios]]
name = "search_multi_turn"
description = "Search query -> tool result -> model summarizes products"
expect_tool_call = true
expected_function = "search_products"
required_arg_keys = ["query"]
[[scenarios.tool_result.results]]
name = "Hands-On Machine Learning"
price = 45.99
rating = 4.8
[[scenarios.tool_result.results]]
name = "Deep Learning with Python"
price = 39.99
rating = 4.6
[[scenarios.messages]]
role = "user"
content = "Search for books about machine learning"
# -- Sequential tool calls --
[[scenarios]]
name = "chained_tool_calls_same"
description = "Thinking + weather(Tokyo) -> result -> model must call weather(London)"
expect_tool_call = true
expected_function = "get_current_weather"
required_arg_keys = ["location"]
[[scenarios.messages]]
role = "user"
content = "Compare the weather in Tokyo and London."
[[scenarios.messages]]
role = "assistant"
content = "I'll check both cities. Let me start with Tokyo."
[[scenarios.messages.tool_calls]]
id = "call_1"
name = "get_current_weather"
arguments = { location = "Tokyo" }
[[scenarios.messages]]
role = "tool"
tool_call_id = "call_1"
content = '{"temperature": "25C", "condition": "sunny"}'
[[scenarios]]
name = "chained_tool_calls_different"
description = "Thinking + weather(Berlin) -> result -> model must call calculator"
expect_tool_call = true
expected_function = "calculate"
required_arg_keys = ["expression"]
[[scenarios.messages]]
role = "user"
content = "What's the weather in Berlin, and also use the calculator to compute 4819 * 37 + 291."
[[scenarios.messages]]
role = "assistant"
content = "I'll handle both. Let me check Berlin's weather first."
[[scenarios.messages.tool_calls]]
id = "call_2"
name = "get_current_weather"
arguments = { location = "Berlin" }
[[scenarios.messages]]
role = "tool"
tool_call_id = "call_2"
content = '{"temperature": "12C", "condition": "rainy"}'
[[scenarios]]
name = "chained_tool_calls_three"
description = "Two prior thinking+tool calls -> results -> model must make a third"
expect_tool_call = true
expected_function = "get_current_weather"
required_arg_keys = ["location"]
[[scenarios.messages]]
role = "user"
content = "Compare weather in Tokyo, Paris, and London."
[[scenarios.messages]]
role = "assistant"
content = "I'll check all three cities. Starting with Tokyo."
[[scenarios.messages.tool_calls]]
id = "call_3"
name = "get_current_weather"
arguments = { location = "Tokyo" }
[[scenarios.messages]]
role = "tool"
tool_call_id = "call_3"
content = '{"temperature": "25C", "condition": "sunny"}'
[[scenarios.messages]]
role = "assistant"
content = "Got Tokyo. Now checking Paris."
[[scenarios.messages.tool_calls]]
id = "call_4"
name = "get_current_weather"
arguments = { location = "Paris" }
[[scenarios.messages]]
role = "tool"
tool_call_id = "call_4"
content = '{"temperature": "18C", "condition": "cloudy"}'
# -- Nested object schema (regression for lossy chat template rendering) --
[[scenarios]]
name = "nested_schema_tool_call"
description = "Tool call with nested object array schema -> create_todos"
expect_tool_call = true
expected_function = "create_todos"
required_arg_keys = ["todos"]
nested_array_key = "todos"
required_item_keys = ["content", "status", "priority"]
tools = ["create_todos"]
[[scenarios.messages]]
role = "user"
content = "Create a todo list with 3 items to learn Python"
# -- Tool name integrity (regression for harmony token leaking into name) --
[tools.glob]
description = "Search for files matching a glob pattern in the codebase"
required = ["pattern"]
[tools.glob.properties.pattern]
type = "string"
description = "The glob pattern to match files against, e.g. '**/*.py'"
[tools.glob.properties.path]
type = "string"
description = "The directory to search in"
[[scenarios]]
name = "tool_name_integrity"
description = "Tool name must not contain harmony tokens like <|channel|>"
expect_tool_call = true
expected_function = "glob"
required_arg_keys = ["pattern"]
tools = ["glob"]
[[scenarios.messages]]
role = "user"
content = "Find all Python files in the src directory"
# -- Should NOT call a tool --
[[scenarios]]
name = "no_tool_joke"
description = "Joke request should NOT trigger any tool"
expect_tool_call = false
[[scenarios.messages]]
role = "user"
content = "Tell me a funny joke about cats."
[[scenarios]]
name = "no_tool_factual"
description = "Factual question answerable from training data"
expect_tool_call = false
[[scenarios.messages]]
role = "user"
content = "What is the capital of Japan?"
+189
View File
@@ -0,0 +1,189 @@
# Single-node M3 Ultra benchmarks
#
# Shared constraints applied to ALL benchmarks in this file.
constraints = [
"All(MacOsBuild(=25D125))",
"Hosts(=1)",
"All(Chip(m3_ultra))",
"All(GpuCores(=80))",
]
[topology]
type = "none"
# Default args merged into each benchmark's args (benchmark-level args win).
[defaults]
pp = [512, 2048, 8192, 16384]
tg = 128
[[benchmark]]
model = "mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/gpt-oss-120b-MXFP4-Q8"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/GLM-4.7-Flash-8bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Qwen3-Coder-Next-6bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Qwen3-30B-A3B-8bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Qwen3-0.6B-4bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Qwen3-0.6B-8bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Llama-3.2-1B-Instruct-4bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Llama-3.2-3B-Instruct-4bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Llama-3.2-3B-Instruct-8bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Meta-Llama-3.1-8B-Instruct-8bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/gpt-oss-20b-MXFP4-Q8"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Qwen3-30B-A3B-4bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/GLM-4.7-Flash-4bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/GLM-4.7-Flash-5bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/GLM-4.7-Flash-6bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Llama-3.3-70B-Instruct-4bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Qwen3-Coder-Next-4bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Qwen3-Coder-Next-5bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Qwen3-Coder-Next-8bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit"
extra_constraints = ["All(Memory(>=96GiB))"]
[[benchmark]]
model = "mlx-community/Llama-3.3-70B-Instruct-8bit"
extra_constraints = ["All(Memory(>=256GiB))"]
[[benchmark]]
model = "mlx-community/llama-3.3-70b-instruct-fp16"
extra_constraints = ["All(Memory(>=256GiB))"]
[[benchmark]]
model = "mlx-community/GLM-4.5-Air-8bit"
extra_constraints = ["All(Memory(>=256GiB))"]
[[benchmark]]
model = "mlx-community/GLM-4.5-Air-bf16"
extra_constraints = ["All(Memory(>=256GiB))"]
[[benchmark]]
model = "mlx-community/GLM-4.7-4bit"
extra_constraints = ["All(Memory(>=256GiB))"]
[[benchmark]]
model = "mlx-community/MiniMax-M2.1-3bit"
extra_constraints = ["All(Memory(>=256GiB))"]
[[benchmark]]
model = "mlx-community/MiniMax-M2.1-8bit"
extra_constraints = ["All(Memory(>=256GiB))"]
[[benchmark]]
model = "mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"
extra_constraints = ["All(Memory(>=256GiB))"]
[[benchmark]]
model = "mlx-community/Qwen3-Coder-Next-bf16"
extra_constraints = ["All(Memory(>=256GiB))"]
[[benchmark]]
model = "mlx-community/Step-3.5-Flash-4bit"
extra_constraints = ["All(Memory(>=256GiB))"]
[[benchmark]]
model = "mlx-community/Step-3.5-Flash-6bit"
extra_constraints = ["All(Memory(>=256GiB))"]
[[benchmark]]
model = "mlx-community/Step-3.5-Flash-8Bit"
extra_constraints = ["All(Memory(>=256GiB))"]
[[benchmark]]
model = "mlx-community/DeepSeek-V3.1-4bit"
extra_constraints = ["All(Memory(>=512GiB))"]
[[benchmark]]
model = "mlx-community/GLM-4.7-6bit"
extra_constraints = ["All(Memory(>=512GiB))"]
[[benchmark]]
model = "mlx-community/GLM-4.7-8bit-gs32"
extra_constraints = ["All(Memory(>=512GiB))"]
[[benchmark]]
model = "mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit"
extra_constraints = ["All(Memory(>=512GiB))"]
[[benchmark]]
model = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"
extra_constraints = ["All(Memory(>=512GiB))"]
View File
Whitespace-only changes.
+593
View File
@@ -0,0 +1,593 @@
# type: ignore
# Vendored from LiveCodeBench (https://github.com/LiveCodeBench/LiveCodeBench)
# File: lcb_runner/evaluation/testing_util.py
# License: MIT
# Vendored 2026-03-07 — do not modify without updating from upstream.
import ast
import faulthandler
import json
import platform
# to run the solution files we're using a timing based approach
import signal
import sys
import time
# used for debugging to time steps
from datetime import datetime
from decimal import Decimal
from enum import Enum
from io import StringIO
# from pyext import RuntimeModule
from types import ModuleType
# used for testing the code that reads from input
from unittest.mock import mock_open, patch
import_string = "from string import *\nfrom re import *\nfrom datetime import *\nfrom collections import *\nfrom heapq import *\nfrom bisect import *\nfrom copy import *\nfrom math import *\nfrom random import *\nfrom statistics import *\nfrom itertools import *\nfrom functools import *\nfrom operator import *\nfrom io import *\nfrom sys import *\nfrom json import *\nfrom builtins import *\nfrom typing import *\nimport string\nimport re\nimport datetime\nimport collections\nimport heapq\nimport bisect\nimport copy\nimport math\nimport random\nimport statistics\nimport itertools\nimport functools\nimport operator\nimport io\nimport sys\nimport json\nsys.setrecursionlimit(50000)\n"
def truncatefn(s, length=300):
if isinstance(s, str):
pass
else:
s = str(s)
if len(s) <= length:
return s
return s[: length // 2] + "...(truncated) ..." + s[-length // 2 :]
class CODE_TYPE(Enum):
call_based = 0
standard_input = 1
# stuff for setting up signal timer
class TimeoutException(Exception):
pass
def timeout_handler(signum, frame):
print("timeout occured: alarm went off")
raise TimeoutException
# used to capture stdout as a list
# from https://stackoverflow.com/a/16571630/6416660
# alternative use redirect_stdout() from contextlib
class Capturing(list):
def __enter__(self):
self._stdout = sys.stdout
sys.stdout = self._stringio = StringIO()
# Make closing the StringIO a no-op
self._stringio.close = lambda x: 1
return self
def __exit__(self, *args):
self.append(self._stringio.getvalue())
del self._stringio # free up some memory
sys.stdout = self._stdout
# Custom mock for sys.stdin that supports buffer attribute
class MockStdinWithBuffer:
def __init__(self, inputs: str):
self.inputs = inputs
self._stringio = StringIO(inputs)
self.buffer = MockBuffer(inputs)
def read(self, *args):
return self.inputs
def readline(self, *args):
return self._stringio.readline(*args)
def readlines(self, *args):
return self.inputs.split("\n")
def __getattr__(self, name):
# Delegate other attributes to StringIO
return getattr(self._stringio, name)
class MockBuffer:
def __init__(self, inputs: str):
self.inputs = inputs.encode("utf-8") # Convert to bytes
def read(self, *args):
# Return as byte strings that can be split
return self.inputs
def readline(self, *args):
return self.inputs.split(b"\n")[0] + b"\n"
def clean_if_name(code: str) -> str:
try:
astree = ast.parse(code)
last_block = astree.body[-1]
if isinstance(last_block, ast.If):
condition = last_block.test
if ast.unparse(condition).strip() == "__name__ == '__main__'":
code = (
ast.unparse(astree.body[:-1]) + "\n" + ast.unparse(last_block.body) # type: ignore
)
except:
pass
return code
def make_function(code: str) -> str:
try:
import_stmts = []
all_other_stmts = []
astree = ast.parse(code)
for stmt in astree.body:
if isinstance(stmt, (ast.Import, ast.ImportFrom)):
import_stmts.append(stmt)
else:
all_other_stmts.append(stmt)
function_ast = ast.FunctionDef(
name="wrapped_function",
args=ast.arguments(
posonlyargs=[], args=[], kwonlyargs=[], kw_defaults=[], defaults=[]
),
body=all_other_stmts,
decorator_list=[],
lineno=-1,
)
main_code = (
import_string
+ "\n"
+ ast.unparse(import_stmts)
+ "\n"
+ ast.unparse(function_ast)
)
return main_code
except Exception:
return code
def call_method(method, inputs):
if isinstance(inputs, list):
inputs = "\n".join(inputs)
inputs_line_iterator = iter(inputs.split("\n"))
# Create custom stdin mock with buffer support
mock_stdin = MockStdinWithBuffer(inputs)
# sys.setrecursionlimit(10000)
# @patch('builtins.input', side_effect=inputs.split("\n"))
@patch("builtins.open", mock_open(read_data=inputs))
@patch("sys.stdin", mock_stdin) # Use our custom mock instead of StringIO
@patch("sys.stdin.readline", lambda *args: next(inputs_line_iterator))
@patch("sys.stdin.readlines", lambda *args: inputs.split("\n"))
@patch("sys.stdin.read", lambda *args: inputs)
# @patch('sys.stdout.write', print)
def _inner_call_method(_method):
try:
return _method()
except SystemExit:
pass
finally:
pass
return _inner_call_method(method)
def get_function(compiled_sol, fn_name: str): # type: ignore
try:
assert hasattr(compiled_sol, fn_name)
return getattr(compiled_sol, fn_name)
except Exception:
return
def compile_code(code: str, timeout: int):
signal.alarm(timeout)
try:
tmp_sol = ModuleType("tmp_sol", "")
exec(code, tmp_sol.__dict__)
if "class Solution" in code:
# leetcode wraps solutions in `Solution`
# this is a hack to check if it is leetcode solution or not
# currently livecodebench only supports LeetCode but
# else condition allows future extensibility to other platforms
compiled_sol = tmp_sol.Solution()
else:
# do nothing in the other case since function is accesible
compiled_sol = tmp_sol
assert compiled_sol is not None
finally:
signal.alarm(0)
return compiled_sol
def convert_line_to_decimals(line: str) -> tuple[bool, list[Decimal]]:
try:
decimal_line = [Decimal(elem) for elem in line.split()]
except:
return False, []
return True, decimal_line
def get_stripped_lines(val: str):
## you don't want empty lines to add empty list after splitlines!
val = val.strip()
return [val_line.strip() for val_line in val.split("\n")]
def grade_call_based(
code: str, all_inputs: list, all_outputs: list, fn_name: str, timeout: int
):
# call-based clean up logic
# need to wrap in try-catch logic after to catch the correct errors, but for now this is fine.
code = import_string + "\n\n" + code
compiled_sol = compile_code(code, timeout)
if compiled_sol is None:
return
method = get_function(compiled_sol, fn_name)
if method is None:
return
all_inputs = [
[json.loads(line) for line in inputs.split("\n")] for inputs in all_inputs
]
all_outputs = [json.loads(output) for output in all_outputs]
total_execution = 0
all_results = []
for idx, (gt_inp, gt_out) in enumerate(zip(all_inputs, all_outputs)):
signal.alarm(timeout)
faulthandler.enable()
try:
# can lock here so time is useful
start = time.time()
prediction = method(*gt_inp)
total_execution += time.time() - start
signal.alarm(0)
# don't penalize model if it produces tuples instead of lists
# ground truth sequences are not tuples
if isinstance(prediction, tuple):
prediction = list(prediction)
tmp_result = prediction == gt_out
# handle floating point comparisons
all_results.append(tmp_result)
if not tmp_result:
return all_results, {
"output": truncatefn(prediction),
"inputs": truncatefn(gt_inp),
"expected": truncatefn(gt_out),
"error_code": -2,
"error_message": "Wrong Answer",
}
except Exception as e:
signal.alarm(0)
if "timeoutexception" in repr(e).lower():
all_results.append(-3)
return all_results, {
"error": repr(e),
"error_code": -3,
"error_message": "Time Limit Exceeded",
"inputs": truncatefn(gt_inp),
"expected": truncatefn(gt_out),
}
else:
all_results.append(-4)
return all_results, {
"error": repr(e),
"error_code": -4,
"error_message": "Runtime Error",
"inputs": truncatefn(gt_inp),
"expected": truncatefn(gt_out),
}
finally:
signal.alarm(0)
faulthandler.disable()
return all_results, {"execution time": total_execution}
def grade_stdio(
code: str,
all_inputs: list,
all_outputs: list,
timeout: int,
):
## runtime doesn't interact well with __name__ == '__main__'
code = clean_if_name(code)
## we wrap the given code inside another function
code = make_function(code)
compiled_sol = compile_code(code, timeout)
if compiled_sol is None:
return
method = get_function(compiled_sol, "wrapped_function")
if method is None:
return
all_results = []
total_execution_time = 0
for idx, (gt_inp, gt_out) in enumerate(zip(all_inputs, all_outputs)):
signal.alarm(timeout)
faulthandler.enable()
signal.alarm(timeout)
with Capturing() as captured_output:
try:
start = time.time()
call_method(method, gt_inp)
total_execution_time += time.time() - start
# reset the alarm
signal.alarm(0)
except Exception as e:
signal.alarm(0)
if "timeoutexception" in repr(e).lower():
all_results.append(-3)
return all_results, {
"error": repr(e),
"error_code": -3,
"error_message": "Time Limit Exceeded",
"inputs": truncatefn(gt_inp),
"expected": truncatefn(gt_out),
}
else:
all_results.append(-4)
return all_results, {
"error": repr(e),
"error_code": -4,
"error_message": "Runtime Error",
"inputs": truncatefn(gt_inp),
"expected": truncatefn(gt_out),
}
finally:
signal.alarm(0)
faulthandler.disable()
prediction = captured_output[0]
stripped_prediction_lines = get_stripped_lines(prediction)
stripped_gt_out_lines = get_stripped_lines(gt_out)
## WA happens in multiple circumstances
## so cache the return to make it clean!
WA_send_args = {
"output": truncatefn(prediction),
"inputs": truncatefn(gt_inp),
"expected": truncatefn(gt_out),
"error_code": -2,
}
if len(stripped_prediction_lines) != len(stripped_gt_out_lines):
all_results.append(-2)
WA_send_args["error_message"] = "Wrong answer: mismatched output length"
return all_results, WA_send_args
for output_line_idx, (
stripped_prediction_line,
stripped_gt_out_line,
) in enumerate(zip(stripped_prediction_lines, stripped_gt_out_lines)):
WA_send_args["error_message"] = (
f"Wrong answer at {output_line_idx=}: {truncatefn(stripped_prediction_line)} != {truncatefn(stripped_gt_out_line)}"
)
## CASE 1: exact match
if stripped_prediction_line == stripped_gt_out_line:
continue
## CASE 2: element-wise comparision
## if there are floating elements
## use `decimal` library for good floating point comparision
## otherwise gotcha: np.isclose(50000000000000000, 50000000000000001) = True
## note that we should always be able to convert to decimals
success, decimal_prediction_line = convert_line_to_decimals(
stripped_prediction_line
)
if not success:
all_results.append(-2)
return all_results, WA_send_args
success, decimal_gtout_line = convert_line_to_decimals(stripped_gt_out_line)
if not success:
all_results.append(-2)
return all_results, WA_send_args
if decimal_prediction_line == decimal_gtout_line:
continue
all_results.append(-2)
return all_results, WA_send_args
all_results.append(True)
return all_results, {"execution time": total_execution_time}
def run_test(sample, test=None, debug=False, timeout=6):
"""
if test(generated_code) is not None it'll try to run the code.
otherwise it'll just return an input and output pair.
"""
signal.signal(signal.SIGALRM, timeout_handler)
# Disable functionalities that can make destructive changes to the test.
# max memory is set to 4GB
reliability_guard()
if debug:
print(f"start = {datetime.now().time()}")
try:
in_outs = json.loads(sample["input_output"])
except ValueError as e:
raise e
in_outs = None
if in_outs:
if in_outs.get("fn_name") is None:
which_type = CODE_TYPE.standard_input # Standard input
method_name = None
else:
which_type = CODE_TYPE.call_based # Call-based
method_name = in_outs["fn_name"]
if debug:
print(f"loaded input_output = {datetime.now().time()}")
if test is None:
assert False, "should not happen: test code is none"
return in_outs, {"error": "no test code provided"}
elif test is not None:
results = []
sol = import_string
if debug:
print(f"loading test code = {datetime.now().time()}")
if which_type == CODE_TYPE.call_based:
signal.alarm(timeout)
try:
results, metadata = grade_call_based(
code=test,
all_inputs=in_outs["inputs"],
all_outputs=in_outs["outputs"],
fn_name=method_name,
timeout=timeout,
)
return results, metadata
except Exception as e:
return [-4], {
"error_code": -4,
"error_message": f"Error during testing: {e}",
}
finally:
signal.alarm(0)
elif which_type == CODE_TYPE.standard_input:
# sol
# if code has if __name__ == "__main__": then remove it
signal.alarm(timeout)
try:
results, metadata = grade_stdio(
code=test,
all_inputs=in_outs["inputs"],
all_outputs=in_outs["outputs"],
timeout=timeout,
)
return results, metadata
except Exception as e:
return [-4], {
"error_code": -4,
"error_message": f"Error during testing: {e}",
}
finally:
signal.alarm(0)
def reliability_guard(maximum_memory_bytes=None):
"""
This disables various destructive functions and prevents the generated code
from interfering with the test (e.g. fork bomb, killing other processes,
removing filesystem files, etc.)
