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541
Commits
| Author | SHA1 | Message | Date | |
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 Ettore Di Giacinto
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ad8af1ade7 |
fix(distributed): make staged release race-safe
Pin each release path component before removing request-owned inputs and sidecars. Stop pruning when a directory identity changes. Assisted-by: Codex:gpt-6 |
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Ettore Di Giacinto
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455421d03d |
feat(worker): enforce ephemeral staging bounds
Share capacity accounting across HTTP and S3 request inputs so workers reject uploads before exhausting their filesystem. Reconcile exact release and crash recovery with the same guard. Assisted-by: Codex:gpt-6 |
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Plamen K. Kosseffandlocalai-org-maint-bot
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e8546965c7 |
fix(gallery): default audio-cpp models to backend:best (#11892)
* fix(gallery): default audio-cpp models to backend:best The audio-cpp engine creates its session on the CPU backend when no backend option is given, so every gallery model ran CPU-only even on machines where a CUDA/Vulkan/Metal device was registered. backend:best selects the best available backend and falls back to CPU. Assisted-by: Claude:claude-fable-5 Signed-off-by: Plamen K. Kosseff <p.kosseff@gmail.com> * docs(audio-cpp): explain gallery device selection Document automatic compute backend selection and the CPU override. Assisted-by: Codex:gpt-6 Signed-off-by: Plamen K. Kosseff <p.kosseff@gmail.com> --------- Signed-off-by: Plamen K. Kosseff <p.kosseff@gmail.com> Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
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Plamen K. Kosseff
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ba88fb13ce |
fix(diffusers): auto-detect CUDA instead of defaulting to CPU (#11891)
The device fell back to CPU unless the model config set cuda: true, while MPS right below was auto-detected — GPU hosts silently rendered on CPU for any gallery entry missing the flag. Use CUDA whenever torch reports it available (ROCm builds included), keep cuda: true as an explicit force, and allow pinning with the device: model option (e.g. options: ["device:cpu"]). Gallery entries stay untouched. Assisted-by: Claude:claude-fable-5 Signed-off-by: Plamen K. Kosseff <p.kosseff@gmail.com> |
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localai-org-maint-botandEttore Di Giacinto
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aff9db9758 |
fix(distributed): resolve paths for virtual models (#11911)
Virtual model names have no primary file to anchor the worker path. Companion assets still stage successfully, but relative options retain an incorrect model directory and fail to load. Derive the worker root from successfully staged option assets when the primary path is absent. Cover Buffalo packs, files, directories, overrides, and failed transfers. Document the frontend upgrade. Assisted-by: Codex:gpt-6 golangci-lint Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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localai-org-maint-botandEttore Di Giacinto
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e494033607 |
fix(distributed): finalize stalled model uploads (#11910)
A worker can retain all model bytes with an unfinished-upload marker. Retries then start at zero and repeatedly fail with HTTP 416. Verify the existing bytes and finalize same-file retries at full size. Reuse the normal integrity checks so corrupt content cannot be accepted. Add regression coverage and document worker recovery. Assisted-by: Codex:gpt-6 golangci-lint Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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localai-org-maint-botandEttore Di Giacinto
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88d19567b8 |
feat(faces): replay saved face enrollments (#11908)
Accept original embeddings and timestamps so clients can restore faces when the in-memory store restarts. Derive stable IDs from exact vectors to make registration retries preserve identity without duplicate entries. Assisted-by: Codex:GPT-6 golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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localai-org-maint-botandEttore Di Giacinto
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fb8b7a359a |
fix(distributed): stage sound detection audio (#11907)
* fix(distributed): stage sound detection audio Sound detection passes frontend temporary paths directly to remote workers, unlike transcription. Stage the WAV before classification so CED can read it without a shared temporary directory. Preserve the original request for retries and propagate staging errors without calling the backend. Cover staging, request preservation, and error handling with regression tests. Assisted-by: Codex:GPT-6 golangci-lint * test(distributed): verify routed sound staging Call sound detection through the client returned by SmartRouter.Route. This checks interface dispatch through both routing wrappers, rather than constructing FileStagingClient directly. The test fails without the sound-staging override and passes with it. Assisted-by: Codex:GPT-6 golangci-lint --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Plamen K. Kosseff
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77b8241c51 |
docs(integrations): add Distribution Packages section (#11904)
Community-maintained packagings that currently track releases — Homebrew, ALT Sisyphus and the Gentoo local-ai overlay — with a note that versions may lag. Placement and scope as discussed in the issue. Assisted-by: Claude:claude-fable-5 Signed-off-by: Plamen K. Kosseff <p.kosseff@gmail.com> |
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57f802aa7a |
chore: ⬆️ Update leejet/stable-diffusion.cpp to d8fb10c02977c8ca999f3fb4e02df9ecf10f7ba6 (#11898)
* ⬆️ Update leejet/stable-diffusion.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(stablediffusion): adapt streaming options Upstream now selects segmented weight streaming automatically and removes the stream_layers field. Keep the old LocalAI option as a no-op for existing model configurations. Assisted-by: Codex:gpt-5 --------- Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Alex Mazzariolandlocalai-org-maint-bot
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4d854f96a8 |
Update containers.md to fix podman image qualification (#11749)
* Update containers.md to fix podman image qualification Signed-off-by: Alex Mazzariol <alex@alex-maz.info> * docs(containers): clarify Podman image names Podman can reject short image names when no registry is configured. Explain why the examples use fully qualified Docker Hub names. Assisted-by: Codex:gpt-5.6 --------- Signed-off-by: Alex Mazzariol <alex@alex-maz.info> Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
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pengmin
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718357219b |
fix(ui): send collection intervals as numbers
Squashed merge of #11819. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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8744de44d4 |
fix(whisperx): reject unconfigured diarization
WhisperX silently returned a plain transcript when diarization lacked the Hugging Face token required to load pyannote. Reject that request clearly so callers do not mistake missing speaker labels for a successful diarization. Convert WhisperX seconds to the nanosecond duration unit used by the transcription API. Assisted-by: Codex:gpt-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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陈志谦
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dd1776a91f |
docs: correct documented env var and CLI flag names (#11886)
- api-errors.md documented LOCALAI_SUBTLEKEY_COMPARISON (missing the KEY underscore); the code defines LOCALAI_SUBTLE_KEY_COMPARISON, so the documented variable silently did nothing - cli-reference.md documented a --csrf flag / $LOCALAI_CSRF env that do not exist, with inverted semantics; the actual flag is --disable-csrf (LOCALAI_DISABLE_CSRF), 'Disable CSRF middleware (enabled by default)' |
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陈志谦
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24f897cd09 |
docs: fix dead anchors and a dead section link (#11885)
- middleware.md: the 'default detector' link used #instance-wide-defaults; the heading is 'Instance-wide default detector' - the advanced/reference landing pages linked an ../installation/ directory that does not exist in docs/content; dropped the dead bullets (deployment content lives under getting-started) |
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Abdullah Mansour | عبد الله منصور
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a98501d6ce |
docs(llama-cpp): clarify multimodal speculative decoding (#11700)
* docs(llama-cpp): clarify multimodal speculative decoding Update the speculative decoding guidance now that modern llama.cpp backends can combine mmproj-based vision with speculative decoding, including MTP. Document compatibility checks, draft acceptance statistics, VRAM tradeoffs, and a combined configuration example. Assisted-by: Codex:GPT-5.6-Sol [gh] [OpenStack] [Docker] Signed-off-by: Abdullah Mansour <abdullahmansour.marketing@gmail.com> * docs(llama-cpp): clarify multimodal MTP references Distinguish the upstream change that removed the general multimodal speculative restriction from the later change that added MTP with explicit vision compatibility. Assisted-by: Codex:GPT-5.6-Sol [gh] [Docker] Signed-off-by: Abdullah Mansour <abdullahmansour.marketing@gmail.com> --------- Signed-off-by: Abdullah Mansour <abdullahmansour.marketing@gmail.com> |
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localai-org-maint-botandEttore Di Giacinto
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44de82e7c7 |
docs(dco): let maintainer-operated automation sign off (#11850)
The AI-assistant policy says an AI agent must never add a Signed-off-by trailer, because only a human can certify the DCO. That is right for the case it was written for: an assistant helping a contributor who then signs off themselves. It does not fit automation a maintainer runs. Those pull requests have no human submitter, so nothing ever signs and the DCO check blocks them permanently. Sixty-one open pull requests from the maintenance bot are in exactly that state, every one of them correctly following the documented rule. Carve out the case: automation a maintainer operates signs off with that maintainer's identity. The maintainer certifies the DCO, as they do for a commit they typed by hand, because they configured the automation, own its output, and take responsibility on merge. The Assisted-by trailer still records that a model wrote the code, so provenance is unchanged. Keep the exception narrow. An assistant helping an outside contributor still must not sign off, and a bot must not sign for anyone but its operator, including on a contributor's branch it pushes to. Assisted-by: Claude:claude-opus-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Claudio Maradonna
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335acce21f |
fix(ds4): cancel abandoned inference (#11822)
Propagate gRPC cancellation into DS4 prompt synchronization and poll it at decode boundaries. Stop on failed stream writes and skip parser finalization and KV persistence for abandoned partial requests. Assisted-by: Codex:gpt-5.6-sol Signed-off-by: Claudio Maradonna <git@codeshifter.xyz> |
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localai-org-maint-botandEttore Di Giacinto
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9afe10ba21 |
fix(distributed): survive a slow control-plane database (#11837)
