[pull] master from mudler:master - #1451
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…5643267164898b4` (#11527) ⬆️ Update ikawrakow/ik_llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
…11522) ⬆️ Update vllm-project/vllm-metal (darwin) Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
…d10053a924322` (#11523) ⬆️ Update CrispStrobe/CrispASR Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
…10674e45a92bd` (#11524) ⬆️ Update ggml-org/whisper.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
Add the UD-Q4_K_XL GGUF build as a 20-shard llama.cpp entry for the latest DeepSeek V4 Pro release. Assisted-by: Codex:gpt-5 Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
…nd/python/transformers (#11499) chore(deps): bump sentence-transformers in /backend/python/transformers Bumps [sentence-transformers](https://github.com/huggingface/sentence-transformers) from 5.6.1 to 5.7.0. - [Release notes](https://github.com/huggingface/sentence-transformers/releases) - [Commits](huggingface/sentence-transformers@v5.6.1...v5.7.0) --- updated-dependencies: - dependency-name: sentence-transformers dependency-version: 5.7.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
The Darwin workflow passes BUILD_TYPE=metal but does not define OS=Darwin. The backend therefore omitted its Metal CMake flags and shipped the runtime source path instead of the embedded library. Map the requested build type directly to the Metal flags and guard the build contract with a dry-run regression test. Assisted-by: Codex:gpt-5 Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
Unexpected runtime exits only reported an exit code, which hid the backend diagnostic. Include the final non-empty stderr line when one exists. Assisted-by: Codex:gpt-5 Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
Add LiquidAI’s compact edge model in Q4_K_M and Q8_0 builds. The variant pair lets LocalAI choose the highest-quality build that fits. Assisted-by: Codex:gpt-5.4 Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
…ding the advisory lock (#11514) * fix(advisorylock): set statement_timeout alongside lock_timeout WithLockCtx already overrides a deployment-wide lock_timeout on its dedicated connection so a blocking pg_advisory_lock() waits its turn instead of failing with 55P03. statement_timeout aborts that exact same statement independently, with SQLSTATE 57014, and was not overridden. Production roles commonly carry statement_timeout=60s. Any guarded section longer than that (a cold model load stages for tens of minutes) therefore killed every concurrent waiter: advisorylock: acquiring lock 9003261067483446873: ERROR: canceling statement due to statement timeout (SQLSTATE 57014) Derive it from the same context budget as lock_timeout, with a matching RESET so the pooled connection is returned clean. Assisted-by: Claude Opus 5 [claude-code] * feat(distributed): add ModelLoadJob, the durable cold-load record A cold load in distributed mode is a long-running background job, but it was modelled as a synchronous side effect of an inference request: the whole of it (backend install, multi-GB staging, checkpoint load) ran inside the per-model advisory lock. Loading a 35.7 GB GGUF held that lock for ~20 minutes, so every concurrent request for the same model blocked on pg_advisory_lock and died at the role's 60s statement_timeout. Introduce the row that lets the lock shrink to a decision. Exactly one ModelLoadJob may be active per tracking key; that uniqueness — not the lifetime of a lock — is what de-duplicates concurrent loaders across replicas. ClaimLoadJob does its read-then-write under the advisory lock and nothing else: no network, file or gRPC I/O inside the guarded section, so a claim costs milliseconds no matter how long the resulting load takes. LastProgress is a heartbeat rather than a byte counter. A checkpoint load legitimately moves zero bytes for many minutes, so a reaper keyed on byte movement would reclaim a healthy job mid-load; byte progress stays the concern of load_deadline.go. A job whose heartbeat stops for longer than the orphan window is reclaimable, so a replica killed mid-load cannot wedge a model permanently. Failed jobs keep their row for a short grace so an immediately-following request reports the real cause instead of silently starting a fresh load of a model that just failed. No caller yet — the router moves onto this in the next commit. Assisted-by: Claude Opus 5 [claude-code] * refactor(distributed): run cold loads as jobs, outside the advisory lock Route wrapped the entire cold load — node selection, backend install, multi-GB staging and the remote LoadModel — in the per-model advisory lock. The lock's job is to de-duplicate concurrent loaders, a decision that takes milliseconds; holding it for the tens of minutes the resulting work takes is what turned a dedup mechanism into a cluster-wide outage for that model. Split it into a claim and a run. The claim is the only thing left inside the lock. The run is a background job owned by the claiming replica and bounded by the same progress-extended deadline as before; every other request for that model — local or on another replica — attaches as a waiter and is served the moment the model is ready, with no duplicate load and no lock contention. Waiters share one broadcast rather than an ordered queue: they all want the identical outcome, so ordering them would add fairness machinery that changes no result. The local channel wakes same-replica waiters instantly and a 2s DB poll is the authority, because