master
443 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 mudler's LocalAI [bot]
|
6d8667f93a |
chore: ⬆️ Update ggml-org/llama.cpp to d59d455fd8ea09e5a2e87ce2a9d668267ffb5ccd (#11618)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
mudler's LocalAI [bot]
|
a57fce10c5 |
chore: ⬆️ Update ggml-org/llama.cpp to 60addddf3c567c43ec3caf70fc953fba3572d96f (#11590)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
mudler's LocalAI [bot]
|
4c4911fe2c |
chore: ⬆️ Update ggml-org/llama.cpp to 0021a77de0a8966059dc94548fb3b96654e0bb12 (#11508)
* chore(llama-cpp): update upstream revision Assisted-by: Codex:gpt-5.6 * fix(llama-cpp): refresh server patch contexts The new llama.cpp pin changed the slot reset and prompt batch code. GNU patch accepted stale hunks with fuzz, which left the L4T build with invalid source. Refresh both server patches against the pinned source so each hunk applies at its intended location. Assisted-by: Codex:gpt-5 * fix(llama-cpp): adapt metrics result fields The updated llama.cpp groups cumulative counters under server_metrics. Probe the result layout so the shared adapter also compiles against older forks. Assisted-by: Codex:gpt-5 * fix(llama-cpp): refresh TTS patch offsets GNU patch rejects the stale pre-decode hunk after the score patch changes the same file. Anchor the TTS hunks to the pinned llama.cpp source so the full series applies without fuzz. Assisted-by: Codex:gpt-5.4 * fix(llama-cpp): normalize batch threads The updated llama.cpp creates its batch threadpool during model initialization, before the context-level fallback can replace the -1 sentinel. Resolve that sentinel from the inference thread count so model loading does not overflow the threadpool allocation.\n\nAssisted-by: Codex:gpt-5.4 --------- Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
||
|
Richard Palethorpe
|
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>
|
||
|
mudler's LocalAI [bot]
|
071952a964 |
chore: ⬆️ Update ggml-org/llama.cpp to 84e908c625fb60992b4cdef8180fb12fa9b4c4bf (#11473)
* ⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(llama-cpp): refresh the TTS patch The llama.cpp update moved and changed the generated-audio pipeline. Refresh the carried patch so backend builds can apply it to the new revision. Assisted-by: Codex:gpt-5.4 --------- 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: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
||
|
mudler's LocalAI [bot]
|
7cfccdc2bf |
chore: ⬆️ Update ggml-org/llama.cpp to 030ebb558a5820b444a8f836ed5cdd46c9b4bd7a (#11454)
* ⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(llama-cpp): rebase server patches Adapt score output limits and TTS backend sampling to the updated llama.cpp server APIs. Assisted-by: Codex:gpt-5.4 --------- 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: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
||
|
mudler's LocalAI [bot]
|
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> |
||
|
mudler's LocalAI [bot]
|
f447faf08d |
chore: ⬆️ Update ggml-org/llama.cpp to 221f0f6356efe2260023208365705ec5d5a7c8f5 (#11303)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
mudler's LocalAI [bot]
|
a0f7faaa2a |
fix(sycl): stop building the ggml CPU variant matrix with icpx (#11321)
Since #11255 and #11276 every GPU image also builds ggml's CPU_ALL_VARIANTS matrix, so a partial offload uses the host's SIMD kernels. That works everywhere except SYCL, where the Makefile compiles the whole tree with icpx -fsycl: icpx never finishes ggml-cpu/arch/x86/repack.cpp at -march=sapphirerapids. In run 30765516644 both sycl_f16 and sycl_f32 stopped at that translation unit and sat there for 5h30m with a single compile in flight until GitHub killed the job at its 6h limit, and turboquant's f16 job lost its runner outright. gcc compiles the same file in seconds in the vulkan and CPU jobs of the same run, so the CPU variant matrix is only unbuildable under icpx. Route SYCL back to the portable fallback binary, which is what these images shipped before #11255. run.sh already prefers *-cpu-all when present and falls back otherwise, so nothing else has to change. Assisted-by: Claude Code:claude-opus-5[1m] [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
mudler's LocalAI [bot]
|
8a80830f33 |
chore: ⬆️ Update ggml-org/llama.cpp to a7a6d0d269c896218b6c78e0933bd6a17519d3f6 (#11283)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
mudler's LocalAI [bot]
|
ad2be8a856 |
chore: ⬆️ Update ggml-org/llama.cpp to 876a4321163249c43ca4e986818fab5ab081f282 (#11177)
* ⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(llama-cpp): drop merged MiniMax-M3 patch The bumped llama.cpp revision includes the MiniMax-M3 parser and template detection, so the carried patch now rejects during backend preparation. Remove the obsolete patch while retaining the independent score-task patch. Assisted-by: Codex:gpt-5 [systematic-debugging] --------- 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: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
||
|
localai-org-maint-bot
|
cedcbf97a9 |
fix(llama-cpp): retain CPU variants in GPU builds (#11255)
Build the runtime CPU variant set alongside x86 GPU backends so partial offload uses the host's SIMD kernels instead of the scalar fallback. Keep arm64 GPU images on the portable binary until their builders consistently provide gcc-14. Assisted-by: Codex:gpt-5 Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
||
|
Dimitris Karakasilis
|
c089caf320 |
feat(sycl): make the intel llama.cpp backend self-contained on any host (#10991)
* feat(sycl): make the intel llama.cpp backend self-contained on any host The SYCL backend shipped an incomplete oneAPI runtime AND relied on a host-provided GPU driver, so it only ran inside the build container. On a bare host it died with "libze_loader.so.1 / libdnnl.so.3: cannot open shared object file", and even with the host's Intel driver installed it SIGSEGV'd during SYCL init when the host driver was built against a newer glibc than the backend's bundled loader (rolling-release distros). package_intel_libs now bundles the complete, coherent oneAPI runtime (the missing MKL ILP64 / sycl_blas / tbb_thread + oneDNN + the dlopen'd UR adapters, plus a sweep of the backend binaries' own direct deps) and the Intel GPU userspace driver (libze_intel_gpu + libigdrcl + IGC + gmm) with its OpenCL ICD manifest, mirroring how package_vulkan_libs bundles Mesa. run.sh points the Level Zero and OpenCL loaders at the bundled driver, and install-base-deps.sh installs it in the SYCL build image. Bundling the driver is safe across kernels because it talks to the host i915/xe via the stable DRM UAPI (unlike NVIDIA's kernel-locked userspace). Validated on Arch (glibc 2.43, i915): the backend loads and runs on an Iris Xe with no host Intel packages installed. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Dimitris Karakasilis <dimitris@karakasilis.me> * fix(sycl): install a driver that exists, and let the user choose their own The driver install added earlier in this branch asked apt for intel-level-zero-gpu, which is not a package in Ubuntu 24.04. apt fails outright on an unknown name, so neither driver was installed, nothing was there to copy, and the images carried no driver at all. It now comes from Intel's own repository, which has 25.18 for this Ubuntu release, against 23.43 from late 2023 in the Ubuntu archive. The archive driver does not know any card released since, so a machine with a recent Intel GPU would end up carrying a driver that cannot drive it. Anything that goes wrong during that install fails the build on purpose: an unreachable repository is a passing problem that a retry fixes, while quietly carrying a different driver, or none, is a difference nobody would notice until a user reports an idle GPU. run.sh used to overwrite whatever driver the user had chosen. Level Zero uses only the driver it is given, so on a machine with a card too new for the carried driver, the GPU would go unused with no way back. Both that setting and the OpenCL one are now left alone when already set, and the docs say how to point a backend at the machine's own driver. The OpenCL setting also used to be applied whenever the backend held a driver list, even when the driver it named had not been copied, which leaves OpenCL with nothing instead of falling back to the machine's own driver. It now requires the copied driver to be present, and the packaging leaves out the list entry of any driver it did not copy. The oneAPI images list a processor-only OpenCL library, which was being carried with nothing behind it. Two more corrections in the packaging. The scan for libraries a program is linked against only looked at files named llama-cpp-*, so turboquant and bonsai, which are also built for Intel GPUs, were left with the incomplete set of libraries this branch set out to fix; it now looks at every program in the directory. And a build that should carry a driver but ends up without one now says so, which is what a stale prebuilt base image looks like: such a backend still runs on a machine that has its own driver, so nothing fails and the only other symptom is a user reporting an idle GPU. Backends now also ask the driver to report how much graphics memory is free, without which llama.cpp reads zero on an integrated GPU, since such a chip shares the system memory instead of having its own. turboquant