* fix(distributed): evict only when a node is known to be full scheduleNewModel asked the registry for a free replica slot and treated every error as "this node is full", so a control-plane database slow enough to time out the lookup evicted a healthy loaded model. The evicted process died, a peer frontend still holding its address dialled the dead port and retried, and the model thrashed between nodes. The comment on the branch already said it meant a full node; the code never tested for it. Evict only on ErrNoFreeSlot. Any other error now returns and names the lookup that failed, so a slow database degrades into a diagnosable load failure instead of into lost work. An audit of the rest of the router found one branch of the same shape: node selection discarded the error from its last-resort finder, so a database timeout there also produced a nil node and evicted for it. That path now returns unless the finder said gorm.ErrRecordNotFound, which is the only answer that means the cluster had no node to give. No other destructive branch in router.go fires on a generic error. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): checkpoint heartbeat writes instead of writing every beat Every heartbeat UPDATEd backend_nodes. Six nodes at a ten second beat is roughly 52,000 writes a day against a six-row table, and that churn is what turned a blocked autovacuum into a 460 MB table whose six-row scan cost 867 ms and timed out the queries that place models. A beat carrying only a fresher timestamp now waits for the checkpoint interval. Each reported field is compared against the value last persisted rather than tested for presence, because a worker sends its disk figures on every beat and presence alone would suppress nothing. A node's first beat, a changed total VRAM, total disk or GPU vendor, and a free VRAM, RAM or disk reading that has moved more than 256 MiB from the persisted value all still write at once. A node that is not active is never suppressed, because it recovers only when the health monitor sees a fresh timestamp. The persisted column is up to one interval stale by design, so the stale-node threshold moves from 60s to 5m to cover it. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): fail worker readiness when a held backend is unreachable The readiness gate tracked only the NATS link, so a worker whose backend processes had died still answered /readyz with 200 and kept receiving loads. One node did exactly that during an incident: it reported healthy while its backend port refused connections, and every load routed to it failed. Readiness is now the NATS link and, for each backend process the worker believes it is running, a short dial of its recorded address. A worker holding no backends stays ready, because idle is a healthy state. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): keep a starting backend out of the readiness dial set A backend process is inserted into the supervisor map with its gRPC address already recorded, but the address refuses connections until the gRPC server binds, which the startup poll allows up to 30 seconds for and which takes 10 to 15 seconds on a slow node. The new data-path readiness probe dialled that address straight away, so a worker answered /readyz with 503 for the whole of every cold backend start. The container HEALTHCHECK absorbs that, but a Kubernetes readinessProbe at 10s does not, and the worker would leave rotation each time it loaded a model. The skip for a stopping process had no counterpart at the other end of the lifecycle. Backend processes now carry a serving flag, set where the startup health-check gate succeeds, and the probe dials only processes that are serving and not yet stopping. backendStartStillValid becomes markBackendServing: the check and the mark must share one lock hold, so the flag can only ever land on the entry the key currently owns. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): export control-plane database health gauges Four transactions wedged on a corrupt index held the vacuum horizon open for 42 days. Nothing measured it, so the first symptom anyone saw was models failing to load six weeks later, by which time a six-row table had grown to 460 MB. Export the oldest xmin age, the longest open transaction, and the dead tuple ratio on the registry tables. The first is the number that would have caught it: it sits near zero in health and was 21,002,291. Sampling is scrape-driven behind a cache, and a failed sample reports the last good values rather than failing the scrape, because these gauges matter most when the database is already struggling. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): rate-limit failed control-plane database samples The cache advanced its clock only on a successful sample, so once the database started failing every scrape retried the query immediately. That turned the cache off in the one regime it exists for: a retry storm at scrape cadence aimed at a database already in trouble. A catalog read that consistently exceeds the 5 second timeout also paid that cost on every scrape, with all scrapes serialised behind the sampler mutex. Time every attempt rather than every success, so failures and timeouts cost the same interval as good samples. Whether a good sample exists moves to its own field, keeping the gauges absent until the first success and holding the last good values through later failures. Also note in the runbook that pg_stat_activity cannot see prepared transactions or replication slot xmins, so a healthy-looking xmin age does not by itself rule out a blocked horizon. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(distributed): pin that a failing database evicts nothing Exercises the real distributed stack against a control-plane database that refuses the router's slot lookup, and asserts the scheduler reports the lookup it could not answer instead of falling through to eviction. The failure is injected with privileges rather than a statement timeout. A timeout set with ALTER DATABASE also breaks AutoMigrate, and it leaks into every later spec in the suite unless it is reset, so the spec would end up testing the migration rather than the scheduler. Instead the spec creates a dedicated login role, points a second gorm handle at it, and revokes that role's SELECT on node_models.replica_index. This has to be a separate role: the test container's owner is a PostgreSQL superuser, and superusers bypass every privilege check, so revoking from CURRENT_USER is recorded and then ignored. The revoke is scoped to one column on purpose. Revoking the whole table would also blind node selection, which runs first and has a guard of its own, so the scheduler would never reach the slot lookup this spec is about. Leaving every other column readable lets selection succeed and lands the refusal exactly on NextFreeReplicaIndex, which plucks replica_index. The grant is restored from BeforeEach via DeferCleanup, so a failing assertion or a panic cannot hand the next spec a role that cannot read. Reverting the eviction guard fails this spec, which is the point of it: the router then reports "no replica slot on keeper and eviction failed" for an error that was never evidence the node was full. The surviving-row assertions are secondary under this injection, because the eviction path reads whole node_models rows and the same revoke blinds it too; a comment in the spec says so, so nobody mistakes them for the load-bearing ones. Also documents why the vector store and the control plane must not share a database: the removable-tuple cutoff is per database, not per table, so one transaction left open anywhere stops autovacuum reclaiming the node registry, and a six-row table bloats into hundreds of megabytes. The note names LOCALAI_AUTH_DATABASE_URL and LOCALAI_AGENT_POOL_DATABASE_URL as the two knobs that must differ, and the localai_control_plane_oldest_xmin_age gauge as the way to see it coming. grep for StaleNodeThreshold and HealthCheckInterval in core/config/runtime_settings_registry.go returns no matches: the distributed duration knobs are not exposed as runtime settings, so the new heartbeat checkpoint interval follows them and needs no registry entry. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): close the review gaps in the heartbeat and health path The stale-node threshold moved from 60 seconds to 5 minutes in this branch because checkpointing makes last_heartbeat up to one checkpoint interval behind by design. Two things were left inconsistent with that. NewHealthMonitor still fell back to a hardcoded 60 seconds when handed a zero threshold, so any future caller that stopped passing the configured value would mark every healthy, beating node offline on every cycle. And the threshold itself had a flag-name constant but no AppOption, no CLI field and no env binding, so an operator who widened --node-heartbeat-checkpoint had no way to widen the threshold to match. The fallback now tracks config.DefaultStaleNodeThreshold, and --stale-node-threshold / LOCALAI_STALE_NODE_THRESHOLD is wired the same way its sibling is. Heartbeat suppression compared the RAW reported free VRAM against the snapshot, but the column persists capAvailable(raw, ceiling). On any node with a VRAM budget set, whose actual free VRAM oscillates above that ceiling, every beat looked material while the persisted value never moved: suppression was defeated on exactly the nodes an operator had configured, and the write amplification this branch exists to remove came straight back there. The comparison and the snapshot now both hold the capped figure, so they measure the same quantity as the column. Fixing that needs the ceiling, and reading it cost a SELECT on every beat, including suppressed ones. The skip decision therefore moved ahead of the updates map and now reuses the ceiling cached on the last durable write, while the write path still re-reads it before capping anything. A ceiling that changed inside the checkpoint window can cost one extra or one late write; it cannot persist a wrong figure. A suppressed beat now costs no query at all. Also: the operations section now says to grant pg_read_all_stats to the LocalAI role, because PostgreSQL blanks backend_xmin and xact_start for sessions owned by other roles, and the transaction that wedged the horizon in the incident was a co-located vector store connecting as a different role, so without the grant the new gauge sees only our own sessions. The compose healthcheck comment now describes readiness covering the backend data path, and the control-plane gauge registration records the otel.SetMeterProvider ordering it depends on. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): resolve the gauge's table names through gorm The dead-tuple gauge queried pg_stat_user_tables against a hardcoded list of three table names. Those three do not agree on where their name comes from: BackendNode and NodeModel take gorm's default pluralisation, while GalleryOperationRecord overrides TableName, and gallery_operations already had a constant of its own that the list duplicated. A literal list keeps compiling after any of that moves, and the query then matches nothing. The failure is silent and it points the wrong way: a dead-tuple ratio that matched no rows reports the same numbers as a cluster with no bloat, so the gauge would look healthiest exactly when it had stopped working. Ask gorm what each model is stored as instead, which follows a TableName override and the default pluralisation alike. A spec pins that the override really is consulted: naive pluralisation of the type would give gallery_operation_records, so the resolution cannot quietly stop asking the model. 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> |
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Claudio Maradonna
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30e53f8d9f |
fix(ds4): enforce generation boundaries (#11821)
Clamp requested generation to the usable context after prompt sync while preserving the legacy 256-token fallback for omitted limits. Constrain each speculative MTP cycle to the remaining request budget so accepted tokens cannot advance beyond the visible output limit. Assisted-by: Codex:gpt-5.6-sol Signed-off-by: Claudio Maradonna <git@codeshifter.xyz> |
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Ettore Di Giacinto
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7aeb47cbf3 |
fix(launcher): auto-start the server so launching the app actually serves