WARNING
This function is NOT a security sandbox. Untrusted code, including, model-
generated code, should not be blindly executed outside of one. See the
Codex paper for more information about OpenAI's code sandbox, and proceed
with caution.
"""
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(
resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes)
)
resource.setrlimit(
resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes)
)
if not platform.uname().system == "Darwin":
resource.setrlimit(
resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes)
)
faulthandler.disable()
import builtins
# builtins.exit = None
builtins.quit = None
import os
os.environ["OMP_NUM_THREADS"] = "1"
os.kill = None
os.system = None
os.putenv = None
os.remove = None
os.removedirs = None
os.rmdir = None
os.fchdir = None
os.setuid = None
os.fork = None
os.forkpty = None
os.killpg = None
os.rename = None
os.renames = None
os.truncate = None
os.replace = None
os.unlink = None
os.fchmod = None
os.fchown = None
os.chmod = None
os.chown = None
os.chroot = None
os.fchdir = None
os.lchflags = None
os.lchmod = None
os.lchown = None
os.getcwd = None
os.chdir = None
import shutil
shutil.rmtree = None
shutil.move = None
shutil.chown = None
import subprocess
subprocess.Popen = None
__builtins__["help"] = None
import sys
sys.modules["ipdb"] = None
sys.modules["joblib"] = None
sys.modules["resource"] = None
sys.modules["psutil"] = None
sys.modules["tkinter"] = None
+272 -1
View File
@@ -11,7 +11,8 @@
"highlight.js": "^11.11.1",
"katex": "^0.16.27",
"marked": "^17.0.1",
"mode-watcher": "^1.1.0"
"mode-watcher": "^1.1.0",
"pdfjs-dist": "^5.6.205"
},
"devDependencies": {
"@sveltejs/adapter-static": "^3.0.10",
@@ -518,6 +519,256 @@
"@jridgewell/sourcemap-codec": "^1.4.14"
}
},
"node_modules/@napi-rs/canvas": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas/-/canvas-0.1.97.tgz",
"integrity": "sha512-8cFniXvrIEnVwuNSRCW9wirRZbHvrD3JVujdS2P5n5xiJZNZMOZcfOvJ1pb66c7jXMKHHglJEDVJGbm8XWFcXQ==",
"license": "MIT",
"optional": true,
"workspaces": [
"e2e/*"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
},
"optionalDependencies": {
"@napi-rs/canvas-android-arm64": "0.1.97",
"@napi-rs/canvas-darwin-arm64": "0.1.97",
"@napi-rs/canvas-darwin-x64": "0.1.97",
"@napi-rs/canvas-linux-arm-gnueabihf": "0.1.97",
"@napi-rs/canvas-linux-arm64-gnu": "0.1.97",
"@napi-rs/canvas-linux-arm64-musl": "0.1.97",
"@napi-rs/canvas-linux-riscv64-gnu": "0.1.97",
"@napi-rs/canvas-linux-x64-gnu": "0.1.97",
"@napi-rs/canvas-linux-x64-musl": "0.1.97",
"@napi-rs/canvas-win32-arm64-msvc": "0.1.97",
"@napi-rs/canvas-win32-x64-msvc": "0.1.97"
}
},
"node_modules/@napi-rs/canvas-android-arm64": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-android-arm64/-/canvas-android-arm64-0.1.97.tgz",
"integrity": "sha512-V1c/WVw+NzH8vk7ZK/O8/nyBSCQimU8sfMsB/9qeSvdkGKNU7+mxy/bIF0gTgeBFmHpj30S4E9WHMSrxXGQuVQ==",
"cpu": [
"arm64"
],
"license": "MIT",
"optional": true,
"os": [
"android"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@napi-rs/canvas-darwin-arm64": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-darwin-arm64/-/canvas-darwin-arm64-0.1.97.tgz",
"integrity": "sha512-ok+SCEF4YejcxuJ9Rm+WWunHHpf2HmiPxfz6z1a/NFQECGXtsY7A4B8XocK1LmT1D7P174MzwPF9Wy3AUAwEPw==",
"cpu": [
"arm64"
],
"license": "MIT",
"optional": true,
"os": [
"darwin"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@napi-rs/canvas-darwin-x64": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-darwin-x64/-/canvas-darwin-x64-0.1.97.tgz",
"integrity": "sha512-PUP6e6/UGlclUvAQNnuXCcnkpdUou6VYZfQOQxExLp86epOylmiwLkqXIvpFmjoTEDmPmXrI+coL/9EFU1gKPA==",
"cpu": [
"x64"
],
"license": "MIT",
"optional": true,
"os": [
"darwin"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@napi-rs/canvas-linux-arm-gnueabihf": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-linux-arm-gnueabihf/-/canvas-linux-arm-gnueabihf-0.1.97.tgz",
"integrity": "sha512-XyXH2L/cic8eTNtbrXCcvqHtMX/nEOxN18+7rMrAM2XtLYC/EB5s0wnO1FsLMWmK+04ZSLN9FBGipo7kpIkcOw==",
"cpu": [
"arm"
],
"license": "MIT",
"optional": true,
"os": [
"linux"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@napi-rs/canvas-linux-arm64-gnu": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-linux-arm64-gnu/-/canvas-linux-arm64-gnu-0.1.97.tgz",
"integrity": "sha512-Kuq/M3djq0K8ktgz6nPlK7Ne5d4uWeDxPpyKWOjWDK2RIOhHVtLtyLiJw2fuldw7Vn4mhw05EZXCEr4Q76rs9w==",
"cpu": [
"arm64"
],
"license": "MIT",
"optional": true,
"os": [
"linux"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@napi-rs/canvas-linux-arm64-musl": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-linux-arm64-musl/-/canvas-linux-arm64-musl-0.1.97.tgz",
"integrity": "sha512-kKmSkQVnWeqg7qdsiXvYxKhAFuHz3tkBjW/zyQv5YKUPhotpaVhpBGv5LqCngzyuRV85SXoe+OFj+Tv0a0QXkQ==",
"cpu": [
"arm64"
],
"license": "MIT",
"optional": true,
"os": [
"linux"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@napi-rs/canvas-linux-riscv64-gnu": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-linux-riscv64-gnu/-/canvas-linux-riscv64-gnu-0.1.97.tgz",
"integrity": "sha512-Jc7I3A51jnEOIAXeLsN/M/+Z28LUeakcsXs07FLq9prXc0eYOtVwsDEv913Gr+06IRo34gJJVgT0TXvmz+N2VA==",
"cpu": [
"riscv64"
],
"license": "MIT",
"optional": true,
"os": [
"linux"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@napi-rs/canvas-linux-x64-gnu": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-linux-x64-gnu/-/canvas-linux-x64-gnu-0.1.97.tgz",
"integrity": "sha512-iDUBe7AilfuBSRbSa8/IGX38Mf+iCSBqoVKLSQ5XaY2JLOaqz1TVyPFEyIck7wT6mRQhQt5sN6ogfjIDfi74tg==",
"cpu": [
"x64"
],
"license": "MIT",
"optional": true,
"os": [
"linux"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@napi-rs/canvas-linux-x64-musl": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-linux-x64-musl/-/canvas-linux-x64-musl-0.1.97.tgz",
"integrity": "sha512-AKLFd/v0Z5fvgqBDqhvqtAdx+fHMJ5t9JcUNKq4FIZ5WH+iegGm8HPdj00NFlCSnm83Fp3Ln8I2f7uq1aIiWaA==",
"cpu": [
"x64"
],
"license": "MIT",
"optional": true,
"os": [
"linux"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@napi-rs/canvas-win32-arm64-msvc": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-win32-arm64-msvc/-/canvas-win32-arm64-msvc-0.1.97.tgz",
"integrity": "sha512-u883Yr6A6fO7Vpsy9YE4FVCIxzzo5sO+7pIUjjoDLjS3vQaNMkVzx5bdIpEL+ob+gU88WDK4VcxYMZ6nmnoX9A==",
"cpu": [
"arm64"
],
"license": "MIT",
"optional": true,
"os": [
"win32"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@napi-rs/canvas-win32-x64-msvc": {
"version": "0.1.97",
"resolved": "https://registry.npmjs.org/@napi-rs/canvas-win32-x64-msvc/-/canvas-win32-x64-msvc-0.1.97.tgz",
"integrity": "sha512-sWtD2EE3fV0IzN+iiQUqr/Q1SwqWhs2O1FKItFlxtdDkikpEj5g7DKQpY3x55H/MAOnL8iomnlk3mcEeGiUMoQ==",
"cpu": [
"x64"
],
"license": "MIT",
"optional": true,
"os": [
"win32"
],
"engines": {
"node": ">= 10"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/Brooooooklyn"
}
},
"node_modules/@polka/url": {
"version": "1.0.0-next.29",
"resolved": "https://registry.npmjs.org/@polka/url/-/url-1.0.0-next.29.tgz",
@@ -2635,6 +2886,26 @@
"node": "^10 || ^12 || ^13.7 || ^14 || >=15.0.1"
}
},
"node_modules/node-readable-to-web-readable-stream": {
"version": "0.4.2",
"resolved": "https://registry.npmjs.org/node-readable-to-web-readable-stream/-/node-readable-to-web-readable-stream-0.4.2.tgz",
"integrity": "sha512-/cMZNI34v//jUTrI+UIo4ieHAB5EZRY/+7OmXZgBxaWBMcW2tGdceIw06RFxWxrKZ5Jp3sI2i5TsRo+CBhtVLQ==",
"license": "MIT",
"optional": true
},
"node_modules/pdfjs-dist": {
"version": "5.6.205",
"resolved": "https://registry.npmjs.org/pdfjs-dist/-/pdfjs-dist-5.6.205.tgz",
"integrity": "sha512-tlUj+2IDa7G1SbvBNN74UHRLJybZDWYom+k6p5KIZl7huBvsA4APi6mKL+zCxd3tLjN5hOOEE9Tv7VdzO88pfg==",
"license": "Apache-2.0",
"engines": {
"node": ">=20.19.0 || >=22.13.0 || >=24"
},
"optionalDependencies": {
"@napi-rs/canvas": "^0.1.96",
"node-readable-to-web-readable-stream": "^0.4.2"
}
},
"node_modules/picocolors": {
"version": "1.1.1",
"resolved": "https://registry.npmjs.org/picocolors/-/picocolors-1.1.1.tgz",
+2 -1
View File
@@ -31,6 +31,7 @@
"highlight.js": "^11.11.1",
"katex": "^0.16.27",
"marked": "^17.0.1",
"mode-watcher": "^1.1.0"
"mode-watcher": "^1.1.0",
"pdfjs-dist": "^5.6.205"
}
}
+65
View File
@@ -202,6 +202,23 @@
filter: drop-shadow(0 0 3px oklch(0.85 0.18 85 / 0.5));
}
/* Onboarding step 2: connection line between devices */
.onboarding-connection-line {
stroke: oklch(0.85 0.18 85 / 0.5);
stroke-width: 1.5px;
stroke-dasharray: 6, 6;
animation: flowAnimation 1s linear infinite;
filter: drop-shadow(0 0 4px oklch(0.85 0.18 85 / 0.4));
}
/* Onboarding step 4: red connection line for disconnect */
.onboarding-connection-line-red {
stroke: rgba(220, 38, 38, 0.7);
stroke-width: 1.5px;
stroke-dasharray: 6, 6;
filter: drop-shadow(0 0 2px rgba(220, 38, 38, 0.3));
}
.graph-link-active {
stroke: oklch(0.85 0.18 85 / 0.8);
stroke-width: 2px;
@@ -320,3 +337,51 @@ input:focus, textarea:focus {
transform: translate(400px, 400px);
}
}
/* Onboarding smooth fade-in */
@keyframes onb-fade-in {
from { opacity: 0; transform: translateY(8px); }
to { opacity: 1; transform: none; }
}
/* Opacity-only fade-in — no transform, safe for containers with fixed-position children */
@keyframes onb-fade-opacity {
from { opacity: 0; }
to { opacity: 1; }
}
/* Respect reduced motion preference */
@media (prefers-reduced-motion: reduce) {
.shooting-star,
.shooting-star::before {
animation: none !important;
opacity: 0 !important;
}
.graph-link {
animation: none;
}
.status-pulse {
animation: none;
}
.cursor-blink {
animation: none;
}
.onboarding-connection-line {
animation: none;
}
.onboarding-connection-line-red {
animation: none;
}
[style*="onb-fade-in"],
[style*="onb-fade-opacity"] {
animation: none !important;
opacity: 1 !important;
}
*,
*::before,
*::after {
transition-duration: 0.01ms !important;
animation-duration: 0.01ms !important;
animation-iteration-count: 1 !important;
}
}
+118 -284
View File
@@ -1,19 +1,15 @@
<script lang="ts">
import {
isLoading,
sendMessage,
generateImage,
editImage,
editingImage,
clearEditingImage,
selectedChatModel,
setSelectedChatModel,
instances,
ttftMs,
tps,
totalTokens,
thinkingEnabled as thinkingEnabledStore,
setConversationThinking,
stopGeneration,
} from "$lib/stores/app.svelte";
import ChatAttachments from "./ChatAttachments.svelte";
import ImageParamsPanel from "./ImageParamsPanel.svelte";
@@ -28,6 +24,19 @@
showModelSelector?: boolean;
modelTasks?: Record<string, string[]>;
modelCapabilities?: Record<string, string[]>;
onSend?: () => void;
onAutoSend: (
content: string,
files?: {
id: string;
name: string;
type: string;
textContent?: string;
preview?: string;
}[],
) => void;
onOpenModelPicker?: () => void;
modelDisplayOverride?: string;
}
let {
@@ -38,6 +47,10 @@
showModelSelector = false,
modelTasks = {},
modelCapabilities = {},
onSend,
onAutoSend,
onOpenModelPicker,
modelDisplayOverride,
}: Props = $props();
let message = $state("");
@@ -48,27 +61,12 @@
const thinkingEnabled = $derived(thinkingEnabledStore());
let loading = $derived(isLoading());
const currentModel = $derived(selectedChatModel());
const instanceData = $derived(instances());
const currentTtft = $derived(ttftMs());
const currentTps = $derived(tps());
const currentTokens = $derived(totalTokens());
const currentEditingImage = $derived(editingImage());
const isEditMode = $derived(currentEditingImage !== null);
// Custom dropdown state
let isModelDropdownOpen = $state(false);
let dropdownButtonRef: HTMLButtonElement | undefined = $state();
let dropdownPosition = $derived(() => {
if (!dropdownButtonRef || !isModelDropdownOpen)
return { top: 0, left: 0, width: 0 };
const rect = dropdownButtonRef.getBoundingClientRect();
return {
top: rect.top,
left: rect.left,
width: rect.width,
};
});
// Accept all supported file types
const acceptString = getAcceptString(["image", "text", "pdf"]);
@@ -103,7 +101,7 @@
const modelSupportsThinking = $derived(() => {
if (!currentModel) return false;
const caps = modelCapabilities[currentModel] || [];
return caps.includes("thinking") && caps.includes("text");
return caps.includes("thinking_toggle") && caps.includes("text");
});
const isEditOnlyWithoutImage = $derived(
@@ -121,73 +119,14 @@
uploadedFiles.length > 0),
);
// Extract available models from running instances
const availableModels = $derived(() => {
const models: Array<{ id: string; label: string; isImageModel: boolean }> =
[];
for (const [, instance] of Object.entries(instanceData)) {
const modelId = getInstanceModelId(instance);
if (
modelId &&
modelId !== "Unknown" &&
!models.some((m) => m.id === modelId)
) {
models.push({
id: modelId,
label: modelId.split("/").pop() || modelId,
isImageModel: modelSupportsImageGeneration(modelId),
});
}
}
return models;
});
// Track previous model IDs to detect newly added models (plain variable to avoid reactive loop)
let previousModelIds: Set<string> = new Set();
// Auto-select the first available model if none is selected, if current selection is stale, or if a new model is added
$effect(() => {
const models = availableModels();
const currentModelIds = new Set(models.map((m) => m.id));
if (models.length > 0) {
// Find newly added models (in current but not in previous)
const newModels = models.filter((m) => !previousModelIds.has(m.id));
// If no model selected, select the first available
if (!currentModel) {
setSelectedChatModel(models[0].id);
}
// If current model is stale (no longer has a running instance), reset to first available
else if (!models.some((m) => m.id === currentModel)) {
setSelectedChatModel(models[0].id);
}
// If a new model was just added, select it
else if (newModels.length > 0 && previousModelIds.size > 0) {
setSelectedChatModel(newModels[0].id);
}
} else {
// No instances running - clear the selected model
if (currentModel) {
setSelectedChatModel("");
}
}
// Update previous model IDs for next comparison
previousModelIds = currentModelIds;
});
function getInstanceModelId(instanceWrapped: unknown): string {
if (!instanceWrapped || typeof instanceWrapped !== "object") return "";
const keys = Object.keys(instanceWrapped as Record<string, unknown>);
if (keys.length === 1) {
const instance = (instanceWrapped as Record<string, unknown>)[
keys[0]
] as { shardAssignments?: { modelId?: string } };
return instance?.shardAssignments?.modelId || "";
}
return "";
}
// Short label for the currently selected model
const currentModelLabel = $derived(
currentModel
? currentModel.split("/").pop() || currentModel
: modelDisplayOverride
? modelDisplayOverride.split("/").pop() || modelDisplayOverride
: "",
);
async function handleFiles(files: File[]) {
if (files.length === 0) return;
@@ -274,38 +213,10 @@
uploadedFiles = [];
resetTextareaHeight();
// Use image editing if in edit mode
if (isEditMode && currentEditingImage && content) {
editImage(content, currentEditingImage.imageDataUrl);
}
// If user attached an image with an ImageToImage model, use edit endpoint
else if (
currentModel &&
modelSupportsImageEditing(currentModel) &&
files.length > 0 &&
content
) {
// Use the first attached image for editing
const imageFile = files[0];
if (imageFile.preview) {
editImage(content, imageFile.preview);
}
} else if (
currentModel &&
modelSupportsTextToImage(currentModel) &&
content
) {
// Use image generation for text-to-image models
generateImage(content);
} else {
sendMessage(
content,
files,
modelSupportsThinking() ? thinkingEnabled : null,
);
}
// Refocus the textarea after sending
// Parent controls all send logic (including image routing,
// launching non-running models before sending, etc.)
