Try wrong shardings

- Add eval with a timeout.
- Add fast synch flag

Timeout mx barrier too

.github/workflows/build-app.yml

@@ -1,6 +1,7 @@
 name: Build EXO macOS DMG
 
 on:
+  workflow_dispatch:
   push:
     tags:
       - "v*"
@@ -35,7 +36,7 @@ jobs:
 
       - name: Derive release version from tag
         run: |
-          if [[ "$GITHUB_REF_NAME" == "test-app" ]]; then
+          if [[ "$GITHUB_REF_NAME" == "test-app" || "${{ github.event_name }}" == "workflow_dispatch" ]]; then
             VERSION="0.0.0-alpha.0"
             echo "IS_ALPHA=true" >> $GITHUB_ENV
           else
@@ -112,11 +113,22 @@ jobs:
           uv python install
           uv sync --locked
 
+      - name: Install Nix
+        uses: cachix/install-nix-action@v31
+        with:
+          nix_path: nixpkgs=channel:nixos-unstable
+
+      - name: Configure Cachix
+        uses: cachix/cachix-action@v14
+        with:
+          name: exo
+          authToken: "${{ secrets.CACHIX_AUTH_TOKEN }}"
+
       - name: Build dashboard
         run: |
-          cd dashboard
-          npm ci
-          npm run build
+          DASHBOARD_OUT=$(nix build .#dashboard --print-build-logs --no-link --print-out-paths)
+          mkdir -p dashboard/build
+          cp -r "$DASHBOARD_OUT"/* dashboard/build/
 
       - name: Install Sparkle CLI
         run: |

.github/workflows/pipeline.yml

@@ -20,6 +20,12 @@ jobs:
         with:
           nix_path: nixpkgs=channel:nixos-unstable
 
+      - uses: cachix/cachix-action@v14
+        name: Configure Cachix
+        with:
+          name: exo
+          authToken: "${{ secrets.CACHIX_AUTH_TOKEN }}"
+
       - name: Configure git user
         run: |
           git config --local user.email "github-actions@users.noreply.github.com"
@@ -88,9 +94,19 @@ jobs:
 
       - uses: ./.github/actions/typecheck
 
-  nix-flake-check:
-    name: Check Nix flake
-    runs-on: ubuntu-latest
+  nix:
+    name: Build and check (${{ matrix.system }})
+    runs-on: ${{ matrix.runner }}
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - runner: macos-26
+            system: aarch64-darwin
+          - runner: ubuntu-latest
+            system: x86_64-linux
+          - runner: ubuntu-24.04-arm
+            system: aarch64-linux
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
@@ -101,83 +117,20 @@ jobs:
         with:
           nix_path: nixpkgs=channel:nixos-unstable
 
-      - name: Run nix flake check
-        run: |
-          nix flake check
-        shell: bash
+      - uses: cachix/cachix-action@v14
+        name: Configure Cachix
+        with:
+          name: exo
+          authToken: "${{ secrets.CACHIX_AUTH_TOKEN }}"
 
-#  ci:
-#    needs: typecheck
-#    runs-on: ubuntu-latest
-#    permissions:
-#      contents: read
-#    env:
-#      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-#    steps:
-#      - name: Checkout repository
-#        uses: actions/checkout@v4
-#        with:
-#          fetch-depth: 0
-#          token: ${{ secrets.GITHUB_TOKEN }}
-#          lfs: true
-#
-#      - name: Configure git user
-#        run: |
-#          git config --local user.email "github-actions@users.noreply.github.com"
-#          git config --local user.name  "github-actions bot"
-#        shell: bash
-#
-#      - name: Pull LFS files
-#        run: |
-#          echo "Pulling Git LFS files..."
-#          git lfs pull
-#        shell: bash
-#
-#      - name: Setup EXO_HOME and API_PORT
-#        run: |
-#          EXO_HOME=$(mktemp -d -t exo-ci-XXXXXXXX)
-#          # Generate random port (macOS compatible method)
-#          API_PORT=$((49152 + RANDOM % (65535 - 49152 + 1)))
-#          echo "EXO_HOME=$EXO_HOME" >> $GITHUB_ENV
-#          echo "API_PORT=$API_PORT" >> $GITHUB_ENV
-#          echo "Created EXO_HOME: $EXO_HOME"
-#          echo "Generated API_PORT: $API_PORT"
-#        shell: bash
-#
-#      - name: Setup Nix Environment
-#        run: |
-#          echo "Checking for nix installation..."
-#          
-#          # Check if nix binary exists directly
-#          if [ -f /nix/var/nix/profiles/default/bin/nix ]; then
-#            echo "Found nix binary at /nix/var/nix/profiles/default/bin/nix"
-#            export PATH="/nix/var/nix/profiles/default/bin:$PATH"
-#            echo "PATH=$PATH" >> $GITHUB_ENV
-#            nix --version
-#          elif [ -f /nix/var/nix/profiles/default/etc/profile.d/nix-daemon.sh ]; then
-#            echo "Found nix profile script, sourcing..."
-#            source /nix/var/nix/profiles/default/etc/profile.d/nix-daemon.sh
-#            nix --version
-#          elif command -v nix >/dev/null 2>&1; then
-#            echo "Nix already in PATH"
-#            nix --version
-#          else
-#            echo "Nix not found. Debugging info:"
-#            echo "Contents of /nix/var/nix/profiles/default/:"
-#            ls -la /nix/var/nix/profiles/default/ 2>/dev/null || echo "Directory not found"
-#            echo "Contents of /nix/var/nix/profiles/default/bin/:"
-#            ls -la /nix/var/nix/profiles/default/bin/ 2>/dev/null || echo "Directory not found"
-#            exit 1
-#          fi
-#        shell: bash
-#
-#      - uses: ./.github/actions/lint-check
-#
-#      - uses: ./.github/actions/unit-test
-#
-#      - name: Cleanup EXO_HOME
-#        run: |
-#          echo "Cleaning up EXO_HOME: $EXO_HOME"
-#          rm -rf "$EXO_HOME"
-#        shell: bash
-#        if: always()
+      - name: Build all Nix outputs
+        run: |
+          nix flake show --json | jq -r '
+            [
+              (.packages."${{ matrix.system }}" // {} | keys[] | ".#packages.${{ matrix.system }}.\(.)"),
+              (.devShells."${{ matrix.system }}" // {} | keys[] | ".#devShells.${{ matrix.system }}.\(.)")
+            ] | .[]
+          ' | xargs nix build
+
+      - name: Run nix flake check
+        run: nix flake check

.mlx_typings/mlx_lm/models/deepseek_v3.pyi

@@ -0,0 +1,156 @@
+"""Type stubs for mlx_lm.models.deepseek_v3"""
+
+from dataclasses import dataclass
+from typing import Any, Dict, 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
+    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: Optional[int]
+    routed_scaling_factor: float
+    kv_lora_rank: int
+    q_lora_rank: Optional[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_theta: float
+    rope_scaling: Optional[Dict[str, Any]]
+    attention_bias: bool
+
+class DeepseekV3Attention(nn.Module):
+    config: ModelArgs
+    hidden_size: int
+    num_heads: int
+    max_position_embeddings: int
+    rope_theta: float
+    q_lora_rank: Optional[int]
+    qk_rope_head_dim: int
+    kv_lora_rank: int
+    v_head_dim: int
+    qk_nope_head_dim: int
+    q_head_dim: int
+    scale: float
+    q_proj: nn.Linear
+    q_a_proj: nn.Linear
+    q_a_layernorm: nn.RMSNorm
+    q_b_proj: nn.Linear
+    kv_a_proj_with_mqa: nn.Linear
+    kv_a_layernorm: nn.RMSNorm
+    kv_b_proj: nn.Linear
+    o_proj: nn.Linear
+    rope: Any
+
+    def __init__(self, config: ModelArgs) -> None: ...
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array: ...
+
+class DeepseekV3MLP(nn.Module):
+    config: ModelArgs
+    hidden_size: int
+    intermediate_size: int
+    gate_proj: nn.Linear
+    up_proj: nn.Linear
+    down_proj: nn.Linear
+
+    def __init__(
+        self,
+        config: ModelArgs,
+        hidden_size: Optional[int] = None,
+        intermediate_size: Optional[int] = None,
+    ) -> None: ...
+    def __call__(self, x: mx.array) -> mx.array: ...
+
+class MoEGate(nn.Module):
+    config: ModelArgs
+    top_k: int
+    norm_topk_prob: bool
+    n_routed_experts: Optional[int]
+    routed_scaling_factor: float
+    n_group: int
+    topk_group: int
+    weight: mx.array
+    e_score_correction_bias: mx.array
+
+    def __init__(self, config: ModelArgs) -> None: ...
+    def __call__(self, x: mx.array) -> tuple[mx.array, mx.array]: ...
+
+class DeepseekV3MoE(nn.Module):
+    config: ModelArgs
+    num_experts_per_tok: int
+    switch_mlp: SwitchGLU
+    gate: MoEGate
+    shared_experts: DeepseekV3MLP
+    sharding_group: Optional[mx.distributed.Group]
+
+    def __init__(self, config: ModelArgs) -> None: ...
+    def __call__(self, x: mx.array) -> mx.array: ...
+
+class DeepseekV3DecoderLayer(nn.Module):
+    self_attn: DeepseekV3Attention
+    mlp: DeepseekV3MLP | DeepseekV3MoE
+    input_layernorm: nn.RMSNorm
+    post_attention_layernorm: nn.RMSNorm
+
+    def __init__(self, config: ModelArgs, layer_idx: int) -> None: ...
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array: ...
+
+class DeepseekV3Model(nn.Module):
+    vocab_size: int
+    embed_tokens: nn.Embedding
+    layers: list[DeepseekV3DecoderLayer]
+    norm: nn.RMSNorm
+
+    def __init__(self, config: ModelArgs) -> None: ...
+    def __call__(
+        self,
+        x: mx.array,
+        cache: Optional[Any] = None,
+    ) -> mx.array: ...
+
+class Model(nn.Module):
+    model_type: str
+    model: DeepseekV3Model
+    lm_head: nn.Linear
+
+    def __init__(self, config: ModelArgs) -> None: ...
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache: Optional[Any] = None,
+    ) -> mx.array: ...
+    def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
+    @property
+    def layers(self) -> list[DeepseekV3DecoderLayer]: ...

.mlx_typings/mlx_lm/models/switch_layers.pyi

@@ -57,6 +57,11 @@ class SwiGLU(nn.Module):
     def __call__(self, x, gate): ...
 
 class SwitchGLU(nn.Module):
+    gate_proj: SwitchLinear
+    up_proj: SwitchLinear
+    down_proj: SwitchLinear
+    activation: SwiGLU
+
     def __init__(
         self,
         input_dims: int,

.mlx_typings/mlx_lm/tokenizer_utils.pyi

@@ -4,6 +4,7 @@ This type stub file was generated by pyright.
 
 from functools import partial
 from pathlib import Path
+from typing import Any
 
 from transformers import PreTrainedTokenizerFast
 
@@ -103,37 +104,55 @@ class TokenizerWrapper:
     Accessing any attribute other than the ``detokenizer`` is forwarded to the
     huggingface tokenizer.
     """
-    def __init__(self, tokenizer, detokenizer_class=..., eos_token_ids=...) -> None: ...
-    def add_eos_token(self, token: str):  # -> None:
-        ...
-    @property
-    def has_thinking(self):  # -> bool:
-        ...
-    @property
-    def think_start(self):  # -> str | None:
-        ...
-    @property
-    def think_end(self):  # -> str | None:
-        ...
-    @property
-    def has_tool_calling(self):  # -> bool:
-        ...
-    @property
-    def tool_call_start(self):  # -> str | None:
-        ...
-    @property
-    def tool_call_end(self):  # -> str | None:
-        ...
-    @property
-    def detokenizer(self):  # -> NaiveStreamingDetokenizer:
-        """
-        Get a stateful streaming detokenizer.
-        """
 
-    def __getattr__(self, attr):  # -> set[Any] | Any:
-        ...
-    def __setattr__(self, attr, value):  # -> None:
-        ...
+    _tokenizer: PreTrainedTokenizerFast
+    eos_token_id: int | None
+    eos_token: str | None
+    bos_token_id: int | None
+    bos_token: str | None
+    vocab_size: int
+    all_special_tokens: list[str]
+
+    def __init__(
+        self,
+        tokenizer: Any,
+        detokenizer_class: Any = ...,
+        eos_token_ids: list[int] | None = ...,
+        chat_template: Any = ...,
+        tool_parser: Any = ...,
+        tool_call_start: str | None = ...,
+        tool_call_end: str | None = ...,
+    ) -> None: ...
+    def encode(self, text: str, **kwargs: Any) -> list[int]: ...
+    def decode(self, token_ids: list[int], **kwargs: Any) -> str: ...
+    def apply_chat_template(
+        self,
+        messages: list[dict[str, Any]],
+        tokenize: bool = False,
+        add_generation_prompt: bool = False,
+        tools: Any = None,
+        **kwargs: Any,
+    ) -> str: ...
+    def get_vocab(self) -> dict[str, int]: ...
+    def add_eos_token(self, token: str) -> None: ...
+    @property
+    def has_thinking(self) -> bool: ...
+    @property
+    def think_start(self) -> str | None: ...
+    @property
+    def think_end(self) -> str | None: ...
+    @property
+    def has_tool_calling(self) -> bool: ...
+    @property
+    def tool_call_start(self) -> str | None: ...
+    @property
+    def tool_call_end(self) -> str | None: ...
+    @property
+    def detokenizer(self) -> NaiveStreamingDetokenizer:
+        """Get a stateful streaming detokenizer."""
+
+    def __getattr__(self, attr: str) -> Any: ...
+    def __setattr__(self, attr: str, value: Any) -> None: ...
 
 class NewlineTokenizer(PreTrainedTokenizerFast):
     """A tokenizer that replaces newlines with <n> and <n> with new line."""
@@ -146,18 +165,11 @@ class NewlineTokenizer(PreTrainedTokenizerFast):
     def batch_decode(self, *args, **kwargs):  # -> list[str]:
         ...
 
-def load_tokenizer(
+def load(
     model_path: Path,
-    tokenizer_config_extra=...,
-    return_tokenizer=...,
-    eos_token_ids=...,
-) -> (
-    TokenizerWrapper
-    | type[SPMStreamingDetokenizer]
-    | partial[SPMStreamingDetokenizer]
-    | type[BPEStreamingDetokenizer]
-    | type[NaiveStreamingDetokenizer]
-):
+    tokenizer_config_extra: dict[str, Any] | None = None,
+    eos_token_ids: list[int] | int | None = None,
+) -> TokenizerWrapper:
     """Load a huggingface tokenizer and try to infer the type of streaming
     detokenizer to use.
 
@@ -165,4 +177,7 @@ def load_tokenizer(
     a Hugging Face repo ID.
     """
 
-def no_bos_or_eos(sequence: list, bos: int, eos: int) -> list: ...
+# Alias for backward compatibility
+load_tokenizer = load
+
+def no_bos_or_eos(sequence: list[int], bos: int, eos: int) -> list[int]: ...

AGENTS.md

@@ -0,0 +1,96 @@
+# AGENTS.md
+
+This file provides guidance to AI coding agents when working with code in this repository.
+
+## Project Overview
+
+exo is a distributed AI inference system that connects multiple devices into a cluster. It enables running large language models across multiple machines using MLX as the inference backend and libp2p for peer-to-peer networking.
+
+## Build & Run Commands
+
+```bash
+# Build the dashboard (required before running exo)
+cd dashboard && npm install && npm run build && cd ..
+
+# Run exo (starts both master and worker with API at http://localhost:52415)
+uv run exo
+
+# Run with verbose logging
+uv run exo -v   # or -vv for more verbose
+
+# Run tests (excludes slow tests by default)
+uv run pytest
+
+# Run all tests including slow tests
+uv run pytest -m ""
+
+# Run a specific test file
+uv run pytest src/exo/shared/tests/test_election.py
+
+# Run a specific test function
+uv run pytest src/exo/shared/tests/test_election.py::test_function_name
+
+# Type checking (strict mode)
+uv run basedpyright
+
+# Linting
+uv run ruff check
+
+# Format code (using nix)
+nix fmt
+```
+
+## Architecture
+
+### Node Composition
+A single exo `Node` (src/exo/main.py) runs multiple components:
+- **Router**: libp2p-based pub/sub messaging via Rust bindings (exo_pyo3_bindings)
+- **Worker**: Handles inference tasks, downloads models, manages runner processes
+- **Master**: Coordinates cluster state, places model instances across nodes
+- **Election**: Bully algorithm for master election
+- **API**: FastAPI server for OpenAI-compatible chat completions
+
+### Message Flow
+Components communicate via typed pub/sub topics (src/exo/routing/topics.py):
+- `GLOBAL_EVENTS`: Master broadcasts indexed events to all workers
+- `LOCAL_EVENTS`: Workers send events to master for indexing
+- `COMMANDS`: Workers/API send commands to master
+- `ELECTION_MESSAGES`: Election protocol messages
+- `CONNECTION_MESSAGES`: libp2p connection updates
+
+### Event Sourcing
+The system uses event sourcing for state management:
+- `State` (src/exo/shared/types/state.py): Immutable state object
+- `apply()` (src/exo/shared/apply.py): Pure function that applies events to state
+- Master indexes events and broadcasts; workers apply indexed events
+
+### Key Type Hierarchy
+- `src/exo/shared/types/`: Pydantic models for all shared types
+  - `events.py`: Event types (discriminated union)
+  - `commands.py`: Command types
+  - `tasks.py`: Task types for worker execution
+  - `state.py`: Cluster state model
+
+### Rust Components
+Rust code in `rust/` provides:
+- `networking`: libp2p networking (gossipsub, peer discovery)
+- `exo_pyo3_bindings`: PyO3 bindings exposing Rust to Python
+- `system_custodian`: System-level operations
+
+### Dashboard
+Svelte 5 + TypeScript frontend in `dashboard/`. Build output goes to `dashboard/build/` and is served by the API.
+
+## Code Style Requirements
+
+From .cursorrules:
+- Strict, exhaustive typing - never bypass the type-checker
+- Use `Literal[...]` for enum-like sets, `typing.NewType` for primitives
+- Pydantic models with `frozen=True` and `strict=True`
+- Pure functions with injectable effect handlers for side-effects
+- Descriptive names - no abbreviations or 3-letter acronyms
+- Catch exceptions only where you can handle them meaningfully
+- Use `@final` and immutability wherever applicable
+
+## Testing
+
+Tests use pytest-asyncio with `asyncio_mode = "auto"`. Tests are in `tests/` subdirectories alongside the code they test. The `EXO_TESTS=1` env var is set during tests.