Fixes #11673: on macOS the DMG launcher appeared to launch nothing. After installing, the app sat in the menu bar with no window, nothing listening on localhost:8080, and empty log files, because nothing ever started the server unless the unrelated 'start on system boot' option was enabled. - Start the LocalAI server automatically when the launcher opens and right after a fresh install. The new auto_start_server config key defaults to enabled and gets a settings checkbox; the legacy auto_start key was never honored nor exposed, so every existing launcher.json carries an unintentional false and is deliberately left behind. - Fix the welcome window suppressing itself: its 'don't show this again' checkbox was initialized with the inverted value, and SetChecked fired the change callback which persisted ShowWelcome=false on the very first showing. - Surface auto-start failures through the systray startup-error dialog, since there is no visible window during auto-start. - Pass --app-version to fyne package so the app stops reporting itself as version 0.0.0 in the About box. - Document the first-launch flow (menu bar app, auto-start, WebUI URL) in the macOS getting-started page. - Repair two launcher specs that never ran in CI: a *bool matched against BeTrue and a /tmp assertion that trips on Linux where the test tempdir itself lives under /tmp. Assisted-by: Claude Code:claude-fable-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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localai-org-maint-botandlocalai-org-maint-bot
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893a45141c |
fix(realtime): accept GA WebRTC signaling (#11778)
OpenAI GA clients send multipart or raw SDP requests. They expect a bare SDP answer. LocalAI only accepted its legacy JSON envelope, so signaling failed before media setup. Keep the JSON contract for existing clients. Accept both GA request shapes and choose the matching response format. Assisted-by: Codex:gpt-5 Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
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mudler's LocalAI [bot]andEttore Di Giacinto
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80e3240f2d |
feat(distributed): key scheduling rules by a model alias (#11771)
Node placement and replica rules could only name a model, so an operator who pinned "llama3" to the GPU tier had to rewrite the rule whenever a different model took over that job. An alias already gives a stable name for whichever model serves it, and a rule on that name makes it a deployment slot: repoint the alias and the placement follows. A rule keeps the name the operator chose. Reads resolve that name through the config loader to the model the rule governs, so the reconciler counts, schedules and trims replicas of the target, and the router finds an alias-keyed rule from the target it is already routing. An alias that resolves to nothing governs nothing loadable, so the reconciler skips it and the write paths refuse it. A replica is shared by every name that resolves to it, so only one rule can decide where it runs. The REST and MCP write paths reject a rule whose target another rule already governs. A pair that arrives some other way, such as a seed file or an alias repointed onto a model that already has a rule, resolves in favour of the rule named after the model itself and then the oldest, and the rest are listed as shadowed. The eviction guard is the exception: it matches rules to replicas in raw SQL inside a locking transaction and cannot resolve an alias. It reads a stored target that the reconciler refreshes each tick, and falls back to the rule's own name when that target is empty. Assisted-by: Claude:claude-opus-5 golangci-lint eslint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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mudler's LocalAI [bot]andEttore Di Giacinto
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29899cd1e0 |
fix(ui): size model fit against the cluster and move node labels into the selector (#11765)
* fix(ui): move node labels into the scheduling selector field The scheduling page kept a node-label browser open above the rules whether or not anyone was writing one, while the field that actually needs labels, the rule's node selector, was two bare text inputs with no hint of what the cluster reports. The browser is gone. The selector's key input now completes against the label keys the cluster uses, and the value input offers only the values that key takes. The roster already loads for the page, so the suggestions cost no request, and a roster that fails to load costs the admin the hints and nothing else. Suggestions stay suggestions: a key no node reports yet still commits as typed, which is how an admin writes a rule before labelling the nodes for it. Assisted-by: Claude:claude-opus-5 golangci-lint eslint playwright Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): size model fit against the cluster, not the frontend The models page asked the frontend how much memory a model may occupy. In distributed mode the frontend is usually a GPU-less pod while every model runs on a worker, so a fleet of GPU nodes was told it could only run the smallest CPU build. The variant picker's fits flag and its auto-selection came from the same place, as did the hardware recommendations. The registry now reports the largest single healthy backend node. The largest node, not the fleet total: a model loads into one node, so four 16GB workers are not a home for a 40GB model. An operator-set VRAM budget caps a node's contribution, because the scheduler refuses a load above that ceiling anyway, and a GPU node beats a CPU node holding more system RAM. GET /api/resources and GET /api/models carry this as an additional cluster object. Their aggregate and ram fields keep reporting the frontend's own hardware, which is what the resource monitor shows. Variant selection judges backends against the union of the capabilities present in the cluster, the way backend discovery already did. Every path degrades to the local host: no cluster object in single-node mode, and none when the registry cannot be read, so a hiccup narrows the answer back to single-node behaviour rather than marking the whole catalog too large. The verdicts now name the node they belong to, since a model fits somewhere or nowhere. Assisted-by: Claude:claude-opus-5 golangci-lint eslint playwright 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> |
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localai-org-maint-botandlocalai-org-maint-bot
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d85577ff5c |
docs: add Apache APISIX reverse proxy example (#11294)
docs: add APISIX reverse proxy example Document the route settings needed for forwarded headers, streaming responses, and long-running inference behind Apache APISIX. Closes #11215 Assisted-by: Codex:gpt-5 Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
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Ettore Di Giacinto
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3953448f60 |
fix(distributed): resync stored config revisions at startup
The controller pins a model's replicas to a stored revision and rejects any request carrying a different one. Nothing ever re-derived that value from the configuration on disk: it moved only on an edit, a gallery install, or a peer's change broadcast. An inference request may only establish a revision, never replace one. So any other way for the two to diverge left the model permanently unroutable. A configuration edited while a frontend was down lands there, and so does a change in what the revision is computed over: an upgrade that alters the hashed form leaves every stored revision describing a configuration that no longer exists. The only recovery was deleting the row by hand, which is not something a cluster should need. Each frontend now reconciles the stored revisions against the loaded configurations at startup and republishes the ones that disagree. Only those: republishing quarantines every replica loaded under the old revision, so doing it for a model that did not drift would unload a healthy replica for nothing. A model with no stored revision has never been served and is left for its first request to establish. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-opus-5 [golangci-lint] |
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Ettore Di Giacinto
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e6269e3cdd |
fix(distributed): reclaim replica slots held by abandoned loads
A replica row in staging or loading holds its slot, because slot allocation counts every state except unloading. Nothing ever reclaimed such a row: every reconciler pass and the router's eviction query filter state = "loaded", and the per-model probe skips rows without an address, which is exactly what a row that never finished loading has. So a worker that dropped out mid-transfer left a row that pinned the only replica slot for that model on that node. Scheduling then found no free slot and eviction found nothing it was allowed to evict, and the request failed with "no replica slot on <node> and eviction failed: all models busy". The state persisted until an operator intervened. The reconciler now reclaims a row stuck before serving when no load job is driving it. Ownership is decided by the job's LastProgress heartbeat, not by elapsed time: staging a large checkpoint legitimately runs for a long while without touching the replica row, so a deadline would either be a model-size cliff or reclaim a healthy transfer. That heartbeat is the same signal job takeover already trusts. Any error reading the job leaves the slot held, because holding one for another pass costs a scheduling opportunity while a wrong reclaim restarts a multi-gigabyte transfer. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-opus-5 [golangci-lint] |
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Ettore Di Giacinto
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c541dbeef4 |
fix(distributed): check a node answers before scheduling onto it
A node's status comes from its HTTP heartbeat. Backend installs travel over NATS. The two are independent, so a worker that dies stops answering on the bus at once but stays healthy in the database until its heartbeat ages out. Inside that window the scheduler picked a node it could not reach, and the request failed with "no responders available" rather than moving to a node that was up. The scheduler now probes the node it selected and, when nothing answers, marks it unhealthy and selects again. The demotion is what makes the retry terminate: the next selection reads only healthy nodes. It also tells the other frontends what this one learned, so the cluster does not rediscover a dead worker one failed request at a time. Only nats.ErrNoResponders counts as absent. A worker that answers slowly stays eligible, because dropping it would cost capacity that is really there. The probe reuses the models.running subject: a new subject would go unanswered by workers that have not been upgraded, and every one of them would then look dead. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-opus-5 [golangci-lint] |
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Ettore Di Giacinto
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cee87d1608 |
fix(distributed): expire staged request files on the worker
A request that carries a file stages it to the worker, which writes it under its staging directory. Nothing removed it afterwards. The frontend expires ephemeral keys from object storage, but that sweep never covered a worker's local disk, so every image, audio clip and video a worker ever served stayed on it. One worker had accumulated 175 request directories over three months. The volume reached 100 percent, and from that point every backend start failed because the process manager could not create a state directory. The worker now sweeps its ephemeral staging directory on a timer and once at startup, so files left by a crash are reclaimed too. Staged model files live beside that directory and are not touched. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-opus-5 [golangci-lint] |
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Ettore Di Giacinto
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04735cd1f6 |
