In distributed mode the registry can list a model as loaded on a node
while the worker has evicted it (autonomous LRU eviction, an out-of-band
unload, etc.) yet the backend process survives. The router's cached-node
check only verifies the process is alive (probeHealth), so it routes there
and inference fails with "<backend>: model not loaded" — and stays broken
until the controller restarts and rebuilds its registry.
InFlightTrackingClient now reconciles this: when a tracked inference call
returns a model-not-loaded error, it drops the stale replica row
(RemoveNodeModel) so the next request reloads the model on a healthy node
instead of routing back to the evicted one. The original error is returned
unchanged; only the registry is corrected.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
FLAG_FACE_RECOGNITION and FLAG_SPEAKER_RECOGNITION already existed as
ModelConfigUsecase bitmask flags, and GuessUsecases already gate-checks
both backends by name — but BackendCapabilities had no entries for
either, so the UI could not classify them.
Also missing were the Method* constants for the five proto-defined RPCs
these backends implement (FaceVerify, FaceAnalyze, VoiceVerify,
VoiceEmbed, VoiceAnalyze) and the corresponding Usecase* strings
and UsecaseInfoMap entries needed to wire them into the rest of the
capability system.
Changes:
- Add MethodFaceVerify, MethodFaceAnalyze, MethodVoiceVerify,
MethodVoiceEmbed, MethodVoiceAnalyze GRPCMethod constants
- Add UsecaseFaceRecognition ("face_recognition") and
UsecaseSpeakerRecognition ("speaker_recognition") Usecase constants
- Add UsecaseInfoMap entries for both new usecases, referencing the
existing FLAG_FACE_RECOGNITION and FLAG_SPEAKER_RECOGNITION flags
- Register insightface: Embedding + Detect + FaceVerify + FaceAnalyze
- Register speaker-recognition: VoiceVerify + VoiceEmbed + VoiceAnalyze
Follows up on #10107 which left these two out because they needed new
constants first.
Assisted-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com>
Distributed file-staging treated every model path field (ModelFile, etc.)
as a single regular file: it os.Open'd the path and streamed its fd as the
HTTP PUT body. For directory-based models — e.g. qwen3-tts-cpp, whose
weights and tokenizer ggufs live under one directory referenced by
parameters.model — opening the directory succeeds but reading its fd
returns EISDIR, so routing the model to a remote NATS worker failed with
"read /models/<model>: is a directory". Single-file models were unaffected,
so only multi-file pipelines (e.g. the realtime TTS stage) broke.
stageModelFiles now detects a directory path field and stages each
contained file individually (via the new stageDirectory helper), preserving
structure with the existing StagingKeyMapper and rewriting the field to the
remote directory (deriving ModelPath as before). countStageableFiles makes
the progress total count a directory's files so the staging tracker stays
accurate.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The qwen3-tts.cpp backend honored the request `language` field only via exact lowercase two-letter codes in the C++ language_to_id table, silently defaulting to English for anything else (en-US, EN, english, ...).
Add normalizeLanguage() in the Go handler: lowercase + trim, strip the region/locale suffix (en-US, pt_BR, zh-Hans -> en/pt/zh), and resolve common English full names (english -> en). The canonical codes match the existing C++ table, so no C++ change is needed. Covered by a pure-Go Ginkgo spec. Also document the language field and accepted forms under the Qwen3-TTS docs.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The OpenAI-compatible TTS endpoint accepts an `instructions` field, but it
was silently dropped at the HTTP->gRPC boundary: neither schema.TTSRequest
nor the gRPC TTSRequest proto carried it, so backends could only read such a
value from static YAML options (identical for every request). This blocked
per-line emotion/style and, for Qwen3-TTS VoiceDesign, limited a model config
to a single designed voice.
Plumb a generic per-request instruction string end to end, plus an optional
backend-specific params map:
- proto: add `optional string instructions` and `map<string,string> params`
to TTSRequest.
- schema: add Instructions (maps OpenAI `instructions`) and Params (LocalAI
extension) to schema.TTSRequest.