CLAUDE.md

@@ -0,0 +1 @@
+AGENTS.md

Cargo.lock

@@ -4340,25 +4340,6 @@ dependencies = [
  "libc",
 ]
 
-[[package]]
-name = "system_custodian"
-version = "0.0.1"
-dependencies = [
- "delegate",
- "derive_more",
- "either",
- "extend",
- "futures",
- "futures-timer",
- "impl-trait-for-tuples",
- "keccak-const",
- "log",
- "thiserror 2.0.17",
- "tokio",
- "tracing-subscriber",
- "util",
-]
-
 [[package]]
 name = "tagptr"
 version = "0.2.0"

Cargo.toml

@@ -3,7 +3,6 @@ resolver = "3"
 members = [
     "rust/networking",
     "rust/exo_pyo3_bindings",
-    "rust/system_custodian",
     "rust/util",
 ]
 
@@ -25,7 +24,6 @@ opt-level = 3
 [workspace.dependencies]
 ## Crate members as common dependencies
 networking = { path = "rust/networking" }
-system_custodian = { path = "rust/system_custodian" }
 util = { path = "rust/util" }
 
 # Proc-macro authoring tools

MISSED_THINGS.md

@@ -0,0 +1,41 @@
+# Missed things
+[X] Log EXO_LIBP2P_NAMESPACE on start in exo/main.py
+[X] Ordering of warmup was changed, which is wrong. It was changed to rank < n-1, then rank=n-1. It should be rank!=0 then rank=0 (this matches the auto_parallel implementation. NOTE: we use a different convention to mlx-lm, our terminal rank is rank=n-1 whereas mlx-lm is rank=0 hence i can see why this was changed wrongly).
+[X] Downloads keying by model_id not shard_metadata (worker/plan.py, worker/main.py).
+[X] Fetching download status of all models on start
+[X] Deduplication of tasks in plan_step.
+[X] resolve_allow_patterns should just be wildcard now.
+[] no mx_barrier in genreate.py mlx_generate at the end.
+[] cache assertion not needed in auto_parallel.py PipelineLastLayer.
+[] GPTOSS support dropped in auto_parallel.py.
+[] sharding changed "all-to-sharded" became _all_to_sharded in auto_parallel.py.
+[] same as above with "sharded-to-all" became _sharded_to_all in auto_parallel.py.
+[] Dropped support for Ministral3Model, DeepseekV32Model, Glm4MoeModel, Qwen3NextModel, GptOssMode in auto_parallel.py.
+[] Dropped prefill/decode code in auto_parallel.py and utils_mlx.py.
+[X] KV_CACHE_BITS should be None to disable quantized KV cache.
+[] Dropped _set_nofile_limit in utils_mlx.py.
+[] We have group optional in load_mlx_items in utils_mlx.py.
+[] Dropped add_missing_chat_templates for GptOss in load_mlx_items in utils_mlx.py.
+[] Dropped model.make_cache in make_kv_cache in utils_mlx.py.
+[X] We put cache limit back in utils_mlx.py.
+[] topology.py remove_node removes the connections after checking if node is is in self._node_id_to_rx_id_map. on beta_1 it checks after, so would remove stale connections I guess?
+[] Missing Glm 4.7 model cards (this isn't ready yet but should be picked up, probably create an issue... the blocker is transforemrs version doesn't support the tokenizer for Glm 4.7. rc-1 does but we can't upgrade as it breaks other things.)
+[] try-except in _command_processor only excepts ValueError. This was silently failing leading to un-debuggable errors (we had a KeyError that was happening ). Changed this to catch Exception instead of ValueError. See exo-v2 89ae38405e0052e3c22405daf094b065878aa873 and fb99fea69b5a39017efc90c5dad0072e677455f0.
+[X] In placement.py, place_instance no longer looks at model_meta.supports_tensor and check if this tensor parallel number of nodes is supported by the model's tensor dimensions.
+[X] In placement.py, place_instanec, we no longer have the special case to exclude DeepSeek v3.1 pipeline parallel (it doesn't work).
+[] logger.warning("You have likely selected ibv for a single node instance; falling back to MlxRing") was changed to debug. That will spam this warning since it happens every time we query instance previews.
+[X] In placement_utils.py, get_mlx_jaccl_coordinators, We no longer prioritise Jaccl Coordinator IP. Now it picks the first one, which is unstable (Jaccl coordinator over TB5 is unstable).
+
+
+
+[X] Downloads keying by model_id not shard_metadata (worker/plan.py, worker/main.py).
+[X] Fetching download status of all models on start
+[X] Deduplication of tasks in plan_step.
+[X] resolve_allow_patterns should just be wildcard now.
+[X] KV_CACHE_BITS should be None to disable quantized KV cache.
+[X] We put cache limit back in utils_mlx.py.
+[X] In placement.py, place_instance no longer looks at model_meta.supports_tensor and check if this tensor parallel number of nodes is supported by the model's tensor dimensions.
+[X] In placement.py, place_instanec, we no longer have the special case to exclude DeepSeek v3.1 pipeline parallel (it doesn't work).
+[X] In placement_utils.py, get_mlx_jaccl_coordinators, We no longer prioritise Jaccl Coordinator IP. Now it picks the first one, which is unstable (Jaccl coordinator over TB5 is unstable).
+
+

README.md

@@ -305,7 +305,10 @@ curl -X DELETE http://localhost:52415/instance/YOUR_INSTANCE_ID
 - List all models: `curl http://localhost:52415/models`
 - Inspect instance IDs and deployment state: `curl http://localhost:52415/state`
 
-For further details, see API types and endpoints in [src/exo/master/api.py](src/exo/master/api.py).
+For further details, see:
+
+- API basic documentation in [docs/api.md](docs/api.md).
+- API types and endpoints in [src/exo/master/api.py](src/exo/master/api.py).
 
 ---
 

app/EXO/EXO/ContentView.swift

@@ -56,6 +56,11 @@ struct ContentView: View {
     }
 
     private var shouldShowLocalNetworkWarning: Bool {
+        // Show warning if local network is not working and EXO is running.
+        // The checker uses a longer timeout on first launch to allow time for
+        // the permission prompt, so this correctly handles both:
+        // 1. User denied permission on first launch
+        // 2. Permission broke after restart (macOS TCC bug)
         if case .notWorking = localNetworkChecker.status {
             return controller.status != .stopped
         }

app/EXO/EXO/Services/LocalNetworkChecker.swift

@@ -5,8 +5,8 @@ import os.log
 /// Checks if the app's local network permission is actually functional.
 ///
 /// macOS local network permission can appear enabled in System Preferences but not
-/// actually work after a restart. This service detects this by creating a UDP
-/// connection to the mDNS multicast address (224.0.0.251:5353).
+/// actually work after a restart. This service uses NWConnection to mDNS multicast
+/// to verify actual connectivity.
 @MainActor
 final class LocalNetworkChecker: ObservableObject {
     enum Status: Equatable {
@@ -35,30 +35,43 @@ final class LocalNetworkChecker: ObservableObject {
     }
 
     private static let logger = Logger(subsystem: "io.exo.EXO", category: "LocalNetworkChecker")
+    private static let hasCompletedInitialCheckKey = "LocalNetworkChecker.hasCompletedInitialCheck"
 
     @Published private(set) var status: Status = .unknown
-    @Published private(set) var lastConnectionState: String = "none"
 
     private var connection: NWConnection?
     private var checkTask: Task<Void, Never>?
 
+    /// Whether we've completed at least one check (stored in UserDefaults)
+    private var hasCompletedInitialCheck: Bool {
+        get { UserDefaults.standard.bool(forKey: Self.hasCompletedInitialCheckKey) }
+        set { UserDefaults.standard.set(newValue, forKey: Self.hasCompletedInitialCheckKey) }
+    }
+
     /// Checks if local network access is working.
     func check() {
         checkTask?.cancel()
         status = .checking
-        lastConnectionState = "connecting"
+
+        // Use longer timeout on first launch to allow time for permission prompt
+        let isFirstCheck = !hasCompletedInitialCheck
+        let timeout: UInt64 = isFirstCheck ? 30_000_000_000 : 3_000_000_000
 
         checkTask = Task { [weak self] in
             guard let self else { return }
-            let result = await self.performCheck()
+
+            Self.logger.info("Checking local network connectivity (first check: \(isFirstCheck))")
+            let result = await self.checkConnectivity(timeout: timeout)
             self.status = result
+            self.hasCompletedInitialCheck = true
+
             Self.logger.info("Local network check complete: \(result.displayText)")
         }
     }
 
-    private func performCheck() async -> Status {
-        Self.logger.info("Checking local network access via UDP multicast")
-
+    /// Checks connectivity using NWConnection to mDNS multicast.
+    /// The connection attempt triggers the permission prompt if not yet shown.
+    private func checkConnectivity(timeout: UInt64) async -> Status {
         connection?.cancel()
         connection = nil
 
@@ -84,22 +97,7 @@ final class LocalNetworkChecker: ObservableObject {
                 continuation.resume(returning: status)
             }
 
-            conn.stateUpdateHandler = { [weak self] state in
-                let stateStr: String
-                switch state {
-                case .setup: stateStr = "setup"
-                case .preparing: stateStr = "preparing"
-                case .ready: stateStr = "ready"
-                case .waiting(let e): stateStr = "waiting(\(e))"
-                case .failed(let e): stateStr = "failed(\(e))"
-                case .cancelled: stateStr = "cancelled"
-                @unknown default: stateStr = "unknown"
-                }
-
-                Task { @MainActor in
-                    self?.lastConnectionState = stateStr
-                }
-
+            conn.stateUpdateHandler = { state in
                 switch state {
                 case .ready:
                     resumeOnce(.working)
@@ -108,6 +106,7 @@ final class LocalNetworkChecker: ObservableObject {
                     if errorStr.contains("54") || errorStr.contains("ECONNRESET") {
                         resumeOnce(.notWorking(reason: "Connection blocked"))
                     }
+                // Otherwise keep waiting - might be showing permission prompt
                 case .failed(let error):
                     let errorStr = "\(error)"
                     if errorStr.contains("65") || errorStr.contains("EHOSTUNREACH")
@@ -127,7 +126,7 @@ final class LocalNetworkChecker: ObservableObject {
             conn.start(queue: .main)
 
             Task {
-                try? await Task.sleep(nanoseconds: 3_000_000_000)
+                try? await Task.sleep(nanoseconds: timeout)
                 let state = conn.state
                 switch state {
                 case .ready:

bench/exo_bench.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import argparse
+import contextlib
 import http.client
 import json
 import os
@@ -26,7 +27,7 @@ class ExoHttpError(RuntimeError):
 
 
 class ExoClient:
-    def __init__(self, host: str, port: int, timeout_s: float = 2400.0):
+    def __init__(self, host: str, port: int, timeout_s: float = 600.0):
         self.host = host
         self.port = port
         self.timeout_s = timeout_s
@@ -104,22 +105,46 @@ 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
     while time.time() - start_time < timeout:
         state = client.request_json("GET", "/state")
         instances = state.get("instances", {})
 
         if instance_id not in instances:
+            if instance_existed:
+                # Instance was deleted after being created - likely due to runner failure
+                raise RuntimeError(
+                    f"Instance {instance_id} was deleted (runner may have failed)"
+                )
             time.sleep(0.1)
             continue
 
+        instance_existed = True
         instance = instances[instance_id]
         runner_ids = runner_ids_from_instance(instance)
         runners = state.get("runners", {})
 
+        # Check for failed runners first
+        for rid in runner_ids:
+            runner = runners.get(rid, {})
+            if runner_failed(runner):
+                error_msg = get_runner_failed_message(runner) or "Unknown error"
+                raise RuntimeError(f"Runner {rid} failed: {error_msg}")
+
         if all(runner_ready(runners.get(rid, {})) for rid in runner_ids):
             return
 
@@ -241,6 +266,9 @@ class PromptSizer:
             ids = tokenizer.apply_chat_template(
                 messages, tokenize=True, add_generation_prompt=True
             )
+            # Fix for transformers 5.x
+            if hasattr(ids, "input_ids"):
+                ids = ids.input_ids
             return int(len(ids))
 
         return count_fn
@@ -296,6 +324,12 @@ def main() -> 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"
     )
@@ -317,7 +351,7 @@ def main() -> int:
         help="Warmup runs per placement (uses first pp/tg).",
     )
     ap.add_argument(
-        "--timeout", type=float, default=2400.0, help="HTTP timeout (seconds)."
+        "--timeout", type=float, default=600.0, help="HTTP timeout (seconds)."
     )
     ap.add_argument(
         "--json-out",
@@ -396,7 +430,7 @@ def main() -> int:
         ):
             continue
 
-        if 0 < n <= args.max_nodes:
+        if args.min_nodes <= n <= args.max_nodes:
             selected.append(p)
 
     if not selected:
@@ -438,7 +472,13 @@ def main() -> int:
         )
 
         client.request_json("POST", "/instance", body={"instance": instance})
-        wait_for_instance_ready(client, instance_id)
+        try:
+            wait_for_instance_ready(client, instance_id)
+        except (RuntimeError, TimeoutError) as e:
+            logger.error(f"Failed to initialize placement: {e}")
+            with contextlib.suppress(ExoHttpError):
+                client.request_json("DELETE", f"/instance/{instance_id}")
+            continue
 
         time.sleep(1)
 

dashboard/dashboard.nix

@@ -0,0 +1,60 @@
+{ lib
+, config
+, dream2nix
+, ...
+}:
+let
+  # Read and parse the lock file
+  rawLockFile = builtins.fromJSON (builtins.readFile "${config.deps.dashboardSrc}/package-lock.json");
+
+  # For packages with bundleDependencies, filter out deps that are bundled
+  # (bundled deps are inside the tarball, not separate lockfile entries)
+  fixedPackages = lib.mapAttrs
+    (path: entry:
+      if entry ? bundleDependencies && entry.bundleDependencies != [ ]
+      then entry // {
+        dependencies = lib.filterAttrs
+          (name: _: !(lib.elem name entry.bundleDependencies))
+          (entry.dependencies or { });
+      }
+      else entry
+    )
+    (rawLockFile.packages or { });
+
+  fixedLockFile = rawLockFile // { packages = fixedPackages; };
+in
+{
+  imports = [
+    dream2nix.modules.dream2nix.nodejs-package-lock-v3
+    dream2nix.modules.dream2nix.nodejs-granular-v3
+  ];
+
+  name = "exo-dashboard";
+  version = "1.0.0";
+
+  mkDerivation = {
+    src = config.deps.dashboardSrc;
+
+    buildPhase = ''
+      runHook preBuild
+      npm run build
+      runHook postBuild
+    '';
+
+    installPhase = ''
+      runHook preInstall
+      cp -r build $out/build
+      runHook postInstall
+    '';
+  };
+
+  deps = { nixpkgs, ... }: {
+    inherit (nixpkgs) stdenv;
+    dashboardSrc = null; # Injected by parts.nix
+  };
+
+  nodejs-package-lock-v3 = {
+    # Don't use packageLockFile - provide the fixed lock content directly
+    packageLock = fixedLockFile;
+  };
+}

dashboard/package-lock.json

@@ -863,6 +863,7 @@
 			"integrity": "sha512-oH8tXw7EZnie8FdOWYrF7Yn4IKrqTFHhXvl8YxXxbKwTMcD/5NNCryUSEXRk2ZR4ojnub0P8rNrsVGHXWqIDtA==",
 			"dev": true,
 			"license": "MIT",
+			"peer": true,
 			"dependencies": {
 				"@standard-schema/spec": "^1.0.0",
 				"@sveltejs/acorn-typescript": "^1.0.5",
@@ -902,6 +903,7 @@
 			"integrity": "sha512-Y1Cs7hhTc+a5E9Va/xwKlAJoariQyHY+5zBgCZg4PFWNYQ1nMN9sjK1zhw1gK69DuqVP++sht/1GZg1aRwmAXQ==",
 			"dev": true,
 			"license": "MIT",
+			"peer": true,
 			"dependencies": {
 				"@sveltejs/vite-plugin-svelte-inspector": "^4.0.1",
 				"debug": "^4.4.1",
@@ -1518,6 +1520,7 @@
 			"integrity": "sha512-LCCV0HdSZZZb34qifBsyWlUmok6W7ouER+oQIGBScS8EsZsQbrtFTUrDX4hOl+CS6p7cnNC4td+qrSVGSCTUfQ==",
 			"dev": true,
 			"license": "MIT",
+			"peer": true,
 			"dependencies": {
 				"undici-types": "~6.21.0"
 			}
@@ -1527,6 +1530,7 @@
 			"resolved": "https://registry.npmjs.org/acorn/-/acorn-8.15.0.tgz",
 			"integrity": "sha512-NZyJarBfL7nWwIq+FDL6Zp/yHEhePMNnnJ0y3qfieCrmNvYct8uvtiV41UvlSe6apAfk0fY1FbWx+NwfmpvtTg==",
 			"license": "MIT",
+			"peer": true,
 			"bin": {
 				"acorn": "bin/acorn"
 			},
@@ -1939,6 +1943,7 @@
 			"integrity": "sha512-fmTRWbNMmsmWq6xJV8D19U/gw/bwrHfNXxrIN+HfZgnzqTHp9jOmKMhsTUjXOJnZOdZY9Q28y4yebKzqDKlxlQ==",
 			"dev": true,
 			"license": "ISC",
+			"peer": true,
 			"engines": {
 				"node": ">=12"
 			}
@@ -2646,6 +2651,7 @@
 			"integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==",
 			"dev": true,
 			"license": "MIT",
+			"peer": true,
 			"engines": {
 				"node": ">=12"
 			},
@@ -2833,6 +2839,7 @@
 			"resolved": "https://registry.npmjs.org/svelte/-/svelte-5.45.3.tgz",
 			"integrity": "sha512-ngKXNhNvwPzF43QqEhDOue7TQTrG09em1sd4HBxVF0Wr2gopAmdEWan+rgbdgK4fhBtSOTJO8bYU4chUG7VXZQ==",
 			"license": "MIT",
+			"peer": true,
 			"dependencies": {
 				"@jridgewell/remapping": "^2.3.4",
 				"@jridgewell/sourcemap-codec": "^1.5.0",
@@ -2977,6 +2984,7 @@
 			"integrity": "sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw==",
 			"dev": true,
 			"license": "Apache-2.0",
+			"peer": true,
 			"bin": {
 				"tsc": "bin/tsc",
 				"tsserver": "bin/tsserver"
@@ -2998,6 +3006,7 @@
 			"integrity": "sha512-+Oxm7q9hDoLMyJOYfUYBuHQo+dkAloi33apOPP56pzj+vsdJDzr+j1NISE5pyaAuKL4A3UD34qd0lx5+kfKp2g==",
 			"dev": true,
 			"license": "MIT",
+			"peer": true,
 			"dependencies": {
 				"esbuild": "^0.25.0",
 				"fdir": "^6.4.4",

dashboard/parts.nix

@@ -0,0 +1,44 @@
+{ inputs, ... }:
+{
+  perSystem =
+    { pkgs, lib, ... }:
+    let
+      # Filter source to only include dashboard directory
+      src = lib.cleanSourceWith {
+        src = inputs.self;
+        filter =
+          path: type:
+          let
+            baseName = builtins.baseNameOf path;
+            inDashboardDir =
+              (lib.hasInfix "/dashboard/" path)
+              || (lib.hasSuffix "/dashboard" (builtins.dirOf path))
+              || (baseName == "dashboard" && type == "directory");
+          in
+          inDashboardDir;
+      };
+
+      # Build the dashboard with dream2nix (includes node_modules in output)
+      dashboardFull = inputs.dream2nix.lib.evalModules {
+        packageSets.nixpkgs = pkgs;
+        modules = [
+          ./dashboard.nix
+          {
+            paths.projectRoot = inputs.self;
+            paths.projectRootFile = "flake.nix";
+            paths.package = inputs.self + "/dashboard";
+          }
+          # Inject the filtered source
+          {
+            deps.dashboardSrc = lib.mkForce "${src}/dashboard";
+          }
+        ];
+      };
+    in
+    {
+      # Extract just the static site from the full build
+      packages.dashboard = pkgs.runCommand "exo-dashboard" { } ''
+        cp -r ${dashboardFull}/build $out
+      '';
+    };
+}

dashboard/src/lib/components/ChatForm.svelte

@@ -60,12 +60,39 @@
 		return models;
 	});
 
-	// Auto-select the first available model if none is selected
+	// 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();
-		if (models.length > 0 && !currentModel) {
-			setSelectedChatModel(models[0].id);
+		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 {

dashboard/src/routes/+page.svelte

@@ -400,10 +400,8 @@ function toggleInstanceDownloadDetails(nodeId: string): void {
 				const errorText = await response.text();
 				console.error('Failed to launch instance:', errorText);
 			} else {
-				// Auto-select the launched model only if no model is currently selected
-				if (!selectedChatModel()) {
-					setSelectedChatModel(modelId);
-				}
+				// Always auto-select the newly launched model so the user chats to what they just launched
+				setSelectedChatModel(modelId);
 				
 				// Scroll to the bottom of instances container to show the new instance
 				// Use multiple attempts to ensure DOM has updated with the new instance
@@ -763,6 +761,10 @@ function toggleInstanceDownloadDetails(nodeId: string): void {
 	async function deleteInstance(instanceId: string) {
 		if (!confirm(`Delete instance ${instanceId.slice(0, 8)}...?`)) return;
 		
+		// Get the model ID of the instance being deleted before we delete it
+		const deletedInstanceModelId = getInstanceModelId(instanceData[instanceId]);
+		const wasSelected = selectedChatModel() === deletedInstanceModelId;
+		
 		try {
 			const response = await fetch(`/instance/${instanceId}`, {
 				method: 'DELETE',
@@ -771,6 +773,24 @@ function toggleInstanceDownloadDetails(nodeId: string): void {
 			
 			if (!response.ok) {
 				console.error('Failed to delete instance:', response.status);
+			} else if (wasSelected) {
+				// If we deleted the currently selected model, switch to another available model
+				// Find another instance that isn't the one we just deleted
+				const remainingInstances = Object.entries(instanceData).filter(([id]) => id !== instanceId);
+				if (remainingInstances.length > 0) {
+					// Select the last instance (most recently added, since objects preserve insertion order)
+					const [, lastInstance] = remainingInstances[remainingInstances.length - 1];
+					const newModelId = getInstanceModelId(lastInstance);
+					if (newModelId && newModelId !== 'Unknown' && newModelId !== 'Unknown Model') {
+						setSelectedChatModel(newModelId);
+					} else {
+						// Clear selection if no valid model found
+						setSelectedChatModel('');
+					}
+				} else {
+					// No more instances, clear the selection
+					setSelectedChatModel('');
+				}
 			}
 		} catch (error) {
 			console.error('Error deleting instance:', error);

dashboard/src/routes/downloads/+page.svelte

@@ -199,7 +199,13 @@
 					const rawProgress = (downloadPayload as Record<string, unknown>).download_progress
 						?? (downloadPayload as Record<string, unknown>).downloadProgress
 						?? {};
-					const totalBytes = getBytes((rawProgress as Record<string, unknown>).total_bytes ?? (rawProgress as Record<string, unknown>).totalBytes);
+					// For DownloadCompleted, total_bytes is at top level; for DownloadOngoing, it's inside download_progress
+					const totalBytes = getBytes(
+						(downloadPayload as Record<string, unknown>).total_bytes
+						?? (downloadPayload as Record<string, unknown>).totalBytes
+						?? (rawProgress as Record<string, unknown>).total_bytes
+						?? (rawProgress as Record<string, unknown>).totalBytes
+					);
 					const downloadedBytes = getBytes((rawProgress as Record<string, unknown>).downloaded_bytes ?? (rawProgress as Record<string, unknown>).downloadedBytes);
 					const speed = (rawProgress as Record<string, unknown>).speed as number ?? 0;
 					const etaMs = (rawProgress as Record<string, unknown>).eta_ms as number ?? (rawProgress as Record<string, unknown>).etaMs as number ?? 0;
@@ -332,8 +338,13 @@
 								<div class="text-lg font-mono text-white truncate">{node.nodeName}</div>
 								<div class="text-xs text-exo-light-gray font-mono truncate">{node.nodeId}</div>
 							</div>
-							<div class="text-xs font-mono uppercase tracking-wider whitespace-nowrap shrink-0">
-								<span class="text-green-400">{node.models.filter(m => m.status === 'completed').length}</span><span class="text-exo-yellow"> /{node.models.length} models</span>
+							<div class="text-xs font-mono uppercase tracking-wider whitespace-nowrap shrink-0 text-right">
+								<div>
+									<span class="text-green-400">{node.models.filter(m => m.status === 'completed').length}</span><span class="text-exo-yellow"> / {node.models.length} models</span>
+								</div>
+								<div class="text-exo-light-gray normal-case tracking-normal">
+									{formatBytes(node.models.filter(m => m.status === 'completed').reduce((sum, m) => sum + m.totalBytes, 0))} on disk
+								</div>
 							</div>
 						</div>
 