and bonsai get the same run.sh handling as llama.cpp. The driver is only carried by the builds that start through run.sh, because run.sh is what points Level Zero and OpenCL at it. The Python backends for Intel GPUs start differently and would never load it, so they keep using the machine's own driver rather than carrying several hundred megabytes they cannot use. Checked in a container on Ubuntu 24.04: the install brings driver 25.18 with the files where the packaging expects them, an unreachable repository fails the build, and the copied set resolves on its own once the machine's Intel packages are moved away. Assisted-by: Claude:claude-opus-5 Signed-off-by: Dimitris Karakasilis <dimitris@karakasilis.me> * fix(ci): rebuild every Linux backend when the GPU packaging script changes scripts/build/package-gpu-libs.sh decides which GPU libraries end up inside an image. The filter that builds the backend matrix listed it as an input of the Python images only, so changing it rebuilt no Go and no C++ backend, even though those run it from their own package.sh. A packaging fix aimed at the Intel llama.cpp backend could merge and reach no image, which is the same failure this rule was written to prevent. Assisted-by: Claude:claude-opus-5 Signed-off-by: Dimitris Karakasilis <dimitris@karakasilis.me> * fix(sycl): carry only the driver Level Zero uses, not the OpenCL one llama.cpp reaches an Intel GPU through Level Zero, which hands the driver programs that are already compiled and so needs only the back end of the graphics compiler. The OpenCL driver can be handed source code instead, so it needs the compiler's front end as well, and that arrives with its own copy of clang. Carrying it cost about 139 MB in every backend built for Intel GPUs, and took the carried set from 123 MB to 261 MB. Nothing here takes that path. No LocalAI code selects an OpenCL device, each backend image holds one backend, and the documentation never described OpenCL as a way to run models: the only mentions are a stale clblas row in the BUILD_TYPE table, for a llama.cpp backend that no longer exists and that no build matrix entry uses, and the sycl-ls troubleshooting hint. Before this branch the packaging carried the OpenCL loader and adapter but no driver, so the path could not work in a released image either. There is nobody to keep working. The driver list that OpenCL reads is no longer carried, and run.sh no longer sets OCL_ICD_VENDORS, so OpenCL inside a container keeps using whatever the image provides rather than being pointed at a directory with no driver in it. Checked in a container against the real 25.18 driver: the carried set is 123 MB with nothing unresolved, and Level Zero still reports the GPU with the machine's own Intel packages moved out of the way. Neither the Level Zero driver nor the compiler back end names the front end or clang among the libraries it opens by name, so the leaner set is complete for this path. Assisted-by: Claude:claude-opus-5 Signed-off-by: Dimitris Karakasilis <dimitris@karakasilis.me> --------- Signed-off-by: Dimitris Karakasilis <dimitris@karakasilis.me> Co-authored-by: localai-org-maint-bot <bot-opensource@localaisrl.com> |
||
|
Leoy
|
632c4b6db2 |
refactor(backends): extract package-system-libs.sh from 31 package.sh (#11095)
refactor(backends): extract shared package-system-libs.sh from package.sh
The arch-detect-and-copy-system-libs block (Darwin rpath / x86_64 / aarch64
loader + libc/libstdc++/libgcc_s/libm/libgomp/libdl/librt/libpthread) was
inlined verbatim in 31 backend package.sh scripts. Extract it into a single
sourced scripts/build/package-system-libs.sh, the CPU-side counterpart to
scripts/build/package-gpu-libs.sh and its sourcing contract.
Consolidating the copies fixes three drift classes that had crept in:
- libgcc_s.so.1 and libstdc++.so.6 were listed twice in 9 backends
(acestep-cpp, crispasr, moss-tts-cpp, omnivoice-cpp, piper,
qwen3-tts-cpp, silero-vad, stablediffusion-ggml, whisper); the shared
script copies each once.
- libgomp.so.1 was omitted from opus. OpenMP consumers dlopen it rather
than link it, so the missing copy only failed at runtime; the shared
script always includes it.
- the Darwin @loader_path/lib rpath was applied only in piper and
silero-vad; both now pass their packaged binary to the shared script,
preserving that behavior. Every other backend passes an empty binary
path so no rpath is added, preserving its current behavior.
Each backend's pre/post packaging steps (binary copy, run.sh, ldd closure
walks, ggml variant bundling, espeak/OpenBLAS extras, the ds4 validate step)
are preserved verbatim; only the inline if/elif/else arch block is replaced
by a single source line.