- core: thread both through ModelTTS/ModelTTSStream via a newTTSRequest helper
that attaches instructions only when non-empty (so backends can fall back to
YAML when unset); forward them from the /v1/audio/speech handler.
- qwen-tts: prefer the per-request instruction over the YAML `instruct` option
(used by both mode detection and generation) and merge per-request params.
- chatterbox: merge per-request params (coerced to float/int/bool) over YAML
options into generate() kwargs.
Fully backward compatible: empty instructions fall back to the YAML option and
backends that don't support style/voice instructions ignore the field.
Closes#10164
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* feat(distributed): NATS JWT auth, TLS/mTLS options, and e2e coverage
Mint per-node NATS user JWTs at registration when LOCALAI_NATS_ACCOUNT_SEED
is set, and connect workers with scoped credentials from the register response.
Add optional LOCALAI_NATS_TLS_CA/CERT/KEY for private CA and mTLS alongside
tls:// URLs, plus test-e2e-distributed and NatsJWT container e2e specs.
Document JWT setup (nats-auth-setup.sh) and TLS env vars in distributed-mode.
Assisted-by: Grok:grok grok-build
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(distributed): correct NATS JWT scoping and harden client auth
The JWT-auth path added in 46467cc7 had several gaps that fail silently
under LOCALAI_NATS_REQUIRE_AUTH:
- Agent-worker minted JWTs did not allow the subjects the agent worker
actually subscribes to (jobs.mcp-ci.new and nodes.<id>.backend.stop),
so MCP-CI jobs and backend-stop session cleanup were silently dropped.
Scope the agent permission set to those subjects.
- NATS subscription permission violations were swallowed (Subscribe
returned a live-but-dead subscription). Confirm subscriptions with a
server round-trip so a denial surfaces synchronously, and log async
permission errors.
- The backend worker connected anonymously when given a JWT without its
paired seed; reject the unpaired credential instead.
- The documented service-user permissions in nats-auth-setup.sh omitted
prefixcache.>, which the frontend publishes and subscribes; add it.
Also: add a credential-provider hook to the messaging client (consumed by
the follow-up credential-lifecycle change), drop the always-nil error from
NatsMessagingOptions, run go mod tidy (jwt/v2 and nkeys are now direct),
and gofmt the feature's files.
Tests: an agent-JWT e2e spec that connects to the enforcing NATS server
and exercises every subscription the agent worker makes, plus permission
allow-list coverage unit tests.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(distributed): acquire and auto-refresh worker NATS credentials
Workers fetched NATS credentials once at startup, which broke two cases
under JWT auth: a worker that registered while still pending admin
approval never received a minted JWT (it connected unauthenticated and
gave up), and a long-running worker's 24h JWT expired with no way to renew
it.
Introduce workerregistry.NATSCredentialManager, built on idempotent
re-registration (the frontend preserves the node row and mints a fresh JWT
each call):
- Acquire re-registers through admin approval until the node is approved
and credentials are minted (or returns the first success when auth is
not required, preserving anonymous-NATS behavior).
- RefreshLoop re-registers before the JWT expires (~75% of its lifetime),
updating the credentials served to the connection.
- Both are bounded (default 100 attempts / consecutive failures) and
return an error on exhaustion, so an unapprovable or unrenewable worker
exits non-zero and surfaces the problem instead of hanging or drifting
toward an expired credential.
The messaging client gains WithUserJWTProvider, fetching credentials on
each (re)connect so the connection transparently adopts a refreshed JWT
when the server expires the old one. RegisterFull exposes the approval
status and full response; Register delegates to it.
Both the backend worker and the agent worker are wired to this: explicit
env credentials are used as-is, minted credentials are acquired-with-wait
and refreshed, and a permanent refresh failure shuts the worker down so it
restarts and re-acquires.
Tests cover Acquire (wait-through-pending, bounded give-up, context
cancel), RefreshLoop (refresh-before-expiry, bounded failure, no-expiry
exit) and jwtExpiry decoding. Docs updated in distributed-mode.md.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
The direct (non-batched) transcription path handed the original upload
path straight to the C library via parakeet_capi_transcribe_path_json.