@@ -385,7 +396,7 @@
 								</div>
 
 								<div class="flex items-center justify-between text-xs font-mono text-exo-light-gray">
-									<span>{model.status === 'completed' ? 'Completed' : `${formatSpeed(model.speed)} • ETA ${formatEta(model.etaMs)}`}</span>
+									<span>{model.status === 'completed' ? `Completed (${formatBytes(model.totalBytes)})` : `${formatSpeed(model.speed)} • ETA ${formatEta(model.etaMs)}`}</span>
 									{#if model.status !== 'completed'}
 										<span>{model.files.length} file{model.files.length === 1 ? '' : 's'}</span>
 									{/if}

docs/api.md

@@ -0,0 +1,212 @@
+# EXO API – Technical Reference
+
+This document describes the REST API exposed by the **EXO ** service, as implemented in:
+
+`src/exo/master/api.py`
+
+The API is used to manage model instances in the cluster, inspect cluster state, and perform inference using an OpenAI-compatible interface.
+
+Base URL example:
+
+```
+http://localhost:52415
+```
+
+## 1. General / Meta Endpoints
+
+### Get Master Node ID
+
+**GET** `/node_id`
+
+Returns the identifier of the current master node.
+
+**Response (example):**
+
+```json
+{
+  "node_id": "node-1234"
+}
+```
+
+### Get Cluster State
+
+**GET** `/state`
+
+Returns the current state of the cluster, including nodes and active instances.
+
+**Response:**
+JSON object describing topology, nodes, and instances.
+
+### Get Events
+
+**GET** `/events`
+
+Returns the list of internal events recorded by the master (mainly for debugging and observability).
+
+**Response:**
+Array of event objects.
+
+## 2. Model Instance Management
+
+### Create Instance
+
+**POST** `/instance`
+
+Creates a new model instance in the cluster.
+
+**Request body (example):**
+
+```json
+{
+  "instance": {
+    "model_id": "llama-3.2-1b",
+    "placement": { }
+  }
+}
+```
+
+**Response:**
+JSON description of the created instance.
+
+### Delete Instance
+
+**DELETE** `/instance/{instance_id}`
+
+Deletes an existing instance by ID.
+
+**Path parameters:**
+
+* `instance_id`: string, ID of the instance to delete
+
+**Response:**
+Status / confirmation JSON.
+
+### Get Instance
+
+**GET** `/instance/{instance_id}`
+
+Returns details of a specific instance.
+
+**Path parameters:**
+
+* `instance_id`: string
+
+**Response:**
+JSON description of the instance.
+
+### Preview Placements
+
+**GET** `/instance/previews?model_id=...`
+
+Returns possible placement previews for a given model.
+
+**Query parameters:**
+
+* `model_id`: string, required
+
+**Response:**
+Array of placement preview objects.
+
+### Compute Placement
+
+**GET** `/instance/placement`
+
+Computes a placement for a potential instance without creating it.
+
+**Query parameters (typical):**
+
+* `model_id`: string
+* `sharding`: string or config
+* `instance_meta`: JSON-encoded metadata
+* `min_nodes`: integer
+
+**Response:**
+JSON object describing the proposed placement / instance configuration.
+
+### Place Instance (Dry Operation)
+
+**POST** `/place_instance`
+
+Performs a placement operation for an instance (planning step), without necessarily creating it.
+
+**Request body:**
+JSON describing the instance to be placed.
+
+**Response:**
+Placement result.
+
+## 3. Models
+
+### List Models
+
+**GET** `/models`
+**GET** `/v1/models` (alias)
+
+Returns the list of available models and their metadata.
+
+**Response:**
+Array of model descriptors.
+
+## 4. Inference / Chat Completions
+
+### OpenAI-Compatible Chat Completions
+
+**POST** `/v1/chat/completions`
+
+Executes a chat completion request using an OpenAI-compatible schema. Supports streaming and non-streaming modes.
+
+**Request body (example):**
+
+```json
+{
+  "model": "llama-3.2-1b",
+  "messages": [
+    { "role": "system", "content": "You are a helpful assistant." },
+    { "role": "user", "content": "Hello" }
+  ],
+  "stream": false
+}
+```
+
+**Response:**
+OpenAI-compatible chat completion response.
+
+### Benchmarked Chat Completions
+
+**POST** `/bench/chat/completions`
+
+Same as `/v1/chat/completions`, but also returns performance and generation statistics.
+
+**Request body:**
+Same schema as `/v1/chat/completions`.
+
+**Response:**
+Chat completion plus benchmarking metrics.
+
+## 5. Complete Endpoint Summary
+
+```
+GET     /node_id
+GET     /state
+GET     /events
+
+POST    /instance
+GET     /instance/{instance_id}
+DELETE  /instance/{instance_id}
+
+GET     /instance/previews
+GET     /instance/placement
+POST    /place_instance
+
+GET     /models
+GET     /v1/models
+
+POST    /v1/chat/completions
+POST    /bench/chat/completions
+```
+
+## 6. Notes
+
+* The `/v1/chat/completions` endpoint is compatible with the OpenAI API format, so existing OpenAI clients can be pointed to EXO by changing the base URL.
+* The instance placement endpoints allow you to plan and preview cluster allocations before actually creating instances.
+* The `/events` and `/state` endpoints are primarily intended for operational visibility and debugging.

flake.lock

@@ -1,5 +1,42 @@
 {
   "nodes": {
+    "crane": {
+      "locked": {
+        "lastModified": 1767744144,
+        "narHash": "sha256-9/9ntI0D+HbN4G0TrK3KmHbTvwgswz7p8IEJsWyef8Q=",
+        "owner": "ipetkov",
+        "repo": "crane",
+        "rev": "2fb033290bf6b23f226d4c8b32f7f7a16b043d7e",
+        "type": "github"
+      },
+      "original": {
+        "owner": "ipetkov",
+        "repo": "crane",
+        "type": "github"
+      }
+    },
+    "dream2nix": {
+      "inputs": {
+        "nixpkgs": [
+          "nixpkgs"
+        ],
+        "purescript-overlay": "purescript-overlay",
+        "pyproject-nix": "pyproject-nix"
+      },
+      "locked": {
+        "lastModified": 1765953015,
+        "narHash": "sha256-5FBZbbWR1Csp3Y2icfRkxMJw/a/5FGg8hCXej2//bbI=",
+        "owner": "nix-community",
+        "repo": "dream2nix",
+        "rev": "69eb01fa0995e1e90add49d8ca5bcba213b0416f",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nix-community",
+        "repo": "dream2nix",
+        "type": "github"
+      }
+    },
     "fenix": {
       "inputs": {
         "nixpkgs": [
@@ -8,11 +45,11 @@
         "rust-analyzer-src": "rust-analyzer-src"
       },
       "locked": {
-        "lastModified": 1761893049,
-        "narHash": "sha256-1TtFDPhC+ZsrOOtBnry1EZC+WipTTvsOVjIEVugqji8=",
+        "lastModified": 1768287139,
+        "narHash": "sha256-nsXFt0OzUi6K7dUzzJD5/v9e0Ic+fvclfIW936/43ZM=",
         "owner": "nix-community",
         "repo": "fenix",
-        "rev": "c2ac9a5c0d6d16630c3b225b874bd14528d1abe6",
+        "rev": "a4a3aa956931f90f35453cb519e4545e9ad7f773",
         "type": "github"
       },
       "original": {
@@ -21,25 +58,59 @@
         "type": "github"
       }
     },
-    "flake-utils": {
-      "inputs": {
-        "systems": "systems"
-      },
+    "flake-compat": {
+      "flake": false,
       "locked": {
-        "lastModified": 1731533236,
-        "narHash": "sha256-l0KFg5HjrsfsO/JpG+r7fRrqm12kzFHyUHqHCVpMMbI=",
-        "owner": "numtide",
-        "repo": "flake-utils",
-        "rev": "11707dc2f618dd54ca8739b309ec4fc024de578b",
+        "lastModified": 1696426674,
+        "narHash": "sha256-kvjfFW7WAETZlt09AgDn1MrtKzP7t90Vf7vypd3OL1U=",
+        "owner": "edolstra",
+        "repo": "flake-compat",
+        "rev": "0f9255e01c2351cc7d116c072cb317785dd33b33",
         "type": "github"
       },
       "original": {
-        "owner": "numtide",
-        "repo": "flake-utils",
+        "owner": "edolstra",
+        "repo": "flake-compat",
+        "type": "github"
+      }
+    },
+    "flake-parts": {
+      "inputs": {
+        "nixpkgs-lib": [
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1768135262,
+        "narHash": "sha256-PVvu7OqHBGWN16zSi6tEmPwwHQ4rLPU9Plvs8/1TUBY=",
+        "owner": "hercules-ci",
+        "repo": "flake-parts",
+        "rev": "80daad04eddbbf5a4d883996a73f3f542fa437ac",
+        "type": "github"
+      },
+      "original": {
+        "owner": "hercules-ci",
+        "repo": "flake-parts",
         "type": "github"
       }
     },
     "nixpkgs": {
+      "locked": {
+        "lastModified": 1768127708,
+        "narHash": "sha256-1Sm77VfZh3mU0F5OqKABNLWxOuDeHIlcFjsXeeiPazs=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "ffbc9f8cbaacfb331b6017d5a5abb21a492c9a38",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "ref": "nixos-unstable",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "nixpkgs-swift": {
       "locked": {
         "lastModified": 1761672384,
         "narHash": "sha256-o9KF3DJL7g7iYMZq9SWgfS1BFlNbsm6xplRjVlOCkXI=",
@@ -50,27 +121,74 @@
       },
       "original": {
         "owner": "NixOS",
-        "ref": "nixos-unstable",
         "repo": "nixpkgs",
+        "rev": "08dacfca559e1d7da38f3cf05f1f45ee9bfd213c",
+        "type": "github"
+      }
+    },
+    "purescript-overlay": {
+      "inputs": {
+        "flake-compat": "flake-compat",
+        "nixpkgs": [
+          "dream2nix",
+          "nixpkgs"
+        ],
+        "slimlock": "slimlock"
+      },
+      "locked": {
+        "lastModified": 1728546539,
+        "narHash": "sha256-Sws7w0tlnjD+Bjck1nv29NjC5DbL6nH5auL9Ex9Iz2A=",
+        "owner": "thomashoneyman",
+        "repo": "purescript-overlay",
+        "rev": "4ad4c15d07bd899d7346b331f377606631eb0ee4",
+        "type": "github"
+      },
+      "original": {
+        "owner": "thomashoneyman",
+        "repo": "purescript-overlay",
+        "type": "github"
+      }
+    },
+    "pyproject-nix": {
+      "inputs": {
+        "nixpkgs": [
+          "dream2nix",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1763017646,
+        "narHash": "sha256-Z+R2lveIp6Skn1VPH3taQIuMhABg1IizJd8oVdmdHsQ=",
+        "owner": "pyproject-nix",
+        "repo": "pyproject.nix",
+        "rev": "47bd6f296502842643078d66128f7b5e5370790c",
+        "type": "github"
+      },
+      "original": {
+        "owner": "pyproject-nix",
+        "repo": "pyproject.nix",
         "type": "github"
       }
     },
     "root": {
       "inputs": {
+        "crane": "crane",
+        "dream2nix": "dream2nix",
         "fenix": "fenix",
-        "flake-utils": "flake-utils",
+        "flake-parts": "flake-parts",
         "nixpkgs": "nixpkgs",
+        "nixpkgs-swift": "nixpkgs-swift",
         "treefmt-nix": "treefmt-nix"
       }
     },
     "rust-analyzer-src": {
       "flake": false,
       "locked": {
-        "lastModified": 1761849405,
-        "narHash": "sha256-igXdvC+WCUN+3gnfk+ptT7rMmxQuY6WbIg1rXMUN1DM=",
+        "lastModified": 1768224240,
+        "narHash": "sha256-Pp1dDrXKPBUJReZnnDElFyHYn67XTd48zRhToheLjtk=",
         "owner": "rust-lang",
         "repo": "rust-analyzer",
-        "rev": "f7de8ae045a5fe80f1203c5a1c3015b05f7c3550",
+        "rev": "725349602e525df37f377701e001fe8aab807878",
         "type": "github"
       },
       "original": {
@@ -80,18 +198,25 @@
         "type": "github"
       }
     },
-    "systems": {
+    "slimlock": {
+      "inputs": {
+        "nixpkgs": [
+          "dream2nix",
+          "purescript-overlay",
+          "nixpkgs"
+        ]
+      },
       "locked": {
-        "lastModified": 1681028828,
-        "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=",
-        "owner": "nix-systems",
-        "repo": "default",
-        "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e",
+        "lastModified": 1688756706,
+        "narHash": "sha256-xzkkMv3neJJJ89zo3o2ojp7nFeaZc2G0fYwNXNJRFlo=",
+        "owner": "thomashoneyman",
+        "repo": "slimlock",
+        "rev": "cf72723f59e2340d24881fd7bf61cb113b4c407c",
         "type": "github"
       },
       "original": {
-        "owner": "nix-systems",
-        "repo": "default",
+        "owner": "thomashoneyman",
+        "repo": "slimlock",
         "type": "github"
       }
     },
@@ -102,11 +227,11 @@
         ]
       },
       "locked": {
-        "lastModified": 1762938485,
-        "narHash": "sha256-AlEObg0syDl+Spi4LsZIBrjw+snSVU4T8MOeuZJUJjM=",
+        "lastModified": 1768158989,
+        "narHash": "sha256-67vyT1+xClLldnumAzCTBvU0jLZ1YBcf4vANRWP3+Ak=",
         "owner": "numtide",
         "repo": "treefmt-nix",
-        "rev": "5b4ee75aeefd1e2d5a1cc43cf6ba65eba75e83e4",
+        "rev": "e96d59dff5c0d7fddb9d113ba108f03c3ef99eca",
         "type": "github"
       },
       "original": {

flake.nix

@@ -3,129 +3,134 @@
 
   inputs = {
     nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable";
-    flake-utils.url = "github:numtide/flake-utils";
-    # Provides Rust dev-env integration:
+
+    flake-parts = {
+      url = "github:hercules-ci/flake-parts";
+      inputs.nixpkgs-lib.follows = "nixpkgs";
+    };
+
+    crane.url = "github:ipetkov/crane";
+
     fenix = {
       url = "github:nix-community/fenix";
       inputs.nixpkgs.follows = "nixpkgs";
     };
-    # Provides formatting infrastructure:
+
     treefmt-nix = {
       url = "github:numtide/treefmt-nix";
       inputs.nixpkgs.follows = "nixpkgs";
     };
+
+    dream2nix = {
+      url = "github:nix-community/dream2nix";
+      inputs.nixpkgs.follows = "nixpkgs";
+    };
+
+    # Pinned nixpkgs for swift-format (swift is broken on x86_64-linux in newer nixpkgs)
+    nixpkgs-swift.url = "github:NixOS/nixpkgs/08dacfca559e1d7da38f3cf05f1f45ee9bfd213c";
   };
 
-  # TODO: figure out caching story
-  # nixConfig = {
-  #   # nix community cachix
-  #   extra-trusted-public-keys = "nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=";
-  #   extra-substituters = "https://nix-community.cachix.org";
-  # };
+  nixConfig = {
+    extra-trusted-public-keys = "exo.cachix.org-1:okq7hl624TBeAR3kV+g39dUFSiaZgLRkLsFBCuJ2NZI=";
+    extra-substituters = "https://exo.cachix.org";
+  };
 
   outputs =
     inputs:
-    let
+    inputs.flake-parts.lib.mkFlake { inherit inputs; } {
       systems = [
         "x86_64-linux"
         "aarch64-darwin"
         "aarch64-linux"
       ];
-      fenixToolchain = system: inputs.fenix.packages.${system}.complete;
-    in
-    inputs.flake-utils.lib.eachSystem systems (
-      system:
-      let
-        pkgs = import inputs.nixpkgs {
-          inherit system;
-          overlays = [ inputs.fenix.overlays.default ];
-        };
-        treefmtEval = inputs.treefmt-nix.lib.evalModule pkgs {
-          projectRootFile = "flake.nix";
-          programs = {
-            nixpkgs-fmt.enable = true;
-            ruff-format = {
-              enable = true;
-              excludes = [ "rust/exo_pyo3_bindings/exo_pyo3_bindings.pyi" ];
+
+      imports = [
+        inputs.treefmt-nix.flakeModule
+        ./dashboard/parts.nix
+        ./rust/parts.nix
+      ];
+
+      perSystem =
+        { config, self', inputs', pkgs, lib, system, ... }:
+        let
+          fenixToolchain = inputs'.fenix.packages.complete;
+          # Use pinned nixpkgs for swift-format (swift is broken on x86_64-linux in newer nixpkgs)
+          pkgsSwift = import inputs.nixpkgs-swift { inherit system; };
+        in
+        {
+          treefmt = {
+            projectRootFile = "flake.nix";
+            programs = {
+              nixpkgs-fmt.enable = true;
+              ruff-format = {
+                enable = true;
+                excludes = [ "rust/exo_pyo3_bindings/exo_pyo3_bindings.pyi" ];
+              };
+              rustfmt = {
+                enable = true;
+                package = config.rust.toolchain;
+              };
+              prettier = {
+                enable = true;
+                includes = [ "*.ts" ];
+              };
+              swift-format = {
+                enable = true;
+                package = pkgsSwift.swiftPackages.swift-format;
+              };
             };
-            rustfmt = {
-              enable = true;
-              package = (fenixToolchain system).rustfmt;
-            };
-            prettier = {
-              enable = true;
-              includes = [ "*.ts" ];
-            };
-            swift-format.enable = true;
           };
-        };
-      in
-      {
-        formatter = treefmtEval.config.build.wrapper;
-        checks.formatting = treefmtEval.config.build.check inputs.self;
-        checks.lint = pkgs.runCommand "lint-check" { } ''
-          export RUFF_CACHE_DIR="$TMPDIR/ruff-cache"
-          ${pkgs.ruff}/bin/ruff check ${inputs.self}/
-          touch $out
-        '';
 
-        devShells.default = pkgs.mkShell {
-          packages =
-            with pkgs;
-            [
-              # PYTHON
-              python313
-              uv
-              ruff
-              basedpyright
-
-              # RUST
-              ((fenixToolchain system).withComponents [
-                "cargo"
-                "rustc"
-                "clippy"
-                "rustfmt"
-                "rust-src"
-              ])
-              rustup # Just here to make RustRover happy
-
-              # NIX
-              nixpkgs-fmt
-
-              # SVELTE
-              nodejs
-
-              # MISC
-              just
-              jq
-            ]
-            ++ (pkgs.lib.optionals pkgs.stdenv.isLinux [
-              # IFCONFIG
-              unixtools.ifconfig
-
-              # Build dependencies for Linux
-              pkg-config
-              openssl
-            ])
-            ++ (pkgs.lib.optionals pkgs.stdenv.isDarwin [
-              # MACMON
-              macmon
-            ]);
-
-          shellHook = ''
-            # PYTHON
-            export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:${pkgs.python313}/lib"
-            ${pkgs.lib.optionalString pkgs.stdenv.isLinux ''
-              # Build environment for Linux
-              export PKG_CONFIG_PATH="${pkgs.openssl.dev}/lib/pkgconfig:$PKG_CONFIG_PATH"
-              export LD_LIBRARY_PATH="${pkgs.openssl.out}/lib:$LD_LIBRARY_PATH"
-            ''}
-            echo
-            echo "🍎🍎 Run 'just <recipe>' to get started"
-            just --list
+          checks.lint = pkgs.runCommand "lint-check" { } ''
+            export RUFF_CACHE_DIR="$TMPDIR/ruff-cache"
+            ${pkgs.ruff}/bin/ruff check ${inputs.self}/
+            touch $out
           '';
 