a waiter on another replica has no channel to close. On wake a waiter re-runs the warm path rather than trusting the signal — the model may have been evicted in between. A waiter whose client disconnects returns immediately and the job keeps running; it belongs to the job record, not to the request. A failure is recorded on the row so every waiter reports the real cause, and the row survives briefly so the next request does not read "no job" as "not loading" and start a duplicate load of a model that just failed. The runner heartbeats the row on a fixed interval whether or not bytes are moving, which is what keeps a legitimately silent checkpoint load from being reclaimed as an orphan. Phase (installing/staging/loading) and placement ride to the heartbeat on the context, the same seam load_deadline.go already uses, so single-host paths are untouched. Non-distributed mode (no DB) keeps the inline load exactly as it was. Assisted-by: Claude Opus 5 [claude-code] * feat(distributed): bound the wait for a loading model and answer with progress A request whose model is cold-loading now attaches to the running job and is served the moment the model is ready. That wait has to be bounded: a held HTTP request cannot survive real infrastructure, and an ingress or LB idle timeout kills a twenty-minute request regardless of what LocalAI does. New LOCALAI_MODEL_LOAD_WAIT (default 60s) bounds the CALLER, never the load — the job keeps running either way. On expiry the request gets 503 with Retry-After and a structured body naming the model, the node, the phase, byte progress and an ETA. The `error` envelope keeps OpenAI clients working; `loading` is additive so they ignore it. The ETA comes from the job's own observed rate and is omitted rather than guessed until enough bytes have moved for that rate to mean anything: a confidently wrong ETA on a twenty-minute wait is worse than none. Retry-After is that ETA when known, clamped to [5s, 300s], and the wait budget otherwise. LOCALAI_MODEL_LOAD_WAIT=0 waits unbounded, for deployments with no proxy in front. Zero in the config struct still means "unset, use the default", so the CLI records the operator's zero as ModelLoadWaitUnbounded rather than losing the distinction. The distributed branch of ModelLoader.loadModel wrapped the router's error with %s, which flattened it to a string. Use %w: the typed error is what the HTTP layer keys the 503 off. Assisted-by: Claude Opus 5 [claude-code] * feat(api): add GET /api/models/{id}/load-status A client that receives 503 while a model stages onto a worker needs somewhere to poll. This returns the same `loading` object the 503 carries — phase, node, byte progress and ETA — or 404 when no load is running. Read-only and observability-shaped, so it is deliberately neither admin-gated nor feature-gated: it explains a 503 the caller just received, and hiding that behind a per-modality feature would make the explanation for a failed image request depend on chat permissions. It also gets no MCP tool, since there is nothing here an admin would manage conversationally. Registered on the surfaces from .agents/api-endpoints-and-auth.md: the swagger block (existing `models` tag, so /api/instructions needs no new area), the endpoint discovery maps in RegisterLocalAIRoutes, regenerated swagger, and the distributed-mode docs page. No FLAG_* usecase is involved, so capabilities.js is unchanged. Assisted-by: Claude Opus 5 [claude-code] * feat(ui): show cold-load progress in Chat and retry when the model is ready A chat request for a model that is still staging onto a worker now gets a 503 carrying live progress instead of an error. Render it: the composer shows the phase (installing / staging / loading), the node, the percent and the ETA, then polls load-status and re-sends the request the moment the model is ready. Reuses the staging progress idiom the page already had rather than inventing a second one — the two sources are folded into one loadProgress, with the load job winning because it is authoritative across frontend replicas and knows the phase, where the staging operation only knows about a byte transfer this replica happens to be performing. Waiting is bounded (three send attempts, ~30 min of polling each), so a load that never finishes still surfaces as an error rather than as a spinner nobody questions. An aborted generation stops the polling too. Assisted-by: Claude Opus 5 [claude-code] * fix(distributed): check warm-path cleanup errors The router moved legacy cleanup calls onto newly linted lines. Report cleanup failures while preserving the fallback to a cold load. Assisted-by: Codex:gpt-5 [golangci-lint] --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
…11402) * chore(deps): bump vllm from 0.25.1 to 0.26.0 in /backend/python/vllm Bumps [vllm](https://github.com/vllm-project/vllm) from 0.25.1 to 0.26.0. - [Release notes](https://github.com/vllm-project/vllm/releases) - [Changelog](https://github.com/vllm-project/vllm/blob/main/RELEASE.md) - [Commits](vllm-project/vllm@v0.25.1...v0.26.0) --- updated-dependencies: - dependency-name: vllm dependency-version: 0.26.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> * fix(vllm): pin Intel source build to release Build the Intel XPU backend from vLLM 0.26.0 instead of the moving main branch, and use the Triton XPU version required by that release's torch 2.12 dependency. Assisted-by: Codex:gpt-5 [systematic-debugging] --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
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