That loader only understands 16 kHz mono WAV/PCM, so any other format
(MP3, etc.) failed with "parakeet: failed to load audio: <file>".
Only the batched path converted the input (via decodeWavMono16k ->
utils.AudioToWav). Every other audio backend (whisper, crispasr)
converts unconditionally with utils.AudioToWav before handing the file
to its engine; the parakeet-cpp fallback was the lone exception.
Extract a convertToWavMono16k helper (reused by decodeWavMono16k) that
produces a 16 kHz mono WAV in a temp dir, and run the non-batched path
through it before calling the C loader. WAV inputs already in the target
format are passed through without ffmpeg.
Add specs covering the helper (decodable copy + cleanup, and an error on
a missing input) that need neither the model, the C library, nor ffmpeg.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
docs: fix distributed-mode diagram - workers coordinate via NATS, not PostgreSQL
The architecture diagram drew the worker-bound arrows from the PostgreSQL area of the control plane, implying workers connect to PostgreSQL. They do not: PostgreSQL is the frontends shared state, while workers coordinate over NATS (backend.install events) and receive LoadModel over gRPC from a frontend. Re-route the worker arrows to originate from the NATS chip.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* docs: add 'how LocalAI works' architecture diagram
Add a blueprint-style architecture diagram: clients -> small core (API,
router, WebUI, agents) -> gRPC -> backend processes pulled on demand as
OCI images. Place it on the overview page and replace the stale external
architecture image on the reference page.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* docs: add blueprint diagrams across feature, distributed & getting-started docs
Add 24 architecture/flow/comparison diagrams (PNG + HTML source) under
docs/static/images/diagrams/, wired into their docs pages, from an
impact-vs-effort audit of the docs. Broaden the API surface on the
overview architecture diagram (OpenAI, Anthropic, ElevenLabs, Ollama,
and LocalAI's own API) and move the gRPC boundary label clear of the arrows.
Pages: distributed mode (architecture, scheduling, ds4 layer-split),
distributed inferencing, MLX, realtime, quantization, MCP, agents,
mitm & cloud proxy, middleware, reverse-proxy TLS, VRAM, voice & face
recognition, reranker, function calling, fine-tuning (recipe + jobs),
diarization, audio transform, quickstart, model resolution.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* docs: add composable-core diagram to README hero
Commit the composable-core card (small core + on-demand backend tiles)
alongside the other diagrams and reference it from the README hero via a
repo-relative path, so it renders on GitHub.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* docs: fix composable-core connectors/badge and federated-vs-worker layout
- composable-core: thicken the plug-in connectors so they read clearly, and
widen the SEPARATE IMAGE badge so its text no longer overflows the box.
- federated-vs-worker: shorten the WHOLE/SPLIT REQUEST pills to fit, and
replace the tangled node-to-node activation arrows with a clean fan-out
(request split across all sharded nodes), mirroring the federated panel.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Reframe the README hero and docs (homepage, overview, FAQ) around the
composable architecture: a small core, with backends built as dedicated
gRPC services around best-in-class engines, shipped as separate OCI
images and pulled on demand. Lead from strength: drop the "36+ backends"
kitchen-sink framing and the "All-in-One Complete AI Stack" / "single
binary that gives you everything" lines that read as a monolith.
- README: small-core differentiator; composable + open/extensible bullets
- _index.md: composable tagline; install only what you use
- overview.md: core vs on-demand backends; gRPC/OCI mechanics as benefits;
bring-your-own model and backend
- faq.md: "Do I need to install all the backends?" and
"Can I bring my own model or backend?"
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The split_mode: tensor description claimed tensor parallelism requires
KV-cache quantization to be disabled. ggml-org/llama.cpp#23792 lifts that
restriction by extending the meta backend to preserve shape information
through KV-cache flatten/reshape, so cache_type_k/cache_type_v
quantization can be combined with -sm tensor on builds that include it.
Documentation only: no backend code, grpc-server.cpp comment, or
llama.cpp pin changes.