onAutoSend(content, files);
onSend?.();
setTimeout(() => textareaRef?.focus(), 10);
}
@@ -415,7 +326,7 @@
{/if}
<!-- Model selector (when enabled) -->
{#if showModelSelector && availableModels().length > 0}
{#if showModelSelector}
<div
class="flex items-center justify-between gap-2 px-3 py-2 border-b border-exo-medium-gray/30"
>
@@ -424,33 +335,22 @@
class="text-xs text-exo-light-gray uppercase tracking-wider flex-shrink-0"
>MODEL:</span
>
<!-- Custom dropdown -->
<!-- Model button — opens the full model picker -->
<div class="relative flex-1 max-w-xs">
<button
bind:this={dropdownButtonRef}
type="button"
onclick={() => (isModelDropdownOpen = !isModelDropdownOpen)}
class="w-full bg-exo-medium-gray/50 border border-exo-yellow/30 rounded pl-3 pr-8 py-1.5 text-xs font-mono text-left tracking-wide cursor-pointer transition-all duration-200 hover:border-exo-yellow/50 focus:outline-none focus:border-exo-yellow/70 {isModelDropdownOpen
? 'border-exo-yellow/70'
: ''}"
onclick={() => onOpenModelPicker?.()}
class="w-full bg-exo-medium-gray/50 border border-exo-yellow/30 rounded pl-3 pr-8 py-1.5 text-xs font-mono text-left tracking-wide cursor-pointer transition-all duration-200 hover:border-exo-yellow/50 focus:outline-none focus:border-exo-yellow/70"
>
{#if availableModels().find((m) => m.id === currentModel)}
<span class="text-exo-yellow truncate"
>{availableModels().find((m) => m.id === currentModel)
?.label}</span
>
{:else if availableModels().length > 0}
<span class="text-exo-yellow truncate"
>{availableModels()[0].label}</span
{#if currentModelLabel}
<span class="text-exo-yellow truncate">{currentModelLabel}</span
>
{:else}
<span class="text-exo-light-gray/50">— SELECT MODEL —</span>
{/if}
</button>
<div
class="absolute right-2 top-1/2 -translate-y-1/2 pointer-events-none transition-transform duration-200 {isModelDropdownOpen
? 'rotate-180'
: ''}"
class="absolute right-2 top-1/2 -translate-y-1/2 pointer-events-none"
>
<svg
class="w-3 h-3 text-exo-yellow/60"
@@ -467,78 +367,6 @@
</svg>
</div>
</div>
{#if isModelDropdownOpen}
<!-- Backdrop to close dropdown -->
<button
type="button"
class="fixed inset-0 z-[9998] cursor-default"
onclick={() => (isModelDropdownOpen = false)}
aria-label="Close dropdown"
></button>
<!-- Dropdown Panel - fixed positioning to escape overflow:hidden -->
<div
class="fixed bg-exo-dark-gray border border-exo-yellow/30 rounded shadow-lg shadow-black/50 z-[9999] max-h-48 overflow-y-auto"
style="bottom: calc(100vh - {dropdownPosition()
.top}px + 4px); left: {dropdownPosition()
.left}px; width: {dropdownPosition().width}px;"
>
<div class="py-1">
{#each availableModels() as model}
<button
type="button"
onclick={() => {
setSelectedChatModel(model.id);
isModelDropdownOpen = false;
}}
class="w-full px-3 py-2 text-left text-xs font-mono tracking-wide transition-colors duration-100 flex items-center gap-2 {currentModel ===
model.id
? 'bg-transparent text-exo-yellow'
: 'text-exo-light-gray hover:text-exo-yellow'}"
>
{#if currentModel === model.id}
<svg
class="w-3 h-3 flex-shrink-0"
fill="currentColor"
viewBox="0 0 20 20"
>
<path
fill-rule="evenodd"
d="M16.707 5.293a1 1 0 010 1.414l-8 8a1 1 0 01-1.414 0l-4-4a1 1 0 011.414-1.414L8 12.586l7.293-7.293a1 1 0 011.414 0z"
clip-rule="evenodd"
/>
</svg>
{:else}
<span class="w-3"></span>
{/if}
{#if model.isImageModel}
<svg
class="w-3.5 h-3.5 flex-shrink-0 text-exo-yellow"
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
stroke-width="2"
aria-label="Image generation model"
>
<rect
x="3"
y="3"
width="18"
height="18"
rx="2"
ry="2"
/>
<circle cx="8.5" cy="8.5" r="1.5" />
<polyline points="21 15 16 10 5 21" />
</svg>
{/if}
<span class="truncate flex-1">{model.label}</span>
</button>
{/each}
</div>
</div>
{/if}
</div>
<!-- Thinking toggle -->
{#if modelSupportsThinking()}
@@ -653,86 +481,92 @@
style="min-height: 28px; max-height: 150px;"
></textarea>
<button
type="submit"
disabled={!canSend || loading || isEditOnlyWithoutImage}
class="px-2.5 sm:px-4 py-1.5 sm:py-2 rounded text-xs sm:text-xs tracking-[0.1em] sm:tracking-[0.15em] uppercase font-medium transition-all duration-200 whitespace-nowrap
{!canSend || loading || isEditOnlyWithoutImage
? 'bg-exo-medium-gray/50 text-exo-light-gray cursor-not-allowed'
: 'bg-exo-yellow text-exo-black hover:bg-exo-yellow-darker hover:shadow-[0_0_20px_rgba(255,215,0,0.3)]'}"
aria-label={shouldShowEditMode
? "Edit image"
: isImageModel()
? "Generate image"
: "Send message"}
>
{#if loading}
{#if loading}
<button
type="button"
onclick={() => stopGeneration()}
class="px-2.5 sm:px-4 py-1.5 sm:py-2 rounded text-xs sm:text-xs tracking-[0.1em] sm:tracking-[0.15em] font-medium transition-all duration-200 whitespace-nowrap bg-exo-medium-gray/70 text-exo-light-gray hover:bg-exo-medium-gray hover:text-white"
aria-label="Stop generation"
>
<span class="inline-flex items-center gap-1 sm:gap-2">
<span
class="w-2.5 h-2.5 sm:w-3 sm:h-3 border-2 border-current border-t-transparent rounded-full animate-spin"
></span>
<span class="hidden sm:inline"
>{shouldShowEditMode
? "EDITING"
: isImageModel()
? "GENERATING"
: "PROCESSING"}</span
>
<span class="sm:hidden">...</span>
</span>
{:else if shouldShowEditMode}
<span class="inline-flex items-center gap-1.5">
<svg
class="w-3.5 h-3.5"
fill="none"
class="w-3 h-3 sm:w-3.5 sm:h-3.5"
fill="currentColor"
viewBox="0 0 24 24"
stroke="currentColor"
stroke-width="2"
>
<path
stroke-linecap="round"
stroke-linejoin="round"
d="M11 5H6a2 2 0 00-2 2v11a2 2 0 002 2h11a2 2 0 002-2v-5m-1.414-9.414a2 2 0 112.828 2.828L11.828 15H9v-2.828l8.586-8.586z"
/>
<rect x="6" y="6" width="12" height="12" rx="1" />
</svg>
<span>EDIT</span>
<span class="hidden sm:inline">Cancel</span>
</span>
{:else if isEditOnlyWithoutImage}
<span class="inline-flex items-center gap-1.5">
<svg
class="w-3.5 h-3.5"
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
stroke-width="2"
>
<path
stroke-linecap="round"
stroke-linejoin="round"
d="M11 5H6a2 2 0 00-2 2v11a2 2 0 002 2h11a2 2 0 002-2v-5m-1.414-9.414a2 2 0 112.828 2.828L11.828 15H9v-2.828l8.586-8.586z"
/>
</svg>
<span>EDIT</span>
</span>
{:else if isImageModel()}
<span class="inline-flex items-center gap-1.5">
<svg
class="w-3.5 h-3.5"
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
stroke-width="2"
>
<rect x="3" y="3" width="18" height="18" rx="2" ry="2" />
<circle cx="8.5" cy="8.5" r="1.5" />
<polyline points="21 15 16 10 5 21" />
</svg>
<span>GENERATE</span>
</span>
{:else}
SEND
{/if}
</button>
</button>
{:else}
<button
type="submit"
disabled={!canSend || isEditOnlyWithoutImage}
class="px-2.5 sm:px-4 py-1.5 sm:py-2 rounded text-xs sm:text-xs tracking-[0.1em] sm:tracking-[0.15em] uppercase font-medium transition-all duration-200 whitespace-nowrap
{!canSend || isEditOnlyWithoutImage
? 'bg-exo-medium-gray/50 text-exo-light-gray cursor-not-allowed'
: 'bg-exo-yellow text-exo-black hover:bg-exo-yellow-darker hover:shadow-[0_0_20px_rgba(255,215,0,0.3)]'}"
aria-label={shouldShowEditMode
? "Edit image"
: isImageModel()
? "Generate image"
: "Send message"}
>
{#if shouldShowEditMode}
<span class="inline-flex items-center gap-1.5">
<svg
class="w-3.5 h-3.5"
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
stroke-width="2"
>
<path
stroke-linecap="round"
stroke-linejoin="round"
d="M11 5H6a2 2 0 00-2 2v11a2 2 0 002 2h11a2 2 0 002-2v-5m-1.414-9.414a2 2 0 112.828 2.828L11.828 15H9v-2.828l8.586-8.586z"
/>
</svg>
<span>EDIT</span>
</span>
{:else if isEditOnlyWithoutImage}
<span class="inline-flex items-center gap-1.5">
<svg
class="w-3.5 h-3.5"
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
stroke-width="2"
>
<path
stroke-linecap="round"
stroke-linejoin="round"
d="M11 5H6a2 2 0 00-2 2v11a2 2 0 002 2h11a2 2 0 002-2v-5m-1.414-9.414a2 2 0 112.828 2.828L11.828 15H9v-2.828l8.586-8.586z"
/>
</svg>
<span>EDIT</span>
</span>
{:else if isImageModel()}
<span class="inline-flex items-center gap-1.5">
<svg
class="w-3.5 h-3.5"
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
stroke-width="2"
>
<rect x="3" y="3" width="18" height="18" rx="2" ry="2" />
<circle cx="8.5" cy="8.5" r="1.5" />
<polyline points="21 15 16 10 5 21" />
</svg>
<span>GENERATE</span>
</span>
{:else}
SEND
{/if}
</button>
{/if}
</div>
<!-- Bottom accent line -->
@@ -3,16 +3,17 @@
messages,
currentResponse,
isLoading,
prefillProgress,
deleteMessage,
editAndRegenerate,
regenerateLastResponse,
regenerateFromToken,
setEditingImage,
} from "$lib/stores/app.svelte";
import type { Message } from "$lib/stores/app.svelte";
import type { MessageAttachment } from "$lib/stores/app.svelte";
import MarkdownContent from "./MarkdownContent.svelte";
import TokenHeatmap from "./TokenHeatmap.svelte";
import PrefillProgressBar from "./PrefillProgressBar.svelte";
import ImageLightbox from "./ImageLightbox.svelte";
interface Props {
@@ -25,6 +26,7 @@
const messageList = $derived(messages());
const response = $derived(currentResponse());
const loading = $derived(isLoading());
const prefill = $derived(prefillProgress());
// Scroll management - user controls scroll, show button when not at bottom
const SCROLL_THRESHOLD = 100;
@@ -137,6 +139,8 @@
return "🖼";
case "text":
return "📄";
case "pdf":
return "📑";
default:
return "📎";
}
@@ -428,6 +432,9 @@
{:else}
<!-- Assistant message styling -->
<div class="p-3 sm:p-4">
{#if loading && isLastAssistantMessage(message.id) && prefill && !message.content}
<PrefillProgressBar progress={prefill} class="mb-3" />
{/if}
{#if message.thinking && message.thinking.trim().length > 0}
<div
class="mb-3 rounded border border-exo-yellow/20 bg-exo-black/40"
@@ -802,8 +809,8 @@
>
AWAITING INPUT
</p>
<p class="text-sm sm:text-xs text-exo-light-gray tracking-wider mt-1">
ENTER A QUERY TO BEGIN
<p class="text-xs text-white/30 tracking-wider mt-1.5 font-mono">
Type a message below &middot; Shift+Enter for newline
</p>
</div>
{/if}
@@ -818,6 +825,7 @@
onclick={scrollToBottom}
class="sticky bottom-4 left-1/2 -translate-x-1/2 w-10 h-10 rounded-full bg-exo-dark-gray/90 border border-exo-medium-gray/50 flex items-center justify-center text-exo-light-gray hover:text-exo-yellow hover:border-exo-yellow/50 transition-all shadow-lg cursor-pointer z-10"
title="Scroll to bottom"
aria-label="Scroll to bottom of messages"
>
<svg
class="w-5 h-5"
@@ -0,0 +1,401 @@
<script lang="ts" module>
export interface ChatModelInfo {
id: string;
name: string;
base_model: string;
storage_size_megabytes: number;
capabilities: string[];
family: string;
quantization: string;
}
// Auto mode tier list (for when user just starts typing)
export const AUTO_TIERS: string[][] = [
// Tier 1 (frontier)
["DeepSeek V3.1", "GLM-5", "Kimi K2.5", "Qwen3 Coder Next"],
// Tier 2 (excellent)
[
"Kimi K2",
"Qwen3 235B",
"MiniMax M2.5",
"Step 3.5 Flash",
"Qwen3 Next 80B",
],
// Tier 3 (great)
[
"GLM 4.7",
"MiniMax M2.1",
"Qwen3 Coder 480B",
"GLM 4.5 Air",
"Llama 3.3 70B",
],
// Tier 4 (good)
["GPT-OSS 120B", "Qwen3 30B", "Llama 3.1 70B", "GLM 4.7 Flash"],
// Tier 5 (small/fast)
[
"Llama 3.1 8B",
"GPT-OSS 20B",
"Llama 3.2 3B",
"Qwen3 0.6B",
"Llama 3.2 1B",
],
];
/** Return the tier index (0 = best) for a base_model name. */
export function getAutoTierIndex(baseModel: string): number {
for (let i = 0; i < AUTO_TIERS.length; i++) {
if (AUTO_TIERS[i].includes(baseModel)) return i;
}
return AUTO_TIERS.length; // not in any tier → lowest priority
}
/** Auto mode: walk tiers top-down, pick biggest fitting variant from highest tier. */
export function pickAutoModel(
modelList: ChatModelInfo[],
memoryGB: number,
): ChatModelInfo | null {
for (const tier of AUTO_TIERS) {
const candidates: ChatModelInfo[] = [];
for (const baseModel of tier) {
const variants = modelList
.filter(
(m) =>
m.base_model === baseModel &&
(m.storage_size_megabytes || 0) / 1024 <= memoryGB &&
(m.storage_size_megabytes || 0) > 0,
)
.sort(
(a, b) =>
(b.storage_size_megabytes || 0) - (a.storage_size_megabytes || 0),
);
if (variants[0]) candidates.push(variants[0]);
}
if (candidates.length > 0) {
candidates.sort(
(a, b) =>
(b.storage_size_megabytes || 0) - (a.storage_size_megabytes || 0),
);
return candidates[0];
}
}
return null;
}
</script>
<script lang="ts">
interface CategoryRecommendation {
category: "coding" | "writing" | "agentic" | "biggest";
label: string;
model: ChatModelInfo | null;
tooltip: string;
}
interface Props {
models: ChatModelInfo[];
clusterLabel: string;
totalMemoryGB: number;
onSelect: (modelId: string, category: string) => void;
onAddModel: () => void;
class?: string;
}
let {
models,
clusterLabel,
totalMemoryGB,
onSelect,
onAddModel,
class: className = "",
}: Props = $props();
// --- Hardcoded Rankings ---
const CODING_RANKING = [
"Qwen3 Coder Next",
"Qwen3 Coder 480B",
"Qwen3 30B",
"GPT-OSS 20B",
"Llama 3.1 8B",
"Llama 3.2 3B",
"Qwen3 0.6B",
];
const WRITING_RANKING = [
"Kimi K2.5",
"Kimi K2",
"Qwen3 Next 80B",
"Llama 3.3 70B",
"MiniMax M2.5",
"GLM 4.5 Air",
"GLM 4.7 Flash",
"GPT-OSS 20B",
"Llama 3.1 8B",
"Llama 3.2 3B",
"Qwen3 0.6B",
];
const AGENTIC_RANKING = [
"DeepSeek V3.1",
"GLM-5",
"Qwen3 235B",
"Step 3.5 Flash",
"GLM 4.7",
"MiniMax M2.1",
"GPT-OSS 120B",
"Llama 3.3 70B",
"Llama 3.1 70B",
"GLM 4.7 Flash",
"GPT-OSS 20B",
"Qwen3 30B",
"Llama 3.1 8B",
"Llama 3.2 3B",
"Qwen3 0.6B",
];
function getModelSizeGB(m: ChatModelInfo): number {
return (m.storage_size_megabytes || 0) / 1024;
}
function fitsInMemory(m: ChatModelInfo): boolean {
return getModelSizeGB(m) <= totalMemoryGB && getModelSizeGB(m) > 0;
}
/** For a given base_model name, find the biggest quant variant that fits in memory. */
function pickBestVariant(baseModel: string): ChatModelInfo | null {
const variants = models
.filter((m) => m.base_model === baseModel && fitsInMemory(m))
.sort((a, b) => getModelSizeGB(b) - getModelSizeGB(a));
return variants[0] ?? null;
}
/** Walk a ranked list of base_model names, return the first that has a fitting variant. */
function pickFromRanking(ranking: string[]): ChatModelInfo | null {
for (const baseModel of ranking) {
const pick = pickBestVariant(baseModel);
if (pick) return pick;
}
return null;
}
/** Pick the single biggest model that fits. */
function pickBiggest(): ChatModelInfo | null {
const fitting = models
.filter((m) => fitsInMemory(m))
.sort((a, b) => getModelSizeGB(b) - getModelSizeGB(a));
return fitting[0] ?? null;
}
const recommendations = $derived.by((): CategoryRecommendation[] => {
return [
{
category: "coding",
label: "Best for Coding",
model: pickFromRanking(CODING_RANKING),
tooltip:
"Ranked by coding benchmark performance (LiveCodeBench, SWE-bench)",
},
{
category: "writing",
label: "Best for Writing",
model: pickFromRanking(WRITING_RANKING),
tooltip: "Ranked by creative writing quality and instruction following",
},
{
category: "agentic",
label: "Best Agentic",
model: pickFromRanking(AGENTIC_RANKING),
tooltip: "Ranked by reasoning, planning, and tool-use capability",
},
{
category: "biggest",
label: "Biggest",
model: pickBiggest(),
tooltip: "Largest model that fits in your available memory",
},
];
});
function formatSize(mb: number): string {
const gb = mb / 1024;
if (gb >= 100) return `${Math.round(gb)} GB`;
return `${gb.toFixed(1)} GB`;
}
// Category icons (SVG paths)
const categoryIcons: Record<string, string> = {
coding:
"M8 9l3 3-3 3m5 0h3M5 20h14a2 2 0 002-2V6a2 2 0 00-2-2H5a2 2 0 00-2 2v12a2 2 0 002 2z",
writing:
"M11 5H6a2 2 0 00-2 2v11a2 2 0 002 2h11a2 2 0 002-2v-5m-1.414-9.414a2 2 0 112.828 2.828L11.828 15H9v-2.828l8.586-8.586z",
agentic:
"M9.663 17h4.673M12 3v1m6.364 1.636l-.707.707M21 12h-1M4 12H3m3.343-5.657l-.707-.707m2.828 9.9a5 5 0 117.072 0l-.548.547A3.374 3.374 0 0014 18.469V19a2 2 0 11-4 0v-.531c0-.895-.356-1.754-.988-2.386l-.548-.547z",
biggest:
"M19 11H5m14 0a2 2 0 012 2v6a2 2 0 01-2 2H5a2 2 0 01-2-2v-6a2 2 0 012-2m14 0V9a2 2 0 00-2-2M5 11V9a2 2 0 012-2m0 0V5a2 2 0 012-2h6a2 2 0 012 2v2M7 7h10",
};
let hoveredTooltip = $state<string | null>(null);
let tooltipAnchor = $state<{ x: number; y: number } | null>(null);
function showTooltip(category: string, e: MouseEvent | FocusEvent) {
hoveredTooltip = category;
const target = e.currentTarget as HTMLElement;
const rect = target.getBoundingClientRect();
tooltipAnchor = { x: rect.left + rect.width / 2, y: rect.top };
}
function hideTooltip() {
hoveredTooltip = null;
tooltipAnchor = null;
}
</script>
<div class="flex flex-col items-center justify-center gap-6 {className}">
<!-- Header -->
<div class="text-center">
<p class="text-xs text-exo-light-gray uppercase tracking-[0.2em] mb-1">
Recommended for your
</p>
<p class="text-sm text-white font-mono tracking-wide">{clusterLabel}</p>
</div>
<!-- Category Cards Grid -->
<div class="grid grid-cols-2 gap-3 w-full max-w-md">
{#each recommendations as rec}
{#if rec.model}
<button
type="button"
onclick={() => rec.model && onSelect(rec.model.id, rec.category)}
class="group relative flex flex-col items-start gap-2 p-4 rounded-lg border border-exo-medium-gray/50 bg-exo-dark-gray/50 hover:border-exo-yellow/40 hover:bg-exo-dark-gray transition-all duration-200 cursor-pointer text-left"
>
<!-- Category icon + label -->
<div class="flex items-center gap-2 w-full">
<svg
class="w-4 h-4 text-exo-yellow/70 group-hover:text-exo-yellow transition-colors flex-shrink-0"
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
stroke-width="1.5"
>
<path
stroke-linecap="round"
stroke-linejoin="round"
d={categoryIcons[rec.category]}
/>
</svg>
<span