fix(distributed): stamp config revision at load time
The request middleware merges the caller's prediction parameters into its copy of the model config. core/backend.ModelOptions then hashed that copy, so the revision identified the request body rather than the persisted configuration. EstablishModelConfigRevision stores the first revision it sees and requires an exact match afterwards. The first request after a restart therefore pinned the model to its own temperature, top_p and stop values, and every later request that sent different ones failed with "stale model config revision". No config edit was involved. The loader now stamps the revision when it materializes a config, before any request override reaches it, and ModelOptions reads that stamp. Model administration keeps hashing the same persisted config, so both paths agree on one revision per configuration. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-opus-5 [golangci-lint] |
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localai-org-maint-botandlocalai-org-maint-bot
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8f56e4e042 |
fix(vram): persist remote probe metadata (#11487)
* fix(vram): persist remote probe metadata The startup warmer repeated remote size and GGUF metadata probes after every restart because both caches lived only in memory. Store successful HTTP probes for 24 hours so frequent restarts reuse the prior results. Bound the cache, reject invalid records, and purge it when gallery data changes. Local model files continue to bypass persistence. Assisted-by: Codex:gpt-5 * fix(vram): check temporary file cleanup The lint gate rejects the unchecked cleanup call in the persistent cache writer. Assisted-by: Codex:gpt-5.6 [golangci-lint] * fix(vram): make persistent cache optional Remote metadata probes can transfer enough data that operators need control over disk reuse and startup warming. Gallery autoload now gates both behaviors, and the runtime setting applies changes immediately. Assisted-by: Codex:gpt-5 * fix(ui): expose gallery startup pre-warm The existing gallery autoload setting also gates the startup metadata warmer. Name both effects in Settings so operators can find the requested boot control. Assisted-by: Codex:gpt-5 --------- Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
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mudler's LocalAI [bot]andEttore Di Giacinto
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82c191afad |
fix(distributed): keep model replicas config-consistent (#11664)
* docs: design configurable copy buffering Document the context-aware copy buffer option and its validation plan. Assisted-by: Codex:gpt-5 * docs: design durable distributed staging operations Assisted-by: Codex:gpt-5 * docs: design distributed model config revisions Assisted-by: Codex:GPT-5 [apply_patch] [exec_command] * feat(config): add stable model revisions Hash typed model configuration and effective protobuf options deterministically for distributed revision comparisons. Assisted-by: Codex:GPT-5 [apply_patch] [exec_command] * feat(worker): acknowledge exact model stops Assisted-by: Codex:GPT-5 [apply_patch] [exec_command] * feat(nodes): track model config revisions Assisted-by: Codex:GPT-5 [apply_patch] * fix(distributed): retry quarantined model cleanup Stop quarantined replicas by exact process identity, retain failed cleanup as durable capped retries, and compare-and-delete only the claimed registry row. Process one sufficiently leased row at a time so multiple frontends cannot duplicate slow cleanup work. Assisted-by: Codex:gpt-5 * fix(distributed): bind loads to config revisions Assisted-by: Codex: GPT-5 [OpenAI Codex] * fix(modeladmin): apply config revisions consistently Route model edits, patches, state changes, deletion, and peer refreshes through the same revision lifecycle. Quarantine stale replicas before exact cleanup and report durable pending cleanup without failing successful config writes. Assisted-by: Codex: GPT-5 [OpenAI Codex] * feat(distributed): expose model config revision state Document replica revision observability and durable cleanup behavior. Keep pending cleanup explicit in model mutation responses and verify endpoint contracts expose revision state without serialized load options. Assisted-by: Codex:GPT-5 [OpenAI Codex] * test(distributed): cover model revision convergence Exercise cross-frontend quarantine, stale replay rejection, exact cleanup retry, worker re-registration, and current-generation replica convergence against the distributed PostgreSQL harness. Assisted-by: Codex:gpt-5 * fix(distributed): pass config revision CI checks Keep configured gallery sources out of authoritative runtime snapshots only after validating their real schema, and harden rollback snapshots against symlink races and non-regular files. Assisted-by: Codex: GPT-5 [OpenAI Codex] --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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localai-org-botandlocalai-org-bot
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3684a534bb |
docs(website): simplify installation paths (#11631)
Keep the homepage focused on runtime capabilities and move engine details to their canonical directory. Make installation choices stable and explicit for users across supported hardware. Assisted-by: Codex:gpt-5 Co-authored-by: localai-org-bot <306113404+localai-org-bot@users.noreply.github.com> |
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mudler's LocalAI [bot]andEttore Di Giacinto
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0ab632b6bd |
fix(auth): protect HTTP routes by default (#11602)
* fix(auth): default to protected HTTP routes Use a method-aware registry for the small anonymous bootstrap surface. Unknown routes now require credentials instead of inheriting fail-open path classification. Keep node self-service routes behind their registration-token middleware. Global auth no longer rejects valid worker credentials first. Assisted-by: Codex:gpt-5 * docs(auth): document public HTTP surface Assisted-by: Codex:gpt-5 * test(auth): align route coverage with default denial Assisted-by: Codex:gpt-5 --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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2383726d6d |
Revert "chore(tests): Avoid network, sleep and more during tests" (#11601)
Revert "chore(tests): Avoid network, sleep and more during tests (#11050)"
This reverts commit
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Richard Palethorpeandlocalai-org-maint-bot
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cb3bf7af3f |
chore(tests): Avoid network, sleep and more during tests (#11050)
* test: make coverage failures observable Keep per-root logs, reject concurrent coverage runs, and avoid relying on /bin/sleep in the worker timeout test. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * test: parallelize coverage without remote fixtures Assisted-by: Codex:gpt-5 [apply_patch] [exec_command] Signed-off-by: Richard Palethorpe <io@richiejp.com> * test: add offline resource infrastructure Introduce versioned resource manifests, a checksum-verified CAS preparer, offline test wrappers, and a guarded network transport. Replace live Hugging Face, GitHub, and OCI cases with deterministic fixtures and inject fixture metadata into importer discovery. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * test: enforce offline resource replay Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * test: harden offline resource refresh Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * test: expose slow coverage waits Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * test: eliminate avoidable wall-clock waits Inject a clock into Hugging Face retry handling, reuse a process-scoped PostgreSQL container with per-spec schemas in the nodes suite, and poll local import jobs promptly. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * test: remove repeated fixture startup waits Share PostgreSQL fixtures across parallel endpoint and agent suite workers, and make the worker Free deadline injectable so the wedged-backend test does not spend five seconds on wall-clock time. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * test: fix offline resource CI portability Normalize Docker archive metadata before content addressing, derive archive checksums during explicit refreshes, make network lint portable to macOS, and prepare distributed images before running their offline suite. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * ci: cache Go modules before offline tests Warm the complete module graph before the Linux and macOS test jobs enter offline replay mode, so tool dependencies such as Ginkgo are not fetched through the guarded proxy. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * test: drop the static network lint in favour of real isolation The offline test suite already prevents tests from reaching the network twice over: run-test-linux-offline.sh puts the test process in a cgroup and REJECTs egress outside the private ranges, and HardenedTransport installs testnetwork.LocalGuard to refuse dials that resolve to a public address. Both fail the test with a precise error at the moment of the dial. test-network-lint.sh added neither. Its diff stage defaulted to a HEAD base, so on a clean checkout it compared the tree against itself and inspected nothing; the branch's own commits were never examined. It only produced output when an earlier job step dirtied the tree, and then it matched a bare https?:// against whatever changed. make react-ui runs npm install rather than npm ci, so CI rewrote core/http/react-ui/package-lock.json and the lint reported an npm registry URL as forbidden test network access: + "resolved": "https://registry.npmjs.org/hono/-/hono-4.12.25.tgz", Its fingerprint stage was self-defeating in a quieter way: hashing the whole tree's network-mechanism inventory meant every rebase onto a master that touched any _test.go needed a manual baseline bump, so the check mostly caught its own staleness. Remove the script, its make target and the two prerequisite edges, along with the test-network: fixture markers that existed only to suppress it. The isolation itself is untouched. Assisted-by: Claude:claude-opus-5 [go vet] Signed-off-by: Richard Palethorpe <io@richiejp.com> * ci: keep hidden files in the offline test bundle artifact Cherry-picked from |
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localai-org-maint-botandlocalai-org-maint-bot
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0761bd02c7 |
feat(chat): add end-to-end context compression (#11556)
* feat(config): add context compression policy Define the opt-in model configuration contract before the chat middleware consumes it. Document each policy field so later request handling does not invent a second schema.\n\nRefs #9534\n\nAssisted-by: Codex:gpt-5 * fix(config): register compression fields The model editor metadata gate rejects new config fields without descriptions and suitable controls. Register the compression policy so operators can edit its six fields safely. Assisted-by: Codex:gpt-5 [monitoring-prs] * feat(chat): compress long contexts Long conversations currently fail once they reach the model context window. The opt-in policy now summarizes complete older turns before primary inference and preserves the newest tool chains. Both OpenAI and MCP chat routes share the same transformation. Usage metadata and metrics expose each compression event. Refs #9534 Assisted-by: Codex:gpt-5 --------- Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
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Richard Palethorpe
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d10374f849 |
feat(router): make KNN a first-class classifier with a persisted, curated corpus (#10652)
* feat(router): make KNN a first-class classifier with a persisted, curated corpus
Add `classifier: knn` — similarity-weighted voting over labelled
example prompts. Unlike score/colbert it needs no classifier model:
label knowledge lives in a corpus seeded and curated through the
admin API, so routing decisions are deterministic, auditable, and
grounded in graded experience rather than a model's opinion.