Assisted-by: Claude Code:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(parakeet-cpp): dynamic-batching scheduler (queue + dispatcher)
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(parakeet-cpp): dynamic batching for AudioTranscription via batched JSON C-API
Drop SingleThread; route unary transcription through the in-process batcher
which coalesces concurrent requests into one batched engine call. Streaming
stays mutually exclusive via engineMu. Adds batch_max_size / batch_max_wait_ms
options (size=1 disables; recommended on CPU).
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(parakeet-cpp): tear down dispatcher in Free; log batch config; preallocate; clarify stream lock
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(parakeet-cpp): Ginkgo batcher tests; optional batch C-API binding with per-request fallback
The batched JSON C-API symbol exists only in newer libparakeet.so (ABI >= 2);
probe it with Dlsym and register optionally so the backend still loads against
an older library, falling back to per-request transcription. Rewrites the
batcher unit tests as Ginkgo/Gomega specs (forbidigo bans t.Fatal in tests).
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(parakeet-cpp): debug-log coalesced batch size in runBatch
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(parakeet-cpp): default batch_max_size to 1 (batching opt-in)
Dynamic batching now defaults off (batch_max_size:1, one request at a
time). Raise batch_max_size to opt in: it is a large throughput win on
GPU under concurrent load, but on CPU and low-concurrency setups it only
adds latency, so off is the safer default. The startup log now states
whether batching is on or off, and the audio-to-text docs are updated to
match.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(parakeet-cpp): bump parakeet.cpp to 8a7c482 (batched decode + B=1 fast-path)
parakeet.cpp PR #1 merged the batched encoder/decode and the B=1 encoder
fast-path to master. Point PARAKEET_VERSION at that commit so the backend
builds the batched C-API (parakeet_capi_transcribe_pcm_batch_json) that the
dynamic batcher calls; the prior pin (30a3075) predated it, so only the
per-request fallback path was exercised. Verified the shared lib builds with
the backend's CMake flags and exports the batch symbol.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Pin texterrors==1.1.6 before nemo_toolkit[asr] in requirements-cublas13.txt.
The texterrors package (a NeMo transitive dependency) contains a compiled
C++ extension (texterrors_align.so) that may be built from source during
OCI image creation. When built on systems with GCC 14+ (e.g. Ubuntu 24.04),
the resulting binary requires GLIBCXX_3.4.32, which is not available in
the default LocalAI container (Ubuntu 22.04, GLIBCXX up to 3.4.30).
Pinning to 1.1.6 (the latest release) ensures:
- Reproducible builds across environments
- pip resolves the pre-built manylinux2014 wheel (needs only GLIBCXX_3.4.11)
instead of potentially building from source with a newer toolchain
Fixes#10056
Signed-off-by: 番茄摔成番茄酱 <fqscfqj@outlook.com>
The UI coverage gate was tightened to 0.1pp against a fast-local
measurement (39.86% baseline); CI's slower runners measure ~0.9pp lower,
so tests-ui-e2e failed there. UI e2e coverage is diffusely
non-deterministic and tracks machine speed — a 0.1pp band can't hold
across environments.
Rather than loosen the gate, raise the floor under it: a render-smoke
spec mounts each lazy page (navigate + assert the header renders),
covering a dozen previously-untested pages and lifting coverage from
~39% to ~42.7% locally. Restore the tolerance to 0.8pp and set the
baseline conservatively (40.0), below the slow-CI floor, so the ratchet
holds without flapping.
Document the coverage policy — install the git hooks and don't bypass
them (no --no-verify, no hand-lowering the baseline or widening the
tolerance); raise coverage by adding tests instead; set the UI baseline
below the slow-CI floor — in AGENTS.md, CONTRIBUTING.md and
.agents/building-and-testing.md.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
Move ReplicaCandidate and PickBestReplica out of core/services/nodes (which depends on gorm) into a new dependency-light leaf package pkg/clusterrouting, so the p2p federation server can later share the same replica-selection policy without pulling in a database driver.
core/services/nodes keeps a type alias and a thin delegator, so every existing reference (the LoadedReplicaStats interface method, the ReplicaCandidate row conversion in registry.go, and the SQL policy-mirror test) compiles and behaves unchanged. This is a pure, behavior-preserving refactor: the full nodes suite, including the policy-mirror spec that pins the SQL ORDER BY to PickBestReplica, stays green.