class="text-xs font-mono uppercase tracking-wider text-exo-light-gray group-hover:text-white transition-colors"
>
{rec.label}
</span>
<!-- Info tooltip -->
<div class="ml-auto flex-shrink-0">
<span
role="button"
tabindex="-1"
class="text-exo-light-gray/40 hover:text-exo-light-gray transition-colors cursor-help inline-flex"
onmouseenter={(e: MouseEvent) => showTooltip(rec.category, e)}
onmouseleave={() => hideTooltip()}
onclick={(e: MouseEvent) => {
e.stopPropagation();
if (hoveredTooltip === rec.category) {
hideTooltip();
} else {
showTooltip(rec.category, e);
}
}}
>
<svg
class="w-3.5 h-3.5"
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
stroke-width="2"
>
<circle cx="12" cy="12" r="10" />
<path d="M12 16v-4m0-4h.01" />
</svg>
</span>
</div>
</div>
<!-- Model name + size -->
<div class="w-full">
<p class="text-sm text-white font-mono truncate">
{rec.model.base_model}
</p>
<p class="text-xs text-exo-light-gray/60 font-mono mt-0.5">
{formatSize(rec.model.storage_size_megabytes)}
{#if rec.model.quantization}
<span class="text-exo-light-gray/40"
>&middot; {rec.model.quantization}</span
>
{/if}
</p>
</div>
</button>
{:else}
<!-- No model fits for this category -->
<div
class="flex flex-col items-start gap-2 p-4 rounded-lg border border-exo-medium-gray/30 bg-exo-dark-gray/30 opacity-50"
>
<div class="flex items-center gap-2">
<svg
class="w-4 h-4 text-exo-light-gray/40 flex-shrink-0"
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
stroke-width="1.5"
>
<path
stroke-linecap="round"
stroke-linejoin="round"
d={categoryIcons[rec.category]}
/>
</svg>
<span
class="text-xs font-mono uppercase tracking-wider text-exo-light-gray/50"
>{rec.label}</span
>
</div>
<p class="text-xs text-exo-light-gray/40 font-mono">No model fits</p>
</div>
{/if}
{/each}
</div>
<!-- Add Model Button -->
<button
type="button"
onclick={onAddModel}
class="flex items-center gap-2 px-4 py-2 text-xs font-mono uppercase tracking-wider text-exo-light-gray hover:text-exo-yellow border border-exo-medium-gray/30 hover:border-exo-yellow/30 rounded-lg transition-all duration-200 cursor-pointer"
>
<svg
class="w-3.5 h-3.5"
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
stroke-width="2"
>
<path stroke-linecap="round" stroke-linejoin="round" d="M12 4v16m8-8H4" />
</svg>
Add Model
</button>
<!-- Auto hint -->
<p class="text-xs text-exo-light-gray/40 font-mono tracking-wide text-center">
Or just start typing &mdash; we'll pick the best model automatically
</p>
</div>
<!-- Fixed-position tooltip (escapes overflow-hidden ancestors) -->
{#if hoveredTooltip && tooltipAnchor}
{@const rec = recommendations.find((r) => r.category === hoveredTooltip)}
{#if rec}
<div
class="fixed z-[9999] px-3 py-2 bg-exo-black border border-exo-medium-gray/50 rounded text-xs text-exo-light-gray whitespace-nowrap shadow-lg pointer-events-none"
style="left: {tooltipAnchor.x}px; top: {tooltipAnchor.y -
8}px; transform: translate(-50%, -100%);"
>
{rec.tooltip}
</div>
{/if}
{/if}
+67 -33
View File
@@ -2,7 +2,6 @@
import {
conversations,
activeConversationId,
createConversation,
loadConversation,
deleteConversation,
deleteAllConversations,
@@ -17,9 +16,21 @@
interface Props {
class?: string;
onNewChat?: () => void;
onSelectConversation?: () => void;
isMobileDrawer?: boolean;
isOpen?: boolean;
onClose?: () => void;
}
let { class: className = "" }: Props = $props();
let {
class: className = "",
onNewChat,
onSelectConversation,
isMobileDrawer = false,
isOpen = false,
onClose,
}: Props = $props();
const conversationList = $derived(conversations());
const activeId = $derived(activeConversationId());
@@ -42,11 +53,16 @@
);
function handleNewChat() {
createConversation();
onNewChat?.();
}
function handleSelectConversation(id: string) {
onSelectConversation?.();
loadConversation(id);
// Close mobile drawer when selecting a conversation
if (isMobileDrawer && isOpen) {
onClose?.();
}
}
function handleStartEdit(id: string, name: string, event: MouseEvent) {
@@ -185,11 +201,7 @@
let instanceType: string | null = null;
if (instanceTag === "MlxRingInstance") instanceType = "MLX Ring";
else if (
instanceTag === "MlxIbvInstance" ||
instanceTag === "MlxJacclInstance"
)
instanceType = "MLX RDMA";
else if (instanceTag === "MlxJacclInstance") instanceType = "MLX RDMA";
let sharding: string | null = null;
const inst = instance as {
@@ -250,9 +262,7 @@
}
</script>
<aside
class="flex flex-col h-full bg-exo-dark-gray border-r border-exo-yellow/10 {className}"
>
{#snippet sidebarContent()}
<!-- Header -->
<div class="p-4">
<button
@@ -307,7 +317,7 @@
<div class="py-2">
<div class="px-4 py-2">
<span
class="text-sm text-white/70 font-mono tracking-wider uppercase"
class="text-xs text-exo-light-gray font-mono tracking-wider uppercase"
>
{searchQuery ? "SEARCH RESULTS" : "CONVERSATIONS"}
</span>
@@ -376,39 +386,37 @@
onkeydown={(e) =>
e.key === "Enter" &&
handleSelectConversation(conversation.id)}
class="group w-full flex items-center justify-between p-2 rounded mb-1 transition-all text-left cursor-pointer
class="group w-full flex items-center justify-between p-2.5 rounded-lg mb-1 transition-all text-left cursor-pointer
{activeId === conversation.id
? 'bg-transparent border border-exo-yellow/30'
: 'hover:border-exo-yellow/20 border border-transparent'}"
? 'bg-exo-yellow/5 border border-exo-yellow/30'
: 'hover:bg-white/[0.03] hover:border-white/10 border border-transparent'}"
>
<div class="flex-1 min-w-0 pr-2">
<div
class="text-sm truncate {activeId === conversation.id
class="text-sm font-medium truncate {activeId ===
conversation.id
? 'text-exo-yellow'
: 'text-white/90'}"
: 'text-white'}"
>
{conversation.name}
</div>
<div class="text-sm text-white/50 mt-0.5">
<div class="text-xs text-white/60 mt-0.5">
{formatDate(conversation.updatedAt)}
</div>
<div class="text-sm text-white/70 truncate">
<div class="text-xs text-exo-light-gray truncate">
{info.modelLabel}
</div>
<div class="text-xs text-white/60 font-mono">
Strategy: <span class="text-white/80"
>{info.strategyLabel}</span
>
</div>
{#if stats}
<div class="text-xs text-white/60 font-mono mt-1">
{#if stats.ttftMs}<span class="text-white/40">TTFT</span>
{stats.ttftMs.toFixed(
0,
)}ms{/if}{#if stats.ttftMs && stats.tps}<span
class="text-white/30 mx-1.5"></span
>{/if}{#if stats.tps}{stats.tps.toFixed(1)}
<span class="text-white/40">tok/s</span>{/if}
<div class="text-xs text-white/70 font-mono mt-1">
{#if stats.ttftMs}<span class="text-white/50">TTFT</span>
<span class="text-exo-yellow/80"
>{stats.ttftMs.toFixed(0)}ms</span
>{/if}{#if stats.ttftMs && stats.tps}<span
class="text-white/30 mx-1.5">·</span
>{/if}{#if stats.tps}<span class="text-exo-yellow/80"
>{stats.tps.toFixed(1)}</span
>
<span class="text-white/50">tok/s</span>{/if}
</div>
{/if}
</div>
@@ -591,4 +599,30 @@
</button>
</div>
</div>
</aside>
{/snippet}
{#if isMobileDrawer}
<!-- Mobile drawer with overlay -->
{#if isOpen}
<!-- Overlay backdrop -->
<button
type="button"
class="fixed inset-0 bg-black/60 backdrop-blur-sm z-40 md:hidden"
onclick={() => onClose?.()}
aria-label="Close sidebar"
></button>
<!-- Drawer panel -->
<aside
class="fixed left-0 top-0 bottom-0 w-72 bg-exo-dark-gray border-r border-exo-yellow/10 z-50 flex flex-col md:hidden"
>
{@render sidebarContent()}
</aside>
{/if}
{:else}
<!-- Desktop sidebar -->
<aside
class="flex flex-col h-full bg-exo-dark-gray border-r border-exo-yellow/10 {className}"
>
{@render sidebarContent()}
</aside>
{/if}
@@ -0,0 +1,20 @@
<script lang="ts">
import { isConnected } from "$lib/stores/app.svelte";
import { slide } from "svelte/transition";
const connected = $derived(isConnected());
</script>
{#if !connected}
<div
transition:slide={{ duration: 200 }}
class="relative z-50 flex items-center justify-center gap-2 px-4 py-2 bg-red-950/80 border-b border-red-500/30"
role="alert"
aria-live="assertive"
>
<div class="w-2 h-2 bg-red-500 rounded-full animate-pulse"></div>
<span class="text-xs font-mono text-red-300 tracking-wider uppercase">
Connection lost &mdash; Reconnecting to backend&hellip;
</span>
</div>
{/if}
@@ -0,0 +1,277 @@
<script lang="ts">
/**
* DeviceIcon — renders a device icon as an SVG <g> element.
* Uses the exact same proportional math as TopologyGraph.svelte
* so that devices look identical in both the topology view and
* the onboarding animation.
*
* Must be placed inside an <svg> element.
*/
interface Props {
/** "macbook pro" | "mac studio" | "mac mini" etc. */
deviceType: string;
/** Center X coordinate in SVG space */
cx: number;
/** Center Y coordinate in SVG space */
cy: number;
/** Base sizing factor (equivalent to TopologyGraph's nodeRadius) */
size?: number;
/** RAM usage 0100 */
ramPercent?: number;
/** Unique id suffix for clip-path ids */
uid?: string;
}
let {
deviceType,
cx,
cy,
size = 60,
ramPercent = 60,
uid = "dev",
}: Props = $props();
// Apple logo path — same constant used by TopologyGraph
const APPLE_LOGO_PATH =
"M788.1 340.9c-5.8 4.5-108.2 62.2-108.2 190.5 0 148.4 130.3 200.9 134.2 202.2-.6 3.2-20.7 71.9-68.7 141.9-42.8 61.6-87.5 123.1-155.5 123.1s-85.5-39.5-164-39.5c-76.5 0-103.7 40.8-165.9 40.8s-105.6-57-155.5-127C46.7 790.7 0 663 0 541.8c0-194.4 126.4-297.5 250.8-297.5 66.1 0 121.2 43.4 162.7 43.4 39.5 0 101.1-46 176.3-46 28.5 0 130.9 2.6 198.3 99.2zm-234-181.5c31.1-36.9 53.1-88.1 53.1-139.3 0-7.1-.6-14.3-1.9-20.1-50.6 1.9-110.8 33.7-147.1 75.8-28.5 32.4-55.1 83.6-55.1 135.5 0 7.8 1.3 15.6 1.9 18.1 3.2.6 8.4 1.3 13.6 1.3 45.4 0 102.5-30.4 135.5-71.3z";
const LOGO_NATIVE_WIDTH = 814;
const LOGO_NATIVE_HEIGHT = 1000;
const wireColor = "rgba(179,179,179,0.8)";
const strokeWidth = 1.5;
const modelLower = $derived(deviceType.toLowerCase());
// ── Mac Studio dimensions (same ratios as TopologyGraph) ──
const studioW = $derived(size * 1.25);
const studioH = $derived(size * 0.85);
const studioX = $derived(cx - studioW / 2);
const studioY = $derived(cy - studioH / 2);
const studioCorner = 4;
const studioTopH = $derived(studioH * 0.15);
// Studio front panel details
const studioSlotH = $derived(studioH * 0.14);
const studioVSlotW = $derived(studioW * 0.05);
const studioVSlotY = $derived(
studioY + studioTopH + (studioH - studioTopH) * 0.6,
);
const studioVSlot1X = $derived(studioX + studioW * 0.18);
const studioVSlot2X = $derived(studioX + studioW * 0.28);
const studioHSlotW = $derived(studioW * 0.2);
const studioHSlotX = $derived(studioX + studioW * 0.5 - studioHSlotW / 2);
// Studio memory fill
const studioMemTotalH = $derived(studioH - studioTopH);
const studioMemH = $derived((ramPercent / 100) * studioMemTotalH);
// ── MacBook dimensions (same ratios as TopologyGraph) ──
const mbW = $derived((size * 1.6 * 0.85) / 1.15);
const mbH = $derived(size * 0.85);
const mbX = $derived(cx - mbW / 2);
const mbY = $derived(cy - mbH / 2);
const mbScreenH = $derived(mbH * 0.7);
const mbBaseH = $derived(mbH * 0.3);
const mbScreenW = $derived(mbW * 0.85);
const mbScreenX = $derived(cx - mbScreenW / 2);
const mbBezel = 3;
// MacBook memory fill
const mbMemTotalH = $derived(mbScreenH - mbBezel * 2);
const mbMemH = $derived((ramPercent / 100) * mbMemTotalH);
// Apple logo sizing
const mbLogoTargetH = $derived(mbScreenH * 0.22);
const mbLogoScale = $derived(mbLogoTargetH / LOGO_NATIVE_HEIGHT);
const mbLogoX = $derived(cx - (LOGO_NATIVE_WIDTH * mbLogoScale) / 2);
const mbLogoY = $derived(
mbY + mbScreenH / 2 - (LOGO_NATIVE_HEIGHT * mbLogoScale) / 2,
);
// MacBook base (trapezoidal)
const mbBaseY = $derived(mbY + mbScreenH);
const mbBaseTopW = $derived(mbScreenW);
const mbBaseBottomW = $derived(mbW);
const mbBaseTopX = $derived(cx - mbBaseTopW / 2);
const mbBaseBottomX = $derived(cx - mbBaseBottomW / 2);
// Keyboard
const mbKbX = $derived(mbBaseTopX + 6);
const mbKbY = $derived(mbBaseY + 3);
const mbKbW = $derived(mbBaseTopW - 12);
const mbKbH = $derived(mbBaseH * 0.55);
// Trackpad
const mbTpW = $derived(mbBaseTopW * 0.4);
const mbTpX = $derived(cx - mbTpW / 2);
const mbTpY = $derived(mbBaseY + mbKbH + 5);
const mbTpH = $derived(mbBaseH * 0.3);
// Clip IDs
const screenClipId = $derived(`di-screen-${uid}`);
const studioClipId = $derived(`di-studio-${uid}`);
</script>
{#if modelLower === "mac studio" || modelLower === "mac mini"}
<!-- Mac Studio / Mac Mini -->
<defs>
<clipPath id={studioClipId}>
<rect
x={studioX}
y={studioY + studioTopH}
width={studioW}
height={studioH - studioTopH}
rx={studioCorner - 1}
/>
</clipPath>
</defs>
<!-- Main body -->
<rect
x={studioX}
y={studioY}
width={studioW}
height={studioH}
rx={studioCorner}
fill="#1a1a1a"
stroke={wireColor}
stroke-width={strokeWidth}
/>
<!-- Memory fill -->
{#if ramPercent > 0}
<rect
x={studioX}
y={studioY + studioTopH + (studioMemTotalH - studioMemH)}
width={studioW}
height={studioMemH}
fill="rgba(255,215,0,0.75)"
clip-path="url(#{studioClipId})"
/>
{/if}
<!-- Top surface divider -->
<line
x1={studioX}
y1={studioY + studioTopH}
x2={studioX + studioW}
y2={studioY + studioTopH}
stroke="rgba(179,179,179,0.3)"
stroke-width="0.5"
/>
<!-- Front panel: vertical slots -->
<rect
x={studioVSlot1X - studioVSlotW / 2}
y={studioVSlotY}
width={studioVSlotW}
height={studioSlotH}
fill="rgba(0,0,0,0.35)"
rx="1.5"
/>
<rect
x={studioVSlot2X - studioVSlotW / 2}
y={studioVSlotY}
width={studioVSlotW}
height={studioSlotH}
fill="rgba(0,0,0,0.35)"
rx="1.5"
/>
<!-- Horizontal slot (SD card) -->
<rect
x={studioHSlotX}
y={studioVSlotY}
width={studioHSlotW}
height={studioSlotH * 0.6}
fill="rgba(0,0,0,0.35)"
rx="1"
/>
{:else}
<!-- MacBook Pro -->
<defs>
<clipPath id={screenClipId}>
<rect
x={mbScreenX + mbBezel}
y={mbY + mbBezel}
width={mbScreenW - mbBezel * 2}
height={mbScreenH - mbBezel * 2}
rx="2"
/>
</clipPath>
</defs>
<!-- Screen outer frame -->
<rect
x={mbScreenX}
y={mbY}
width={mbScreenW}
height={mbScreenH}
rx="3"
fill="#1a1a1a"
stroke={wireColor}
stroke-width={strokeWidth}
/>
<!-- Screen inner (dark) -->
<rect
x={mbScreenX + mbBezel}
y={mbY + mbBezel}
width={mbScreenW - mbBezel * 2}
height={mbScreenH - mbBezel * 2}
rx="2"
fill="#0a0a12"
/>
<!-- Memory fill on screen -->
{#if ramPercent > 0}
<rect
x={mbScreenX + mbBezel}
y={mbY + mbBezel + (mbMemTotalH - mbMemH)}
width={mbScreenW - mbBezel * 2}
height={mbMemH}
fill="rgba(255,215,0,0.85)"
clip-path="url(#{screenClipId})"
/>
{/if}
<!-- Apple logo -->
<path
d={APPLE_LOGO_PATH}
transform="translate({mbLogoX}, {mbLogoY}) scale({mbLogoScale})"
fill="#FFFFFF"
opacity="0.9"
/>
<!-- Keyboard base (trapezoidal) -->
<path
d="M {mbBaseTopX} {mbBaseY} L {mbBaseTopX +
mbBaseTopW} {mbBaseY} L {mbBaseBottomX + mbBaseBottomW} {mbBaseY +
mbBaseH} L {mbBaseBottomX} {mbBaseY + mbBaseH} Z"
fill="#2c2c2c"
stroke={wireColor}
stroke-width="1"
/>
<!-- Keyboard area -->
<rect
x={mbKbX}
y={mbKbY}
width={mbKbW}
height={mbKbH}
fill="rgba(0,0,0,0.2)"
rx="2"
/>
<!-- Trackpad -->
<rect
x={mbTpX}
y={mbTpY}
width={mbTpW}
height={mbTpH}
fill="rgba(255,255,255,0.08)"
rx="2"
/>
{/if}
@@ -82,6 +82,24 @@
d="M12.025 1.13c-5.77 0-10.449 4.647-10.449 10.378 0 1.112.178 2.181.503 3.185.064-.222.203-.444.416-.577a.96.96 0 0 1 .524-.15c.293 0 .584.124.84.284.278.173.48.408.71.694.226.282.458.611.684.951v-.014c.017-.324.106-.622.264-.874s.403-.487.762-.543c.3-.047.596.06.787.203s.31.313.4.467c.15.257.212.468.233.542.01.026.653 1.552 1.657 2.54.616.605 1.01 1.223 1.082 1.912.055.537-.096 1.059-.38 1.572.637.121 1.294.187 1.967.187.657 0 1.298-.063 1.921-.178-.287-.517-.44-1.041-.384-1.581.07-.69.465-1.307 1.081-1.913 1.004-.987 1.647-2.513 1.657-2.539.021-.074.083-.285.233-.542.09-.154.208-.323.4-.467a1.08 1.08 0 0 1 .787-.203c.359.056.604.29.762.543s.247.55.265.874v.015c.225-.34.457-.67.683-.952.23-.286.432-.52.71-.694.257-.16.547-.284.84-.285a.97.97 0 0 1 .524.151c.228.143.373.388.43.625l.006.04a10.3 10.3 0 0 0 .534-3.273c0-5.731-4.678-10.378-10.449-10.378M8.327 6.583a1.5 1.5 0 0 1 .713.174 1.487 1.487 0 0 1 .617 2.013c-.183.343-.762-.214-1.102-.094-.38.134-.532.914-.917.71a1.487 1.487 0 0 1 .69-2.803m7.486 0a1.487 1.487 0 0 1 .689 2.803c-.385.204-.536-.576-.916-.71-.34-.12-.92.437-1.103.094a1.487 1.487 0 0 1 .617-2.013 1.5 1.5 0 0 1 .713-.174m-10.68 1.55a.96.96 0 1 1 0 1.921.96.96 0 0 1 0-1.92m13.838 0a.96.96 0 1 1 0 1.92.96.96 0 0 1 0-1.92M8.489 11.458c.588.01 1.965 1.157 3.572 1.164 1.607-.007 2.984-1.155 3.572-1.164.196-.003.305.12.305.454 0 .886-.424 2.328-1.563 3.202-.22-.756-1.396-1.366-1.63-1.32q-.011.001-.02.006l-.044.026-.01.008-.03.024q-.018.017-.035.036l-.032.04a1 1 0 0 0-.058.09l-.014.025q-.049.088-.11.19a1 1 0 0 1-.083.116 1.2 1.2 0 0 1-.173.18q-.035.029-.075.058a1.3 1.3 0 0 1-.251-.243 1 1 0 0 1-.076-.107c-.124-.193-.177-.363-.337-.444-.034-.016-.104-.008-.2.022q-.094.03-.216.087-.06.028-.125.063l-.13.074q-.067.04-.136.086a3 3 0 0 0-.135.096 3 3 0 0 0-.26.219 2 2 0 0 0-.12.121 2 2 0 0 0-.106.128l-.002.002a2 2 0 0 0-.09.132l-.001.001a1.2 1.2 0 0 0-.105.212q-.013.036-.024.073c-1.139-.875-1.563-2.317-1.563-3.203 0-.334.109-.457.305-.454m.836 10.354c.824-1.19.766-2.082-.365-3.194-1.13-1.112-1.789-2.738-1.789-2.738s-.246-.945-.806-.858-.97 1.499.202 2.362c1.173.864-.233 1.45-.685.64-.45-.812-1.683-2.896-2.322-3.295s-1.089-.175-.938.647 2.822 2.813 2.562 3.244-1.176-.506-1.176-.506-2.866-2.567-3.49-1.898.473 1.23 2.037 2.16c1.564.932 1.686 1.178 1.464 1.53s-3.675-2.511-4-1.297c-.323 1.214 3.524 1.567 3.287 2.405-.238.839-2.71-1.587-3.216-.642-.506.946 3.49 2.056 3.522 2.064 1.29.33 4.568 1.028 5.713-.624m5.349 0c-.824-1.19-.766-2.082.365-3.194 1.13-1.112 1.789-2.738 1.789-2.738s.246-.945.806-.858.97 1.499-.202 2.362c-1.173.864.233 1.45.685.64.451-.812 1.683-2.896 2.322-3.295s1.089-.175.938.647-2.822 2.813-2.562 3.244 1.176-.506 1.176-.506 2.866-2.567 3.49-1.898-.473 1.23-2.037 2.16c-1.564.932-1.686 1.178-1.464 1.53s3.675-2.511 4-1.297c.323 1.214-3.524 1.567-3.287 2.405.238.839 2.71-1.587 3.216-.642.506.946-3.49 2.056-3.522 2.064-1.29.33-4.568 1.028-5.713-.624"