+          devShells.default = with pkgs; pkgs.mkShell {
+            inputsFrom = [ self'.checks.cargo-build ];
+
+            packages =
+              [
+                # FORMATTING
+                config.treefmt.build.wrapper
+
+                # PYTHON
+                python313
+                uv
+                ruff
+                basedpyright
+
+                # RUST
+                config.rust.toolchain
+                maturin
+
+                # NIX
+                nixpkgs-fmt
+
+                # SVELTE
+                nodejs
+
+                # MISC
+                just
+                jq
+              ]
+              ++ lib.optionals stdenv.isLinux [
+                unixtools.ifconfig
+              ]
+              ++ lib.optionals stdenv.isDarwin [
+                macmon
+              ];
+
+            OPENSSL_NO_VENDOR = "1";
+
+            shellHook = ''
+              export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:${python313}/lib"
+              ${lib.optionalString stdenv.isLinux ''
+                export LD_LIBRARY_PATH="${openssl.out}/lib:$LD_LIBRARY_PATH"
+              ''}
+            '';
+          };
         };
-      }
-    );
+    };
 }

justfile

@@ -1,3 +1,5 @@
+export NIX_CONFIG := "extra-experimental-features = nix-command flakes"
+
 fmt:
     nix fmt
 

pyproject.toml

@@ -17,12 +17,13 @@ dependencies = [
     "loguru>=0.7.3",
     "exo_pyo3_bindings", # rust bindings
     "anyio==4.11.0",
-    "mlx>=0.30.1; sys_platform == 'darwin'",
-    "mlx[cpu]>=0.30.1; sys_platform == 'linux'",
-    "mlx-lm>=0.28.3",
+    "mlx==0.30.1; sys_platform == 'darwin'",
+    "mlx[cpu]==0.30.1; sys_platform == 'linux'",
+    "mlx-lm @ git+https://github.com/AlexCheema/mlx-lm.git@fix-transformers-5.0.0rc2",
     "tiktoken>=0.12.0", # required for kimi k2 tokenizer
     "hypercorn>=0.18.0",
     "openai-harmony>=0.0.8",
+    "httpx>=0.28.1",
 ]
 
 [project.scripts]
@@ -33,6 +34,7 @@ exo = "exo.main:main"
 # dependencies only required for development
 [dependency-groups]
 dev = [
+    "basedpyright>=1.29.0",
     "pyinstaller>=6.17.0",
     "pytest>=8.4.0",
     "pytest-asyncio>=1.0.0",
@@ -98,6 +100,7 @@ root = "src"
 
 # supported platforms for this project
 [tool.uv]
+prerelease = "allow"
 environments = [
     "sys_platform == 'darwin'",
     "sys_platform == 'linux'",

rust/parts.nix

@@ -0,0 +1,145 @@
+{ inputs, ... }:
+{
+  perSystem =
+    { config, self', inputs', pkgs, lib, ... }:
+    let
+      # Fenix nightly toolchain with all components
+      fenixPkgs = inputs'.fenix.packages;
+      rustToolchain = fenixPkgs.complete.withComponents [
+        "cargo"
+        "rustc"
+        "clippy"
+        "rustfmt"
+        "rust-src"
+        "rust-analyzer"
+      ];
+
+      # Crane with fenix toolchain
+      craneLib = (inputs.crane.mkLib pkgs).overrideToolchain rustToolchain;
+
+      # Source filtering - only include rust/ directory and root Cargo files
+      # This ensures changes to Python/docs/etc don't trigger Rust rebuilds
+      src = lib.cleanSourceWith {
+        src = inputs.self;
+        filter =
+          path: type:
+          let
+            baseName = builtins.baseNameOf path;
+            parentDir = builtins.dirOf path;
+            inRustDir =
+              (lib.hasInfix "/rust/" path)
+              || (lib.hasSuffix "/rust" parentDir)
+              || (baseName == "rust" && type == "directory");
+            isRootCargoFile =
+              (baseName == "Cargo.toml" || baseName == "Cargo.lock")
+              && (builtins.dirOf path == toString inputs.self);
+          in
+          isRootCargoFile
+          || (inRustDir && (craneLib.filterCargoSources path type || lib.hasSuffix ".toml" path || lib.hasSuffix ".md" path));
+      };
+
+      # Common arguments for all Rust builds
+      commonArgs = {
+        inherit src;
+        pname = "exo-rust";
+        version = "0.0.1";
+        strictDeps = true;
+
+        nativeBuildInputs = [
+          pkgs.pkg-config
+          pkgs.python313 # Required for pyo3-build-config
+        ];
+
+        buildInputs = [
+          pkgs.openssl
+          pkgs.python313 # Required for pyo3 tests
+        ];
+
+        OPENSSL_NO_VENDOR = "1";
+
+        # Required for pyo3 tests to find libpython
+        LD_LIBRARY_PATH = lib.makeLibraryPath [ pkgs.python313 ];
+      };
+
+      # Build dependencies once for caching
+      cargoArtifacts = craneLib.buildDepsOnly (
+        commonArgs
+        // {
+          cargoExtraArgs = "--workspace";
+        }
+      );
+    in
+    {
+      # Export toolchain for use in treefmt and devShell
+      options.rust = {
+        toolchain = lib.mkOption {
+          type = lib.types.package;
+          default = rustToolchain;
+          description = "The Rust toolchain to use";
+        };
+      };
+
+      config = {
+        packages = {
+          # Python bindings wheel via maturin
+          exo_pyo3_bindings = craneLib.buildPackage (
+            commonArgs
+            // {
+              inherit cargoArtifacts;
+              pname = "exo_pyo3_bindings";
+
+              nativeBuildInputs = commonArgs.nativeBuildInputs ++ [
+                pkgs.maturin
+              ];
+
+              buildPhaseCargoCommand = ''
+                maturin build \
+                  --release \
+                  --manylinux off \
+                  --manifest-path rust/exo_pyo3_bindings/Cargo.toml \
+                  --features "pyo3/extension-module,pyo3/experimental-async" \
+                  --interpreter ${pkgs.python313}/bin/python \
+                  --out dist
+              '';
+
+              # Don't use crane's default install behavior
+              doNotPostBuildInstallCargoBinaries = true;
+
+              installPhaseCommand = ''
+                mkdir -p $out
+                cp dist/*.whl $out/
+              '';
+            }
+          );
+        };
+
+        checks = {
+          # Full workspace build (all crates)
+          cargo-build = craneLib.buildPackage (
+            commonArgs
+            // {
+              inherit cargoArtifacts;
+              cargoExtraArgs = "--workspace";
+            }
+          );
+          # Run tests with nextest
+          cargo-nextest = craneLib.cargoNextest (
+            commonArgs
+            // {
+              inherit cargoArtifacts;
+              cargoExtraArgs = "--workspace";
+            }
+          );
+
+          # Build documentation
+          cargo-doc = craneLib.cargoDoc (
+            commonArgs
+            // {
+              inherit cargoArtifacts;
+              cargoExtraArgs = "--workspace";
+            }
+          );
+        };
+      };
+    };
+}

rust/system_custodian/Cargo.toml

@@ -1,47 +0,0 @@
-[package]
-name = "system_custodian"
-version = { workspace = true }
-edition = { workspace = true }
-publish = false
-
-[lib]
-doctest = false
-name = "system_custodian"
-path = "src/lib.rs"
-
-[[bin]]
-path = "src/bin/main.rs"
-name = "system_custodian"
-doc = false
-
-[lints]
-workspace = true
-
-[dependencies]
-# datastructures
-either = { workspace = true }
-
-# macro dependencies
-extend = { workspace = true }
-delegate = { workspace = true }
-impl-trait-for-tuples = { workspace = true }
-derive_more = { workspace = true }
-
-# async
-tokio = { workspace = true, features = ["full"] }
-futures = { workspace = true }
-futures-timer = { workspace = true }
-
-# utility dependencies
-util = { workspace = true }
-thiserror = { workspace = true }
-#internment = { workspace = true }
-#recursion = { workspace = true }
-#generativity = { workspace = true }
-#itertools = { workspace = true }
-tracing-subscriber = { version = "0.3.19", features = ["default", "env-filter"] }
-keccak-const = { workspace = true }
-
-# tracing/logging
-log = { workspace = true }
-

rust/system_custodian/src/bin/main.rs

@@ -1,4 +0,0 @@
-//! TODO: documentation
-//!
-
-fn main() {}

rust/system_custodian/src/lib.rs

@@ -1,69 +0,0 @@
-//! This crate defines the logic of, and ways to interact with, Exo's **_System Custodian_** daemon.
-//!
-//! The **_System Custodian_** daemon is supposed to be a long-living process that precedes the
-//! launch of the Exo application, and responsible for ensuring the system (configuration, settings,
-//! etc.) is in an appropriate state to facilitate the running of Exo application.
-//! The **_System Custodian_** daemon shall expose a [D-Bus](https://www.freedesktop.org/wiki/Software/dbus/)
-//! service which Exo application use to _control & query_ it.
-//!
-//! # Lifecycle
-//! When the Exo application starts, it will _wake_ the **_System Custodian_** daemon for the
-//! duration of its lifetime, and after it has terminated the daemon will go back to sleep. When
-//! the daemon wakes up, it will configure the system into a state suitable for the Exo Application;
-//! When the daemon goes to sleep, it will revert those changes as much as it can in case they were
-//! destructive to the user's pre-existing configurations.
-//!
-//! # Responsibilities
-//! TODO: these are purely on MacOS, but change to be more broad
-//! The **_System Custodian_** daemon is responsible for using System Configuration framework to
-//!  1. duplicate the current network set
-//!  2. modify existing services to turn on IPv6 if not there
-//!  3. remove any bridge services & add any missing services that AREN'T bridge
-//! TODO: In the future:
-//!  1. run a dummy AWDL service to [allow for macOS peer-to-peer wireless networking](https://yggdrasil-network.github.io/2019/08/19/awdl.html)
-//!  2. toggle some GPU/memory configurations to speed up GPU (ask Alex what those configurations are)
-//!  3. if we ever decide to provide our **own network interfaces** that abstract over some userland
-//!     logic, this would be the place to spin that up.
-//!
-//! Then it will watch the SCDynamicStore for:
-//!  1. all __actual__ network interfaces -> collect information on them e.g. their BSD name, MAC
-//!     address, MTU, IPv6 addresses, etc. -> and set up watchers/notifiers to inform the DBus
-//!     interface of any changes
-//!  2. watch for any __undesirable__ changes to configuration and revert it
-//!
-//! It should somehow (probably through system sockets and/or BSD interface) trigger IPv6 NDP on
-//! each of the interfaces & also listen to/query for any changes on the OS routing cache??
-//! Basically emulate the `ping6 ff02::1%enX` and `ndp -an` commands BUT BETTER!!!
-//!  1. all that info should coalesce back to the overall state colleted -> should be queryable
-//!     over D-Bus
-//! TODO:
-//!  1. we might potentially add to this step a handshake of some kind...? To ensure that we can
-//!     ACTUALLY communicate with that machine over that link over e.g. TCP, UDP, etc. Will the
-//!     handshake require to know Node ID? Will the handshake require heartbeats? Who knows...
-//!  2. if we ever decide to write proprietary L2/L3 protocols for quicker communication,
-//!     e.g. [AF_NDRV](https://www.zerotier.com/blog/how-zerotier-eliminated-kernel-extensions-on-macos/)
-//!     for raw ethernet frame communication, or even a [custom thunderbolt PCIe driver](https://developer.apple.com/documentation/pcidriverkit/creating-custom-pcie-drivers-for-thunderbolt-devices),
-//!     then this would be the place to carry out discovery and propper handshakes with devices
-//!     on the other end of the link.
-//!
-
-// enable Rust-unstable features for convenience
-#![feature(trait_alias)]
-#![feature(stmt_expr_attributes)]
-#![feature(type_alias_impl_trait)]
-#![feature(specialization)]
-#![feature(unboxed_closures)]
-#![feature(const_trait_impl)]
-#![feature(fn_traits)]
-
-pub(crate) mod private {
-    // sealed traits support
-    pub trait Sealed {}
-    impl<T: ?Sized> Sealed for T {}
-}
-
-/// Namespace for all the type/trait aliases used by this crate.
-pub(crate) mod alias {}
-
-/// Namespace for crate-wide extension traits/methods
-pub(crate) mod ext {}

src/exo/main.py

@@ -1,6 +1,7 @@
 import argparse
 import multiprocessing as mp
 import os
+import resource
 import signal
 from dataclasses import dataclass, field
 from typing import Self
@@ -195,6 +196,8 @@ class Node:
 
 def main():
     args = Args.parse()
+    soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
+    resource.setrlimit(resource.RLIMIT_NOFILE, (max(soft, 65535), hard))
 
     mp.set_start_method("spawn")
     # TODO: Refactor the current verbosity system
@@ -202,6 +205,14 @@ def main():
     logger.info("Starting EXO")
     logger.info(f"EXO_LIBP2P_NAMESPACE: {os.getenv('EXO_LIBP2P_NAMESPACE')}")
 
+    # Set FAST_SYNCH override env var for runner subprocesses
+    if args.fast_synch is True:
+        os.environ["EXO_FAST_SYNCH"] = "on"
+        logger.info("FAST_SYNCH forced ON")
+    elif args.fast_synch is False:
+        os.environ["EXO_FAST_SYNCH"] = "off"
+        logger.info("FAST_SYNCH forced OFF")
+
     node = anyio.run(Node.create, args)
     anyio.run(node.run)
     logger.info("EXO Shutdown complete")
@@ -215,6 +226,7 @@ class Args(CamelCaseModel):
     api_port: PositiveInt = 52415
     tb_only: bool = False
     no_worker: bool = False
+    fast_synch: bool | None = None  # None = auto, True = force on, False = force off
 
     @classmethod
     def parse(cls) -> Self:
@@ -256,6 +268,20 @@ class Args(CamelCaseModel):
             "--no-worker",
             action="store_true",
         )
+        fast_synch_group = parser.add_mutually_exclusive_group()
+        fast_synch_group.add_argument(
+            "--fast-synch",
+            action="store_true",
+            dest="fast_synch",
+            default=None,
+            help="Force MLX FAST_SYNCH on (for JACCL backend)",
+        )
+        fast_synch_group.add_argument(
+            "--no-fast-synch",
+            action="store_false",
+            dest="fast_synch",
+            help="Force MLX FAST_SYNCH off",
+        )
 
         args = parser.parse_args()
         return cls(**vars(args))  # pyright: ignore[reportAny] - We are intentionally validating here, we can't do it statically

src/exo/master/api.py

@@ -1,24 +1,19 @@
 import time
 from collections.abc import AsyncGenerator
+from http import HTTPStatus
 from typing import cast
 
 import anyio
-from anyio import create_task_group
+from anyio import BrokenResourceError, create_task_group
 from anyio.abc import TaskGroup
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import StreamingResponse
+from fastapi.responses import JSONResponse, StreamingResponse
 from fastapi.staticfiles import StaticFiles
 from hypercorn.asyncio import serve  # pyright: ignore[reportUnknownVariableType]
 from hypercorn.config import Config
 from hypercorn.typing import ASGIFramework
 from loguru import logger
-from openai_harmony import (  # pyright: ignore[reportMissingTypeStubs]
-    HarmonyEncodingName,
-    Role,
-    StreamableParser,
-    load_harmony_encoding,
-)
 
 from exo.master.placement import place_instance as get_instance_placements
 from exo.shared.apply import apply
@@ -35,6 +30,8 @@ from exo.shared.types.api import (
     CreateInstanceParams,
     CreateInstanceResponse,
     DeleteInstanceResponse,
+    ErrorInfo,
+    ErrorResponse,
     FinishReason,
     GenerationStats,
     ModelList,
@@ -55,7 +52,12 @@ from exo.shared.types.commands import (
     TaskFinished,
 )
 from exo.shared.types.common import CommandId, NodeId, SessionId
-from exo.shared.types.events import ChunkGenerated, Event, ForwarderEvent, IndexedEvent
+from exo.shared.types.events import (
+    ChunkGenerated,
+    Event,
+    ForwarderEvent,
+    IndexedEvent,
+)
 from exo.shared.types.memory import Memory
 from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.state import State
@@ -67,8 +69,6 @@ from exo.utils.channels import Receiver, Sender, channel
 from exo.utils.dashboard_path import find_dashboard
 from exo.utils.event_buffer import OrderedBuffer
 
-encoding = load_harmony_encoding(HarmonyEncodingName.HARMONY_GPT_OSS)
-
 
 def chunk_to_response(
     chunk: TokenChunk, command_id: CommandId
@@ -123,6 +123,7 @@ class API:
         self.paused_ev: anyio.Event = anyio.Event()
 
         self.app = FastAPI()
+        self._setup_exception_handlers()
         self._setup_cors()
         self._setup_routes()
 
@@ -153,6 +154,20 @@ class API:
         self.paused_ev.set()
         self.paused_ev = anyio.Event()
 
+    def _setup_exception_handlers(self) -> None:
+        @self.app.exception_handler(HTTPException)
+        async def http_exception_handler(  # pyright: ignore[reportUnusedFunction]
+            _: Request, exc: HTTPException
+        ) -> JSONResponse:
+            err = ErrorResponse(
+                error=ErrorInfo(
+                    message=exc.detail,
+                    type=HTTPStatus(exc.status_code).phrase,
+                    code=exc.status_code,
+                )
+            )
+            return JSONResponse(err.model_dump(), status_code=exc.status_code)
+
     def _setup_cors(self) -> None:
         self.app.add_middleware(
             CORSMiddleware,
@@ -381,35 +396,8 @@ class API:
             instance_id=instance_id,
         )
 
-    async def _process_gpt_oss(self, token_chunks: Receiver[TokenChunk]):
-        stream = StreamableParser(encoding, role=Role.ASSISTANT)
-        thinking = False
-
-        async for chunk in token_chunks:
-            stream.process(chunk.token_id)
-
-            delta = stream.last_content_delta
-            ch = stream.current_channel
-
-            if ch == "analysis" and not thinking:
-                thinking = True
-                yield chunk.model_copy(update={"text": "<think>"})
-
-            if ch != "analysis" and thinking:
-                thinking = False
-                yield chunk.model_copy(update={"text": "</think>"})
-
-            if delta:
-                yield chunk.model_copy(update={"text": delta})
-
-            if chunk.finish_reason is not None:
-                if thinking:
-                    yield chunk.model_copy(update={"text": "</think>"})
-                yield chunk
-                break
-
     async def _chat_chunk_stream(
-        self, command_id: CommandId, parse_gpt_oss: bool
+        self, command_id: CommandId
     ) -> AsyncGenerator[TokenChunk, None]:
         """Yield `TokenChunk`s for a given command until completion."""
 
@@ -417,16 +405,10 @@ class API:
             self._chat_completion_queues[command_id], recv = channel[TokenChunk]()
 
             with recv as token_chunks:
-                if parse_gpt_oss:
-                    async for chunk in self._process_gpt_oss(token_chunks):
-                        yield chunk
-                        if chunk.finish_reason is not None:
-                            break
-                else:
-                    async for chunk in token_chunks:
-                        yield chunk
-                        if chunk.finish_reason is not None:
-                            break
+                async for chunk in token_chunks:
+                    yield chunk
+                    if chunk.finish_reason is not None:
+                        break
 
         except anyio.get_cancelled_exc_class():
             # TODO: TaskCancelled
@@ -442,11 +424,23 @@ class API:
             del self._chat_completion_queues[command_id]
 
     async def _generate_chat_stream(
-        self, command_id: CommandId, parse_gpt_oss: bool
+        self, command_id: CommandId
     ) -> AsyncGenerator[str, None]:
         """Generate chat completion stream as JSON strings."""
 
-        async for chunk in self._chat_chunk_stream(command_id, parse_gpt_oss):
+        async for chunk in self._chat_chunk_stream(command_id):
+            if chunk.finish_reason == "error":
+                error_response = ErrorResponse(
+                    error=ErrorInfo(
+                        message=chunk.error_message or "Internal server error",
+                        type="InternalServerError",
+                        code=500,
+                    )
+                )
+                yield f"data: {error_response.model_dump_json()}\n\n"
+                yield "data: [DONE]\n\n"
+                return
+
             chunk_response: ChatCompletionResponse = chunk_to_response(
                 chunk, command_id
             )
@@ -458,7 +452,7 @@ class API:
                 yield "data: [DONE]\n\n"
 
     async def _collect_chat_completion(
-        self, command_id: CommandId, parse_gpt_oss: bool
+        self, command_id: CommandId
     ) -> ChatCompletionResponse:
         """Collect all token chunks for a chat completion and return a single response."""
 