Assisted-by: Claude Code:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* chore(localvqe): update backend to v1.3, add v1.2/v1.3 gallery models
Bump the LocalVQE backend pin 72bfb4c6 -> b0f0378a, which adds the v1.2
(1.3 M) and v1.3 (4.8 M) GGUF SHA-256s to the upstream released-models
allowlist (and the arch_version=3 loader) so both load without
LOCALVQE_ALLOW_UNHASHED.
Add gallery entries for localvqe-v1.2-1.3m and localvqe-v1.3-4.8m
(SHA-256 verified against the downloaded weights) and update the
audio-transform docs to make v1.3 the current default while noting the
compact v1.1/v1.2 alternatives.
Assisted-by: Claude:claude-opus-4-8 Claude-Code
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* chore(flake): add ffmpeg-headless to the dev shell
pkg/utils/ffmpeg_test.go shells out to the `ffmpeg` CLI, and the
pre-commit gate runs those tests via `make test-coverage`. Without
ffmpeg in the dev shell the gate fails with "executable file not found
in $PATH". The headless build provides the CLI without GUI/X deps.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(localvqe): parse WAV by walking RIFF sub-chunks
Walk the RIFF chunk list instead of assuming the canonical 44-byte
header layout. Real inputs (browser-recorded clips, ffmpeg output with
an 18/40-byte extensible `fmt ` chunk or trailing LIST/INFO metadata)
would otherwise splice header/metadata bytes into the PCM stream as an
audible impulse. Honour the `data` chunk size and validate that both
`fmt ` and `data` chunks are present.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(security-headers): allow blob: in connect-src for waveform fetch
The waveform renderer XHRs/fetches a freshly-created blob: object URL
(e.g. an uploaded or enhanced clip before it has a server URL). XHR/fetch
of blob: is governed by connect-src, not media-src, so it was blocked by
the CSP. Add blob: to connect-src.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(react-ui): add input/output spectrogram view to AudioTransform
The transform page only showed time-domain amplitude waveforms, so you
could see how loud a clip was but not which frequencies the model
touched. Add a time x frequency spectrogram heatmap and render the input
and output spectrums side by side, so it's visible which bands the
enhancement attenuates (bright input bands that go dark in the output).
Computed client-side via a Hann-windowed STFT over both clips (a small
dependency-free radix-2 FFT), defaulting to the LocalVQE 512/256 frame
geometry. This shows the net input->output spectral change; the model's
internal gain mask is not exposed by the backend.
- src/utils/fft.js radix-2 FFT
- src/hooks/useSpectrogram.js decode + STFT -> normalised dB magnitude grid
- src/components/audio/Spectrogram.jsx canvas heatmap (magma colormap)
- AudioTransform.jsx dual-spectrogram panel + CSS
- e2e spec + UI coverage baseline bump (38.29 -> 39.0; measured ~39.4-40.2)
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* test(react-ui): make UI coverage deterministic, tighten the gate
UI e2e line coverage swung ~1pp run-to-run (39.1% <-> 40.2%), which forced
a loose 0.8pp tolerance on the monotonic gate — a band wide enough to let
a real ~300-line regression through silently. The swing was a bug, not
inherent jitter: the 'Create Agent navigates' spec ended on the URL
assertion, so AgentCreate.jsx's ~400 lines were collected only when its
render happened to beat the coverage teardown.