/>
</svg>
{:else if family === "step"}
<svg class="w-6 h-6 {className}" viewBox="0 0 24 24" fill="currentColor">
<path
d="M22.012 0h1.032v.927H24v.968h-.956V3.78h-1.032V1.896h-1.878v-.97h1.878V0zM2.6 12.371V1.87h.969v10.502h-.97zm10.423.66h10.95v.918h-6.208v9.579h-4.742V13.03zM5.629 3.333v12.356H0v4.51h10.386V8L20.859 8l-.003-4.668-15.227.001z"
/>
</svg>
{:else if family === "nemotron"}
<svg class="w-6 h-6 {className}" viewBox="0 0 24 24" fill="currentColor">
<path
d="M8.948 8.798v-1.43a6.7 6.7 0 0 1 .424-.018c3.922-.124 6.493 3.374 6.493 3.374s-2.774 3.851-5.75 3.851c-.398 0-.787-.062-1.158-.185v-4.346c1.528.185 1.837.857 2.747 2.385l2.04-1.714s-1.492-1.952-4-1.952a6.016 6.016 0 0 0-.796.035m0-4.735v2.138l.424-.027c5.45-.185 9.01 4.47 9.01 4.47s-4.08 4.964-8.33 4.964c-.37 0-.733-.035-1.095-.097v1.325c.3.035.61.062.91.062 3.957 0 6.82-2.023 9.593-4.408.459.371 2.34 1.263 2.73 1.652-2.633 2.208-8.772 3.984-12.253 3.984-.335 0-.653-.018-.971-.053v1.864H24V4.063zm0 10.326v1.131c-3.657-.654-4.673-4.46-4.673-4.46s1.758-1.944 4.673-2.262v1.237H8.94c-1.528-.186-2.73 1.245-2.73 1.245s.68 2.412 2.739 3.11M2.456 10.9s2.164-3.197 6.5-3.533V6.201C4.153 6.59 0 10.653 0 10.653s2.35 6.802 8.948 7.42v-1.237c-4.84-.6-6.492-5.936-6.492-5.936z"
/>
</svg>
{:else if family === "gemma"}
<svg class="w-6 h-6 {className}" viewBox="0 0 24 24" fill="currentColor">
<path
d="M12.48 10.92v3.28h7.84c-.24 1.84-.853 3.187-1.787 4.133-1.147 1.147-2.933 2.4-6.053 2.4-4.827 0-8.6-3.893-8.6-8.72s3.773-8.72 8.6-8.72c2.6 0 4.507 1.027 5.907 2.347l2.307-2.307C18.747 1.44 16.133 0 12.48 0 5.867 0 .307 5.387.307 12s5.56 12 12.173 12c3.573 0 6.267-1.173 8.373-3.36 2.16-2.16 2.84-5.213 2.84-7.667 0-.76-.053-1.467-.173-2.053H12.48z"
/>
</svg>
{:else}
<svg class="w-6 h-6 {className}" viewBox="0 0 24 24" fill="currentColor">
<path
@@ -31,6 +31,8 @@
kimi: "Kimi",
flux: "FLUX",
"qwen-image": "Qwen Img",
nemotron: "NVIDIA",
gemma: "Google",
};
function getFamilyName(family: string): string {
@@ -41,31 +43,20 @@
</script>
<div
class="flex flex-col gap-1 py-2 px-1 border-r border-exo-yellow/10 bg-exo-medium-gray/30 min-w-[64px] overflow-y-auto scrollbar-hide"
class="flex flex-col gap-1 py-2 px-1 border-r border-exo-yellow/10 bg-exo-medium-gray/30 min-w-[80px] sm:min-w-[72px] overflow-y-auto scrollbar-hide"
>
<!-- All models (no filter) -->
<button
type="button"
onclick={() => onSelect(null)}
class="group flex flex-col items-center justify-center p-2 rounded transition-all duration-200 cursor-pointer {selectedFamily ===
class="group flex items-center justify-center px-3 py-2.5 rounded transition-all duration-200 cursor-pointer min-h-[44px] sm:min-h-0 {selectedFamily ===
null
? 'bg-exo-yellow/20 border-l-2 border-exo-yellow'
: 'hover:bg-white/5 border-l-2 border-transparent'}"
title="All models"
>
<svg
class="w-5 h-5 {selectedFamily === null
? 'text-exo-yellow'
: 'text-white/50 group-hover:text-white/70'}"
viewBox="0 0 24 24"
fill="currentColor"
>
<path
d="M4 8h4V4H4v4zm6 12h4v-4h-4v4zm-6 0h4v-4H4v4zm0-6h4v-4H4v4zm6 0h4v-4h-4v4zm6-10v4h4V4h-4zm-6 4h4V4h-4v4zm6 6h4v-4h-4v4zm0 6h4v-4h-4v4z"
/>
</svg>
<span
class="text-[9px] font-mono mt-0.5 {selectedFamily === null
class="text-[12px] font-mono font-medium {selectedFamily === null
? 'text-exo-yellow'
: 'text-white/40 group-hover:text-white/60'}">All</span
>
@@ -89,7 +80,7 @@
: "text-white/50 group-hover:text-amber-400/70"}
/>
<span
class="text-[9px] font-mono mt-0.5 {selectedFamily === 'favorites'
class="text-[11px] font-mono mt-0.5 {selectedFamily === 'favorites'
? 'text-amber-400'
: 'text-white/40 group-hover:text-white/60'}">Faves</span
>
@@ -114,7 +105,7 @@
: "text-white/50 group-hover:text-white/70"}
/>
<span
class="text-[9px] font-mono mt-0.5 {selectedFamily === 'recents'
class="text-[11px] font-mono mt-0.5 {selectedFamily === 'recents'
? 'text-exo-yellow'
: 'text-white/40 group-hover:text-white/60'}">Recent</span
>
@@ -138,7 +129,7 @@
: "text-white/50 group-hover:text-orange-400/70"}
/>
<span
class="text-[9px] font-mono mt-0.5 {selectedFamily === 'huggingface'
class="text-[11px] font-mono mt-0.5 {selectedFamily === 'huggingface'
? 'text-orange-400'
: 'text-white/40 group-hover:text-white/60'}">Hub</span
>
@@ -164,7 +155,7 @@
: "text-white/50 group-hover:text-white/70"}
/>
<span
class="text-[9px] font-mono mt-0.5 truncate max-w-full {selectedFamily ===
class="text-[11px] font-mono mt-0.5 truncate max-w-full {selectedFamily ===
family
? 'text-exo-yellow'
: 'text-white/40 group-hover:text-white/60'}"
+249 -51
View File
@@ -1,11 +1,41 @@
<script lang="ts">
import { browser } from "$app/environment";
import { featureFlags } from "$lib/stores/app.svelte";
export let showHome = true;
export let onHome: (() => void) | null = null;
export let showSidebarToggle = false;
export let sidebarVisible = true;
export let onToggleSidebar: (() => void) | null = null;
const showAdvanced = $derived(featureFlags()["disaggregation"] === true);
interface Props {
showHome?: boolean;
onHome?: (() => void) | null;
showSidebarToggle?: boolean;
sidebarVisible?: boolean;
onToggleSidebar?: (() => void) | null;
showMobileMenuToggle?: boolean;
mobileMenuOpen?: boolean;
onToggleMobileMenu?: (() => void) | null;
showMobileRightToggle?: boolean;
mobileRightOpen?: boolean;
onToggleMobileRight?: (() => void) | null;
downloadProgress?: {
count: number;
percentage: number;
} | null;
}
let {
showHome = true,
onHome = null,
showSidebarToggle = false,
sidebarVisible = true,
onToggleSidebar = null,
showMobileMenuToggle = false,
mobileMenuOpen = false,
onToggleMobileMenu = null,
showMobileRightToggle = false,
mobileRightOpen = false,
onToggleMobileRight = null,
downloadProgress = null,
}: Props = $props();
function handleHome(): void {
if (onHome) {
@@ -23,45 +53,96 @@
onToggleSidebar();
}
}
function handleToggleMobileMenu(): void {
if (onToggleMobileMenu) {
onToggleMobileMenu();
}
}
function handleToggleMobileRight(): void {
if (onToggleMobileRight) {
onToggleMobileRight();
}
}
</script>
<header
class="relative z-20 flex items-center justify-center px-6 pt-8 pb-4 bg-exo-dark-gray"
class="relative z-20 flex items-center justify-center px-4 md:px-6 pt-4 md:pt-8 pb-3 md:pb-4 bg-exo-dark-gray"
>
<!-- Left: Sidebar Toggle -->
{#if showSidebarToggle}
<div class="absolute left-6 top-1/2 -translate-y-1/2">
<button
onclick={handleToggleSidebar}
class="p-2 rounded border border-exo-medium-gray/40 hover:border-exo-yellow/50 transition-colors cursor-pointer"
title={sidebarVisible ? "Hide sidebar" : "Show sidebar"}
<!-- Left: Sidebar Toggle (desktop) or Mobile Sidebar Toggle (mobile) -->
<div
class="absolute left-4 md:left-6 top-1/2 -translate-y-1/2 flex items-center gap-2"
>
<!-- Mobile sidebar toggle -->
<button
onclick={handleToggleMobileMenu}
class="p-2 rounded border border-exo-light-gray/30 hover:border-exo-yellow/50 hover:bg-exo-medium-gray/30 transition-colors cursor-pointer md:hidden"
title={mobileMenuOpen ? "Hide sidebar" : "Show sidebar"}
aria-label={mobileMenuOpen
? "Hide conversation sidebar"
: "Show conversation sidebar"}
aria-pressed={mobileMenuOpen}
>
<svg
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
stroke-width="2"
class="w-5 h-5 {mobileMenuOpen
? 'text-exo-yellow'
: 'text-exo-light-gray'}"
>
<svg
class="w-5 h-5 {sidebarVisible
? 'text-exo-yellow'
: 'text-exo-medium-gray'}"
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
stroke-width="2"
>
{#if sidebarVisible}
<path
stroke-linecap="round"
stroke-linejoin="round"
d="M11 19l-7-7 7-7m8 14l-7-7 7-7"
/>
{:else}
<path
stroke-linecap="round"
stroke-linejoin="round"
d="M13 5l7 7-7 7M5 5l7 7-7 7"
/>
{/if}
</svg>
</button>
</div>
{/if}
{#if mobileMenuOpen}
<path
stroke-linecap="round"
stroke-linejoin="round"
d="M11 19l-7-7 7-7m8 14l-7-7 7-7"
></path>
{:else}
<path
stroke-linecap="round"
stroke-linejoin="round"
d="M13 5l7 7-7 7M5 5l7 7-7 7"
></path>
{/if}
</svg>
</button>
<!-- Desktop sidebar toggle -->
<button
onclick={handleToggleSidebar}
class="p-2 rounded border border-exo-light-gray/30 hover:border-exo-yellow/50 hover:bg-exo-medium-gray/30 transition-colors cursor-pointer hidden md:block"
title={sidebarVisible ? "Hide sidebar" : "Show sidebar"}
aria-label={sidebarVisible
? "Hide conversation sidebar"
: "Show conversation sidebar"}
aria-pressed={sidebarVisible}
>
<svg
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
stroke-width="2"
class="w-5 h-5 {sidebarVisible
? 'text-exo-yellow'
: 'text-exo-light-gray'}"
>
{#if sidebarVisible}
<path
stroke-linecap="round"
stroke-linejoin="round"
d="M11 19l-7-7 7-7m8 14l-7-7 7-7"
></path>
{:else}
<path
stroke-linecap="round"
stroke-linejoin="round"
d="M13 5l7 7-7 7M5 5l7 7-7 7"
></path>
{/if}
</svg>
</button>
</div>
<!-- Center: Logo (clickable to go home) -->
<button
@@ -75,18 +156,55 @@
<img
src="/exo-logo.png"
alt="EXO"
class="h-18 drop-shadow-[0_0_20px_rgba(255,215,0,0.5)]"
class="h-12 md:h-18 drop-shadow-[0_0_4px_rgba(255,215,0,0.3)]"
/>
</button>
<!-- Right: Home + Downloads -->
<div
class="absolute right-6 top-1/2 -translate-y-1/2 flex items-center gap-4"
<!-- Right: Home + Downloads + Mobile Right Toggle -->
<nav
class="absolute right-4 md:right-6 top-1/2 -translate-y-1/2 flex items-center gap-2 md:gap-4"
aria-label="Main navigation"
>
<!-- Mobile right sidebar toggle (instances/models) - only show when not in chat mode -->
{#if showMobileRightToggle}
<button
onclick={handleToggleMobileRight}
class="p-2 rounded border border-exo-light-gray/30 hover:border-exo-yellow/50 hover:bg-exo-medium-gray/30 transition-colors cursor-pointer md:hidden"
title={mobileRightOpen ? "Hide instances" : "Show instances"}
aria-label={mobileRightOpen
? "Hide instances panel"
: "Show instances panel"}
aria-pressed={mobileRightOpen}
>
<svg
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
stroke-width="2"
class="w-5 h-5 {mobileRightOpen
? 'text-exo-yellow'
: 'text-exo-light-gray'}"
>
{#if mobileRightOpen}
<path
stroke-linecap="round"
stroke-linejoin="round"
d="M13 5l7 7-7 7M5 5l7 7-7 7"
></path>
{:else}
<path
stroke-linecap="round"
stroke-linejoin="round"
d="M11 19l-7-7 7-7m8 14l-7-7 7-7"
></path>
{/if}
</svg>
</button>
{/if}
{#if showHome}
<button
onclick={handleHome}
class="text-sm text-exo-light-gray hover:text-exo-yellow transition-colors tracking-wider uppercase flex items-center gap-2 cursor-pointer"
class="flex text-sm text-white/70 hover:text-exo-yellow transition-colors tracking-wider uppercase items-center gap-2 cursor-pointer"
title="Back to topology view"
>
<svg
@@ -102,13 +220,69 @@
d="M3 12l2-2m0 0l7-7 7 7M5 10v10a1 1 0 001 1h3m10-11l2 2m-2-2v10a1 1 0 01-1 1h-3m-6 0a1 1 0 001-1v-4a1 1 0 011-1h2a1 1 0 011 1v4a1 1 0 001 1m-6 0h6"
/>
</svg>
Home
<span class="hidden sm:inline">Home</span>
</button>
{/if}
<a
href="/#/downloads"
class="text-sm text-exo-light-gray hover:text-exo-yellow transition-colors tracking-wider uppercase flex items-center gap-2 cursor-pointer"
class="text-xs md:text-sm text-white/70 hover:text-exo-yellow transition-colors tracking-wider uppercase flex items-center gap-1.5 md:gap-2 cursor-pointer"
title="View downloads overview"
>
{#if downloadProgress}
<!-- Compact download progress indicator -->
<div class="relative w-4 h-4 flex-shrink-0">
<svg class="w-4 h-4 -rotate-90" viewBox="0 0 20 20">
<circle
cx="10"
cy="10"
r="8"
fill="none"
stroke="currentColor"
stroke-width="2"
opacity="0.2"
/>
<circle
cx="10"
cy="10"
r="8"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-dasharray={2 * Math.PI * 8}
stroke-dashoffset={2 *
Math.PI *
8 *
(1 - downloadProgress.percentage / 100)}
class="text-blue-400 transition-all duration-300"
/>
</svg>
<div
class="absolute inset-0 flex items-center justify-center text-[6px] font-mono text-blue-400"
>
{downloadProgress.count}
</div>
</div>
{:else}
<svg
class="w-4 h-4"
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
>
<path d="M12 3v12" />
<path d="M7 12l5 5 5-5" />
<path d="M5 21h14" />
</svg>
{/if}
<span class="hidden sm:inline">Downloads</span>
</a>
<a
href="/#/integrations"
class="text-xs md:text-sm text-white/70 hover:text-exo-yellow transition-colors tracking-wider uppercase flex items-center gap-1.5 md:gap-2 cursor-pointer"
title="Integration configs for external tools"
>
<svg
class="w-4 h-4"
@@ -119,11 +293,35 @@
stroke-linecap="round"
stroke-linejoin="round"
>
<path d="M12 3v12" />
<path d="M7 12l5 5 5-5" />
<path d="M5 21h14" />
<path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71" />
<path
d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"
/>
</svg>
Downloads
<span class="hidden sm:inline">Integrations</span>
</a>
</div>
{#if showAdvanced}
<a
href="/#/advanced"
class="text-xs md:text-sm text-white/70 hover:text-exo-yellow transition-colors tracking-wider uppercase flex items-center gap-1.5 md:gap-2 cursor-pointer"
title="Advanced cluster settings"
>
<svg
class="w-4 h-4"
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
>
<circle cx="12" cy="12" r="3" />
<path
d="M19.4 15a1.65 1.65 0 0 0 .33 1.82l.06.06a2 2 0 0 1 0 2.83 2 2 0 0 1-2.83 0l-.06-.06a1.65 1.65 0 0 0-1.82-.33 1.65 1.65 0 0 0-1 1.51V21a2 2 0 0 1-4 0v-.09A1.65 1.65 0 0 0 9 19.4a1.65 1.65 0 0 0-1.82.33l-.06.06a2 2 0 0 1-2.83 0 2 2 0 0 1 0-2.83l.06-.06a1.65 1.65 0 0 0 .33-1.82 1.65 1.65 0 0 0-1.51-1H3a2 2 0 0 1 0-4h.09A1.65 1.65 0 0 0 4.6 9a1.65 1.65 0 0 0-.33-1.82l-.06-.06a2 2 0 0 1 0-2.83 2 2 0 0 1 2.83 0l.06.06a1.65 1.65 0 0 0 1.82.33H9a1.65 1.65 0 0 0 1-1.51V3a2 2 0 0 1 4 0v.09a1.65 1.65 0 0 0 1 1.51 1.65 1.65 0 0 0 1.82-.33l.06-.06a2 2 0 0 1 2.83 0 2 2 0 0 1 0 2.83l-.06.06a1.65 1.65 0 0 0-.33 1.82V9a1.65 1.65 0 0 0 1.51 1H21a2 2 0 0 1 0 4h-.09a1.65 1.65 0 0 0-1.51 1z"
/>
</svg>
<span class="hidden sm:inline">Advanced</span>
</a>
{/if}
</nav>
</header>
@@ -26,7 +26,8 @@
downloadedOnNodes = [],
}: HuggingFaceResultItemProps = $props();
function formatNumber(num: number): string {
function formatNumber(num: number | undefined): string {
if (num == null) return "0";
if (num >= 1000000) {
return `${(num / 1000000).toFixed(1)}M`;
} else if (num >= 1000) {
@@ -35,8 +36,12 @@
return num.toString();
}
// Extract model name from full ID (e.g., "mlx-community/Llama-3.2-1B" -> "Llama-3.2-1B")
const modelName = $derived(model.id.split("/").pop() || model.id);
// Show short name for mlx-community models, full ID for everything else
const modelName = $derived(
model.author === "mlx-community"
? model.id.split("/").pop() || model.id
: model.id,
);
</script>
<div
@@ -59,13 +59,14 @@
}
const sizeOptions: ImageGenerationParams["size"][] = [
"auto",
"512x512",
"768x768",
"1024x1024",
"1024x768",
"768x1024",
"1024x1365",
"1365x1024",
"1024x1536",
"1536x1024",
];
const qualityOptions: ImageGenerationParams["quality"][] = [
@@ -176,92 +177,90 @@
<div class="border-b border-exo-medium-gray/30 px-3 py-2">
<!-- Basic params row -->
<div class="flex items-center gap-3 flex-wrap">
<!-- Size (hidden in edit mode - output size comes from input image) -->
{#if !isEditMode}
<div class="flex items-center gap-1.5">
<span class="text-xs text-exo-light-gray uppercase tracking-wider"
>SIZE:</span
<!-- Size -->
<div class="flex items-center gap-1.5">
<span class="text-xs text-exo-light-gray uppercase tracking-wider"
>SIZE:</span
>
<div class="relative">
<button
bind:this={sizeButtonRef}
type="button"
onclick={() => (isSizeDropdownOpen = !isSizeDropdownOpen)}
class="bg-exo-medium-gray/50 border border-exo-yellow/30 rounded pl-2 pr-6 py-1 text-xs font-mono text-exo-yellow cursor-pointer transition-all duration-200 hover:border-exo-yellow/50 focus:outline-none focus:border-exo-yellow/70 {isSizeDropdownOpen
? 'border-exo-yellow/70'
: ''}"
>
<div class="relative">
<button
bind:this={sizeButtonRef}
type="button"
onclick={() => (isSizeDropdownOpen = !isSizeDropdownOpen)}
class="bg-exo-medium-gray/50 border border-exo-yellow/30 rounded pl-2 pr-6 py-1 text-xs font-mono text-exo-yellow cursor-pointer transition-all duration-200 hover:border-exo-yellow/50 focus:outline-none focus:border-exo-yellow/70 {isSizeDropdownOpen
? 'border-exo-yellow/70'
: ''}"
{params.size.toUpperCase()}
</button>
<div
class="absolute right-1.5 top-1/2 -translate-y-1/2 pointer-events-none transition-transform duration-200 {isSizeDropdownOpen
? 'rotate-180'
: ''}"
>
<svg
class="w-3 h-3 text-exo-yellow/60"
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
>
{params.size}
</button>
<div
class="absolute right-1.5 top-1/2 -translate-y-1/2 pointer-events-none transition-transform duration-200 {isSizeDropdownOpen
? 'rotate-180'
: ''}"
>
<svg
class="w-3 h-3 text-exo-yellow/60"
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
>
<path
stroke-linecap="round"
stroke-linejoin="round"
stroke-width="2"
d="M19 9l-7 7-7-7"
/>
</svg>
<path
stroke-linecap="round"
stroke-linejoin="round"
stroke-width="2"
d="M19 9l-7 7-7-7"
/>
</svg>
</div>
</div>
{#if isSizeDropdownOpen}
<!-- Backdrop to close dropdown -->
<button
type="button"
class="fixed inset-0 z-[9998] cursor-default"
onclick={() => (isSizeDropdownOpen = false)}
aria-label="Close dropdown"
></button>