@@ -466,7 +460,13 @@ class API:
         model: str | None = None
         finish_reason: FinishReason | None = None
 
-        async for chunk in self._chat_chunk_stream(command_id, parse_gpt_oss):
+        async for chunk in self._chat_chunk_stream(command_id):
+            if chunk.finish_reason == "error":
+                raise HTTPException(
+                    status_code=500,
+                    detail=chunk.error_message or "Internal server error",
+                )
+
             if model is None:
                 model = chunk.model
 
@@ -495,7 +495,7 @@ class API:
         )
 
     async def _collect_chat_completion_with_stats(
-        self, command_id: CommandId, parse_gpt_oss: bool
+        self, command_id: CommandId
     ) -> BenchChatCompletionResponse:
         text_parts: list[str] = []
         model: str | None = None
@@ -503,7 +503,13 @@ class API:
 
         stats: GenerationStats | None = None
 
-        async for chunk in self._chat_chunk_stream(command_id, parse_gpt_oss):
+        async for chunk in self._chat_chunk_stream(command_id):
+            if chunk.finish_reason == "error":
+                raise HTTPException(
+                    status_code=500,
+                    detail=chunk.error_message or "Internal server error",
+                )
+
             if model is None:
                 model = chunk.model
 
@@ -544,8 +550,6 @@ class API:
         """Handle chat completions, supporting both streaming and non-streaming responses."""
         model_meta = await resolve_model_meta(payload.model)
         payload.model = model_meta.model_id
-        parse_gpt_oss = "gpt-oss" in model_meta.model_id.lower()
-        logger.info(f"{parse_gpt_oss=}")
 
         if not any(
             instance.shard_assignments.model_id == payload.model
@@ -562,17 +566,16 @@ class API:
         await self._send(command)
         if payload.stream:
             return StreamingResponse(
-                self._generate_chat_stream(command.command_id, parse_gpt_oss),
+                self._generate_chat_stream(command.command_id),
                 media_type="text/event-stream",
             )
 
-        return await self._collect_chat_completion(command.command_id, parse_gpt_oss)
+        return await self._collect_chat_completion(command.command_id)
 
     async def bench_chat_completions(
         self, payload: BenchChatCompletionTaskParams
     ) -> BenchChatCompletionResponse:
         model_meta = await resolve_model_meta(payload.model)
-        parse_gpt_oss = "gpt-oss" in model_meta.model_id.lower()
         payload.model = model_meta.model_id
 
         if not any(
@@ -589,10 +592,7 @@ class API:
         command = ChatCompletion(request_params=payload)
         await self._send(command)
 
-        response = await self._collect_chat_completion_with_stats(
-            command.command_id,
-            parse_gpt_oss,
-        )
+        response = await self._collect_chat_completion_with_stats(command.command_id)
         return response
 
     def _calculate_total_available_memory(self) -> Memory:
@@ -654,14 +654,14 @@ class API:
                 for idx, event in self.event_buffer.drain_indexed():
                     self._event_log.append(event)
                     self.state = apply(self.state, IndexedEvent(event=event, idx=idx))
-                    if (
-                        isinstance(event, ChunkGenerated)
-                        and event.command_id in self._chat_completion_queues
-                    ):
+                    if isinstance(event, ChunkGenerated):
                         assert isinstance(event.chunk, TokenChunk)
-                        await self._chat_completion_queues[event.command_id].send(
-                            event.chunk
-                        )
+                        queue = self._chat_completion_queues.get(event.command_id)
+                        if queue is not None:
+                            try:
+                                await queue.send(event.chunk)
+                            except BrokenResourceError:
+                                self._chat_completion_queues.pop(event.command_id, None)
 
     async def _pause_on_new_election(self):
         with self.election_receiver as ems:

src/exo/master/tests/test_api_error_handling.py

@@ -0,0 +1,107 @@
+# pyright: reportUnusedFunction=false, reportAny=false
+from typing import Any, get_args
+
+from fastapi import FastAPI, HTTPException
+from fastapi.testclient import TestClient
+
+from exo.shared.types.api import ErrorInfo, ErrorResponse, FinishReason
+from exo.shared.types.chunks import TokenChunk
+from exo.worker.tests.constants import MODEL_A_ID
+
+
+def test_http_exception_handler_formats_openai_style() -> None:
+    """Test that HTTPException is converted to OpenAI-style error format."""
+    from exo.master.api import API
+
+    app = FastAPI()
+
+    # Setup exception handler
+    api = object.__new__(API)
+    api.app = app
+    api._setup_exception_handlers()  # pyright: ignore[reportPrivateUsage]
+
+    # Add test routes that raise HTTPException
+    @app.get("/test-error")
+    async def _test_error() -> None:
+        raise HTTPException(status_code=500, detail="Test error message")
+
+    @app.get("/test-not-found")
+    async def _test_not_found() -> None:
+        raise HTTPException(status_code=404, detail="Resource not found")
+
+    client = TestClient(app)
+
+    # Test 500 error
+    response = client.get("/test-error")
+    assert response.status_code == 500
+    data: dict[str, Any] = response.json()
+    assert "error" in data
+    assert data["error"]["message"] == "Test error message"
+    assert data["error"]["type"] == "Internal Server Error"
+    assert data["error"]["code"] == 500
+
+    # Test 404 error
+    response = client.get("/test-not-found")
+    assert response.status_code == 404
+    data = response.json()
+    assert "error" in data
+    assert data["error"]["message"] == "Resource not found"
+    assert data["error"]["type"] == "Not Found"
+    assert data["error"]["code"] == 404
+
+
+def test_finish_reason_includes_error() -> None:
+    valid_reasons = get_args(FinishReason)
+    assert "error" in valid_reasons
+
+
+def test_token_chunk_with_error_fields() -> None:
+    chunk = TokenChunk(
+        idx=0,
+        model=MODEL_A_ID,
+        text="",
+        token_id=0,
+        finish_reason="error",
+        error_message="Something went wrong",
+    )
+
+    assert chunk.finish_reason == "error"
+    assert chunk.error_message == "Something went wrong"
+
+
+def test_token_chunk_without_error() -> None:
+    chunk = TokenChunk(
+        idx=1,
+        model=MODEL_A_ID,
+        text="Hello",
+        token_id=42,
+        finish_reason=None,
+    )
+
+    assert chunk.finish_reason is None
+    assert chunk.error_message is None
+
+
+def test_error_response_construction() -> None:
+    error_response = ErrorResponse(
+        error=ErrorInfo(
+            message="Generation failed",
+            type="InternalServerError",
+            code=500,
+        )
+    )
+
+    assert error_response.error.message == "Generation failed"
+    assert error_response.error.code == 500
+
+
+def test_normal_finish_reasons_still_work() -> None:
+    for reason in ["stop", "length", "tool_calls", "content_filter", "function_call"]:
+        chunk = TokenChunk(
+            idx=0,
+            model=MODEL_A_ID,
+            text="done",
+            token_id=100,
+            finish_reason=reason,  # type: ignore[arg-type]
+        )
+        assert chunk.finish_reason == reason

src/exo/shared/logging.py

@@ -29,6 +29,11 @@ class _InterceptHandler(logging.Handler):
 
 def logger_setup(log_file: Path | None, verbosity: int = 0):
     """Set up logging for this process - formatting, file handles, verbosity and output"""
+
+    logging.getLogger("exo_pyo3_bindings").setLevel(logging.WARNING)
+    logging.getLogger("httpx").setLevel(logging.WARNING)
+    logging.getLogger("httpcore").setLevel(logging.WARNING)
+
     logger.remove()
 
     # replace all stdlib loggers with _InterceptHandlers that log to loguru

src/exo/shared/models/model_cards.py

@@ -14,32 +14,6 @@ class ModelCard(CamelCaseModel):
 
 MODEL_CARDS: dict[str, ModelCard] = {
     # deepseek v3
-    # "deepseek-v3-0324:4bit": ModelCard(
-    #     short_id="deepseek-v3-0324:4bit",
-    #     model_id="mlx-community/DeepSeek-V3-0324-4bit",
-    #     name="DeepSeek V3 0324 (4-bit)",
-    #     description="""DeepSeek V3 is a large language model trained on the DeepSeek V3 dataset.""",
-    #     tags=[],
-    #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/DeepSeek-V3-0324-4bit"),
-    #         pretty_name="DeepSeek V3 0324 (4-bit)",
-    #         storage_size=Memory.from_kb(409706307),
-    #         n_layers=61,
-    #     ),
-    # ),
-    # "deepseek-v3-0324": ModelCard(
-    #     short_id="deepseek-v3-0324",
-    #     model_id="mlx-community/DeepSeek-v3-0324-8bit",
-    #     name="DeepSeek V3 0324 (8-bit)",
-    #     description="""DeepSeek V3 is a large language model trained on the DeepSeek V3 dataset.""",
-    #     tags=[],
-    #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/DeepSeek-v3-0324-8bit"),
-    #         pretty_name="DeepSeek V3 0324 (8-bit)",
-    #         storage_size=Memory.from_kb(754706307),
-    #         n_layers=61,
-    #     ),
-    # ),
     "deepseek-v3.1-4bit": ModelCard(
         short_id="deepseek-v3.1-4bit",
         model_id=ModelId("mlx-community/DeepSeek-V3.1-4bit"),
@@ -70,63 +44,6 @@ MODEL_CARDS: dict[str, ModelCard] = {
             supports_tensor=True,
         ),
     ),
-    # "deepseek-v3.2": ModelCard(
-    #     short_id="deepseek-v3.2",
-    #     model_id=ModelId("mlx-community/DeepSeek-V3.2-8bit"),
-    #     name="DeepSeek V3.2 (8-bit)",
-    #     description="""DeepSeek V3.2 is a large language model trained on the DeepSeek V3.2 dataset.""",
-    #     tags=[],
-    #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/DeepSeek-V3.2-8bit"),
-    #         pretty_name="DeepSeek V3.2 (8-bit)",
-    #         storage_size=Memory.from_kb(754706307),
-    #         n_layers=61,
-    #         hidden_size=7168,
-    #     ),
-    # ),
-    # "deepseek-v3.2-4bit": ModelCard(
-    #     short_id="deepseek-v3.2-4bit",
-    #     model_id=ModelId("mlx-community/DeepSeek-V3.2-4bit"),
-    #     name="DeepSeek V3.2 (4-bit)",
-    #     description="""DeepSeek V3.2 is a large language model trained on the DeepSeek V3.2 dataset.""",
-    #     tags=[],
-    #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/DeepSeek-V3.2-4bit"),
-    #         pretty_name="DeepSeek V3.2 (4-bit)",
-    #         storage_size=Memory.from_kb(754706307 // 2),  # TODO !!!!!
-    #         n_layers=61,
-    #         hidden_size=7168,
-    #     ),
-    # ),
-    # deepseek r1
-    # "deepseek-r1-0528-4bit": ModelCard(
-    #     short_id="deepseek-r1-0528-4bit",
-    #     model_id="mlx-community/DeepSeek-R1-0528-4bit",
-    #     name="DeepSeek-R1-0528 (4-bit)",
-    #     description="""DeepSeek R1 is a large language model trained on the DeepSeek R1 dataset.""",
-    #     tags=[],
-    #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/DeepSeek-R1-0528-4bit"),
-    #         pretty_name="DeepSeek R1 671B (4-bit)",
-    #         storage_size=Memory.from_kb(409706307),
-    #         n_layers=61,
-    #         hidden_size=7168,
-    #     ),
-    # ),
-    # "deepseek-r1-0528": ModelCard(
-    #     short_id="deepseek-r1-0528",
-    #     model_id="mlx-community/DeepSeek-R1-0528-8bit",
-    #     name="DeepSeek-R1-0528 (8-bit)",
-    #     description="""DeepSeek R1 is a large language model trained on the DeepSeek R1 dataset.""",
-    #     tags=[],
-    #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/DeepSeek-R1-0528-8bit"),
-    #         pretty_name="DeepSeek R1 671B (8-bit)",
-    #         storage_size=Memory.from_bytes(754998771712),
-    #         n_layers=61,
-    # .       hidden_size=7168,
-    #     ),
-    # ),
     # kimi k2
     "kimi-k2-instruct-4bit": ModelCard(
         short_id="kimi-k2-instruct-4bit",
@@ -508,23 +425,24 @@ MODEL_CARDS: dict[str, ModelCard] = {
             supports_tensor=True,
         ),
     ),
-    "gpt-oss-20b-4bit": ModelCard(
-        short_id="gpt-oss-20b-4bit",
-        model_id=ModelId("mlx-community/gpt-oss-20b-MXFP4-Q4"),
-        name="GPT-OSS 20B (MXFP4-Q4, MLX)",
-        description="""OpenAI's GPT-OSS 20B is a medium-sized MoE model for lower-latency and local or specialized use cases; this MLX variant uses MXFP4 4-bit quantization.""",
+    "gpt-oss-20b-MXFP4-Q8": ModelCard(
+        short_id="gpt-oss-20b-MXFP4-Q8",
+        model_id=ModelId("mlx-community/gpt-oss-20b-MXFP4-Q8"),
+        name="GPT-OSS 20B (MXFP4-Q8, MLX)",
+        description="""OpenAI's GPT-OSS 20B is a medium-sized MoE model for lower-latency and local or specialized use cases; this variant is a 4-bit MLX conversion for Apple Silicon.""",
         tags=[],
         metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/gpt-oss-20b-MXFP4-Q4"),
-            pretty_name="GPT-OSS 20B (MXFP4-Q4, MLX)",
+            model_id=ModelId("mlx-community/gpt-oss-20b-MXFP4-Q8"),
+            pretty_name="GPT-OSS 20B (MXFP4-Q8, MLX)",
             storage_size=Memory.from_kb(11_744_051),
             n_layers=24,
             hidden_size=2880,
             supports_tensor=True,
         ),
     ),
-    # Needs to be quantized g32 or g16.
+    # glm 4.5
     "glm-4.5-air-8bit": ModelCard(
+        # Needs to be quantized g32 or g16 to work with tensor parallel
         short_id="glm-4.5-air-8bit",
         model_id=ModelId("mlx-community/GLM-4.5-Air-8bit"),
         name="GLM 4.5 Air 8bit",
@@ -554,19 +472,81 @@ MODEL_CARDS: dict[str, ModelCard] = {
             supports_tensor=True,
         ),
     ),
-    # "devstral-2-123b-instruct-2512-8bit": ModelCard(
-    #     short_id="devstral-2-123b-instruct-2512-8bit",
-    #     model_id=ModelId("mlx-community/Devstral-2-123B-Instruct-2512-8bit"),
-    #     name="Devstral 2 123B Instruct 2512 (8-bit, MLX)",
-    #     description="""Mistral AI's Devstral 2 123B Instruct (2512) is an agentic coding model.""",
-    #     tags=[],
-    #     metadata=ModelMetadata(
-    #         model_id=ModelId("mlx-community/Devstral-2-123B-Instruct-2512-8bit"),
-    #         pretty_name="Devstral 2 123B Instruct 2512 (8-bit, MLX)",
-    #         storage_size=Memory.from_kb(133_000_000),
-    #         n_layers=88,
-    #         hidden_size=12288,
-    #         supports_tensor=True,
-    #     ),
-    # ),
+    # glm 4.7
+    "glm-4.7-4bit": ModelCard(
+        short_id="glm-4.7-4bit",
+        model_id=ModelId("mlx-community/GLM-4.7-4bit"),
+        name="GLM 4.7 4bit",
+        description="GLM 4.7 4bit",
+        tags=[],
+        metadata=ModelMetadata(
+            model_id=ModelId("mlx-community/GLM-4.7-4bit"),
+            pretty_name="GLM 4.7 4bit",
+            storage_size=Memory.from_bytes(198556925568),
+            n_layers=91,
+            hidden_size=5120,
+            supports_tensor=True,
+        ),
+    ),
+    "glm-4.7-6bit": ModelCard(
+        short_id="glm-4.7-6bit",
+        model_id=ModelId("mlx-community/GLM-4.7-6bit"),
+        name="GLM 4.7 6bit",
+        description="GLM 4.7 6bit",
+        tags=[],
+        metadata=ModelMetadata(
+            model_id=ModelId("mlx-community/GLM-4.7-6bit"),
+            pretty_name="GLM 4.7 6bit",
+            storage_size=Memory.from_bytes(286737579648),
+            n_layers=91,
+            hidden_size=5120,
+            supports_tensor=True,
+        ),
+    ),
+    "glm-4.7-8bit-gs32": ModelCard(
+        short_id="glm-4.7-8bit-gs32",
+        model_id=ModelId("mlx-community/GLM-4.7-8bit-gs32"),
+        name="GLM 4.7 8bit (gs32)",
+        description="GLM 4.7 8bit (gs32)",
+        tags=[],
+        metadata=ModelMetadata(
+            model_id=ModelId("mlx-community/GLM-4.7-8bit-gs32"),
+            pretty_name="GLM 4.7 8bit (gs32)",
+            storage_size=Memory.from_bytes(396963397248),
+            n_layers=91,
+            hidden_size=5120,
+            supports_tensor=True,
+        ),
+    ),
+    # minimax-m2
+    "minimax-m2.1-8bit": ModelCard(
+        short_id="minimax-m2.1-8bit",
+        model_id=ModelId("mlx-community/MiniMax-M2.1-8bit"),
+        name="MiniMax M2.1 8bit",
+        description="MiniMax M2.1 8bit",
+        tags=[],
+        metadata=ModelMetadata(
+            model_id=ModelId("mlx-community/MiniMax-M2.1-8bit"),
+            pretty_name="MiniMax M2.1 8bit",
+            storage_size=Memory.from_bytes(242986745856),
+            n_layers=61,
+            hidden_size=3072,
+            supports_tensor=True,
+        ),
+    ),
+    "minimax-m2.1-3bit": ModelCard(
+        short_id="minimax-m2.1-3bit",
+        model_id=ModelId("mlx-community/MiniMax-M2.1-3bit"),
+        name="MiniMax M2.1 3bit",
+        description="MiniMax M2.1 3bit",
+        tags=[],
+        metadata=ModelMetadata(
+            model_id=ModelId("mlx-community/MiniMax-M2.1-3bit"),
+            pretty_name="MiniMax M2.1 3bit",
+            storage_size=Memory.from_bytes(100086644736),
+            n_layers=61,
+            hidden_size=3072,
+            supports_tensor=True,
+        ),
+    ),
 }

src/exo/shared/tests/test_apply/test_apply_node_download.py

@@ -2,6 +2,7 @@ from exo.shared.apply import apply_node_download_progress
 from exo.shared.tests.conftest import get_pipeline_shard_metadata
 from exo.shared.types.common import NodeId
 from exo.shared.types.events import NodeDownloadProgress
+from exo.shared.types.memory import Memory
 from exo.shared.types.state import State
 from exo.shared.types.worker.downloads import DownloadCompleted
 from exo.worker.tests.constants import MODEL_A_ID, MODEL_B_ID
@@ -13,6 +14,7 @@ def test_apply_node_download_progress():
     event = DownloadCompleted(
         node_id=NodeId("node-1"),
         shard_metadata=shard1,
+        total_bytes=Memory(),
     )
 
     new_state = apply_node_download_progress(
@@ -28,10 +30,12 @@ def test_apply_two_node_download_progress():
     event1 = DownloadCompleted(
         node_id=NodeId("node-1"),
         shard_metadata=shard1,
+        total_bytes=Memory(),
     )
     event2 = DownloadCompleted(
         node_id=NodeId("node-1"),
         shard_metadata=shard2,
+        total_bytes=Memory(),
     )
     state = State(downloads={NodeId("node-1"): [event1]})
 

src/exo/shared/types/api.py

@@ -11,10 +11,21 @@ from exo.shared.types.worker.instances import Instance, InstanceId, InstanceMeta
 from exo.shared.types.worker.shards import Sharding
 
 FinishReason = Literal[
-    "stop", "length", "tool_calls", "content_filter", "function_call"
+    "stop", "length", "tool_calls", "content_filter", "function_call", "error"
 ]
 
 
+class ErrorInfo(BaseModel):
+    message: str
+    type: str
+    param: str | None = None
+    code: int
+
+
+class ErrorResponse(BaseModel):
+    error: ErrorInfo
+
+
 class ModelListModel(BaseModel):
     id: str
     object: str = "model"

src/exo/shared/types/chunks.py

@@ -22,6 +22,7 @@ class TokenChunk(BaseChunk):
     token_id: int
     finish_reason: FinishReason | None = None
     stats: GenerationStats | None = None
+    error_message: str | None = None
 
 
 class ImageChunk(BaseChunk):

src/exo/shared/types/worker/downloads.py

@@ -28,7 +28,7 @@ class DownloadPending(BaseDownloadProgress):
 
 
 class DownloadCompleted(BaseDownloadProgress):
-    pass
+    total_bytes: Memory
 
 
 class DownloadFailed(BaseDownloadProgress):

src/exo/worker/engines/mlx/auto_parallel.py

@@ -10,18 +10,24 @@ from mlx.nn.layers.distributed import (
     shard_linear,
     sum_gradients,
 )
-from mlx_lm.models.cache import (
-    _BaseCache,  # pyright: ignore[reportPrivateUsage]
-)
 from mlx_lm.models.deepseek_v3 import DeepseekV3MLP
 from mlx_lm.models.deepseek_v3 import Model as DeepseekV3Model
+from mlx_lm.models.deepseek_v32 import DeepseekV32MLP
+from mlx_lm.models.deepseek_v32 import Model as DeepseekV32Model
+from mlx_lm.models.glm4_moe import Model as Glm4MoeModel
+from mlx_lm.models.glm4_moe import MoE
+from mlx_lm.models.gpt_oss import GptOssMoeModel
+from mlx_lm.models.gpt_oss import Model as GptOssModel
 from mlx_lm.models.llama import Model as LlamaModel
+from mlx_lm.models.minimax import Model as MiniMaxModel
+from mlx_lm.models.ministral3 import Model as Ministral3Model
 from mlx_lm.models.qwen3_moe import Model as Qwen3MoeModel
 from mlx_lm.models.qwen3_moe import Qwen3MoeSparseMoeBlock
+from mlx_lm.models.qwen3_next import Model as Qwen3NextModel
+from mlx_lm.models.qwen3_next import Qwen3NextSparseMoeBlock
 
-from exo.shared.types.worker.shards import (
-    PipelineShardMetadata,
-)
+from exo.shared.logging import logger
+from exo.shared.types.worker.shards import PipelineShardMetadata
 
 
 class _LayerCallable(Protocol):
@@ -91,8 +97,6 @@ class PipelineLastLayer(CustomMlxLayer):
             x, *args, **kwargs
         ).arguments.get("cache", None)
 