Wait for the page to actually render (assert its heading) so those lines
are covered every run. With the race gone, repeated runs land within
~0.013pp of each other, so:
- tighten UI_COVERAGE_TOLERANCE 0.8 -> 0.1 (noise floor, not a drift band)
- set the baseline to the real, reliably-achieved value (39.0 -> 39.86)
Localised by running the V8-coverage suite repeatedly and diffing per-file
line coverage; AgentCreate.jsx was the sole ~1pp flipper.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
fix(parakeet-cpp): forward PARAKEET_GGML_* so cublas/hipblas/vulkan builds aren't silently CPU-only
parakeet.cpp gates its GGML backends behind PARAKEET_GGML_CUDA/HIP/VULKAN and
does set(GGML_CUDA ${PARAKEET_GGML_CUDA} CACHE BOOL "" FORCE), which overwrites
a bare -DGGML_CUDA=ON back to OFF. So the backend's BUILD_TYPE=cublas (and hipblas,
vulkan) produced a CPU-only libparakeet.so. Forward the PARAKEET_GGML_* options
instead. Verified on a GB10 (CUDA 13): the lib now links libcudart/libcublas and
registers the CUDA backend, vs a CPU-only lib before.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Large model GGUFs (multi-GB) transferred between master and worker over
flaky / bandwidth-throttled paths (e.g. libp2p relays with byte caps) used
to restart from byte 0 on every transport error. This change adds standard
HTTP Range/resume semantics to the worker's PUT /v1/files/<key> endpoint
and teaches the master-side HTTPFileStager to consult the worker for the
last accepted offset and resume from there.
Server side (file_transfer_server.go):
- PUT now honors Content-Range: bytes <start>-<end>/<total>. The handler
validates that <start> matches the current on-disk size; mismatches
return 416 with the actual size in X-File-Size.
- Mid-upload chunks return 308 Permanent Redirect ("Resume Incomplete")
with the new size, so the client can keep going.
- An optional X-Content-SHA256 request header binds an upload to a target
hash; cross-attempt drift returns 409. On the final chunk the server
re-computes SHA-256 and returns 400 if it doesn't match.
- HEAD now advertises Accept-Ranges: bytes and Content-Length, and exposes
X-Target-SHA256 for in-progress files (so clients can resume only when
the partial bytes belong to the file they want to upload).
- Legacy PUTs with no Content-Range keep the original truncate-create
semantics — zero behavior change on the happy path.
Client side (file_stager_http.go):
- Pre-PUT HEAD probe reads X-File-Size + X-Target-SHA256 to determine the
resume offset.
- doUpload seeks to that offset and sends Content-Range + X-Content-SHA256.
- Retry loop switches from fixed 3 attempts / 5s-10s-20s backoff to an
outer time budget
with exponential backoff (1s -> 30s cap), so a 5GB upload over a flaky
link can outlast many short disconnects.
- 308 and 416 responses are treated as transient: the next iteration
re-HEADs to learn the correct offset.
Tests:
- Two-chunk Content-Range round-trip produces the correct file + sidecar.
- 416 on a Content-Range/file-size mismatch.
- 409 on X-Content-SHA256 drift between chunks.
- 400 on final-hash mismatch.
- HEAD on a partial upload exposes X-Target-SHA256 (not a misleading
hash-of-partial-bytes via X-Content-SHA256).
- Pre-existing finished file with a different hash is transparently
overwritten when a new PUT starts at byte 0.
- End-to-end resume: EnsureRemote against a worker that already holds a
partial file transfers only the remainder.
- Mid-stream connection drop on attempt #1 is recovered by attempt #2
resuming from the partial offset.
Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* ⬆️ Update ggml-org/llama.cpp
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* fix(llama-cpp): skip begin-of-stream null partial in PredictStream
Upstream llama.cpp (ggml-org/llama.cpp#23884), pulled in by this bump,
now emits an initial "begin" partial whose to_json() returns null. It
exists only to signal the HTTP layer to flush 200 status headers before
any token is produced.
gRPC has no such concept, and PredictStream had no guard: the null result
was fed straight into build_reply_from_json, which threw an uncaught
exception. That surfaced as a generic "Unexpected error in RPC handling"
and the task was cancelled the instant it launched, breaking the
PredictStream e2e spec.
Skip null results in both the first-result handling and the streaming
loop, mirroring upstream's own `if (first_result_json == nullptr)` guard.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
---------
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