<!-- Dropdown Panel - fixed positioning to escape overflow:hidden -->
<div
class="fixed bg-exo-dark-gray border border-exo-yellow/30 rounded shadow-lg shadow-black/50 z-[9999] max-h-48 overflow-y-auto overflow-x-hidden min-w-max"
style="bottom: calc(100vh - {sizeDropdownPosition()
.top}px + 4px); left: {sizeDropdownPosition().left}px;"
>
<div class="py-1">
{#each sizeOptions as size}
<button
type="button"
onclick={() => selectSize(size)}
class="w-full px-3 py-1.5 text-left text-xs font-mono tracking-wide transition-colors duration-100 flex items-center gap-2 {params.size ===
size
? 'bg-transparent text-exo-yellow'
: 'text-exo-light-gray hover:text-exo-yellow'}"
>
{#if params.size === size}
<svg
class="w-3 h-3 flex-shrink-0"
fill="currentColor"
viewBox="0 0 20 20"
>
<path
fill-rule="evenodd"
d="M16.707 5.293a1 1 0 010 1.414l-8 8a1 1 0 01-1.414 0l-4-4a1 1 0 011.414-1.414L8 12.586l7.293-7.293a1 1 0 011.414 0z"
clip-rule="evenodd"
/>
</svg>
{:else}
<span class="w-3"></span>
{/if}
<span>{size.toUpperCase()}</span>
</button>
{/each}
</div>
</div>
{#if isSizeDropdownOpen}
<!-- Backdrop to close dropdown -->
<button
type="button"
class="fixed inset-0 z-[9998] cursor-default"
onclick={() => (isSizeDropdownOpen = false)}
aria-label="Close dropdown"
></button>
<!-- Dropdown Panel - fixed positioning to escape overflow:hidden -->
<div
class="fixed bg-exo-dark-gray border border-exo-yellow/30 rounded shadow-lg shadow-black/50 z-[9999] max-h-48 overflow-y-auto min-w-max"
style="bottom: calc(100vh - {sizeDropdownPosition()
.top}px + 4px); left: {sizeDropdownPosition().left}px;"
>
<div class="py-1">
{#each sizeOptions as size}
<button
type="button"
onclick={() => selectSize(size)}
class="w-full px-3 py-1.5 text-left text-xs font-mono tracking-wide transition-colors duration-100 flex items-center gap-2 {params.size ===
size
? 'bg-transparent text-exo-yellow'
: 'text-exo-light-gray hover:text-exo-yellow'}"
>
{#if params.size === size}
<svg
class="w-3 h-3 flex-shrink-0"
fill="currentColor"
viewBox="0 0 20 20"
>
<path
fill-rule="evenodd"
d="M16.707 5.293a1 1 0 010 1.414l-8 8a1 1 0 01-1.414 0l-4-4a1 1 0 011.414-1.414L8 12.586l7.293-7.293a1 1 0 011.414 0z"
clip-rule="evenodd"
/>
</svg>
{:else}
<span class="w-3"></span>
{/if}
<span>{size}</span>
</button>
{/each}
</div>
</div>
{/if}
</div>
{/if}
{/if}
</div>
<!-- Quality -->
<div class="flex items-center gap-1.5">
@@ -311,7 +310,7 @@
<!-- Dropdown Panel - fixed positioning to escape overflow:hidden -->
<div
class="fixed bg-exo-dark-gray border border-exo-yellow/30 rounded shadow-lg shadow-black/50 z-[9999] max-h-48 overflow-y-auto min-w-max"
class="fixed bg-exo-dark-gray border border-exo-yellow/30 rounded shadow-lg shadow-black/50 z-[9999] max-h-48 overflow-y-auto overflow-x-hidden min-w-max"
style="bottom: calc(100vh - {qualityDropdownPosition()
.top}px + 4px); left: {qualityDropdownPosition().left}px;"
>
@@ -0,0 +1,62 @@
<script lang="ts">
import { copyText } from "$lib/utils/clipboard";
interface Props {
title: string;
subtitle: string;
config: string;
description?: string;
language?: "json" | "bash";
}
let {
title,
subtitle,
config,
description = "",
language = "json",
}: Props = $props();
let copied = $state(false);
let failed = $state(false);
async function copyToClipboard() {
const ok = await copyText(config);
if (ok) {
copied = true;
setTimeout(() => (copied = false), 2000);
} else {
failed = true;
setTimeout(() => (failed = false), 2000);
}
}
</script>
<div
class="border border-exo-light-gray/20 rounded-lg bg-exo-medium-gray/20 overflow-hidden"
>
<div class="flex items-center justify-between px-5 py-4">
<div>
<h3 class="text-white text-sm font-semibold tracking-wide">{title}</h3>
<p class="text-exo-light-gray/60 text-xs mt-0.5 font-mono">{subtitle}</p>
</div>
<button
onclick={copyToClipboard}
class="px-3 py-1.5 text-xs rounded border transition-all duration-200 cursor-pointer
{copied
? 'border-green-500/50 text-green-400 bg-green-500/10'
: failed
? 'border-red-500/50 text-red-400 bg-red-500/10'
: 'border-exo-light-gray/30 text-exo-light-gray hover:border-exo-yellow/50 hover:text-exo-yellow'}"
>
{copied ? "Copied!" : failed ? "Copy failed" : "Copy"}
</button>
</div>
{#if description}
<p class="text-exo-light-gray/70 text-xs px-5 pb-3">{description}</p>
{/if}
<div class="bg-black/30 border-t border-exo-light-gray/10">
<pre
class="text-xs text-exo-light-gray/90 font-mono p-4 overflow-x-auto whitespace-pre">{config}</pre>
</div>
</div>
@@ -507,9 +507,29 @@
});
$effect(() => {
if (containerRef && processedHtml) {
setupCopyButtons();
if (!containerRef || !browser) return;
function handleDelegatedClick(event: MouseEvent) {
const codeBtn = (event.target as HTMLElement).closest(
".copy-code-btn",
) as HTMLButtonElement | null;
if (codeBtn) {
handleCopyClick({ currentTarget: codeBtn } as unknown as Event);
return;
}
const mathBtn = (event.target as HTMLElement).closest(
".copy-math-btn",
) as HTMLButtonElement | null;
if (mathBtn) {
handleMathCopyClick({ currentTarget: mathBtn } as unknown as Event);
return;
}
}
containerRef.addEventListener("click", handleDelegatedClick);
return () => {
containerRef?.removeEventListener("click", handleDelegatedClick);
};
});
</script>
+66 -18
View File
@@ -16,12 +16,14 @@
perNode?: Array<{
nodeId: string;
nodeName: string;
progress: DownloadProgress;
status: "completed" | "partial" | "pending" | "downloading";
percentage: number;
progress: DownloadProgress | null;
}>;
} | null;
nodes?: Record<string, NodeInfo>;
sharding?: "Pipeline" | "Tensor";
runtime?: "MlxRing" | "MlxIbv" | "MlxJaccl";
runtime?: "MlxRing" | "MlxJaccl";
onLaunch?: () => void;
tags?: string[];
apiPreview?: PlacementPreview | null;
@@ -145,10 +147,7 @@
return `${s}s`;
}
const isDownloading = $derived(downloadStatus?.isDownloading ?? false);
const progress = $derived(downloadStatus?.progress);
const percentage = $derived(progress?.percentage ?? 0);
let expandedNodes = $state<Set<string>>(new Set());
const perNode = $derived(downloadStatus?.perNode ?? []);
function toggleNodeDetails(nodeId: string): void {
const next = new Set(expandedNodes);
@@ -348,7 +347,7 @@
// Debug mode state
const isDebugMode = $derived(debugMode());
const topology = $derived(topologyData());
const isRdma = $derived(runtime === "MlxIbv" || runtime === "MlxJaccl");
const isRdma = $derived(runtime === "MlxJaccl");
// Get interface name for an IP from node data
function getInterfaceForIp(nodeId: string, ip?: string): string | null {
@@ -567,20 +566,72 @@
<div class="flex items-center gap-1.5 mb-2">
<span
class="px-1.5 py-0.5 text-xs font-mono tracking-wider uppercase bg-exo-medium-gray/30 text-exo-light-gray border border-exo-medium-gray/40"
title={sharding === "Pipeline"
? "Pipeline: splits model into sequential stages across devices. Lower network overhead."
: "Tensor: splits each layer across devices. Best with high-bandwidth connections (Thunderbolt)."}
>
{sharding}
</span>
<span
class="px-1.5 py-0.5 text-xs font-mono tracking-wider uppercase bg-exo-medium-gray/30 text-exo-light-gray border border-exo-medium-gray/40"
title={runtime === "MlxRing"
? "Ring: standard networking. Works over any connection (Wi-Fi, Ethernet, Thunderbolt)."
: "RDMA: direct memory access over Thunderbolt. Significantly faster for multi-device inference."}
>
{runtime === "MlxRing"
? "MLX Ring"
: runtime === "MlxIbv" || runtime === "MlxJaccl"
: runtime === "MlxJaccl"
? "MLX RDMA"
: runtime}
</span>
</div>
<!-- Download Status (per-node) -->
{#if perNode.length > 0}
<div class="mb-2 space-y-1">
<div
class="text-[10px] font-mono text-white/20 tracking-widest uppercase"
>
Download progress
</div>
{#each perNode as node}
<div class="flex items-center gap-2 text-xs font-mono">
<span class="text-white/40 w-20 truncate" title={node.nodeId}
>{node.nodeName}</span
>
<div
class="flex-1 h-1 bg-exo-medium-gray/30 rounded overflow-hidden"
>
<div
class="h-full transition-all duration-300 {node.status ===
'downloading'
? 'bg-blue-500/70'
: node.status === 'completed'
? 'bg-exo-yellow/40'
: 'bg-white/20'}"
style="width: {node.percentage}%"
></div>
</div>
<span
class="text-right {node.status === 'completed'
? 'text-exo-yellow/60'
: node.status === 'downloading'
? 'text-blue-400/60'
: 'text-white/30'}"
>
{#if node.status === "downloading" && node.progress}
{Math.round(node.percentage)}% {formatSpeed(
node.progress.speed,
)}
{:else}
{node.percentage > 0 ? `${Math.round(node.percentage)}%` : "0%"}
{/if}
</span>
</div>
{/each}
</div>
{/if}
<!-- Mini Topology Preview -->
{#if placementPreview().nodes.length > 0}
{@const preview = placementPreview()}
@@ -636,15 +687,7 @@
{@const allConnections =
isDebugMode && usedNodes.length > 1
? (() => {
const conns: Array<{
ip: string;
iface: string | null;
from: string;
to: string;
midX: number;
midY: number;
arrow: string;
}> = [];
const conns: Array = [];
for (let i = 0; i < usedNodes.length; i++) {
for (let j = i + 1; j < usedNodes.length; j++) {
const n1 = usedNodes[i];
@@ -656,7 +699,12 @@
const toPos = nodePositions[c.to];
const arrow =
fromPos && toPos ? getArrow(fromPos, toPos) : "→";
conns.push({ ...c, midX, midY, arrow });
conns.push({
...c,
midX,
midY,
arrow,
});
}
}
}
@@ -6,6 +6,7 @@
capabilities: string[];
sizeRange: { min: number; max: number } | null;
downloadedOnly: boolean;
readyOnly: boolean;
}
type ModelFilterPopoverProps = {
@@ -72,7 +73,7 @@
<!-- svelte-ignore a11y_no_static_element_interactions -->
<div
class="filter-popover absolute right-0 top-full mt-2 w-64 bg-exo-dark-gray border border-exo-yellow/10 rounded-lg shadow-xl z-10"
class="filter-popover absolute right-0 top-full mt-2 w-64 bg-exo-dark-gray border border-exo-yellow/10 rounded-lg shadow-xl z-20"
transition:fly={{ y: -10, duration: 200, easing: cubicOut }}
onclick={(e) => e.stopPropagation()}
role="dialog"
@@ -190,34 +191,58 @@
</div>
</div>
<!-- Downloaded only -->
<!-- Availability filters -->
<div>
<h4 class="text-xs font-mono text-white/50 mb-2">Availability</h4>
<button
type="button"
class="px-2 py-1 text-xs font-mono rounded transition-colors {filters.downloadedOnly
? 'bg-green-500/20 text-green-400 border border-green-500/30'
: 'bg-white/5 text-white/60 hover:bg-white/10 border border-transparent'}"
onclick={() =>
onChange({ ...filters, downloadedOnly: !filters.downloadedOnly })}
>
<svg
class="w-3.5 h-3.5 inline-block"
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
<div class="flex flex-wrap gap-1.5">
<button
type="button"
class="px-2 py-1 text-xs font-mono rounded transition-colors {filters.downloadedOnly
? 'bg-green-500/20 text-green-400 border border-green-500/30'
: 'bg-white/5 text-white/60 hover:bg-white/10 border border-transparent'}"
onclick={() =>
onChange({ ...filters, downloadedOnly: !filters.downloadedOnly })}
>
<path
class="text-white/40"
d="M20 20a2 2 0 0 0 2-2V8a2 2 0 0 0-2-2h-7.9a2 2 0 0 1-1.69-.9L9.6 3.9A2 2 0 0 0 7.93 3H4a2 2 0 0 0-2 2v13a2 2 0 0 0 2 2Z"
/>
<path class="text-green-400" d="m9 13 2 2 4-4" />
</svg>
<span class="ml-1">Downloaded</span>
</button>
<svg
class="w-3.5 h-3.5 inline-block"
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
>
<path
class="text-white/40"
d="M20 20a2 2 0 0 0 2-2V8a2 2 0 0 0-2-2h-7.9a2 2 0 0 1-1.69-.9L9.6 3.9A2 2 0 0 0 7.93 3H4a2 2 0 0 0-2 2v13a2 2 0 0 0 2 2Z"
/>
<path class="text-green-400" d="m9 13 2 2 4-4" />
</svg>
<span class="ml-1">Downloaded</span>
</button>
<button
type="button"
class="px-2 py-1 text-xs font-mono rounded transition-colors {filters.readyOnly
? 'bg-green-500/20 text-green-400 border border-green-500/30'
: 'bg-white/5 text-white/60 hover:bg-white/10 border border-transparent'}"
onclick={() =>
onChange({ ...filters, readyOnly: !filters.readyOnly })}
>
<svg
class="w-3.5 h-3.5 inline-block"
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
>
<circle cx="12" cy="12" r="10" />
<path d="m9 12 2 2 4-4" />
</svg>
<span class="ml-1">Ready</span>
</button>
</div>
</div>
<!-- Size range -->
@@ -32,6 +32,7 @@
group: ModelGroup;
isExpanded: boolean;
isFavorite: boolean;
isHighlighted?: boolean;
selectedModelId: string | null;
canModelFit: (id: string) => boolean;
getModelFitStatus: (id: string) => ModelFitStatus;
@@ -41,12 +42,14 @@
onShowInfo: (group: ModelGroup) => void;
downloadStatusMap?: Map<string, DownloadAvailability>;
launchedAt?: number;
instanceStatuses?: Record<string, { status: string; statusClass: string }>;
};
let {
group,
isExpanded,
isFavorite,
isHighlighted = false,
selectedModelId,
canModelFit,
getModelFitStatus,
@@ -56,6 +59,7 @@
onShowInfo,
downloadStatusMap,
launchedAt,
instanceStatuses = {},
}: ModelPickerGroupProps = $props();
// Group-level download status: show if any variant is downloaded
@@ -92,6 +96,12 @@
const anyVariantFits = $derived(
group.variants.some((v) => canModelFit(v.id)),
);
// Check if any variant has an active instance (ready, loading, downloading)
const anyVariantHasInstance = $derived(
instanceStatuses
? group.variants.some((v) => instanceStatuses[v.id] != null)
: false,
);
const groupFitStatus = $derived.by((): ModelFitStatus => {
let hasClusterCapacityOnly = false;
for (const variant of group.variants) {
@@ -122,16 +132,36 @@
!group.hasMultipleVariants &&
group.variants.some((v) => v.id === selectedModelId),
);
// Group-level instance status: show the "best" status across all variants
const groupInstanceStatus = $derived.by(() => {
if (!instanceStatuses) return null;
const readyStatuses = ["READY", "LOADED", "RUNNING"];
const loadingStatuses = ["LOADING", "WARMING UP"];
let bestStatus: { status: string; statusClass: string } | null = null;
for (const variant of group.variants) {
const s = instanceStatuses[variant.id];
if (!s) continue;
if (readyStatuses.includes(s.status)) return s; // Ready is best
if (loadingStatuses.includes(s.status) || s.status === "DOWNLOADING") {
bestStatus = s;
}
}
return bestStatus;
});
</script>
<div
class="border-b border-white/5 last:border-b-0 {!anyVariantFits
data-model-ids={group.variants.map((v) => v.id).join(" ")}
class="border-b border-white/5 last:border-b-0 {!anyVariantFits &&
!anyVariantHasInstance
? 'opacity-50'
: ''}"
: ''} {isHighlighted ? 'model-just-added' : ''}"
>
<!-- Main row -->
<div
class="flex items-center gap-2 px-3 py-2.5 transition-colors {anyVariantFits
class="flex items-center gap-2 px-3 py-2.5 transition-colors {anyVariantFits ||
anyVariantHasInstance
? 'hover:bg-white/5 cursor-pointer'
: 'cursor-not-allowed'} {isMainSelected
? 'bg-exo-yellow/10 border-l-2 border-exo-yellow'
@@ -141,7 +171,7 @@
onToggleExpand();
} else {
const modelId = group.variants[0]?.id;
if (modelId && canModelFit(modelId)) {
if (modelId && (canModelFit(modelId) || instanceStatuses[modelId])) {
onSelectModel(modelId);
}
}
@@ -155,7 +185,7 @@
onToggleExpand();
} else {
const modelId = group.variants[0]?.id;
if (modelId && canModelFit(modelId)) {
if (modelId && (canModelFit(modelId) || instanceStatuses[modelId])) {
onSelectModel(modelId);
}
}
@@ -346,6 +376,47 @@
</span>
{/if}
<!-- Instance status badge -->
{#if groupInstanceStatus}
{#if groupInstanceStatus.status === "READY" || groupInstanceStatus.status === "LOADED" || groupInstanceStatus.status === "RUNNING"}
<span class="flex-shrink-0" title="Running">
<svg
class="w-3 h-3 text-green-400"
viewBox="0 0 12 12"
fill="currentColor"
>
<circle cx="6" cy="6" r="5" />
</svg>
</span>
{:else if groupInstanceStatus.status === "DOWNLOADING"}
<span class="flex-shrink-0 animate-pulse" title="Downloading">
<svg
class="w-3.5 h-3.5 text-blue-400"
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
>
<path d="M21 15v4a2 2 0 01-2 2H5a2 2 0 01-2-2v-4" />
<polyline points="7 10 12 15 17 10" />
<line x1="12" y1="15" x2="12" y2="3" />
</svg>
</span>
{:else if groupInstanceStatus.status === "LOADING" || groupInstanceStatus.status === "WARMING UP"}
<span class="flex-shrink-0 animate-pulse" title="Loading">
<svg
class="w-3 h-3 text-yellow-400"
viewBox="0 0 12 12"
fill="currentColor"
>
<circle cx="6" cy="6" r="5" />
</svg>
</span>
{/if}
{/if}
<!-- Check mark if selected (single-variant) -->
{#if isMainSelected}
<svg
@@ -420,20 +491,30 @@
{#each group.variants as variant}
{@const fitStatus = getModelFitStatus(variant.id)}
{@const modelCanFit = canModelFit(variant.id)}
{@const variantHasInstance = instanceStatuses[variant.id] != null}
{@const isSelected = selectedModelId === variant.id}
<button
type="button"
class="w-full flex items-center gap-3 px-3 py-2 pl-10 hover:bg-white/5 transition-colors text-left {!modelCanFit
<div
class="w-full flex items-center gap-3 px-3 py-2 pl-10 hover:bg-white/5 transition-colors text-left {!modelCanFit &&
!variantHasInstance
? 'opacity-50 cursor-not-allowed'
: 'cursor-pointer'} {isSelected
? 'bg-exo-yellow/10 border-l-2 border-exo-yellow'
: 'border-l-2 border-transparent'}"
disabled={!modelCanFit}
role="button"
tabindex="0"
onclick={() => {
if (modelCanFit) {
if (modelCanFit || variantHasInstance) {
onSelectModel(variant.id);
}
}}
onkeydown={(e) => {
if (e.key === "Enter" || e.key === " ") {
e.preventDefault();
if (modelCanFit) {
onSelectModel(variant.id);
}
}
}}
>
<!-- Quantization badge -->
<span
@@ -478,6 +559,48 @@
{/if}
{/if}
<!-- Instance status badge -->
{#if instanceStatuses[variant.id]}
{@const instStatus = instanceStatuses[variant.id]}
{#if instStatus.status === "READY" || instStatus.status === "LOADED" || instStatus.status === "RUNNING"}
<span class="flex-shrink-0" title="Running">
<svg
class="w-3 h-3 text-green-400"