-        assert cache is None or issubclass(type(cache), _BaseCache)  # type: ignore
-
         output: mx.array = self.original_layer(x, *args, **kwargs)
 
         if self.r != self.s - 1:
@@ -100,7 +104,6 @@ class PipelineLastLayer(CustomMlxLayer):
                 output, (self.r + 1) % self.s, group=self.group
             )
             if cache is not None:
-                # This change happened upstream - check out mlx github somewhere??
                 cache.keys = mx.depends(cache.keys, output)  # type: ignore[reportUnknownMemberType]
 
         output = mx.distributed.all_gather(output, group=self.group)[-output.shape[0] :]
@@ -132,24 +135,6 @@ def _get_layers(inner_model_instance: nn.Module) -> list[_LayerCallable]:
     return layers
 
 
-def _set_layers(model: nn.Module, layers: list[_LayerCallable]) -> None:
-    inner_model_instance = _inner_model(model)
-    if hasattr(inner_model_instance, "layers"):
-        inner_model_instance.layers = layers
-
-        # Update DeepSeek V3 specific parameters when layers are shrunk
-        if isinstance(model, DeepseekV3Model) and hasattr(
-            inner_model_instance, "num_layers"
-        ):
-            inner_model_instance.start_idx = 0
-            inner_model_instance.end_idx = len(layers)
-            inner_model_instance.num_layers = len(layers)
-    elif hasattr(inner_model_instance, "h"):
-        inner_model_instance.h = layers
-    else:
-        raise ValueError("Model must have either a 'layers' or 'h' attribute")
-
-
 def pipeline_auto_parallel(
     model: nn.Module,
     group: mx.distributed.Group,
@@ -165,8 +150,7 @@ def pipeline_auto_parallel(
     """
     inner_model_instance: nn.Module = _inner_model(model)
 
-    # Handle both model.layers and model.h cases
-    layers: list[_LayerCallable] = _get_layers(inner_model_instance)
+    layers = _get_layers(inner_model_instance)
 
     start_layer, end_layer = model_shard_meta.start_layer, model_shard_meta.end_layer
     device_rank, world_size = model_shard_meta.device_rank, model_shard_meta.world_size
@@ -180,6 +164,17 @@ def pipeline_auto_parallel(
         group=group,
     )
 
+    if isinstance(inner_model_instance, GptOssMoeModel):
+        inner_model_instance.layer_types = inner_model_instance.layer_types[  # type: ignore
+            start_layer:end_layer
+        ]
+        inner_model_instance.swa_idx = inner_model_instance.layer_types.index(  # type: ignore
+            "sliding_attention"
+        )
+        inner_model_instance.ga_idx = inner_model_instance.layer_types.index(  # type: ignore
+            "full_attention"
+        )
+
     _set_layers(model, layers)
 
     assert isinstance(layers, list), (
@@ -204,18 +199,44 @@ def tensor_auto_parallel(
         group=group,
     )
 
+    segments: int = 1
+
+    def _all_to_sharded(path: str, weight: mx.array):
+        # if path.endswith("bias"):
+        #     logger.info(f"Sharding bias for {path} - all to sharded")
+        #     return weight.ndim - 1, segments
+        return max(weight.ndim - 2, 0), segments
+
     all_to_sharded_linear_in_place = partial(
         shard_inplace,
-        sharding="all-to-sharded",
-        group=group,
-    )
-    sharded_to_all_linear_in_place = partial(
-        shard_inplace,
-        sharding="sharded-to-all",
+        sharding=_all_to_sharded,  # type: ignore
         group=group,
     )
 
-    if isinstance(model, LlamaModel):
+    n = group.size()
+
+    def _sharded_to_all(path: str, weight: mx.array):
+        # if path.endswith("bias"):
+        #     logger.info(f"Sharding bias for {path} - sharded to all")
+        #     weight /= n
+        #     return None
+        return -1, segments
+
+    sharded_to_all_linear_in_place = partial(
+        shard_inplace,
+        sharding=_sharded_to_all,  # type: ignore
+        group=group,
+    )
+
+    if hasattr(model, "shard"):
+        try:
+            model.shard(group)  # type: ignore
+            return model
+        except (AttributeError, TypeError, NameError):
+            pass
+
+    if isinstance(model, (LlamaModel, Ministral3Model)):
+        logger.warning("shouldn't be hit - upstream sharding exists")
         tensor_parallel_sharding_strategy = LlamaShardingStrategy(
             group,
             all_to_sharded_linear,
@@ -223,7 +244,8 @@ def tensor_auto_parallel(
             all_to_sharded_linear_in_place,
             sharded_to_all_linear_in_place,
         )
-    elif isinstance(model, DeepseekV3Model):
+    elif isinstance(model, (DeepseekV3Model, DeepseekV32Model)):
+        logger.warning("shouldn't be hit - upstream sharding exists")
         tensor_parallel_sharding_strategy = DeepSeekShardingStrategy(
             group,
             all_to_sharded_linear,
@@ -231,7 +253,15 @@ def tensor_auto_parallel(
             all_to_sharded_linear_in_place,
             sharded_to_all_linear_in_place,
         )
-    elif isinstance(model, Qwen3MoeModel):
+    elif isinstance(model, MiniMaxModel):
+        tensor_parallel_sharding_strategy = MiniMaxShardingStrategy(
+            group,
+            all_to_sharded_linear,
+            sharded_to_all_linear,
+            all_to_sharded_linear_in_place,
+            sharded_to_all_linear_in_place,
+        )
+    elif isinstance(model, (Qwen3MoeModel, Glm4MoeModel, Qwen3NextModel)):
         tensor_parallel_sharding_strategy = QwenShardingStrategy(
             group,
             all_to_sharded_linear,
@@ -239,6 +269,15 @@ def tensor_auto_parallel(
             all_to_sharded_linear_in_place,
             sharded_to_all_linear_in_place,
         )
+    elif isinstance(model, GptOssModel):
+        tensor_parallel_sharding_strategy = GptOssShardingStrategy(
+            group,
+            all_to_sharded_linear,
+            sharded_to_all_linear,
+            all_to_sharded_linear_in_place,
+            sharded_to_all_linear_in_place,
+        )
+
     else:
         raise ValueError(f"Unsupported model type: {type(model)}")
 
@@ -284,13 +323,38 @@ class LlamaShardingStrategy(TensorParallelShardingStrategy):
         return model
 
 
+def _set_layers(model: nn.Module, layers: list[_LayerCallable]) -> None:
+    inner_model_instance = _inner_model(model)
+    if hasattr(inner_model_instance, "layers"):
+        inner_model_instance.layers = layers
+
+        # Update DeepSeek V3 specific parameters when layers are shrunk
+        if isinstance(
+            model, (DeepseekV3Model, DeepseekV32Model, Glm4MoeModel)
+        ) and hasattr(inner_model_instance, "num_layers"):
+            logger.info(
+                f"Setting num_layers to {len(layers)} for model {model.model.__class__.__name__}"
+            )
+            inner_model_instance.start_idx = 0
+            inner_model_instance.end_idx = len(layers)
+            inner_model_instance.num_layers = len(layers)
+        elif isinstance(model, Qwen3MoeModel):
+            logger.info(
+                f"Setting num_hidden_layers to {len(layers)} for model {model.model.__class__.__name__}"
+            )
+            inner_model_instance.num_hidden_layers = len(layers)
+    elif hasattr(inner_model_instance, "h"):
+        inner_model_instance.h = layers
+    else:
+        raise ValueError("Model must have either a 'layers' or 'h' attribute")
+
+
 class DeepSeekShardingStrategy(TensorParallelShardingStrategy):
     def shard_model(self, model: nn.Module) -> nn.Module:
         model = cast(DeepseekV3Model, model)
         for layer in model.layers:
             # Shard the self attention
-            if layer.self_attn.q_lora_rank is None:  # pyright: ignore[reportUnnecessaryComparison]
-                # Unfortunately, q_lora_rank can be None despite typing hints.
+            if layer.self_attn.q_lora_rank is None:
                 layer.self_attn.q_proj = self.all_to_sharded_linear(
                     layer.self_attn.q_proj
                 )
@@ -305,7 +369,7 @@ class DeepSeekShardingStrategy(TensorParallelShardingStrategy):
             layer.self_attn.num_heads //= self.N
 
             # Shard the MLP
-            if isinstance(layer.mlp, DeepseekV3MLP):
+            if isinstance(layer.mlp, (DeepseekV3MLP, DeepseekV32MLP)):
                 layer.mlp.gate_proj = self.all_to_sharded_linear(layer.mlp.gate_proj)
                 layer.mlp.down_proj = self.sharded_to_all_linear(layer.mlp.down_proj)
                 layer.mlp.up_proj = self.all_to_sharded_linear(layer.mlp.up_proj)
@@ -339,6 +403,35 @@ class ShardedDeepseekV3MoE(CustomMlxLayer):
         return y
 
 
+class MiniMaxShardingStrategy(TensorParallelShardingStrategy):
+    def shard_model(self, model: nn.Module) -> nn.Module:
+        model = cast(MiniMaxModel, model)
+        for layer in model.layers:
+            # Shard the self attention
+            layer.self_attn.q_proj = self.all_to_sharded_linear(layer.self_attn.q_proj)
+            layer.self_attn.k_proj = self.all_to_sharded_linear(layer.self_attn.k_proj)
+            layer.self_attn.v_proj = self.all_to_sharded_linear(layer.self_attn.v_proj)
+            layer.self_attn.o_proj = self.sharded_to_all_linear(layer.self_attn.o_proj)
+            layer.self_attn.num_attention_heads //= self.N
+            layer.self_attn.num_key_value_heads //= self.N
+
+            # Shard the MoE. Shard in place since the MoE should be responsible
+            # for aggregating the results.
+            self.all_to_sharded_linear_in_place(
+                layer.block_sparse_moe.switch_mlp.gate_proj
+            )
+            self.sharded_to_all_linear_in_place(
+                layer.block_sparse_moe.switch_mlp.down_proj
+            )
+            self.all_to_sharded_linear_in_place(
+                layer.block_sparse_moe.switch_mlp.up_proj
+            )
+            layer.block_sparse_moe = ShardedQwenMoE(layer.block_sparse_moe)  # pyright: ignore[reportAttributeAccessIssue, reportArgumentType]
+            layer.block_sparse_moe.sharding_group = self.group
+
+        return model
+
+
 class QwenShardingStrategy(TensorParallelShardingStrategy):
     def shard_model(self, model: nn.Module) -> nn.Module:
         model = cast(Qwen3MoeModel, model)
@@ -353,11 +446,13 @@ class QwenShardingStrategy(TensorParallelShardingStrategy):
 
             # Shard the MoE. Shard in place since the MoE should be responsible
             # for aggregating the results.
-            if isinstance(layer.mlp, Qwen3MoeSparseMoeBlock):
+            if isinstance(
+                layer.mlp, (Qwen3MoeSparseMoeBlock, MoE, Qwen3NextSparseMoeBlock)
+            ):
                 self.all_to_sharded_linear_in_place(layer.mlp.switch_mlp.gate_proj)
                 self.sharded_to_all_linear_in_place(layer.mlp.switch_mlp.down_proj)
                 self.all_to_sharded_linear_in_place(layer.mlp.switch_mlp.up_proj)
-                layer.mlp = ShardedQwenMoE(layer.mlp)  # type: ignore
+                layer.mlp = ShardedQwenMoE(layer.mlp)  # pyright: ignore[reportAttributeAccessIssue, reportArgumentType]
                 layer.mlp.sharding_group = self.group
 
             # Shard the MLP
@@ -381,3 +476,50 @@ class ShardedQwenMoE(CustomMlxLayer):
         if self.sharding_group is not None:
             y = mx.distributed.all_sum(y, group=self.sharding_group)
         return y
+
+
+class GptOssShardingStrategy(TensorParallelShardingStrategy):
+    def shard_model(self, model: nn.Module) -> nn.Module:
+        model = cast(GptOssMoeModel, model)
+
+        for layer in model.layers:
+            layer.self_attn.q_proj = self.all_to_sharded_linear(layer.self_attn.q_proj)
+            layer.self_attn.k_proj = self.all_to_sharded_linear(layer.self_attn.k_proj)
+            layer.self_attn.v_proj = self.all_to_sharded_linear(layer.self_attn.v_proj)
+            layer.self_attn.o_proj = self.sharded_to_all_linear(layer.self_attn.o_proj)
+
+            layer.self_attn.num_attention_heads //= self.N
+            layer.self_attn.num_key_value_heads //= self.N
+            layer.self_attn.num_key_value_groups = (
+                layer.self_attn.num_attention_heads
+                // layer.self_attn.num_key_value_heads
+            )
+
+            layer.self_attn.sinks = layer.self_attn.sinks[
+                layer.self_attn.num_attention_heads
+                * self.group.rank() : layer.self_attn.num_attention_heads
+                * (self.group.rank() + 1)
+            ]
+
+            self.all_to_sharded_linear_in_place(layer.mlp.experts.gate_proj)
+            self.sharded_to_all_linear_in_place(layer.mlp.experts.down_proj)
+            self.all_to_sharded_linear_in_place(layer.mlp.experts.up_proj)
+
+            layer.mlp = ShardedGptOssMoE(layer.mlp)  # type: ignore
+            layer.mlp.sharding_group = self.group
+
+        return model
+
+
+class ShardedGptOssMoE(CustomMlxLayer):
+    def __init__(self, layer: nn.Module):
+        super().__init__(layer)
+        self.sharding_group: mx.distributed.Group | None = None
+
+    def __call__(self, x: mx.array) -> mx.array:
+        if self.sharding_group is not None:
+            x = sum_gradients(self.sharding_group)(x)
+        y = self.original_layer(x)
+        if self.sharding_group is not None:
+            y = mx.distributed.all_sum(y, group=self.sharding_group)
+        return y

src/exo/worker/engines/mlx/utils_mlx.py

@@ -1,12 +1,28 @@
 import json
 import os
 import resource
+import sys
+import threading
 import time
+from collections.abc import Callable
 from pathlib import Path
 from typing import Any, cast
 
+# Monkey-patch for transformers 5.x compatibility
+# Kimi's tokenization_kimi.py imports bytes_to_unicode from the old location
+# which was moved in transformers 5.0.0rc2
+try:
+    import transformers.models.gpt2.tokenization_gpt2 as gpt2_tokenization
+    from transformers.convert_slow_tokenizer import bytes_to_unicode
+
+    if not hasattr(gpt2_tokenization, "bytes_to_unicode"):
+        gpt2_tokenization.bytes_to_unicode = bytes_to_unicode  # type: ignore[attr-defined]
+except ImportError:
+    pass  # transformers < 5.0 or bytes_to_unicode not available
+
 from mlx_lm.models.cache import KVCache, QuantizedKVCache, RotatingKVCache
 from mlx_lm.models.deepseek_v3 import DeepseekV3Model
+from mlx_lm.models.gpt_oss import Model as GptOssModel
 from mlx_lm.tokenizer_utils import TokenizerWrapper
 
 from exo.worker.engines.mlx.constants import (
@@ -18,7 +34,7 @@ from exo.worker.engines.mlx.constants import (
 try:
     from mlx_lm.tokenizer_utils import load_tokenizer
 except ImportError:
-    from mlx_lm.tokenizer_utils import load as load_tokenizer  # type: ignore
+    from mlx_lm.tokenizer_utils import load as load_tokenizer
 import contextlib
 
 import mlx.core as mx
@@ -68,6 +84,45 @@ def get_weights_size(model_shard_meta: ShardMetadata) -> Memory:
     )
 
 
+class ModelLoadingTimeoutError(Exception):
+    pass
+
+
+TimeoutCallback = Callable[[], None]
+
+
+def eval_with_timeout(
+    mlx_item: Any,  # pyright: ignore[reportAny]
+    timeout_seconds: float = 60.0,
+    on_timeout: TimeoutCallback | None = None,
+) -> None:
+    """Evaluate MLX item with a hard timeout.
+
+    If on_timeout callback is provided, it will be called before terminating
+    the process. This allows the runner to send a failure event before exit.
+    """
+    completed = threading.Event()
+
+    def watchdog() -> None:
+        if not completed.wait(timeout=timeout_seconds):
+            logger.error(
+                f"mlx_item evaluation timed out after {timeout_seconds:.0f}s. "
+                "This may indicate an issue with FAST_SYNCH and tensor parallel sharding. "
+                "Terminating process."
+            )
+            if on_timeout is not None:
+                on_timeout()
+            os._exit(1)
+
+    watchdog_thread = threading.Thread(target=watchdog, daemon=True)
+    watchdog_thread.start()
+
+    try:
+        mx.eval(mlx_item)  # pyright: ignore[reportAny]
+    finally:
+        completed.set()
+
+
 def mx_barrier(group: Group | None = None):
     mx.eval(
         mx.distributed.all_sum(
@@ -174,7 +229,9 @@ def initialize_mlx(
 
 
 def load_mlx_items(
-    bound_instance: BoundInstance, group: Group | None
+    bound_instance: BoundInstance,
+    group: Group | None,
+    on_timeout: TimeoutCallback | None = None,
 ) -> tuple[Model, TokenizerWrapper]:
     if group is None:
         logger.info(f"Single device used for {bound_instance.instance}")
@@ -188,7 +245,9 @@ def load_mlx_items(
     else:
         logger.info("Starting distributed init")
         start_time = time.perf_counter()
-        model, tokenizer = shard_and_load(bound_instance.bound_shard, group=group)
+        model, tokenizer = shard_and_load(
+            bound_instance.bound_shard, group=group, on_timeout=on_timeout
+        )
         end_time = time.perf_counter()
         logger.info(
             f"Time taken to shard and load model: {(end_time - start_time):.2f}s"
@@ -202,6 +261,7 @@ def load_mlx_items(
 def shard_and_load(
     shard_metadata: ShardMetadata,
     group: Group,
+    on_timeout: TimeoutCallback | None = None,
 ) -> tuple[nn.Module, TokenizerWrapper]:
     model_path = build_model_path(shard_metadata.model_meta.model_id)
 
@@ -238,7 +298,14 @@ def shard_and_load(
             logger.info(f"loading model from {model_path} with pipeline parallelism")
             model = pipeline_auto_parallel(model, group, shard_metadata)
 
-    mx.eval(model.parameters())
+    # Estimate timeout based on model size
+    model_size_gb = get_weights_size(shard_metadata).in_bytes / (1024**3)
+    timeout_seconds = 60 + model_size_gb / 5
+    logger.info(
+        f"Evaluating model parameters with timeout of {timeout_seconds:.0f}s "
+        f"(model size: {model_size_gb:.1f}GB)"
+    )
+    eval_with_timeout(model.parameters(), timeout_seconds, on_timeout)
 
     # TODO: Do we need this?
     mx.eval(model)
@@ -252,26 +319,70 @@ def shard_and_load(
     return model, tokenizer
 
 
-def get_tokenizer(model_path: Path, shard_metadata: ShardMetadata):
-    # TODO: Let's move away from this custom logic to mlx_lm.load()
-    if "kimi-k2" in shard_metadata.model_meta.model_id.lower():
-        eos_token_ids = [163586]
+def get_tokenizer(model_path: Path, shard_metadata: ShardMetadata) -> TokenizerWrapper:
+    """Load tokenizer for a model shard. Delegates to load_tokenizer_for_model_id."""
+    return load_tokenizer_for_model_id(shard_metadata.model_meta.model_id, model_path)
 
-    elif "glm" in shard_metadata.model_meta.model_id.lower():
-        eos_token_ids = [151336, 151329, 151338]
 
-    else:
-        eos_token_ids = None
+def get_eos_token_ids_for_model(model_id: str) -> list[int] | None:
+    """
+    Get the EOS token IDs for a model based on its ID.
 