Epistemic gate: corpus entries below knn.similarity_threshold cannot
vote; when none clears it the classifier activates no labels and the
router uses the fallback — a prompt unlike all labelled experience is
treated as undecidable, not guessed. Decisions record
nearest_similarity (also on fallback rows) so admins can see how far
the nearest labelled experience was; the Routing tab explains
out-of-corpus fallbacks and shows per-label corpus counts.
Persistence: one JSONL file per router under
<data path>/router-corpus (text, labels, vector, embedder
fingerprint). The file is the source of truth; the local-store index
is rebuilt from it at classifier build time and stays a pure
in-memory index. Entries recorded under a different embedding model
re-embed on load. Also corrects the docs' false claim that
local-store collections persist — the embedding cache never survived
restarts (and still doesn't); the corpus does.
Corpus input is API-only by design (entries may contain example user
content): POST /api/router/{name}/corpus seeds (labels validated
against declared policies, embedded server-side, indexed
immediately), GET .../corpus/stats inspects — label counts only,
entry texts are never returned by any surface — DELETE .../corpus
wipes. Admin-gated like the sibling router endpoints, and exposed as
MCP tools (seed_router_corpus / get_router_corpus_stats /
clear_router_corpus) in both the httpapi and inproc clients with
coverage-test route mappings.
Plumbing: VectorStore gains SearchK (top-K was hardcoded to 1);
local-store gets InsertBatch/Delete as optional fast paths;
RouterConfig gains a knn block (embedding_model, k,
similarity_threshold, vote_threshold, store_name) with meta-registry
fields; the classifier dropdown now offers knn and the
previously-missing colbert; embedding_cache is ignored (with a
warning) for knn — it IS an embedding-KNN lookup; the stale
/api/instructions intelligent-routing entry is rewritten (it
described a classifier that no longer exists); swagger regenerated.
Tests: KNN vote/gate specs with hand-computed vote shares, corpus
manager suite (restart reload without re-embedding, fingerprint
re-embed, dedupe, hostile store names), middleware specs (corpus
routing, gate fallback, config validation, cache-wrap refusal),
corpus endpoint specs pinning the texts-never-returned contract, MCP
catalog + route-mapping gates, and a Playwright spec for corpus
stats and the out-of-corpus decision detail.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(router): name consulted corpus neighbours in knn decisions
Every knn decision (decision log rows and the /api/router/decide
response) now carries neighbors: the K retrieved corpus entries by
descending similarity - including ones below the epistemic gate, which
is what makes fallback decisions diagnosable - each as {id, similarity,
labels}. The id is the entry's content hash (first 8 bytes of the
SHA-256 of its text, hex): stable across reseeds and re-embeds, and
text-free, so an external platform that seeded the corpus can recompute
text->id on its own copy and bucket decisions by corpus region (per-
region reliability accounting) without corpus text ever leaving the
server. A corrupt index payload surfaces as an id-less neighbour at a
real similarity instead of disappearing.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* refactor(router): deduplicate knn plumbing and cut corpus hot-path waste
Post-review cleanup of the knn-first-class-router branch; no behaviour
changes on the API surface.
Reuse/altitude:
- RouterKNNConfig.ResolvedStoreName is now the single source of the
router-corpus-<name> default (was hand-derived in four files).
- corpus.ResolveKNNRouter + corpus.Seed carry the shared model
resolution and seed validation; the REST endpoints and the assistant
MCP client are thin transport adapters over them, with sentinel
errors mapped to HTTP statuses at the echo boundary.
- middleware.NewClassifierDeps assembles the classifier dependency set
once for all five entry points (OpenAI, Anthropic, realtime, decide,
corpus) instead of five hand-copied literals.
- router.AllClassifiers feeds both the status endpoint and the
unknown-classifier error, ending the classifier-list drift.
- Per-classifier requirements moved out of validateRouterPolicies into
their buildClassifier arms; the knn arm owns its embedding_cache
opt-out instead of a name-check in the shared wrap tail.
- adminOnly replaces four inline copies of the admin gate in the
middleware routes.
- localVectorStore.Search delegates to SearchK (identical traces).
Efficiency:
- Manager.Add embeds outside the manager mutex and appends to the
JSONL file (O(new) instead of O(corpus) rewrite); a torn tail from a
crash mid-append is tolerated on read and repaired on next write.
- Stats memoises per store keyed on the file's stat fingerprint and no
longer takes the manager mutex, so the 5s status poll stops parsing
vector-laden JSONL and stops blocking behind seeds.
- KNN Classify decodes each neighbour payload once (was twice) and
builds refs and votes in a single pass with one fallback return.
- Corpus file writes fsync before rename/close.
- The corpus manager is built eagerly in newApplication (sync.Once
dropped); test helper dead branch removed.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(router): bind knn corpus vectors to an embedder fingerprint and fail closed on mismatch
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* chore(mcp): align corpus tool prompts and the mutating-tool safety list
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(proto,backend): report embedding shape from the llama-cpp backend
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(embeddings): Go-side pooling — mean/last/decayed_mean with half-life
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(embeddings): accept chat messages[] and per-request pooling on /v1/embeddings
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* chore(middleware): name the failing fields when post-merge validation 400s
An intermittent post-merge validation failure surfaced as an opaque 400
during integration (pooling scheme mismatch that no client had sent).
Log the model, the request's pooling override, and the merged config's
pooling fields at the failure point so the next occurrence identifies
whether the request or the stored config carried the bad value.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(embeddings): scheme override must not inherit the config's half-life
A model config defaulting to decayed_mean pooling carries
pooling_half_life_tokens; a request overriding the scheme to mean/last
without its own half-life inherited that value, and post-merge
validation rejected the pair the server itself had assembled. Zero the
inherited half-life when the overridden scheme is not decayed_mean; a
request that explicitly pairs a half-life with a non-decayed scheme
still 400s.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix embedding pooling validation and router bounds
Declare backend embedding layouts and reject incompatible pooling modes. Reset local-store dimensions after a full clear, validate KNN thresholds, and add real backend and store integration coverage.
Assisted-by: Codex:gpt-5
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* ci: run local-store integration tests
Build and install the local-store backend in the Linux test job, then run the existing store integration suite so new specs are discovered automatically.