viewBox="0 0 12 12"
fill="currentColor"
>
<circle cx="6" cy="6" r="5" />
</svg>
</span>
{:else if instStatus.status === "DOWNLOADING"}
<span class="flex-shrink-0 animate-pulse" title="Downloading">
<svg
class="w-3.5 h-3.5 text-blue-400"
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
>
<path d="M21 15v4a2 2 0 01-2 2H5a2 2 0 01-2-2v-4" />
<polyline points="7 10 12 15 17 10" />
<line x1="12" y1="15" x2="12" y2="3" />
</svg>
</span>
{:else if instStatus.status === "LOADING" || instStatus.status === "WARMING UP"}
<span class="flex-shrink-0 animate-pulse" title="Loading">
<svg
class="w-3 h-3 text-yellow-400"
viewBox="0 0 12 12"
fill="currentColor"
>
<circle cx="6" cy="6" r="5" />
</svg>
</span>
{/if}
{/if}
<!-- Check mark if selected -->
{#if isSelected}
<svg
@@ -490,8 +613,55 @@
/>
</svg>
{/if}
</button>
<!-- Info button -->
<button
type="button"
class="p-1 rounded hover:bg-white/10 transition-colors flex-shrink-0"
onclick={(e) => {
e.stopPropagation();
onShowInfo({
id: variant.id,
name: variant.name || variant.id,
capabilities: group.capabilities,
family: group.family,
variants: [variant],
smallestVariant: variant,
hasMultipleVariants: false,
});
}}
title="View variant details"
>
<svg
class="w-4 h-4 text-white/30 hover:text-white/50"
viewBox="0 0 24 24"
fill="currentColor"
>
<path
d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm1 15h-2v-6h2v6zm0-8h-2V7h2v2z"
/>
</svg>
</button>
</div>
{/each}
</div>
{/if}
</div>
<style>
.model-just-added {
animation: highlightFade 4s ease-out forwards;
}
@keyframes highlightFade {
0%,
40% {
background-color: rgba(20, 83, 45, 0.25);
box-shadow: inset 0 0 0 1px rgba(74, 222, 128, 0.4);
}
100% {
background-color: transparent;
box-shadow: none;
}
}
</style>
@@ -1,4 +1,5 @@
<script lang="ts">
import { tick } from "svelte";
import { fade, fly } from "svelte/transition";
import { cubicOut } from "svelte/easing";
import FamilySidebar from "./FamilySidebar.svelte";
@@ -7,6 +8,7 @@
import HuggingFaceResultItem from "./HuggingFaceResultItem.svelte";
import { getNodesWithModelDownloaded } from "$lib/utils/downloads";
import { getRecentEntries } from "$lib/stores/recents.svelte";
import { addToast } from "$lib/stores/toast.svelte";
interface ModelInfo {
id: string;
@@ -36,6 +38,7 @@
capabilities: string[];
sizeRange: { min: number; max: number } | null;
downloadedOnly: boolean;
readyOnly: boolean;
}
interface HuggingFaceModel {
@@ -49,6 +52,11 @@
type ModelFitStatus = "fits_now" | "fits_cluster_capacity" | "too_large";
export type InstanceStatus = {
status: string;
statusClass: string;
};
type ModelPickerModalProps = {
isOpen: boolean;
models: ModelInfo[];
@@ -75,6 +83,7 @@
macmon_info?: { memory?: { ram_total?: number } };
}
>;
instanceStatuses?: Record<string, InstanceStatus>;
};
let {
@@ -96,6 +105,7 @@
usedMemoryGB,
downloadsData,
topologyNodes,
instanceStatuses = {},
}: ModelPickerModalProps = $props();
// Local state
@@ -107,6 +117,7 @@
capabilities: [],
sizeRange: null,
downloadedOnly: false,
readyOnly: false,
});
let infoGroup = $state<ModelGroup | null>(null);
@@ -182,6 +193,13 @@
let hfSearchDebounceTimer: ReturnType<typeof setTimeout> | null = null;
let manualModelId = $state("");
let addModelError = $state<string | null>(null);
let justAddedModelId = $state<string | null>(null);
let justAddedTimer: ReturnType<typeof setTimeout> | null = null;
// Inline HuggingFace search in main search bar
let mainSearchHfResults = $state<HuggingFaceModel[]>([]);
let mainSearchHfLoading = $state(false);
let mainSearchDebounceTimer: ReturnType<typeof setTimeout> | null = null;
// Reset transient state when modal opens, but preserve tab selection
$effect(() => {
@@ -191,6 +209,11 @@
showFilters = false;
manualModelId = "";
addModelError = null;
justAddedModelId = null;
if (justAddedTimer) {
clearTimeout(justAddedTimer);
justAddedTimer = null;
}
}
});
@@ -205,6 +228,50 @@
}
});
// Inline HuggingFace search when local search returns no results
$effect(() => {
const query = searchQuery.trim();
const noLocalResults = filteredGroups.length === 0;
if (mainSearchDebounceTimer) {
clearTimeout(mainSearchDebounceTimer);
mainSearchDebounceTimer = null;
}
if (
selectedFamily === "huggingface" ||
selectedFamily === "recents" ||
selectedFamily === "favorites" ||
query.length < 2 ||
!noLocalResults
) {
mainSearchHfResults = [];
mainSearchHfLoading = false;
return;
}
mainSearchHfLoading = true;
mainSearchDebounceTimer = setTimeout(async () => {
try {
const response = await fetch(
`/models/search?query=${encodeURIComponent(query)}&limit=10`,
);
if (response.ok) {
const results: HuggingFaceModel[] = await response.json();
mainSearchHfResults = results.filter(
(r) => !existingModelIds.has(r.id),
);
} else {
mainSearchHfResults = [];
}
} catch {
mainSearchHfResults = [];
} finally {
mainSearchHfLoading = false;
}
}, 500);
});
async function fetchTrendingModels() {
hfIsLoadingTrending = true;
try {
@@ -265,6 +332,24 @@
addModelError = null;
try {
await onAddModel(modelId);
// Success: show toast, switch to All Models, highlight the model
const shortName = modelId.split("/").pop() || modelId;
addToast({ type: "success", message: `Added ${shortName}` });
justAddedModelId = modelId;
selectedFamily = null;
searchQuery = "";
// Scroll to the newly added model after DOM update
await tick();
const el = document.querySelector(
`[data-model-ids~="${CSS.escape(modelId)}"]`,
);
el?.scrollIntoView({ behavior: "smooth", block: "center" });
// Clear highlight after 4 seconds
if (justAddedTimer) clearTimeout(justAddedTimer);
justAddedTimer = setTimeout(() => {
justAddedModelId = null;
justAddedTimer = null;
}, 4000);
} catch (error) {
addModelError =
error instanceof Error ? error.message : "Failed to add model";
@@ -372,8 +457,10 @@
"deepseek",
"gpt-oss",
"llama",
"gemma",
"flux",
"qwen-image",
"nemotron",
];
return Array.from(families).sort((a, b) => {
const aIdx = familyOrder.indexOf(a);
@@ -440,6 +527,16 @@
);
}
// Filter to ready/running models only
if (filters.readyOnly) {
result = result.filter((g) =>
g.variants.some((v) => {
const s = instanceStatuses[v.id];
return s && s.statusClass === "ready";
}),
);
}
// Sort: fits-now first, then fits-cluster-capacity, then too-large
result.sort((a, b) => {
const getGroupFitRank = (group: ModelGroup): number => {
@@ -512,6 +609,18 @@
);
});
// Split filtered groups into recommended (fits_now) and others for visual separation
const recommendedGroups = $derived(
filteredGroups.filter((g) =>
g.variants.some((v) => getModelFitStatus(v.id) === "fits_now"),
),
);
const otherGroups = $derived(
filteredGroups.filter(
(g) => !g.variants.some((v) => getModelFitStatus(v.id) === "fits_now"),
),
);
function toggleGroupExpanded(groupId: string) {
const next = new Set(expandedGroups);
if (next.has(groupId)) {
@@ -538,13 +647,19 @@
}
function clearFilters() {
filters = { capabilities: [], sizeRange: null, downloadedOnly: false };
filters = {
capabilities: [],
sizeRange: null,
downloadedOnly: false,
readyOnly: false,
};
}
const hasActiveFilters = $derived(
filters.capabilities.length > 0 ||
filters.sizeRange !== null ||
filters.downloadedOnly,
filters.downloadedOnly ||
filters.readyOnly,
);
</script>
@@ -804,6 +919,8 @@
{group}
isExpanded={expandedGroups.has(group.id)}
isFavorite={favorites.has(group.id)}
isHighlighted={justAddedModelId !== null &&
group.variants.some((v) => v.id === justAddedModelId)}
{selectedModelId}
{canModelFit}
{getModelFitStatus}
@@ -813,6 +930,7 @@
onShowInfo={(g) => (infoGroup = g)}
downloadStatusMap={getVariantDownloadMap(group)}
launchedAt={recentTimestamps.get(group.variants[0]?.id ?? "")}
{instanceStatuses}
/>
{/each}
{/if}
@@ -840,11 +958,40 @@
{/if}
</div>
{:else}
{#each filteredGroups as group}
<!-- Recommended for your cluster -->
{#if recommendedGroups.length > 0 && otherGroups.length > 0 && !searchQuery.trim()}
<div
class="sticky top-0 z-10 flex items-center gap-2 px-3 py-2 bg-green-950/60 border-b border-green-500/20 backdrop-blur-sm"
>
<svg
class="w-3.5 h-3.5 text-green-400 flex-shrink-0"
fill="none"
viewBox="0 0 24 24"
stroke="currentColor"
stroke-width="2"
>
<path
stroke-linecap="round"
stroke-linejoin="round"
d="M9 12.75L11.25 15 15 9.75M21 12a9 9 0 11-18 0 9 9 0 0118 0z"
/>
</svg>
<span
class="text-xs font-mono text-green-400 tracking-wider uppercase"
>Recommended for your cluster</span
>
<span class="text-xs font-mono text-green-400/50"
>— fits in available memory</span
>
</div>
{/if}
{#each recommendedGroups as group}
<ModelPickerGroup
{group}
isExpanded={expandedGroups.has(group.id)}
isFavorite={favorites.has(group.id)}
isHighlighted={justAddedModelId !== null &&
group.variants.some((v) => v.id === justAddedModelId)}
{selectedModelId}
{canModelFit}
{getModelFitStatus}
@@ -853,8 +1000,87 @@
{onToggleFavorite}
onShowInfo={(g) => (infoGroup = g)}
downloadStatusMap={getVariantDownloadMap(group)}
{instanceStatuses}
/>
{/each}
<!-- Other models -->
{#if otherGroups.length > 0 && recommendedGroups.length > 0 && !searchQuery.trim()}
<div
class="sticky top-0 z-10 flex items-center gap-2 px-3 py-2 bg-exo-dark-gray/80 border-y border-exo-medium-gray/20 backdrop-blur-sm"
>
<span
class="text-xs font-mono text-white/40 tracking-wider uppercase"
>Other models</span
>
</div>
{/if}
{#each otherGroups as group}
<ModelPickerGroup
{group}
isExpanded={expandedGroups.has(group.id)}
isFavorite={favorites.has(group.id)}
isHighlighted={justAddedModelId !== null &&
group.variants.some((v) => v.id === justAddedModelId)}
{selectedModelId}
{canModelFit}
{getModelFitStatus}
onToggleExpand={() => toggleGroupExpanded(group.id)}
onSelectModel={handleSelect}
{onToggleFavorite}
onShowInfo={(g) => (infoGroup = g)}
downloadStatusMap={getVariantDownloadMap(group)}
{instanceStatuses}
/>
{/each}
<!-- Inline HuggingFace search results (shown when no local results match) -->
{#if filteredGroups.length === 0 && searchQuery.trim().length >= 2 && selectedFamily !== "huggingface" && selectedFamily !== "recents" && selectedFamily !== "favorites"}
{#if mainSearchHfLoading}
<div
class="flex items-center gap-2 px-3 py-2 border-t border-orange-400/20 bg-orange-950/20"
>
<span
class="w-4 h-4 border-2 border-orange-400 border-t-transparent rounded-full animate-spin"
></span>
<span class="text-xs font-mono text-orange-400/60"
>Searching HuggingFace...</span
>
</div>
{:else if mainSearchHfResults.length > 0}
<div
class="sticky top-0 z-10 flex items-center gap-2 px-3 py-2 bg-orange-950/30 border-y border-orange-400/20 backdrop-blur-sm"
>
<span
class="text-xs font-mono text-orange-400 tracking-wider uppercase"
>From HuggingFace</span
>
</div>
{#each mainSearchHfResults as model}
<HuggingFaceResultItem
{model}
isAdded={existingModelIds.has(model.id)}
isAdding={addingModelId === model.id}
onAdd={() => handleAddModel(model.id)}
onSelect={() => handleSelectHfModel(model.id)}
downloadedOnNodes={downloadsData
? getNodesWithModelDownloaded(downloadsData, model.id).map(
getNodeName,
)
: []}
/>
{/each}
<button
type="button"
class="w-full px-3 py-2 text-xs font-mono text-orange-400/60 hover:text-orange-400 hover:bg-orange-500/10 transition-colors text-center"
onclick={() => {
hfSearchQuery = searchQuery;
searchHuggingFace(searchQuery);
selectedFamily = "huggingface";
}}
>
See all results on Hub
</button>
{/if}
{/if}
{/if}
</div>
</div>
@@ -875,6 +1101,11 @@
>Downloaded</span
>
{/if}
{#if filters.readyOnly}
<span class="px-1.5 py-0.5 bg-green-500/20 text-green-400 rounded"
>Ready</span
>
{/if}
{#if filters.sizeRange}
<span class="px-1.5 py-0.5 bg-exo-yellow/20 text-exo-yellow rounded">
{filters.sizeRange.min}GB - {filters.sizeRange.max}GB
@@ -0,0 +1,565 @@
<script lang="ts">
import { onMount, onDestroy } from "svelte";
import FamilyLogos from "$lib/components/FamilyLogos.svelte";
import {
instances,
instanceLinks,
nodeIdentities,
refreshState,
createInstanceLink,
updateInstanceLink,
deleteInstanceLink,
type Instance,
} from "$lib/stores/app.svelte";
import { deriveBaseModel, deriveFamily } from "$lib/utils/model_family";
type InstanceWrapper = {
MlxRingInstance?: Instance;
MlxJacclInstance?: Instance;
VllmInstance?: Instance;
};
let interval: ReturnType<typeof setInterval> | null = null;
onMount(() => {
refreshState();
interval = setInterval(refreshState, 3000);
});
onDestroy(() => {
if (interval) clearInterval(interval);
});
type InstanceRow = {
id: string;
modelId: string;
family: string;
baseModel: string;
nodeNames: string[];
nodeCount: number;
};
const instanceRows = $derived.by<InstanceRow[]>(() => {
const rows: InstanceRow[] = [];
const ids = nodeIdentities();
for (const [id, raw] of Object.entries(instances())) {
const wrapper = raw as InstanceWrapper;
const inst =
wrapper.MlxRingInstance ??
wrapper.MlxJacclInstance ??
wrapper.VllmInstance;
const modelId = inst?.shardAssignments?.modelId ?? "";
const nodeToRunner = inst?.shardAssignments?.nodeToRunner ?? {};
const nodeIds = Object.keys(nodeToRunner);
const nodeNames = nodeIds
.map((nodeId) => ids[nodeId]?.friendlyName ?? nodeId.slice(0, 6))
.filter((name) => !!name);
rows.push({
id,
modelId,
family: deriveFamily(modelId),
baseModel: deriveBaseModel(modelId),
nodeNames,
nodeCount: nodeIds.length,
});
}
rows.sort((a, b) => a.modelId.localeCompare(b.modelId));
return rows;
});
const instanceById = $derived(
Object.fromEntries(instanceRows.map((r) => [r.id, r])),
);
type LinkRow = {
linkId: string;
prefill: string[];
decode: string[];
families: string[];
multiNode: boolean;
};
const linkRows = $derived.by<LinkRow[]>(() => {
const rows: LinkRow[] = [];
for (const [, link] of Object.entries(instanceLinks())) {
const fams = new Set<string>();
let multiNode = false;
for (const id of [...link.prefillInstances, ...link.decodeInstances]) {
const r = instanceById[id];
if (r && r.baseModel) fams.add(r.baseModel.toLowerCase());
if (r && r.nodeCount > 1) multiNode = true;
}
rows.push({
linkId: link.linkId,
prefill: link.prefillInstances,
decode: link.decodeInstances,
families: Array.from(fams),
multiNode,
});
}
return rows;
});
let editingLinkId = $state<string | null>(null);
let editingPrefill = $state<Set<string>>(new Set());
let editingDecode = $state<Set<string>>(new Set());
let saving = $state(false);
let errorMessage = $state<string | null>(null);
function startCreate() {
editingLinkId = "new";
editingPrefill = new Set();
editingDecode = new Set();
errorMessage = null;
}
function startEdit(row: LinkRow) {
editingLinkId = row.linkId;
editingPrefill = new Set(row.prefill);
editingDecode = new Set(row.decode);
errorMessage = null;
}
function cancelEdit() {
editingLinkId = null;
editingPrefill = new Set();
editingDecode = new Set();
errorMessage = null;
}
type Role = "prefill" | "decode" | "none";
function roleOf(id: string): Role {
if (editingPrefill.has(id)) return "prefill";
if (editingDecode.has(id)) return "decode";
return "none";
}
function setRole(id: string, role: Role) {
const p = new Set(editingPrefill);
const d = new Set(editingDecode);
p.delete(id);
d.delete(id);
if (role === "prefill") p.add(id);
if (role === "decode") d.add(id);
editingPrefill = p;
editingDecode = d;
}
const editingFamilies = $derived.by<string[]>(() => {
const fams = new Set<string>();
for (const id of [...editingPrefill, ...editingDecode]) {
const r = instanceById[id];
if (r && r.baseModel) fams.add(r.baseModel.toLowerCase());
}
return Array.from(fams);
});
const editingMultiNode = $derived.by<string[]>(() => {
const names: string[] = [];
for (const id of [...editingPrefill, ...editingDecode]) {
const r = instanceById[id];
if (r && r.nodeCount > 1) {
names.push(r.baseModel || r.modelId);
}
}
return names;
});
const editingMismatch = $derived(editingFamilies.length > 1);
const canSave = $derived(
editingLinkId !== null &&
editingPrefill.size > 0 &&
editingDecode.size > 0 &&
!saving,
);
async function save() {
if (editingLinkId === null) return;
saving = true;
errorMessage = null;
try {
const prefill = Array.from(editingPrefill);
const decode = Array.from(editingDecode);
if (editingLinkId === "new") {
await createInstanceLink(prefill, decode);
} else {
await updateInstanceLink(editingLinkId, prefill, decode);
}
cancelEdit();
await refreshState();
} catch (err) {
errorMessage = err instanceof Error ? err.message : String(err);
} finally {
saving = false;
}
}
async function remove(linkId: string) {
if (!confirm("Remove this routing?")) return;
try {
await deleteInstanceLink(linkId);
if (editingLinkId === linkId) cancelEdit();
await refreshState();
} catch (err) {
errorMessage = err instanceof Error ? err.message : String(err);
}
}
</script>
<div class="font-mono text-foreground">
<div class="mb-6 space-y-4">
<details open class="group [&_summary::-webkit-details-marker]:hidden">
<summary
class="cursor-pointer list-none text-exo-yellow text-xs font-mono tracking-widest uppercase flex items-center gap-2 hover:opacity-80 transition-opacity"
>
<span
class="inline-block transition-transform group-open:rotate-90 text-exo-light-gray"
>▶</span
>
Prefill vs Decode
</summary>
<div class="mt-2 text-white/80 text-sm leading-relaxed">
Prefill is the compute-heavy pass that consumes the entire prompt and
builds a KV cache. Decode is the memory-bandwidth-bound loop that emits
tokens sequentially from that cache. The two phases have very different
bottlenecks, so running them on different hardware can be substantially
faster than doing both on one node.