-    tokenizer = cast(
-        TokenizerWrapper,
-        load_tokenizer(
-            model_path,
-            tokenizer_config_extra={"trust_remote_code": TRUST_REMOTE_CODE},
-            eos_token_ids=eos_token_ids,
-        ),
+    Some models require explicit EOS token configuration that isn't in their
+    tokenizer config. This function returns the known EOS token IDs for such models.
+
+    Args:
+        model_id: The HuggingFace model ID
+
+    Returns:
+        List of EOS token IDs, or None if the model uses standard tokenizer config
+    """
+    model_id_lower = model_id.lower()
+    if "kimi-k2" in model_id_lower:
+        return [163586]
+    elif "glm" in model_id_lower:
+        return [151336, 151329, 151338]
+    return None
+
+
+def load_tokenizer_for_model_id(model_id: str, model_path: Path) -> TokenizerWrapper:
+    """
+    Load tokenizer for a model given its ID and local path.
+
+    This is the core tokenizer loading logic, handling special cases for different
+    model families (Kimi, GLM, etc.) and transformers 5.x compatibility.
+
+    Args:
+        model_id: The HuggingFace model ID (e.g., "moonshotai/Kimi-K2-Instruct")
+        model_path: Local path where the model/tokenizer files are stored
+
+    Returns:
+        TokenizerWrapper instance configured for the model
+    """
+    model_id_lower = model_id.lower()
+    eos_token_ids = get_eos_token_ids_for_model(model_id)
+
+    # Kimi uses a custom TikTokenTokenizer that transformers 5.x can't load via AutoTokenizer
+    if "kimi-k2" in model_id_lower:
+        sys.path.insert(0, str(model_path))
+        from tokenization_kimi import TikTokenTokenizer  # type: ignore[import-not-found]  # noqa: I001
+
+        hf_tokenizer: Any = TikTokenTokenizer.from_pretrained(model_path)  # pyright: ignore[reportUnknownVariableType,reportUnknownMemberType]
+
+        # Patch encode to use internal tiktoken model directly
+        # transformers 5.x has a bug in the encode->pad path for slow tokenizers
+        def _patched_encode(text: str, **_kwargs: object) -> list[int]:
+            # Pass allowed_special="all" to handle special tokens like <|im_user|>
+            return list(hf_tokenizer.model.encode(text, allowed_special="all"))  # pyright: ignore[reportUnknownMemberType,reportUnknownArgumentType]
+
+        hf_tokenizer.encode = _patched_encode
+        return TokenizerWrapper(hf_tokenizer, eos_token_ids=eos_token_ids)
+
+    tokenizer = load_tokenizer(
+        model_path,
+        tokenizer_config_extra={"trust_remote_code": TRUST_REMOTE_CODE},
+        eos_token_ids=eos_token_ids,
     )
-    assert isinstance(tokenizer, TokenizerWrapper)
 
     return tokenizer
 
@@ -301,14 +412,16 @@ def apply_chat_template(
             {k: v for k, v in message.model_dump().items() if v is not None}  # type: ignore
         )
 
-    prompt: str = tokenizer.apply_chat_template(  # type: ignore
+    prompt: str = tokenizer.apply_chat_template(
         formatted_messages,
         tokenize=False,
         add_generation_prompt=True,
         tools=chat_task_data.tools,
     )
 
-    return prompt  # type: ignore
+    logger.info(prompt)
+
+    return prompt
 
 
 class NullKVCache(KVCache):
@@ -339,6 +452,11 @@ def make_kv_cache(
 ) -> list[KVCache | RotatingKVCache | QuantizedKVCache]:
     assert hasattr(model, "layers")
 
+    # TODO: Do this for all models
+    if hasattr(model, "make_cache") and isinstance(model, GptOssModel):
+        logger.info("Using MLX LM's make cache")
+        return model.make_cache()  # type: ignore
+
     if max_kv_size is None:
         if KV_CACHE_BITS is None:
             logger.info("Using default KV cache")

src/exo/worker/main.py

@@ -217,7 +217,9 @@ class Worker:
                     )
                     if initial_progress.status == "complete":
                         progress = DownloadCompleted(
-                            shard_metadata=shard, node_id=self.node_id
+                            shard_metadata=shard,
+                            node_id=self.node_id,
+                            total_bytes=initial_progress.total_bytes,
                         )
                         self.download_status[shard.model_meta.model_id] = progress
                         await self.event_sender.send(
@@ -364,7 +366,11 @@ class Worker:
             nonlocal self
             nonlocal last_progress_time
             if progress.status == "complete":
-                status = DownloadCompleted(shard_metadata=shard, node_id=self.node_id)
+                status = DownloadCompleted(
+                    shard_metadata=shard,
+                    node_id=self.node_id,
+                    total_bytes=progress.total_bytes,
+                )
                 self.download_status[shard.model_meta.model_id] = status
                 # Footgun!
                 self.event_sender.send_nowait(
@@ -457,7 +463,9 @@ class Worker:
                 ) in self.shard_downloader.get_shard_download_status():
                     if progress.status == "complete":
                         status = DownloadCompleted(
-                            node_id=self.node_id, shard_metadata=progress.shard
+                            node_id=self.node_id,
+                            shard_metadata=progress.shard,
+                            total_bytes=progress.total_bytes,
                         )
                     elif progress.status in ["in_progress", "not_started"]:
                         if progress.downloaded_bytes_this_session.in_bytes == 0:

src/exo/worker/runner/bootstrap.py

@@ -17,15 +17,23 @@ def entrypoint(
     task_receiver: MpReceiver[Task],
     _logger: "loguru.Logger",
 ) -> None:
-    if (
-        isinstance(bound_instance.instance, MlxJacclInstance)
-        and len(bound_instance.instance.ibv_devices) >= 2
+    fast_synch_override = os.environ.get("EXO_FAST_SYNCH")
+    if fast_synch_override == "on" or (
+        fast_synch_override != "off"
+        and (
+            isinstance(bound_instance.instance, MlxJacclInstance)
+            and len(bound_instance.instance.ibv_devices) >= 2
+        )
     ):
         os.environ["MLX_METAL_FAST_SYNCH"] = "1"
+    else:
+        os.environ["MLX_METAL_FAST_SYNCH"] = "0"
 
     global logger
     logger = _logger
 
+    logger.info(f"Fast synch flag: {os.environ['MLX_METAL_FAST_SYNCH']}")
+
     # Import main after setting global logger - this lets us just import logger from this module
     try:
         from exo.worker.runner.runner import main

src/exo/worker/runner/runner.py

@@ -1,9 +1,21 @@
 import time
+from collections.abc import Generator
+from contextlib import contextmanager
+from functools import cache
+from typing import cast
 
 import mlx.core as mx
+from mlx_lm.models.gpt_oss import Model as GptOssModel
+from openai_harmony import (  # pyright: ignore[reportMissingTypeStubs]
+    HarmonyEncodingName,
+    Role,
+    StreamableParser,
+    load_harmony_encoding,
+)
 
 from exo.shared.types.api import ChatCompletionMessageText
 from exo.shared.types.chunks import TokenChunk
+from exo.shared.types.common import CommandId
 from exo.shared.types.events import (
     ChunkGenerated,
     Event,
@@ -11,6 +23,7 @@ from exo.shared.types.events import (
     TaskAcknowledged,
     TaskStatusUpdated,
 )
+from exo.shared.types.models import ModelId
 from exo.shared.types.tasks import (
     ChatCompletion,
     ConnectToGroup,
@@ -39,6 +52,7 @@ from exo.shared.types.worker.runners import (
     RunnerWarmingUp,
 )
 from exo.utils.channels import MpReceiver, MpSender
+from exo.worker.engines.mlx import Model
 from exo.worker.engines.mlx.generator.generate import mlx_generate, warmup_inference
 from exo.worker.engines.mlx.utils_mlx import (
     initialize_mlx,
@@ -48,6 +62,33 @@ from exo.worker.engines.mlx.utils_mlx import (
 from exo.worker.runner.bootstrap import logger
 
 
+@contextmanager
+def send_error_chunk_on_exception(
+    event_sender: MpSender[Event],
+    command_id: CommandId,
+    model_id: ModelId,
+    device_rank: int,
+):
+    try:
+        yield
+    except Exception as e:
+        logger.error(e)
+        if device_rank == 0:
+            event_sender.send(
+                ChunkGenerated(
+                    command_id=command_id,
+                    chunk=TokenChunk(
+                        idx=0,
+                        model=model_id,
+                        text="",
+                        token_id=0,
+                        finish_reason="error",
+                        error_message=str(e),
+                    ),
+                )
+            )
+
+
 def main(
     bound_instance: BoundInstance,
     event_sender: MpSender[Event],
@@ -109,7 +150,20 @@ def main(
                         )
                     )
 
-                    model, tokenizer = load_mlx_items(bound_instance, group)
+                    def on_model_load_timeout() -> None:
+                        event_sender.send(
+                            RunnerStatusUpdated(
+                                runner_id=runner_id,
+                                runner_status=RunnerFailed(
+                                    error_message="Model loading timed out"
+                                ),
+                            )
+                        )
+                        time.sleep(0.5)
+
+                    model, tokenizer = load_mlx_items(
+                        bound_instance, group, on_timeout=on_model_load_timeout
+                    )
 
                     current_status = RunnerLoaded()
                     logger.info("runner loaded")
@@ -126,7 +180,7 @@ def main(
 
                     logger.info(f"warming up inference for instance: {instance}")
                     toks = warmup_inference(
-                        model=model,
+                        model=cast(Model, model),
                         tokenizer=tokenizer,
                         # kv_prefix_cache=kv_prefix_cache,  # supply for warmup-time prefix caching
                     )
@@ -139,8 +193,6 @@ def main(
                 case ChatCompletion(task_params=task_params, command_id=command_id) if (
                     isinstance(current_status, RunnerReady)
                 ):
-                    assert model
-                    assert tokenizer
                     logger.info(f"received chat request: {str(task)[:500]}")
                     current_status = RunnerRunning()
                     logger.info("runner running")
@@ -149,33 +201,47 @@ def main(
                             runner_id=runner_id, runner_status=current_status
                         )
                     )
-                    assert task_params.messages[0].content is not None
-                    _check_for_debug_prompts(task_params.messages[0].content)
-
-                    # Generate responses using the actual MLX generation
-                    for response in mlx_generate(
-                        model=model,
-                        tokenizer=tokenizer,
-                        task=task_params,
+                    with send_error_chunk_on_exception(
+                        event_sender,
+                        command_id,
+                        shard_metadata.model_meta.model_id,
+                        shard_metadata.device_rank,
                     ):
-                        match response:
-                            case GenerationResponse():
-                                if shard_metadata.device_rank == 0:
-                                    event_sender.send(
-                                        ChunkGenerated(
-                                            command_id=command_id,
-                                            chunk=TokenChunk(
-                                                idx=response.token,
-                                                model=shard_metadata.model_meta.model_id,
-                                                text=response.text,
-                                                token_id=response.token,
-                                                finish_reason=response.finish_reason,
-                                                stats=response.stats,
-                                            ),
+                        assert model
+                        assert tokenizer
+                        assert task_params.messages[0].content is not None
+                        _check_for_debug_prompts(task_params.messages[0].content)
+
+                        # Generate responses using the actual MLX generation
+                        mlx_generator = mlx_generate(
+                            model=cast(Model, model),
+                            tokenizer=tokenizer,
+                            task=task_params,
+                        )
+
+                        # GPT-OSS specific parsing to match other model formats.
+                        if isinstance(model, GptOssModel):
+                            mlx_generator = parse_gpt_oss(mlx_generator)
+
+                        # TODO: Add tool call parser here
+
+                        for response in mlx_generator:
+                            match response:
+                                case GenerationResponse():
+                                    if shard_metadata.device_rank == 0:
+                                        event_sender.send(
+                                            ChunkGenerated(
+                                                command_id=command_id,
+                                                chunk=TokenChunk(
+                                                    idx=response.token,
+                                                    model=shard_metadata.model_meta.model_id,
+                                                    text=response.text,
+                                                    token_id=response.token,
+                                                    finish_reason=response.finish_reason,
+                                                    stats=response.stats,
+                                                ),
+                                            )
                                         )
-                                    )
-                                # case TokenizedResponse():
-                                # TODO: something here ig
 
                     current_status = RunnerReady()
                     logger.info("runner ready")
@@ -207,6 +273,43 @@ def main(
                 break
 
 
+@cache
+def get_gpt_oss_encoding():
+    encoding = load_harmony_encoding(HarmonyEncodingName.HARMONY_GPT_OSS)
+    return encoding
+
+
+def parse_gpt_oss(
+    responses: Generator[GenerationResponse],
+) -> Generator[GenerationResponse]:
+    encoding = get_gpt_oss_encoding()
+    stream = StreamableParser(encoding, role=Role.ASSISTANT)
+    thinking = False
+
+    for response in responses:
+        stream.process(response.token)
+
+        delta = stream.last_content_delta
+        ch = stream.current_channel
+
+        if ch == "analysis" and not thinking:
+            thinking = True
+            yield response.model_copy(update={"text": "<think>"})
+
+        if ch != "analysis" and thinking:
+            thinking = False
+            yield response.model_copy(update={"text": "</think>"})
+
+        if delta:
+            yield response.model_copy(update={"text": delta})
+
+        if response.finish_reason is not None:
+            if thinking:
+                yield response.model_copy(update={"text": "</think>"})
+            yield response
+            break
+
+
 EXO_RUNNER_MUST_FAIL = "EXO RUNNER MUST FAIL"
 EXO_RUNNER_MUST_OOM = "EXO RUNNER MUST OOM"
 EXO_RUNNER_MUST_TIMEOUT = "EXO RUNNER MUST TIMEOUT"

src/exo/worker/tests/unittests/test_mlx/test_tokenizers.py

@@ -0,0 +1,386 @@
+"""
+Unit tests for tokenizer loading and functionality across all supported models.
+
+This test downloads only tokenizer-related files (not full model weights) to verify
+that tokenizers can be loaded and used correctly for encoding/decoding.
+"""
+
+import asyncio
+import contextlib
+from pathlib import Path
+
+import pytest
+
+from exo.shared.models.model_cards import MODEL_CARDS, ModelCard
+from exo.worker.download.download_utils import (
+    download_file_with_retry,
+    ensure_models_dir,
+    fetch_file_list_with_cache,
+)
+from exo.worker.engines.mlx.utils_mlx import (
+    get_eos_token_ids_for_model,
+    load_tokenizer_for_model_id,
+)
+
+# Files needed for tokenizer functionality
+TOKENIZER_FILE_PATTERNS = [
+    "tokenizer.json",
+    "tokenizer_config.json",
+    "special_tokens_map.json",
+    "vocab.json",
+    "vocab.txt",
+    "merges.txt",
+    "tiktoken.model",
+    "added_tokens.json",
+    "tokenizer.model",
+    "tokenization_*.py",  # Custom tokenizer implementations
+]
+
+
+def is_tokenizer_file(filename: str) -> bool:
+    """Check if a file is needed for tokenizer functionality."""
+    for pattern in TOKENIZER_FILE_PATTERNS:
+        if "*" in pattern:
+            prefix = pattern.split("*")[0]
+            suffix = pattern.split("*")[1]
+            if filename.startswith(prefix) and filename.endswith(suffix):
+                return True
+        elif filename == pattern:
+            return True
+    return False
+
+
+async def download_tokenizer_files(model_id: str) -> Path:
+    """Download only the tokenizer-related files for a model."""
+    target_dir = await ensure_models_dir() / model_id.replace("/", "--")
+    target_dir.mkdir(parents=True, exist_ok=True)
+
+    file_list = await fetch_file_list_with_cache(model_id, "main", recursive=True)
+
+    tokenizer_files = [f for f in file_list if is_tokenizer_file(f.path)]
+
+    if not tokenizer_files:
+        pytest.skip(f"No tokenizer files found for {model_id}")
+
+    for file_entry in tokenizer_files:
+        with contextlib.suppress(FileNotFoundError):
+            await download_file_with_retry(
+                model_id, "main", file_entry.path, target_dir
+            )
+
+    return target_dir
+
+
+# Get a sample of models to test (one per family to keep tests fast)
+def get_test_models() -> list[tuple[str, ModelCard]]:
+    """Get a representative sample of models to test."""
+    # Pick one model from each family to test
+    families: dict[str, tuple[str, ModelCard]] = {}
+    for short_id, card in MODEL_CARDS.items():
+        # Extract family name (e.g., "llama-3.1" from "llama-3.1-8b")
+        parts = short_id.split("-")
+        family = "-".join(parts[:2]) if len(parts) >= 2 else parts[0]
+
+        if family not in families:
+            families[family] = (short_id, card)
+
+    return list(families.values())
+
+
+TEST_MODELS: list[tuple[str, ModelCard]] = get_test_models()
+
+
+@pytest.fixture(scope="module")
+def event_loop():
+    """Create event loop for async tests."""
+    loop = asyncio.new_event_loop()
+    yield loop
+    loop.close()
+
+
+@pytest.mark.parametrize(
+    "short_id,model_card",
+    TEST_MODELS,
+    ids=[m[0] for m in TEST_MODELS],
+)
+@pytest.mark.asyncio
+async def test_tokenizer_encode_decode(short_id: str, model_card: ModelCard) -> None:
+    """Test that tokenizer can encode and decode text correctly."""
+    model_id = str(model_card.model_id)
+
+    # Download tokenizer files
+    model_path = await download_tokenizer_files(model_id)
+
+    # Verify required files exist
+    has_tokenizer = (
+        (model_path / "tokenizer.json").exists()
+        or (model_path / "tokenizer_config.json").exists()
+        or (model_path / "tiktoken.model").exists()
+        or (model_path / "tokenizer.model").exists()
+    )
+    if not has_tokenizer:
+        pytest.skip(f"Required tokenizer files not found for {model_id}")
+
+    # Load tokenizer
+    tokenizer = load_tokenizer_for_model_id(model_id, model_path)
+
+    # Test basic encoding
+    test_text = "Hello, world!"
+    encoded = tokenizer.encode(test_text)
+    assert isinstance(encoded, list), f"encode() should return a list for {model_id}"
+    assert len(encoded) > 0, f"encode() should return non-empty list for {model_id}"
+    assert all(isinstance(t, int) for t in encoded), (
+        f"All tokens should be integers for {model_id}"
+    )
+
+    # Test decoding
+    decoded = tokenizer.decode(encoded)
+    assert isinstance(decoded, str), f"decode() should return a string for {model_id}"
+    assert test_text in decoded or decoded.strip() == test_text.strip(), (
+        f"decode(encode(x)) should preserve text for {model_id}: got {decoded!r}"
+    )
+
+    # Test with longer text
+    long_text = "The quick brown fox jumps over the lazy dog. " * 10
+    long_encoded = tokenizer.encode(long_text)
+    assert len(long_encoded) > len(encoded), (
+        f"Longer text should produce more tokens for {model_id}"
+    )
+
+    # Test empty string
+    empty_encoded = tokenizer.encode("")
+    assert isinstance(empty_encoded, list), (
+        f"encode('') should return a list for {model_id}"
+    )
+
+    # Test special characters
+    special_text = 'Hello!\n\tWorld? <test> & "quotes"'
+    special_encoded = tokenizer.encode(special_text)
+    assert len(special_encoded) > 0, f"Special chars should encode for {model_id}"
+
+    # Test unicode
+    unicode_text = "Hello 世界 🌍"
+    unicode_encoded = tokenizer.encode(unicode_text)
+    assert len(unicode_encoded) > 0, f"Unicode should encode for {model_id}"
+
+
+@pytest.mark.parametrize(
+    "short_id,model_card",
+    TEST_MODELS,
+    ids=[m[0] for m in TEST_MODELS],
+)
+@pytest.mark.asyncio
+async def test_tokenizer_has_required_attributes(
+    short_id: str, model_card: ModelCard
+) -> None:
+    """Test that tokenizer has required attributes for inference."""
+    model_id = str(model_card.model_id)
+
+    model_path = await download_tokenizer_files(model_id)
+
+    has_tokenizer = (
+        (model_path / "tokenizer.json").exists()
+        or (model_path / "tokenizer_config.json").exists()
+        or (model_path / "tiktoken.model").exists()
+        or (model_path / "tokenizer.model").exists()
+    )
+    if not has_tokenizer:
+        pytest.skip(f"Required tokenizer files not found for {model_id}")
+
+    tokenizer = load_tokenizer_for_model_id(model_id, model_path)
+    eos_token_ids = get_eos_token_ids_for_model(model_id)
+
+    # Check for vocabulary size
+    empty_vocab: dict[str, int] = {}
+    vocab_size: int = getattr(tokenizer, "vocab_size", None) or len(
+        getattr(tokenizer, "get_vocab", lambda: empty_vocab)()
+    )
+    assert vocab_size > 0, f"Tokenizer should have vocab_size > 0 for {model_id}"
+
+    # Check for EOS token (either from tokenizer or explicitly provided)
+    has_eos = (
+        eos_token_ids is not None
+        or getattr(tokenizer, "eos_token_id", None) is not None
+        or getattr(tokenizer, "eos_token", None) is not None
+    )
+    assert has_eos, f"Tokenizer should have EOS token for {model_id}"
+
+
+@pytest.mark.parametrize(
+    "short_id,model_card",
+    TEST_MODELS,
+    ids=[m[0] for m in TEST_MODELS],
+)
+@pytest.mark.asyncio
+async def test_tokenizer_special_tokens(short_id: str, model_card: ModelCard) -> None:
+    """Test that tokenizer can encode text containing special tokens.
+
+    This is critical because the actual inference path uses prompts with
+    special tokens from chat templates. If special tokens aren't handled
+    correctly, encoding will fail.
+    """
+    model_id = str(model_card.model_id)
+
+    model_path = await download_tokenizer_files(model_id)
+
+    has_tokenizer = (
+        (model_path / "tokenizer.json").exists()
+        or (model_path / "tokenizer_config.json").exists()
+        or (model_path / "tiktoken.model").exists()
+        or (model_path / "tokenizer.model").exists()
+    )
+    assert has_tokenizer, f"Required tokenizer files not found for {model_id}"
+
+    tokenizer = load_tokenizer_for_model_id(model_id, model_path)
+
+    # Get special tokens from the tokenizer
+    special_tokens: list[str] = []
+
+    # Try to get special tokens from various sources
+    if hasattr(tokenizer, "all_special_tokens"):
+        special_tokens.extend(tokenizer.all_special_tokens)
+    elif hasattr(tokenizer, "_tokenizer") and hasattr(
+        tokenizer._tokenizer,
+        "all_special_tokens",
+    ):
+        special_tokens.extend(tokenizer._tokenizer.all_special_tokens)
+
+    # Also check for common special token attributes
+    for attr in [
+        "bos_token",
+        "eos_token",
+        "pad_token",
+        "unk_token",
+        "sep_token",
+        "cls_token",
+    ]:
+        token = getattr(tokenizer, attr, None)
+        if token is None and hasattr(tokenizer, "_tokenizer"):
+            token = getattr(tokenizer._tokenizer, attr, None)
+        if token and isinstance(token, str) and token not in special_tokens:
+            special_tokens.append(token)
+
+    # If we found special tokens, test encoding text that contains them
+    if special_tokens:
+        # Create text with special tokens interspersed
+        test_with_special = f"{special_tokens[0]}Hello world"
+        if len(special_tokens) > 1:
+            test_with_special += f"{special_tokens[1]}"
+
+        encoded = tokenizer.encode(test_with_special)
+        assert isinstance(encoded, list), (
+            f"encode() with special tokens should return list for {model_id}"
+        )
+        assert len(encoded) > 0, (
+            f"encode() with special tokens should return non-empty list for {model_id}"
+        )
+        assert all(isinstance(t, int) for t in encoded), (
+            f"All tokens should be integers for {model_id}"
+        )
+
+        # Verify we can decode
+        decoded = tokenizer.decode(encoded)
+        assert isinstance(decoded, str), f"decode() should return string for {model_id}"
+
+    # Test with angle-bracket tokens (common format for special tokens)
+    # These should not raise errors even if they're not actual special tokens
+    angle_bracket_text = "<|test|>Hello<|end|>"
+    encoded = tokenizer.encode(angle_bracket_text)
+    assert isinstance(encoded, list), (
+        f"encode() with angle brackets should return list for {model_id}"
+    )
+    assert len(encoded) > 0, (
+        f"encode() with angle brackets should be non-empty for {model_id}"
+    )
+
+
+# Specifically test Kimi tokenizer since it has special handling
+@pytest.mark.asyncio
+async def test_kimi_tokenizer_specifically():
+    """Test Kimi tokenizer with its specific patches and quirks."""
+    kimi_models = [
+        (short_id, card)
+        for short_id, card in MODEL_CARDS.items()
+        if "kimi" in short_id.lower()
+    ]
+
+    if not kimi_models:
+        pytest.skip("No Kimi models found in MODEL_CARDS")
+
+    _, model_card = kimi_models[0]
+    model_id = str(model_card.model_id)
+
+    model_path = await download_tokenizer_files(model_id)
+
+    # Ensure the custom tokenizer file exists
+    if not (model_path / "tokenization_kimi.py").exists():
+        pytest.skip("tokenization_kimi.py not found")
+
+    tokenizer = load_tokenizer_for_model_id(model_id, model_path)
+    eos_token_ids = get_eos_token_ids_for_model(model_id)
+
+    # Test encode/decode cycle
+    test_text = "Hello, world!"
+    encoded = tokenizer.encode(test_text)
+    decoded = tokenizer.decode(encoded)
+
+    assert len(encoded) > 0, "Kimi tokenizer should encode text"
+    assert isinstance(decoded, str), "Kimi tokenizer should decode to string"
+
+    # Test that the patched encode works (returns list of ints)
+    assert all(isinstance(t, int) for t in encoded), "Tokens should be integers"
+
+    # Test encoding text with special tokens (like from chat templates)
+    # This is critical - the warmup inference uses prompts with special tokens
+    special_token_text = "<|im_user|>user<|im_middle|>Hello<|im_end|><|im_assistant|>"
+    special_encoded = tokenizer.encode(special_token_text)
+    assert len(special_encoded) > 0, "Kimi tokenizer should handle special tokens"
+    assert all(isinstance(t, int) for t in special_encoded), (
+        "Special token encoding should return integers"
+    )
+
+    # Verify EOS token is set
+    assert eos_token_ids == [163586], "Kimi EOS token should be [163586]"
+
+
+# Test GLM tokenizer since it also has special handling
+@pytest.mark.asyncio
+async def test_glm_tokenizer_specifically():
+    """Test GLM tokenizer with its specific EOS tokens."""
+    glm_models = [
+        (short_id, card)
+        for short_id, card in MODEL_CARDS.items()
+        if "glm" in short_id.lower()
+    ]
+
+    if not glm_models:
+        pytest.skip("No GLM models found in MODEL_CARDS")
+
+    _, model_card = glm_models[0]
+    model_id = str(model_card.model_id)
+
+    model_path = await download_tokenizer_files(model_id)
+
+    has_tokenizer = (model_path / "tokenizer.json").exists() or (
+        model_path / "tokenizer_config.json"
+    ).exists()
+    if not has_tokenizer:
+        pytest.skip("GLM tokenizer files not found")
+
+    tokenizer = load_tokenizer_for_model_id(model_id, model_path)
+    eos_token_ids = get_eos_token_ids_for_model(model_id)
+
+    # Test encode/decode
+    test_text = "Hello, world!"
+    encoded = tokenizer.encode(test_text)
+    decoded = tokenizer.decode(encoded)
+
+    assert len(encoded) > 0, "GLM tokenizer should encode text"
+    assert isinstance(decoded, str), "GLM tokenizer should decode to string"
+
+    # Verify EOS tokens
+    assert eos_token_ids == [
+        151336,
+        151329,
+        151338,
+    ], "GLM EOS tokens should be correct"