Assisted-by: Codex:gpt-5
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
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localai-org-maint-botandlocalai-org-maint-bot
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a7bce6a128 |
fix(audio): reject incompatible transform streams (#11565)
The transform WebSocket accepted any model and opened its frame-based RPC. Any-to-any models use a different stream contract, so liquid-audio failed with an unimplemented RPC after the handshake. Reject incompatible model use cases before loading the backend. Direct realtime-audio callers to the OpenAI Realtime API. Assisted-by: Codex:gpt-5 Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
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Richard Palethorpe
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799cc9f211 |
feat: bound global admission and expose running backend traces (#11560)
feat: bound backend admission and expose running traces Add process-wide backend execution admission without blocking UI or administrative HTTP work. Represent backend operations while they are in flight, surface running traces with immediate log links, and tie streaming admission leases to the gRPC receive lifecycle. Assisted-by: OpenAI Codex: GPT-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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mudler's LocalAI [bot]andEttore Di Giacinto
|
0aaff91ebd |
feat(ui): unify model and backend lifecycle (#11548)
* feat(ui): add installed model lifecycle Models now owns catalog exploration and installed runtime controls under one canonical route. URL-owned state keeps lifecycle context recoverable through links and browser history. Assisted-by: Codex:gpt-5 Playwright * feat(ui): add installed backend lifecycle Backends split discovery from backend-binary management. The canonical page now keeps both lifecycle views under one URL-backed shell while it preserves target-node placement. Assisted-by: Codex:gpt-5 Playwright * fix(ui): repair lifecycle state updates Installed models lost distributed refreshes and kept a deleted selection. Backend searches also stopped tracking URL changes, while batch upgrades stopped after their first error. Preserve background refreshes and finish each requested batch action. Drive catalog results from URL-backed state without losing full metadata. Assisted-by: Codex:gpt-5 [Playwright] * feat(ui): make resource pages canonical Replace Host navigation with canonical Models and Backends lifecycle routes, preserve legacy management URLs, and surface shared host capacity on the Operate overview. Assisted-by: Codex:gpt-5 [Playwright] * feat(ui): complete canonical resource lifecycle Finish the responsive list-to-detail behavior, remove the retired Host implementation, and keep Explore focused on discovery while Installed owns destructive actions. Update regression coverage, localization, documentation, and development binding for the canonical resource pages. Assisted-by: Codex:gpt-5 [Playwright] * docs(ui): record the UI design context Record the approved users, brand character, and design principles so future interface work uses the same product direction. Index the context from the repository's agent instructions. Assisted-by: Codex:gpt-5 --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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mudler's LocalAI [bot]andEttore Di Giacinto
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6fb9ab38aa |
feat(gallery): add vllm.cpp text-generation models (#11511)
Adds eight curated vllm-cpp entries to the model gallery. Until now the backend had gallery coverage only for MiniMax-H3 video, so serving text on it meant hand-writing engine_args. The flagship tier is what vllm.cpp gates its correctness and speed claims on: Qwen3.6-27B and Qwen3.6-35B-A3B in NVFP4, each with a speculative sibling (MTP on both, DFlash on the 27B). Qwen3-Coder-30B-A3B covers agentic tool use, and Qwen3-4B / Qwen3-0.6B in bf16 are the entries that run where NVFP4 cannot, CPU included. Three details are load-bearing rather than incidental: - The 27B entries pin revision 890bdef7. That repository was later re-quantized in place from NVFP4 to FP8 W8A8 under the same name, so an unpinned entry resolves to different weights and reports nothing. - Qwen3-Coder names tool_parser: qwen3_coder explicitly. Its dialect is byte-identical on the wire to step3p5's, so chat-template sniffing cannot separate them and auto-detection picks wrong. - enable_prefix_caching is deliberately left unset everywhere. It defaults on for dense models and off for the GDN hybrids, and that per-model default is the right answer. num_blocks is sized per model from its real KV footprint rather than copied between entries, which ranges from 20 KiB/token on the 35B to 144 KiB/token on the 4B. Docs: adds features/vllm-cpp.md covering installation, the model table, the pinning rationale and how to choose between the speculative variants, and cross-links it from the existing engine_args reference. It also records that the CUDA images are built for Blackwell only, which is narrower than vllm.cpp's own ten-architecture release and makes an otherwise cryptic "no kernel image is available" failure legible. Verified: gallery suite green; all eight decode and validate as a ModelConfig. qwen3-0.6b-vllm-cpp confirmed end to end on a real cluster, chat plus engine-parsed tool_calls. The NVFP4 entries are not yet runtime-verified: no available node has kernels for them. Assisted-by: Claude Code:claude-opus-5[1m] [Read] [Bash] [Edit] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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88edd7fc7f |
fix(distributed): run cold model loads as durable jobs instead of holding 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>
|
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mudler's LocalAI [bot]andEttore Di Giacinto
|
0c9d4bf9cc |
fix(vllm-cpp): build every CUDA architecture the platform can host (#11512)
The vllm-cpp CUDA images were built for Blackwell only: 120a;121a on amd64 and 121a alone on arm64. vllm.cpp's own release archive builds ten architectures, so LocalAI shipped one or two of them. The failure mode is the problem. An unlisted card is not slower, it dies at the first request with "no kernel image is available for execution on the device", long after `backends install` reported success. That covers A100, A10/3090, L4/4090/RTX 6000 Ada, H100/H200, B200, B300, Jetson Orin and Jetson Thor, and it is how a Jetson Thor node was found serving nothing at all. amd64 now builds 80;86;89;90a;100a;103a;120a;121a and arm64 builds 87;90a;100a;110;121a, split by where the silicon exists: Jetson is arm64-only, desktop 120a is amd64-only, and 90a/100a are on both because of GH200/GB200. Triton-AOT stays ON for both, which the old comment said was impossible. It is not, at the version we pin: only maintainer REGEN needs a single arch, while the BUILDER path embeds every vendored cubin tree and selects by exact SM, so 87/103a/110/120a take the portable CUDA kernels and can never load a neighbouring cubin. Upstream ships its ten-SM archive that way. The CUDA 13 guard now covers both branches rather than amd64 alone. arm64 needs compute_121a just as much, and CI already builds it with 13. Cost is smaller than the arch count suggests, because gencode is per-source: fp4-mma still resolves to 120a;121a, and the CUTLASS scaled-mm kernels to one arch each, so the added architectures do not multiply the expensive translation units. Verified: flag generation checked for both branches, CUDA 12 still refused, CPU build untouched; both arch lists expanded through vllm.cpp's own vt_cuda_gencode_options and per-feature arch gating, and all six vendored Triton trees confirmed intact, at the exact pinned commit. A real compile is CI-only: there is no CUDA toolchain on the dev box. Assisted-by: Claude Code:claude-opus-5[1m] [Read] [Bash] [Edit] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
|
5c63969760 |
fix: Show MCP connection errors in the UI (#11495)
* fix(mcp): surface configured server failures Keep model-configured MCP servers visible when discovery or connection setup fails, propagate status through distributed discovery, and let the Chat UI show actionable errors while retrying unavailable servers. Add model-editor metadata for remote and stdio configuration and document the expected format, deployment networking boundary, and alternate MCP scopes. Assisted-by: Codex:gpt-5 Ordino golangci-lint Signed-off-by: Richard Palethorpe <io@richiejp.com> * build(compose): match CUDA development image Configure the API image with the cublas, CUDA 13, auth-tagged build settings used by the local development Makefile invocation, including the 24-way Docker build. Assisted-by: Codex:gpt-5 Ordino Signed-off-by: Richard Palethorpe <io@richiejp.com> * revert: keep host build settings out of compose The CUDA development deployment is managed from ~/docker/localai, not the repository example Compose file. Restore the generic example and keep machine-specific build settings in the host deployment. Assisted-by: Codex:gpt-5 Ordino Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(docker): exclude local agent artifacts Keep Claude worktrees and locally installed verification tools out of the Docker build context. These host-only directories added roughly 1.9 GB to every root image build. Assisted-by: Codex:gpt-5 Ordino Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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mudler's LocalAI [bot]andEttore Di Giacinto
|
7a7fb00730 |
feat(ui): rebuild the import form on the restyled design language (#11461)
The import page took the new palette in #11305 but kept its old layout, so it stayed a 760px column with the primary action detached from the form it submits. Two of the problems were outright bugs. The Import button carried no className at all, so the page's single most important control fell through to the user-agent button: system chrome, wrong radius, no design-system focus ring. The YAML button carried `fas fa-save fa-upload`, which sets Font Awesome as the button's own font family (its label text inherits it) and points two glyph classes at one ::before. On the layout: `page--narrow` is documented for "forms / single-record edit views", and in Advanced mode this page held a URI field, a six-section format guide, ten modality chips, nine preference fields, a key-value repeater and a YAML editor at `calc(100vh - 400px)`. The width was the symptom; one column was the disease. - `page--medium` with a work column and a format reference beside it. The reference answers the only question a first-time admin has and used to sit behind a chevron, closed by default. Below 1024px it becomes a disclosure rather than disappearing. - The source field is the hero: monospace, because it holds something you paste, and it carries its own Import button. That removes the hidden aria-hidden submit button that existed only because the real action sat outside the form. - Simple and Advanced are gone. They were ~80% the same surface, and the overlap cost a mode switch, a localStorage key and a three-button Keep/Discard/Cancel dialog whose only job was protecting state that switching modes would hide. One form with a collapsible options panel hides nothing, so none of it is needed. What genuinely differs is the kind of input, which is now the two tabs: a source, or YAML. - The size/VRAM estimate reports under the field that produced it instead of as a banner above the page header, and an import in flight gets the progress, phase and byte counts the poller already returned and the old status card threw away. - ModalityChips resolves its labels through the same `modality.*` keys as the dropdown it filters. It hardcoded English shorthand, so one modality carried two names on one screen ("Speech" on the chip, "Speech recognition" on the group it scrolled to) and seven locales had neither. Its inline styles and its pill radius move onto the design system. - Three inline styles go, including both conditional-padding hacks; the only one left is the progress bar's runtime width. Baseline 538 -> 535. Docs updated in the same change: the WebUI section described a Simple and an Advanced mode and told the reader to "Toggle to Advanced Mode". e2e: 426 passed. The mode-switch suite is replaced by one covering the tabs and the disclosure, and a new layout suite pins the width, the styled primary action, the absence of an icon-font button, the reference column at both widths, and the estimate's position. Assisted-by: Claude Code:claude-opus-5[1m] [Read] [Edit] [Bash] [Playwright] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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localai-org-maint-botandlocalai-org-maint-bot