</div>
</details>
<details class="group [&_summary::-webkit-details-marker]:hidden">
<summary
class="cursor-pointer list-none text-exo-yellow text-xs font-mono tracking-widest uppercase flex items-center gap-2 hover:opacity-80 transition-opacity"
>
<span
class="inline-block transition-transform group-open:rotate-90 text-exo-light-gray"
>▶</span
>
Linking Instances
</summary>
<div class="mt-2 text-white/80 text-sm leading-relaxed space-y-2">
<p>
A linked route here tells the cluster: when a request is sent to a
model in that cluster, the decode node (or the least active one if
there are multiple) will handle it. If it decides it must do a lot of
prefill not already cached in the prefix cache, it routes the request
to the prefill node over TCP IP. The prefill node streams the KV cache
back to the decode node which picks up from there.
</p>
<p>
Linked instances must be running the same model family — KV layouts
differ across architectures. More on the <a
class="text-exo-yellow underline underline-offset-2 hover:text-exo-yellow-darker transition-colors"
href="https://blog.exolabs.net/nvidia-dgx-spark/"
target="_blank"
rel="noreferrer noopener">blog</a
>.
</p>
</div>
</details>
</div>
{#if errorMessage}
<div
class="mb-4 px-4 py-3 bg-red-500/10 border border-red-500/40 text-red-300 text-sm"
>
{errorMessage}
</div>
{/if}
<section class="mt-12">
<h2
class="text-exo-yellow text-xs font-mono tracking-widest uppercase m-0 mb-3"
>
Existing routes
</h2>
{#if linkRows.length === 0}
{#if editingLinkId === null}
<div class="flex items-center justify-between">
<p class="text-exo-light-gray italic text-sm m-0">
No routes yet. Create one to enable remote prefill.
</p>
<button
class="px-3 py-1.5 text-xs font-mono tracking-wider uppercase bg-exo-yellow/15 border border-exo-yellow/50 text-exo-yellow hover:bg-exo-yellow/25 hover:border-exo-yellow/80 transition-colors"
onclick={startCreate}
>
+ New route
</button>
</div>
{/if}
{:else}
{#if editingLinkId === null}
<div class="flex justify-end mb-3">
<button
class="px-3 py-1.5 text-xs font-mono tracking-wider uppercase bg-exo-yellow/15 border border-exo-yellow/50 text-exo-yellow hover:bg-exo-yellow/25 hover:border-exo-yellow/80 transition-colors"
onclick={startCreate}
>
+ New route
</button>
</div>
{/if}
<div
class="bg-exo-dark-gray/60 border border-exo-medium-gray/40 flex flex-col"
>
{#each linkRows as row (row.linkId)}
{#if editingLinkId !== row.linkId}
<article
class="p-4 border-b border-exo-light-gray/25 last:border-b-0"
>
{#if row.multiNode}
<div
class="mb-3 px-3 py-2 bg-red-500/10 border border-red-500/40 text-red-300 text-xs tracking-wide"
>
⚠ Multi-node instance detected. Remote prefill currently only
works on single-node (rank-0) instances. This route will not
function until that's supported.
</div>
{/if}
{#if row.families.length > 1}
<div
class="mb-3 px-3 py-2 bg-amber-500/10 border border-amber-500/40 text-amber-300 text-xs tracking-wide"
>
⚠ Mixed model families: {row.families.join(", ")}
</div>
{/if}
<div
class="grid grid-cols-[1fr_auto_1fr_auto] items-center gap-x-3 gap-y-2"
>
<span
class="inline-block justify-self-start text-[10px] font-mono tracking-widest uppercase px-2 py-0.5 bg-exo-yellow/15 border border-exo-yellow/40 text-exo-yellow"
>Prefill</span
>
<span></span>
<span
class="inline-block justify-self-start text-[10px] font-mono tracking-widest uppercase px-2 py-0.5 bg-exo-medium-gray/40 border border-exo-medium-gray/60 text-foreground"
>Decode</span
>
<span></span>
<div class="min-w-0">
<ul class="list-none p-0 m-0 flex flex-col gap-2">
{#each row.prefill as id (id)}
{@const r = instanceById[id]}
{#if r}
<li
class="flex items-center gap-2 px-2.5 py-2 bg-exo-medium-gray/20 border border-exo-medium-gray/40"
>
<FamilyLogos family={r.family} />
<div class="min-w-0 flex-1">
<div
class="text-exo-yellow text-xs font-mono truncate"
>
{r.baseModel || r.modelId}
</div>
<div
class="text-exo-light-gray text-[11px] truncate"
>
{r.nodeNames.join(", ") || "?"}{r.nodeCount > 1
? ` (${r.nodeCount} nodes)`
: ""}
</div>
<div
class="text-exo-light-gray/40 text-[10px] font-mono truncate"
title={r.id}
>
{r.id.slice(0, 8)}
</div>
</div>
</li>
{/if}
{/each}
</ul>
</div>
<div class="text-exo-yellow/60 text-xl px-2" aria-hidden="true">
</div>
<div class="min-w-0">
<ul class="list-none p-0 m-0 flex flex-col gap-2">
{#each row.decode as id (id)}
{@const r = instanceById[id]}
{#if r}
<li
class="flex items-center gap-2 px-2.5 py-2 bg-exo-medium-gray/20 border border-exo-medium-gray/40"
>
<FamilyLogos family={r.family} />
<div class="min-w-0 flex-1">
<div
class="text-exo-yellow text-xs font-mono truncate"
>
{r.baseModel || r.modelId}
</div>
<div
class="text-exo-light-gray text-[11px] truncate"
>
{r.nodeNames.join(", ") || "?"}{r.nodeCount > 1
? ` (${r.nodeCount} nodes)`
: ""}
</div>
<div
class="text-exo-light-gray/40 text-[10px] font-mono truncate"
title={r.id}
>
{r.id.slice(0, 8)}
</div>
</div>
</li>
{/if}
{/each}
</ul>
</div>
<div class="flex gap-2 pl-3">
<button
class="px-2 py-0.5 text-[11px] font-mono tracking-wider uppercase bg-exo-medium-gray/30 border border-exo-medium-gray/60 rounded text-foreground hover:border-exo-yellow/60 hover:text-exo-yellow disabled:opacity-40 disabled:cursor-not-allowed transition-colors"
onclick={() => startEdit(row)}
disabled={editingLinkId !== null}
>
Edit
</button>
<button
class="px-2 py-0.5 text-[11px] font-mono tracking-wider uppercase bg-red-500/15 border border-red-500/40 rounded text-red-300 hover:bg-red-500/25 transition-colors"
onclick={() => remove(row.linkId)}
>
Remove
</button>
</div>
</div>
</article>
{/if}
{/each}
</div>
{/if}
</section>
{#if editingLinkId !== null && instanceRows.length === 0}
<section
class="mt-6 bg-exo-dark-gray/60 border border-exo-yellow/30 px-4 py-2.5 flex items-center justify-between gap-3"
>
<span class="text-exo-light-gray italic text-sm font-mono"
>No instances available.</span
>
<button
class="px-3 py-1 text-xs font-mono tracking-wider uppercase bg-exo-medium-gray/30 border border-exo-medium-gray/60 rounded text-foreground hover:border-exo-yellow/60 transition-colors"
onclick={cancelEdit}
>
Cancel
</button>
</section>
{:else if editingLinkId !== null}
<section class="mt-6 bg-exo-dark-gray/60 border border-exo-yellow/30 p-5">
<h2
class="text-exo-yellow text-xs font-mono tracking-widest uppercase m-0 mb-3"
>
{editingLinkId === "new" ? "New route" : "Edit route"}
</h2>
{#if editingMismatch}
<div
class="mb-3 px-3 py-2 bg-amber-500/10 border border-amber-500/40 text-amber-300 text-xs tracking-wide"
>
⚠ Selected instances span multiple model families: <strong
>{editingFamilies.join(", ")}</strong
>. Linking across families produces a corrupt KV cache.
</div>
{/if}
{#if editingMultiNode.length > 0}
<div
class="mb-3 px-3 py-2 bg-red-500/10 border border-red-500/40 text-red-300 text-xs tracking-wide"
>
⚠ Multi-node instance(s) selected: <strong
>{editingMultiNode.join(", ")}</strong
>. Remote prefill currently only works on single-node instances. This
route will not function until multi-node support lands.
</div>
{/if}
<p class="text-exo-light-gray text-xs mb-4">
Pick a role for each instance:
<span class="text-exo-yellow">Prefill</span>
serves KV cache,
<span class="text-foreground">Decode</span> consumes it.
</p>
<div
class="grid gap-2.5"
style="grid-template-columns: repeat(auto-fill, minmax(360px, 1fr));"
>
{#each instanceRows as row (row.id)}
{@const role = roleOf(row.id)}
<div
class="border p-3 flex flex-col gap-2.5 transition-colors {role ===
'prefill'
? 'border-exo-yellow/60 bg-exo-dark-gray/60'
: role === 'decode'
? 'border-exo-light-gray/60 bg-exo-dark-gray/60'
: 'border-exo-medium-gray/40 bg-exo-dark-gray/40'}"
>
<div class="flex items-center gap-2">
<FamilyLogos family={row.family} />
<div class="min-w-0 flex-1">
<div class="text-exo-yellow text-xs font-mono truncate">
{row.baseModel || row.modelId}
</div>
<div class="text-exo-light-gray text-[11px] truncate">
{row.nodeNames.join(", ") || "?"}{row.nodeCount > 1
? ` (${row.nodeCount} nodes)`
: ""}
</div>
<div
class="text-exo-light-gray/40 text-[10px] font-mono truncate"
title={row.id}
>
{row.id.slice(0, 8)}
</div>
</div>
{#if row.nodeCount > 1}
<span
class="text-[9px] font-mono tracking-widest uppercase px-1.5 py-0.5 bg-red-500/15 border border-red-500/40 text-red-300"
title="Multi-node instances are not supported by remote prefill yet."
>Unsupported</span
>
{/if}
</div>
<div
class="flex rounded-md overflow-hidden border border-exo-light-gray/40 divide-x divide-exo-light-gray/40"
>
<button
class="flex-1 px-2 py-1 text-[11px] font-mono tracking-wider uppercase transition-colors {role ===
'prefill'
? 'bg-exo-yellow/20 text-exo-yellow'
: 'bg-transparent text-white/80 hover:text-exo-yellow'}"
onclick={() =>
setRole(row.id, role === "prefill" ? "none" : "prefill")}
>Prefill</button
>
<button
class="flex-1 px-2 py-1 text-[11px] font-mono tracking-wider uppercase transition-colors {role ===
'decode'
? 'bg-exo-medium-gray/50 text-foreground'
: 'bg-transparent text-white/80 hover:text-foreground'}"
onclick={() =>
setRole(row.id, role === "decode" ? "none" : "decode")}
>Decode</button
>
</div>
</div>
{/each}
</div>
<div class="flex gap-2 mt-5 justify-end">
<button
class="px-3 py-1.5 text-xs font-mono tracking-wider uppercase bg-exo-yellow/15 border border-exo-yellow/50 text-exo-yellow hover:bg-exo-yellow/25 hover:border-exo-yellow/80 disabled:opacity-40 disabled:cursor-not-allowed transition-colors"
onclick={save}
disabled={!canSave}
>
{saving ? "Saving..." : "Save route"}
</button>
<button
class="px-3 py-1.5 text-xs font-mono tracking-wider uppercase bg-exo-medium-gray/30 border border-exo-medium-gray/60 text-foreground hover:border-exo-yellow/60 disabled:opacity-40 disabled:cursor-not-allowed transition-colors"
onclick={cancelEdit}
disabled={saving}
>
Cancel
</button>
</div>
</section>
{/if}
</div>
@@ -0,0 +1,70 @@
<script lang="ts">
import type { PrefillProgress } from "$lib/stores/app.svelte";
interface Props {
progress: PrefillProgress;
class?: string;
}
let { progress, class: className = "" }: Props = $props();
const percentage = $derived(
progress.total > 0
? Math.round((progress.processed / progress.total) * 100)
: 0,
);
const etaText = $derived.by(() => {
if (progress.processed <= 0 || progress.total <= 0) return null;
const elapsedMs = performance.now() - progress.startedAt;
if (elapsedMs < 200) return null; // need a minimum sample window
const tokensPerMs = progress.processed / elapsedMs;
const remainingTokens = progress.total - progress.processed;
const remainingMs = remainingTokens / tokensPerMs;
const remainingSec = Math.ceil(remainingMs / 1000);
if (remainingSec <= 0) return null;
if (remainingSec < 60) return `~${remainingSec}s remaining`;
const mins = Math.floor(remainingSec / 60);
const secs = remainingSec % 60;
return `~${mins}m ${secs}s remaining`;
});
function formatTokenCount(count: number | undefined): string {
if (count == null) return "0";
if (count >= 1000) {
return `${(count / 1000).toFixed(1)}k`;
}
return count.toString();
}
</script>
<div class="prefill-progress {className}">
<div
class="flex items-center justify-between text-xs text-exo-light-gray mb-1"
>
<span>Processing prompt</span>
<span class="font-mono">
{formatTokenCount(progress.processed)} / {formatTokenCount(
progress.total,
)} tokens
</span>
</div>
<div class="h-1.5 bg-exo-black/60 rounded-full overflow-hidden">
<div
class="h-full bg-exo-yellow rounded-full transition-all duration-150 ease-out"
style="width: {percentage}%"
></div>
</div>
<div
class="flex items-center justify-between text-xs text-exo-light-gray/70 mt-0.5 font-mono"
>
<span>{etaText ?? ""}</span>
<span>{percentage}%</span>
</div>
</div>
<style>
.prefill-progress {
width: 100%;
}
</style>
@@ -0,0 +1,117 @@
<script lang="ts">
import { toasts, dismissToast, type Toast } from "$lib/stores/toast.svelte";
import { fly, fade } from "svelte/transition";
import { flip } from "svelte/animate";
const items = $derived(toasts());
const typeStyles: Record<
Toast["type"],
{ border: string; icon: string; iconColor: string }
> = {
success: {
border: "border-l-green-500",
icon: "M9 12.75L11.25 15 15 9.75M21 12a9 9 0 11-18 0 9 9 0 0118 0z",
iconColor: "text-green-400",
},
error: {
border: "border-l-red-500",
icon: "M12 9v3.75m9-.75a9 9 0 11-18 0 9 9 0 0118 0zm-9 3.75h.008v.008H12v-.008z",
iconColor: "text-red-400",
},
warning: {
border: "border-l-yellow-500",
icon: "M12 9v3.75m-9.303 3.376c-.866 1.5.217 3.374 1.948 3.374h14.71c1.73 0 2.813-1.874 1.948-3.374L13.949 3.378c-.866-1.5-3.032-1.5-3.898 0L2.697 16.126z",
iconColor: "text-yellow-400",
},
info: {
border: "border-l-blue-500",
icon: "M11.25 11.25l.041-.02a.75.75 0 011.063.852l-.708 2.836a.75.75 0 001.063.853l.041-.021M21 12a9 9 0 11-18 0 9 9 0 0118 0zm-9-3.75h.008v.008H12V8.25z",
iconColor: "text-blue-400",
},
};
</script>
{#if items.length > 0}
<div
class="fixed bottom-6 right-6 z-[9999] flex flex-col gap-2 pointer-events-none"
role="log"
aria-live="polite"
aria-label="Notifications"
>
{#each items as toast (toast.id)}
{@const style = typeStyles[toast.type]}
<div
class="pointer-events-auto max-w-sm w-80 bg-exo-dark-gray/95 backdrop-blur-sm border border-exo-medium-gray/60 border-l-[3px] {style.border} rounded shadow-lg shadow-black/40"
in:fly={{ x: 80, duration: 250 }}
out:fade={{ duration: 150 }}
animate:flip={{ duration: 200 }}
role="alert"
>
<div class="flex items-start gap-3 px-4 py-3">
<!-- Icon -->
<svg
class="w-5 h-5 flex-shrink-0 mt-0.5 {style.iconColor}"
fill="none"
viewBox="0 0 24 24"
stroke-width="1.5"
stroke="currentColor"
>
<path
stroke-linecap="round"
stroke-linejoin="round"
d={style.icon}
/>
</svg>
<!-- Message -->
<p class="flex-1 text-sm text-white/90 font-mono leading-snug">
{toast.message}
</p>
<!-- Dismiss button -->
<button
onclick={() => dismissToast(toast.id)}
class="flex-shrink-0 p-0.5 text-white/40 hover:text-white/80 transition-colors cursor-pointer"
aria-label="Dismiss notification"
>
<svg
class="w-4 h-4"
fill="none"
viewBox="0 0 24 24"
stroke-width="2"
stroke="currentColor"
>
<path
stroke-linecap="round"
stroke-linejoin="round"
d="M6 18L18 6M6 6l12 12"
/>
</svg>
</button>
</div>
<!-- Auto-dismiss progress bar -->
{#if toast.duration > 0}
<div class="h-0.5 bg-white/5 rounded-b overflow-hidden">
<div
class="h-full {style.border.replace('border-l-', 'bg-')}/60"
style="animation: shrink {toast.duration}ms linear forwards"
></div>
</div>
{/if}
</div>
{/each}
</div>
{/if}
<style>
@keyframes shrink {
from {
width: 100%;
}
to {
width: 0%;
}
}
</style>
+2
View File
@@ -12,3 +12,5 @@ export { default as HuggingFaceResultItem } from "./HuggingFaceResultItem.svelte
export { default as ModelFilterPopover } from "./ModelFilterPopover.svelte";
export { default as ModelPickerGroup } from "./ModelPickerGroup.svelte";
export { default as ModelPickerModal } from "./ModelPickerModal.svelte";
export { default as ChatModelSelector } from "./ChatModelSelector.svelte";
export { default as IntegrationCard } from "./IntegrationCard.svelte";
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