src/exo/worker/tests/unittests/test_plan/test_download_and_loading.py

@@ -1,5 +1,6 @@
 import exo.worker.plan as plan_mod
 from exo.shared.types.common import NodeId
+from exo.shared.types.memory import Memory
 from exo.shared.types.models import ModelId
 from exo.shared.types.tasks import LoadModel
 from exo.shared.types.worker.downloads import DownloadCompleted, DownloadProgress
@@ -94,13 +95,23 @@ def test_plan_loads_model_when_all_shards_downloaded_and_waiting():
 
     # Local node has already marked its shard as downloaded (not actually used by _load_model)
     local_download_status = {
-        MODEL_A_ID: DownloadCompleted(shard_metadata=shard1, node_id=NODE_A)
+        MODEL_A_ID: DownloadCompleted(
+            shard_metadata=shard1, node_id=NODE_A, total_bytes=Memory()
+        )
     }
 
     # Global view has completed downloads for both nodes
     global_download_status = {
-        NODE_A: [DownloadCompleted(shard_metadata=shard1, node_id=NODE_A)],
-        NODE_B: [DownloadCompleted(shard_metadata=shard2, node_id=NODE_B)],
+        NODE_A: [
+            DownloadCompleted(
+                shard_metadata=shard1, node_id=NODE_A, total_bytes=Memory()
+            )
+        ],
+        NODE_B: [
+            DownloadCompleted(
+                shard_metadata=shard2, node_id=NODE_B, total_bytes=Memory()
+            )
+        ],
     }
 
     result = plan_mod.plan(
@@ -140,7 +151,9 @@ def test_plan_does_not_request_download_when_shard_already_downloaded():
 
     # Local status claims the shard is downloaded already
     local_download_status = {
-        MODEL_A_ID: DownloadCompleted(shard_metadata=shard, node_id=NODE_A)
+        MODEL_A_ID: DownloadCompleted(
+            shard_metadata=shard, node_id=NODE_A, total_bytes=Memory()
+        )
     }
 
     # Global view hasn't caught up yet (no completed shards recorded for NODE_A)
@@ -192,10 +205,16 @@ def test_plan_does_not_load_model_until_all_shards_downloaded_globally():
 
     # Only NODE_A's shard is recorded as downloaded globally
     local_download_status = {
-        MODEL_A_ID: DownloadCompleted(shard_metadata=shard1, node_id=NODE_A)
+        MODEL_A_ID: DownloadCompleted(
+            shard_metadata=shard1, node_id=NODE_A, total_bytes=Memory()
+        )
     }
     global_download_status = {
-        NODE_A: [DownloadCompleted(shard_metadata=shard1, node_id=NODE_A)],
+        NODE_A: [
+            DownloadCompleted(
+                shard_metadata=shard1, node_id=NODE_A, total_bytes=Memory()
+            )
+        ],
         NODE_B: [],  # NODE_B has no downloads completed yet
     }
 
@@ -212,9 +231,15 @@ def test_plan_does_not_load_model_until_all_shards_downloaded_globally():
     assert result is None
 
     global_download_status = {
-        NODE_A: [DownloadCompleted(shard_metadata=shard1, node_id=NODE_A)],
+        NODE_A: [
+            DownloadCompleted(
+                shard_metadata=shard1, node_id=NODE_A, total_bytes=Memory()
+            )
+        ],
         NODE_B: [
-            DownloadCompleted(shard_metadata=shard2, node_id=NODE_B)
+            DownloadCompleted(
+                shard_metadata=shard2, node_id=NODE_B, total_bytes=Memory()
+            )
         ],  # NODE_B has no downloads completed yet
     }
 

src/exo/worker/tests/unittests/test_runner/test_error_handling.py

@@ -0,0 +1,50 @@
+# pyright: reportAny=false
+from unittest.mock import MagicMock
+
+from exo.shared.types.chunks import TokenChunk
+from exo.shared.types.common import CommandId
+from exo.shared.types.events import ChunkGenerated
+from exo.worker.runner.runner import send_error_chunk_on_exception
+from exo.worker.tests.constants import MODEL_A_ID
+
+
+def test_send_error_chunk_on_exception_no_error() -> None:
+    event_sender = MagicMock()
+    command_id = CommandId()
+
+    with send_error_chunk_on_exception(
+        event_sender, command_id, MODEL_A_ID, device_rank=0
+    ):
+        _ = 1 + 1
+
+    event_sender.send.assert_not_called()
+
+
+def test_send_error_chunk_on_exception_catches_error() -> None:
+    event_sender = MagicMock()
+    command_id = CommandId()
+
+    with send_error_chunk_on_exception(
+        event_sender, command_id, MODEL_A_ID, device_rank=0
+    ):
+        raise ValueError("test error")
+
+    event_sender.send.assert_called_once()
+    call_args = event_sender.send.call_args[0][0]
+    assert isinstance(call_args, ChunkGenerated)
+    assert call_args.command_id == command_id
+    assert isinstance(call_args.chunk, TokenChunk)
+    assert call_args.chunk.finish_reason == "error"
+    assert call_args.chunk.error_message == "test error"
+
+
+def test_send_error_chunk_on_exception_skips_non_rank_zero() -> None:
+    event_sender = MagicMock()
+    command_id = CommandId()
+
+    with send_error_chunk_on_exception(
+        event_sender, command_id, MODEL_A_ID, device_rank=1
+    ):
+        raise ValueError("test error")
+
+    event_sender.send.assert_not_called()

src/exo/worker/utils/net_profile.py

@@ -1,49 +1,64 @@
-import http.client
-
-from anyio import create_task_group, to_thread
+import anyio
+import httpx
+from anyio import create_task_group
 from loguru import logger
 
 from exo.shared.topology import Topology
 from exo.shared.types.common import NodeId
 
+REACHABILITY_ATTEMPTS = 3
+
 
 async def check_reachability(
     target_ip: str,
     expected_node_id: NodeId,
-    self_node_id: NodeId,
     out: dict[NodeId, set[str]],
+    client: httpx.AsyncClient,
 ) -> None:
     """Check if a node is reachable at the given IP and verify its identity."""
+    if ":" in target_ip:
+        # TODO: use real IpAddress types
+        target_ip = f"[{target_ip}]"
+    url = f"http://{target_ip}:52415/node_id"
 
-    def _fetch_remote_node_id() -> NodeId | None:
-        connection = http.client.HTTPConnection(target_ip, 52415, timeout=1)
+    remote_node_id = None
+    last_error = None
+
+    for _ in range(REACHABILITY_ATTEMPTS):
         try:
-            connection.request("GET", "/node_id")
-            response = connection.getresponse()
-            if response.status != 200:
-                return None
+            r = await client.get(url)
+            if r.status_code != 200:
+                await anyio.sleep(1)
+                continue
 
-            body = response.read().decode("utf-8").strip()
+            body = r.text.strip().strip('"')
+            if not body:
+                await anyio.sleep(1)
+                continue
 
-            # Strip quotes if present (JSON string response)
-            if body.startswith('"') and body.endswith('"') and len(body) >= 2:
-                body = body[1:-1]
+            remote_node_id = NodeId(body)
+            break
 
-            return NodeId(body) or None
-        except OSError:
-            return None
-        except http.client.HTTPException:
-            return None
-        finally:
-            connection.close()
+        # expected failure cases
+        except (
+            httpx.TimeoutException,
+            httpx.NetworkError,
+        ):
+            await anyio.sleep(1)
+
+        # other failures should be logged on last attempt
+        except httpx.HTTPError as e:
+            last_error = e
+            await anyio.sleep(1)
+
+    if last_error is not None:
+        logger.warning(
+            f"connect error {type(last_error).__name__} from {target_ip} after {REACHABILITY_ATTEMPTS} attempts; treating as down"
+        )
 
-    remote_node_id = await to_thread.run_sync(_fetch_remote_node_id)
     if remote_node_id is None:
         return
 
-    if remote_node_id == self_node_id:
-        return
-
     if remote_node_id != expected_node_id:
         logger.warning(
             f"Discovered node with unexpected node_id; "
@@ -61,18 +76,33 @@ async def check_reachable(
     topology: Topology, self_node_id: NodeId
 ) -> dict[NodeId, set[str]]:
     """Check which nodes are reachable and return their IPs."""
+
     reachable: dict[NodeId, set[str]] = {}
-    async with create_task_group() as tg:
+
+    # these are intentionally httpx's defaults so we can tune them later
+    timeout = httpx.Timeout(timeout=5.0)
+    limits = httpx.Limits(
+        max_connections=100,
+        max_keepalive_connections=20,
+        keepalive_expiry=5,
+    )
+
+    async with (
+        httpx.AsyncClient(timeout=timeout, limits=limits) as client,
+        create_task_group() as tg,
+    ):
         for node in topology.list_nodes():
             if not node.node_profile:
                 continue
+            if node.node_id == self_node_id:
+                continue
             for iface in node.node_profile.network_interfaces:
                 tg.start_soon(
                     check_reachability,
                     iface.ip_address,
                     node.node_id,
-                    self_node_id,
                     reachable,
+                    client,
                 )
 
     return reachable

tests/headless_runner.py

@@ -49,14 +49,12 @@ class Tests(BaseModel):
     kind: typing.Literal["init", "warmup", "inference"]
 
 
-hn = socket.gethostname()
 mp.set_start_method("spawn", force=True)
 logger_setup(None)
 
 
 async def main():
     logger.info("starting cool server majig")
-    logger.info(hn)
     await assert_downloads()
     cfg = Config()
     cfg.bind = "0.0.0.0:52415"
@@ -81,20 +79,41 @@ async def main():
 async def assert_downloads():
     sd = exo_shard_downloader()
     # await sd.ensure_shard(await build_full_shard(MODEL_CARDS["qwen3-0.6b"].model_id))
-    await sd.ensure_shard(await build_full_shard(MODEL_CARDS["llama-3.2-1b"].model_id))
+    await sd.ensure_shard(
+        await build_full_shard(MODEL_CARDS["llama-3.1-8b-bf16"].model_id)
+    )
+    await sd.ensure_shard(await build_full_shard(MODEL_CARDS["qwen3-30b"].model_id))
+    await sd.ensure_shard(
+        await build_full_shard(MODEL_CARDS["gpt-oss-120b-MXFP4-Q8"].model_id)
+    )
+    await sd.ensure_shard(
+        await build_full_shard(MODEL_CARDS["gpt-oss-20b-4bit"].model_id)
+    )
+    await sd.ensure_shard(
+        await build_full_shard(MODEL_CARDS["glm-4.7-8bit-gs32"].model_id)
+    )
+    await sd.ensure_shard(
+        await build_full_shard(MODEL_CARDS["minimax-m2.1-8bit"].model_id)
+    )
 
 
 async def ring_backend(test: Tests):
     iid = InstanceId(str(hash(str(test.devs))))
-    return await execute_test(test, ring_instance(test, iid))
+    weird_hn = socket.gethostname()
+    for dev in test.devs:
+        if weird_hn.startswith(dev[0]) or dev[0].startswith(weird_hn):
+            hn = dev[0]
+            break
+    else:
+        raise ValueError(f"{weird_hn} not in {test.devs}")
+    return await execute_test(test, ring_instance(test, iid, hn), hn)
 
 
-def ring_instance(test: Tests, iid: InstanceId) -> Instance:
-    global hn
+def ring_instance(test: Tests, iid: InstanceId, hn: str) -> Instance:
     hbn = [Host(ip="i dont care", port=52416) for _ in test.devs]
     world_size = len(test.devs)
     for i in range(world_size):
-        if hn.startswith(test.devs[i][0]):
+        if test.devs[i][0] == hn:
             hn = test.devs[i][0]
             if i - 1 >= 0:
                 hbn[i - 1] = Host(ip=test.devs[i - 1][1], port=52416)
@@ -102,6 +121,8 @@ def ring_instance(test: Tests, iid: InstanceId) -> Instance:
                 hbn[i + 1] = Host(ip=test.devs[i + 1][1], port=52416)
             hbn[i] = Host(ip="0.0.0.0", port=52416)
             break
+    else:
+        raise ValueError(f"{hn} not in {test.devs}")
 
     meta = MODEL_CARDS[test.model_id].metadata
     instance = MlxRingInstance(
@@ -131,10 +152,10 @@ def ring_instance(test: Tests, iid: InstanceId) -> Instance:
     return instance
 
 
-async def execute_test(test: Tests, instance: Instance):
+async def execute_test(test: Tests, instance: Instance, hn: str):
     world_size = len(test.devs)
     iid = InstanceId(str(hash(str(test.devs))))
-    _handle, recv, send = new_runner(instance)
+    _handle, recv, send = new_runner(instance, hn)
     if world_size > 1:
         send.send(ConnectToGroup(instance_id=iid))
     send.send(LoadModel(instance_id=iid))
@@ -181,17 +202,19 @@ async def execute_test(test: Tests, instance: Instance):
 
 async def jaccl_backend(test: Tests):
     iid = InstanceId(str(hash(str(test.devs))))
-    return await execute_test(test, jaccl_instance(test, iid))
+    weird_hn = socket.gethostname()
+    for dev in test.devs:
+        if weird_hn.startswith(dev[0]) or dev[0].startswith(weird_hn):
+            hn = dev[0]
+            break
+    else:
+        raise ValueError(f"{weird_hn} not in {test.devs}")
+    return await execute_test(test, jaccl_instance(test, iid, hn), hn)
 
 
-def jaccl_instance(test: Tests, iid: InstanceId):
-    global hn
+def jaccl_instance(test: Tests, iid: InstanceId, hn: str):
     meta = MODEL_CARDS[test.model_id].metadata
     world_size = len(test.devs)
-    for name, _ in test.devs:
-        if hn.startswith(name):
-            hn = name
-            break
 
     return MlxJacclInstance(
         instance_id=iid,
@@ -220,6 +243,7 @@ def jaccl_instance(test: Tests, iid: InstanceId):
 
 def new_runner(
     instance: Instance,
+    hn: str,
 ) -> tuple[mp.Process, MpReceiver[Event], MpSender[Task]]:
     bound_instance = BoundInstance(
         instance=instance, bound_runner_id=RunnerId(hn), bound_node_id=NodeId(hn)

tests/start_distributed_test.sh

@@ -34,19 +34,23 @@ done
 devs_raw=$(printf "[\"%s\", \"%s\"], " "${weaved[@]}")
 devs="[${devs_raw%, }]"
 
-for i in "${!ips[@]}"; do  
-  { 
-    req="{
-      \"model_id\": \"llama-3.2-1b\",
-      \"devs\": ${devs},
-      \"kind\": \"inference\"
-     }"
-    echo "req $req"
-    curl -sN \
-      -X POST "http://${ips[$i]}:52415/${kind}" \
-      -H "Content-Type: application/json" -d "$req" \
-    2>&1 | sed "s/^/\n${hostnames[$i]}@${ips[$i]}: /" || echo "curl to ${hostnames[$i]} failed"
-  } &
+model_ids=("qwen3-30b" "gpt-oss-120b-MXFP4-Q8" "kimi-k2-thinking")
+
+for model_id in "${model_ids[@]}"; do
+  for i in "${!ips[@]}"; do  
+    { 
+      req="{
+        \"model_id\": \"${model_id}\",
+        \"devs\": ${devs},
+        \"kind\": \"inference\"
+       }"
+      echo "req $req"
+      curl -sN \
+        -X POST "http://${ips[$i]}:52415/${kind}" \
+        -H "Content-Type: application/json" -d "$req" \
+      2>&1 | sed "s/^/\n${hostnames[$i]}@${ips[$i]}: /" || echo "curl to ${hostnames[$i]} failed" && exit 1
+    } &
+  done
+  wait
 done
 
-wait

uv.lock

@@ -10,6 +10,9 @@ supported-markers = [
     "sys_platform == 'linux'",
 ]
 
+[options]
+prerelease-mode = "allow"
+
 [manifest]
 members = [
     "exo",
@@ -36,7 +39,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.13.2"
+version = "3.13.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
@@ -47,53 +50,53 @@ dependencies = [
     { name = "propcache", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "yarl", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/50/42/32cf8e7704ceb4481406eb87161349abb46a57fee3f008ba9cb610968646/aiohttp-3.13.3.tar.gz", hash = "sha256:a949eee43d3782f2daae4f4a2819b2cb9b0c5d3b7f7a927067cc84dafdbb9f88", size = 7844556, upload-time = "2026-01-03T17:33:05.204Z" }
 wheels = [
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