|
f2cdc06781 |
fix(model): surface backend startup exits (#11447)
* fix(model): surface backend startup exits Preserve the local backend process exit code and bounded stderr diagnostic when the process dies before its gRPC service becomes ready. Fixes #9050 Assisted-by: Codex:gpt-5 * fix(model): satisfy startup diagnostic checks Assisted-by: Codex:gpt-5.6 [Codex] --------- Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
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localai-org-maint-botandlocalai-org-maint-bot
|
16193e1982 |
feat(gallery): add Higgs Audio v3 TTS (#11456)
Expose the existing audio.cpp Higgs support as an installable Q8 gallery model and document voice cloning and licensing constraints. Assisted-by: Codex:gpt-5 Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
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localai-org-maint-botandlocalai-org-maint-bot
|
f7db51bdf5 |
feat(realtime): add shared WebRTC UDP port (#11436)
* feat(realtime): add shared WebRTC UDP port Allow realtime WebRTC peer connections to reuse one configurable UDP mux, and surface listener bind failures through signaling. Assisted-by: Codex:gpt-5 * test(realtime): keep UDP mux alive during bind check The returned SettingEngine owns the UDP listener. Retain it through the duplicate-bind assertion so macOS cannot finalize the listener early and make the exclusivity check spuriously pass. Assisted-by: Codex:gpt-5 [systematic-debugging] * test(realtime): use IPv4 for UDP mux checks Match the socket family used by the WebRTC UDP mux so macOS does not allocate an IPv6 probe that can coexist with the IPv4 listener.\n\nAssisted-by: Codex:gpt-5 --------- Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
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mudler's LocalAI [bot]andEttore Di Giacinto
|
7b9167eaad |
feat(llama-cpp): serve Qwen3-TTS through the llama.cpp backend (#11392)
* fix(config): do not read a TTS speaker-encoder mmproj as vision support Qwen3-TTS on llama-cpp ships an mmproj holding the speaker encoder and code predictor. VisionSupported() treated any non-empty MMProj as proof of image input, so every such model would be advertised as vision-capable. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): add TTS request option parsing helper Validates text and speaker reference presence and strictly parses the top_k / top_p per-request params, in a header with no llama.cpp or gRPC dependencies so the standalone C++ unit test gate picks it up. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(llama-cpp): range-check the TTS top_k and top_p request params Format validation alone let NaN, infinity and out-of-range values through. The consumer copies both values into the audio generation input unconditionally and only guards its separate sampler assignment with "> 0", a test NaN also fails, so a NaN reached llama.cpp with the guard never firing. top_k must now be >= 0 and top_p must fall within 0.0 to 1.0 inclusive, with the bound written as a negated in-range test so NaN is rejected rather than silently accepted. Also cover the two checks the suite could not previously kill: the whole-string check in the float parser and the int32 range check. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(llama-cpp): bump pin to f9e832c10 and carry the TTS server task Picks up ggml-org/llama.cpp#26254 (Qwen3-TTS via mtmd) and #26536 (the short-input audio chunk fix). Adds 0002-add-server-task-type-tts.patch, the server-side half of the still-draft #26603, so TTS runs through the slot scheduler instead of racing it. Remove that patch when #26603 merges. The patch is rebased on top of the score patch: its tokenize-switch hunk collided with the SERVER_TASK_TYPE_SCORE case, and its lone SRV_WRN call passes no variadic argument, which the macro cannot expand. The score patch itself needed no refresh. Also fixes fallout from the bump in grpc-server.cpp: upstream dropped the per-slot n_ctx argument from server_schema::eval_llama_cmpl_schema. Only the schema branch loses it, since forks predating the server-schema split still expect the old argument list. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): implement the TTS and TTSStream RPCs Both were declared in backend.proto but unimplemented. They now submit a SERVER_TASK_TYPE_TTS task and drain the response reader, the same shape PredictStream uses. The streaming path emits a leading sample_rate message and then raw PCM, because ModelTTSStream builds the WAV header itself; the non-streaming path emits a complete WAV to the requested dst. The streamed samples are converted from the pipeline's float32 to signed 16-bit first. MTMD_HELPER_GEN_AUDIO_OUTTYPE_PCM hands back floats, while the header ModelTTSStream writes announces 16-bit samples, so shipping the floats verbatim would decode as noise. prepare.sh and CMakeLists.txt now stage tts_request_options.h alongside the other grpc-server helpers, and register its standalone test with ctest the way passthrough_options_test is registered. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(llama-cpp): mask non-codec tokens for Qwen3-TTS generation The Qwen3-TTS gen-audio pipeline maps a sampled backbone token to a codebook row with an unchecked subtraction, in mtmd-helper-gen.cpp: inp.code0 = sampled - codec_0; For ggml-org/Qwen3-TTS-12Hz-1.7B-Base-GGUF the vocab is 155008 tokens, <|codec_0|> is 151936 and the codec codes end at 153983. The model's own tokenizer.ggml.suppress_tokens holds 1023 ids covering 153984..155007, every special above the codec range except <|codec_eos_token|> (154086) which stays reachable as the stop token. Nothing masks the text range 0..151935, so the backbone can sample a text token at any step, the subtraction goes negative, and ggml_compute_forward_get_rows aborts the whole backend process on GGML_ASSERT(i01 >= 0 && i01 < ne01). Complete the mask upstream started: bias every token below <|codec_0|> to -INFINITY for TTS tasks so only codec codes and the codec EOS remain reachable. The biases are appended to task.params.sampling.logit_bias, which common_sampler_init already merges with the model's suppress tokens into one llama_sampler_init_logit_bias, so no sampler is added to the chain. Measured cost is 0.082 ms per sampled token and 1.16 MB, set against a forward pass in the multi-millisecond range. It lands in launch_slot_with_task rather than in a route handler so that llama.cpp's own POST /tts and LocalAI's TTS/TTSStream RPCs are both covered, and <|codec_0|> is resolved from the vocab rather than hardcoded so a model without it is left alone. This is reproducible with upstream's own llama-tts and no LocalAI code loaded, aborting at frame 55 on Q4_K_M and frame 71 on Q8_0, so it is neither a quantization artifact nor an artifact of the gRPC adapter. Two further defects in the same draft pipeline still prevent end-to-end audio; they are independent of this one and are recorded in the task report for an upstream bug report. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(llama-cpp): bump pin to 9de0fcf2b and drop the TTS codec mask Upstream fixed the Qwen3-TTS abort in ggml-org/llama.cpp c8e03ce81 ("mtmd/ggml: add ggml_build_forward_order", #26649), landed one hour after the previous pin. ggml_build_forward_expand marks a tensor and all its ancestors for compute, so using it as a pure ordering hint defeated ggml_build_forward_select and made GEN_WAV calls execute the GEN_CODE branch against a stale inp_code0, hitting the get_rows bound assert in ggml_compute_forward_get_rows. That single defect accounts for every abort seen on this model, so 0003-mask-non-codec-tokens-for-tts.patch is removed rather than rebased. The mask changed the observed behavior, but it was perturbing a graph ordering bug rather than fixing a sampling one: at the new pin the whole path works without it. Keeping it would have meant carrying a 152k-entry logit bias, and rebasing it on every pin bump, for no benefit. Verified at 9de0fcf2b with only 0001 and 0002 applied, which both apply clean with no fuzz and needed no rebase: non-streaming HTTP 200, 410924 bytes, 8.56 s RIFF (little-endian) data, WAVE audio, Microsoft PCM, 16 bit, mono 24000 Hz streaming HTTP 200, 560684 bytes, 11.68 s, exactly one RIFF at byte 0, same format, which also exercises the float32-to-s16 conversion at runtime for the first time Pristine unpatched llama-tts at the same pin now also completes, 130 frames to a valid WAV, where it aborted at frame 55 before. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(llama-cpp): clear the TTS slot sequence between requests Only the first TTS request in a backend process succeeded. Every later one failed instantly, in about 0.13 s, with "TTS prompt processing failed" from step_prompt, regardless of streaming or non-streaming and regardless of the text. With LOCALAI_SINGLE_ACTIVE_BACKEND=true the process is kept alive between requests, so a deployment would have served exactly one utterance per backend start. The cause is missing KV hygiene, not anything in the gRPC adapter. TTS slots never enter the shared batch: pre_decode() returns early for them and process_tts_slots() drives them instead, so they skip the prompt-cache bookkeeping that clears a slot's sequence between requests. Nothing in the gen-audio path makes up for it: mtmd_helper_gen_audio_reset only clears host-side buffers, and the pipeline always decodes from position 0 into the sequence identified by slot.id. So the second task on a slot writes positions 0..N over the first task's tokens and llama_decode fails. Fix is one call to slot.prompt_clear(), the same helper the normal path uses, in the SERVER_TASK_TYPE_TTS branch of launch_slot_with_task before set_input. It goes into 0002 rather than a new patch file because it is a defect in the code that patch introduces, and the header now records it as ours so we know whether it still needs carrying if #26603 merges without it. Verified in one backend process, different text on every request: three consecutive non-streaming requests, three consecutive streaming requests, and an interleaved non-streaming, streaming, non-streaming, streaming run. All ten returned HTTP 200 with RIFF ... WAVE audio, Microsoft PCM, 16 bit, mono 24000 Hz, the streamed ones carrying exactly one RIFF header at byte 0, and every output measured as real speech rather than silence or a truncated fragment. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): expose max_frames for TTS requests The Qwen3-TTS backbone does not always emit <|codec_eos_token|>, and when it does not, generation runs to upstream's 512-frame n_predict default. At the model's 12.5 Hz frame rate that is 40.96 s of audio, which a short input can trigger: one request in this session produced 40.96 s for a ten-word sentence. prepareTTSTask hardcoded n_predict to -1, so callers had no way to bound it. Add a max_frames key alongside top_k and top_p, parsed with the same strict whole-string parsing so a typo is an error rather than a silently truncated value, and rejected with a field-naming message when negative. 0 keeps the existing sentinel convention and means unset, so a request that omits it behaves exactly as before. Named max_frames rather than n_predict because frames are what the parameter means at a TTS endpoint: one frame is 0.08 s of audio. The 512-frame default is deliberately unchanged. Lowering it would truncate legitimately long inputs, which is a worse failure than an occasionally overlong one. Verified end to end on one text of thirty words: max_frames=25 HTTP 200, 96044 bytes, 2.00 s, exactly 25 frames max_frames=50 HTTP 200, 192044 bytes, 4.00 s, exactly 50 frames no max_frames HTTP 200, 572204 bytes, 11.92 s, stopped at its own codec EOS after 149 frames, unchanged behavior max_frames=-1 InvalidArgument "max_frames must be >= 0, got \"-1\"" max_frames=many InvalidArgument "max_frames must be an integer, got \"many\"" Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(llama-cpp): send the TTS sample rate up front, and tidy three review items Four items from the Task 4 review. Streaming first-byte latency. TTSStream sent the sample-rate reply only once the first audio result arrived, and a chunk needs a whole 72-frame window, roughly 5.8 s of audio and far longer in wall time on CPU. The Go side blocks on that reply before it can emit the WAV header, so a streaming client sat at zero bytes for the whole stretch. The rate is a property of the loaded model and is available synchronously from mtmd_gen_audio_get_info, so it now goes out immediately after post_task and the rate_sent bookkeeping is gone. Measured on a warm model, first byte drops from 30.48 s to 0.014 s, and the output is still a valid WAV with exactly one RIFF header at byte 0. Unchecked close. The non-streaming path ignored ofstream::close(), so a failure that only surfaces on flush was reported as success while leaving a truncated file at dst. It now returns INTERNAL like the other write failures. Wrong comment on set_lang. gen_audio::inp::get() already maps a stored blank to nullptr, so our guard is behavior-preserving, not behavior-fixing. The comment claimed otherwise; the code was right. Repetition penalty. penalty_last_n = -1 is inert at this pin, because llama_sampler_init_penalties clamps it with std::max(penalty_last_n, 0) and then builds a disabled sampler, so the 1.05 penalty never applies. Upstream's README attributes looping to a missing repeat_penalty, so it was worth testing as a root-cause fix for the model running to the frame cap. Dropping the line lets the sampling default of 64 apply, which was confirmed in the sampler chain trace as penalty_last_n = 64 with repeat_penalty = 1.050. Over 15 uncapped short requests each way it did not help: 0 of 15 ran to the cap with the penalty inert, 1 of 15 with it active. Both lines are therefore kept for parity with upstream's draft, and a comment now records that the pair is inert and why, so the next reader does not believe a penalty is applied. max_frames remains the way to bound output. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * build(llama-cpp): let unpatched forks opt out of the TTS task turboquant and bonsai copy grpc-server.cpp into llama.cpp forks that do not carry our patches. disable-tts-task.sh injects the same kind of preprocessor switch disable-score-task.sh already uses, so those builds answer UNIMPLEMENTED rather than failing to compile. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(config): keep a TTS speaker-encoder projector out of vision detection Task 1 exempted a declared-TTS model's mmproj from VisionSupported, but the first real gallery entry with an mmproj still came back vision-capable through two paths the earlier fix did not close. GuessUsecases has no FLAG_VISION branch, so it falls through to true for any chat-ish model. That is not just a wrong answer at the call site: syncKnownUsecasesFromString rewrites KnownUsecaseStrings from HasUsecases, and the loader calls it more than once per config file, so the guessed FLAG_VISION is written out and parsed back into KnownUsecases as if the operator had declared it. Give GuessUsecases a FLAG_VISION branch that defers to the same explicit signals VisionSupported uses. Second, llama.cpp builds an mtmd context for the speaker-encoder projector and reports its media marker on the first chat probe, which resurrected vision after the model had been used once. Apply the same declared-TTS exemption to MediaMarker that the mmproj check already had. Verified against the qwen3-tts-llamacpp-q4 gallery entry: no vision capability and no image input modality, before load, after a TTS request, and after a chat probe. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add Qwen3-TTS entries for the llama-cpp backend Two entries over upstream's own GGUF conversion, Q8_0 and Q4_K_M, each pairing a backbone with the Q8_0 projector. Named to sit alongside the existing qwen3-tts-cpp entries rather than replace them. Also tags the llama-cpp backend text-to-speech / TTS so the backend browser surfaces the capability. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: cover Qwen3-TTS on the llama-cpp backend Adds the gallery variants, the two-file mmproj configuration, the required voice reference, and the language and sampling knobs. Also corrects the streaming-support list, which named only voxcpm. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(config): register llama-cpp as a TTS and voice-cloning backend The branch taught the llama-cpp backend to serve Qwen3-TTS and shipped two gallery entries for it, but never told the capability table. llama-cpp still declared only the text RPCs and usecases, so: - VoiceCloningForModel returned nil at the capability check, before it ever reached the model's own tts.voice_cloning override, and /tts answered 400 "selected model does not support reference-audio voice cloning" for any localai://voice-profiles/... voice. No model YAML could opt back in. - GET /api/backends/usecases did not list tts for llama-cpp, so the gallery greyed out the TTS filter for the entries this branch adds. - The React TTS page saw voice_cloning: null and kept both models out of the Voice Library. Add the TTS RPCs and usecase, and the reference-audio contract. The contract needs narrowing, because the per-backend switch in VoiceCloningForModel ends in a permissive default: an unnarrowed entry would have advertised reference-audio cloning on every GGUF chat model in the gallery. Narrow on the declared TTS usecase rather than the model name. The TTS checkpoints are the only llama-cpp models carrying known_usecases: [tts]; name matching would have to guess at third-party repacks, and "base", the substring the neighbouring Qwen and vLLM cases key on, is a routine word in text-model names. The check reads the declared bit directly instead of going through HasUsecases, which falls through to GuessUsecases and would hand the decision to a heuristic that never had a llama.cpp TTS model in mind. DefaultUsecases stays [chat]: a bare GGUF served by llama.cpp is a chat model, and both the gallery filter and the importer read that field. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(gallery): declare what nemotron-3-nano-omni actually accepts The entry is backend: vllm-omni with known_usecases: [chat, completion], no mmproj and no media marker, so it used to report vision only through the blanket GuessUsecases fallthrough that the vision branch in this branch removed. Nemotron 3 Nano Omni is a multimodal understanding model: image, video and audio in, text out. Declaring that is what the sibling vllm-omni-qwen3-omni-30b already does. known_usecases gains vision only. FLAG_VIDEO is video GENERATION, an output modality, and this model generates none; video and audio input belong in known_input_modalities, which is where AudioInputSupported and VideoInputSupported read them from. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(importers): import a Qwen3-TTS GGUF repo as TTS, not chat The llama-cpp importer hardcodes known_usecases: [chat] and assigns any mmproj-matching file as a vision projector, so ggml-org/Qwen3-TTS-12Hz-1.7B- Base-GGUF imported as a chat model with vision. Both fields were wrong, and the model was unreachable from /tts and from the Voice Library. Filenames cannot fix this. A Qwen3-TTS repo has the exact shape of a vision repo, one backbone GGUF plus one mmproj-*.gguf, so the projector's own header is the only honest signal: mtmd writes clip.has_gen_audio_encoder for the projectors it can drive as a speech pipeline and refuses to build one without it. Probe the selected mmproj for that flag, reusing the range-fetch the MTP detection already does, and declare tts when it is set. The mmproj assignment then stops reading as vision on its own, since a declared-TTS model already exempts its projector from vision detection. The probe is best-effort like the MTP one: a network blip leaves the chat default in place rather than failing the import. Verified against the real artifacts on disk: the Qwen3-TTS projector reports gen-audio, its backbone does not. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(llama-cpp): stop non-TTS models crashing on the new pin Two regressions, both hit every ordinary llama-cpp model and neither was caught locally because every test on this branch loaded a TTS model. The first is a null dereference. server_slot::tts_ctx::reset() called mtmd_helper_gen_audio_reset() unconditionally, but the gen-audio pipeline is only allocated for models carrying a gen-audio mmproj, and upstream's implementation reads ctx->pipeline before null-checking anything. Since server_slot::reset() runs during slot initialization for every model, any non-TTS model segfaulted the backend the moment it loaded. Guard the call on the is_supported() predicate already defined beside it, and keep the plain field resets unconditional. The second is unrelated to TTS and came in with the pin bump. PredictOptions.Penalty is a bare proto float, so a caller that names no repetition penalty sends 0 rather than omitting the field. Since 9de0fcf2b, common_sampler_init() rejects a non-positive penalty_repeat outright because it would divide logits by zero, turning every such request into "Failed to initialize samplers". Treat 0 as unset and leave llama.cpp's own neutral default in place. Verified with the same suite CI runs, which is what caught both: tests/e2e-backends passes 6 of 6 including the load and predict specs that were red. Qwen3-TTS still synthesises on both paths, 24 kHz mono 16-bit WAV with exactly one RIFF header on the streamed output. Assisted-by: Claude:claude-fable-5 [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> |