Signed-off-by: supermario_leo <leo.stack@outlook.com>
Co-authored-by: localai-org-maint-bot <bot-opensource@localaisrl.com>
|
||
|
Adira
|
ef724a3c9d |
feat(api): add /v1/detokenize endpoint (#9620)
* feat(api): add /v1/detokenize endpoint Closes #1649. Mirror of the existing /v1/tokenize path, requested by @benniekiss in the issue thread for "complete API workflow" use cases that need to turn token IDs back into text without local processing. - Add Detokenize gRPC RPC with DetokenizeRequest{tokens} / DetokenizeResponse{content} messages. - Implement in the llama.cpp backend using common_token_to_piece, the same primitive TokenizeString already uses internally. - Other backends inherit the default Unimplemented from base.Base, in line with how Detect, Rerank, etc. are gated per-backend. - Wire up the Go gRPC interface, server, client, and in-process embed wrapper alongside their TokenizeString counterparts. - Add the schema types, ModelDetokenize wrapper, HTTP handler, route registration, RouteFeatureRegistry entry (gated by FeatureTokenize so no new feature flag is needed), and the discovery map entry under ai_functions. - Regenerated swagger reflects the new endpoint and types. - Update authentication.md to list /v1/detokenize alongside /v1/tokenize. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com> * test(e2e): add mock backend tests for /v1/detokenize Add Detokenize to the mock gRPC backend and wire up two e2e tests in the MockBackend suite: one that posts known token IDs and asserts a non-empty content response, and a round-trip that tokenizes first then detokenizes the returned IDs. Addresses reviewer feedback on #9620. Assisted-by: Claude:claude-sonnet-4-6 Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com> * fix(kokoros): implement detokenize in the Rust backend service The Detokenize RPC added in this PR grows the tonic-generated Backend trait. Unlike the other languages there is nothing to inherit a default from — Rust trait impls must list every method — so backend/rust/kokoros failed to compile: error[E0046]: not all trait items implemented, missing: `detokenize` --> src/service.rs:72:1 72 | impl Backend for KokorosService { Go backends pick up the Unimplemented default from base.Base, and the generated C++/Python servicer bases default to UNIMPLEMENTED, which is why the Rust backend was the only one that broke. kokoros is the sole Rust crate in the tree, so this is the full extent of the fallout. Return Status::unimplemented("Not supported"), matching how this same file already gates tokenize_string and ~20 other unsupported RPCs. Fixes the tests-kokoros and backend-jobs-singlearch-4 (-cpu-kokoros) failures on the previous head. Assisted-by: Claude:claude-opus-5 cargo Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com> --------- Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com> Co-authored-by: localai-org-maint-bot <bot-opensource@localaisrl.com> |
||
|
localai-org-maint-bot
|
5e541894df |
fix(llama-cpp): preserve GPU layers during option passthrough (#11193)
Stage the negative GPU-layer sentinels expected by the upstream argument parser, then restore LocalAI resolved values unless a passthrough flag explicitly overrides them. This avoids the parser assertion that terminated the backend for any generic option. Assisted-by: Codex:gpt-5 Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
||
|
Richard Palethorpe
|
49ef40a187 |
feat(classifier/VAD): support voice control on low power devices (#10804)
* feat(llama-cpp): route Score through the slot loop Score previously bypassed the slot loop with a direct llama_decode: a conflict guard aborted the whole process if scoring raced generation, the config validator had to reject score alongside chat/completion/embeddings, and every candidate re-decoded the full shared prompt. Add SERVER_TASK_TYPE_SCORE to the (patched) upstream server so score tasks are scheduled like any other slot work: generation and scoring serialize naturally, the shared prompt is decoded once per call, and the slot's prompt cache carries the conversation prefix across calls. Context checkpoints at the score boundary and at the cache-divergence point keep SWA/hybrid/recurrent models (e.g. LFM2.5) from re-prefilling the whole prompt per candidate: warm-turn scoring on a 6-option set drops from ~8s to ~0.5s on a desktop CPU. The conflict guard and the validation split are removed; declaring score with generation usecases on one config is now supported and shares the slot cache. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): classifier wire types and pipeline config Wire types and YAML config for realtime classifier mode: sessions carry a localai_classifier extension (options with canned replies/tool calls, softmax threshold, normalization, history trimming, fallback modes, and a deterministic wake-word address gate), mirrored by pipeline.classifier in the model YAML and surfaced in the config-meta registry. The localai.classifier.result server event reports the full score distribution per turn. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): classifier response flow Classifier-mode responses: instead of autoregressive generation, each user turn is prefill-scored against the option list (router.ScoreClassifier prompt/candidate shapes over the Score primitive) and the winning option's canned reply and tool call are emitted through the existing response machinery. Below-threshold turns take the configured fallback (none / canned reply / generate); empty transcripts and unaddressed turns (wake word not mentioned) skip scoring entirely. The scoring probe defaults to the latest user message only — small scorers echo canned replies from prior turns back as the top option otherwise. Built for hardware that can afford prompt processing but not decode: with slot-based Score the option list stays KV-cached across turns, so a turn costs roughly one forward pass over the new words. session_update_error events now carry the validation cause instead of a generic message. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): bound the VAD tick's scan window and buffer retention The VAD tick loop re-scanned the entire input buffer every 300ms and only trimmed it on zero-segment ticks or commits. Audio that keeps producing segments without a committing pause (steady noise a mic pipeline lets through, music, continuous speech) grew the buffer toward the 100MB cap with each tick rescanning all of it — O(n^2), measured at ~3.3ms of silero per buffered second: past ~90s retained, ticks run back to back and pin ~4 cores until the stream stops. Silero's recurrent state only carries a few hundred ms of context, so rescanning old audio buys nothing. Clip the slice handed to the VAD to the largest silence the commit test can need to measure (server_vad silence window or the semantic eagerness fallback) plus a warm-up margin, and rebase the returned segment times so every downstream consumer keeps whole-buffer coordinates. An open turn whose clipped window is all silence now commits (the silence outran the window) instead of being discarded as no-speech. Independently, retain at most 90s of raw buffer, rebasing the live-feed and EOU cursors on trim — this also bounds the previously unbounded VAD-error path. Turn boundaries are otherwise unchanged: no forced commits, no new coordinator states. pipeline.turn_detection.vad_window_sec can widen the scan window; values below the automatic floor are ignored. The tick body is extracted into vadTick so specs can drive turn detection synchronously (same shape as classifySoundWindow); the babble reproduction that pinned 4 cores now plateaus under 10% of one core. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(backend): let per-model threads override the global default ModelOptions overrode a set per-model threads value with the app-level --threads whenever the latter was non-zero — and WithThreads defaults it to the physical core count, so it always was. The YAML threads: knob has been dead config: a tiny VAD model could never opt down from the global pool size. SetDefaults already fills an unset per-model value from the app config, which is the intended precedence; resolve threads through a helper that honors it (explicit threads: 0 still means unset). Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * chore(gallery): single-thread the silero VAD Silero is a ~2MB recurrent model with no exploitable graph parallelism: measured per-call latency is identical at 1 and 10 ORT threads, while every extra pool thread just spin-waits between the realtime loop's frequent tiny inferences. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * docs(realtime): classifier mode, VAD scan window, threads precedence Document the realtime classifier mode (options, threshold guidance, wake-word address gate, empty-transcript handling), the VAD scan window and 90s buffer retention (pipeline.turn_detection.vad_window_sec), the per-model threads precedence, and the M3 classifier note in the realtime state-machine design doc. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * perf(llama-cpp): score all candidates in one batched decode One scoring call is now a single SERVER_TASK_TYPE_SCORE task: the slot decodes the shared prefix (prompt + longest common candidate token prefix) once, then forks one sequence per candidate off it (metadata-only for the unified KV cache, copy-on-write for recurrent state) and decodes every candidate's unique tail in one llama_decode. Previously each candidate was its own task that restored the boundary checkpoint and re-decoded its full tail sequentially, paying per-candidate task and decode overhead. The context reserves SERVER_SCORE_FORK_SEQS extra sequence ids (and recurrent-state cells) beyond the parallel slots via the new common_params::n_seq_score_forks. Forking requires the unified KV cache (already this backend's default) since per-sequence streams would shrink n_ctx_seq; an explicit kv_unified:false disables forking and Score calls that need it fail cleanly. Candidates beyond the fork/output budget decode in successive chunks. Wire contract and scores are unchanged: per-token logprobs are stitched from the shared region and the forked tails. Verified bitwise deterministic call-to-call and independent of candidate order (no cross-fork leakage via equal-length candidate swap); ranking matches the per-candidate implementation on the drone battery (winner softmax 0.99996 vs 0.99997), and >16-candidate chunking, prefix-of-another and empty candidates all pass. Measured on a desktop CPU: warm /api/score calls 0.52s -> 0.23s; warm realtime classifier turns 196-303ms. The 9-candidate drone turn decodes ~17 unique tail tokens in one batch instead of nine sequential ~220ms checkpoint-restore tasks. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): gate scoring capacity by model usecase Reserve llama.cpp scoring slots only for models that explicitly declare the score usecase, while allowing score to coexist with chat and completion. Reject incompatible unified-KV settings and classifier activation on models without scoring capacity. Propagate application defaults when resolving realtime and preload pipeline stages so unset thread counts are resolved consistently without overriding explicit model settings. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(ci): honor APT mirrors in the prebuilt llama-cpp compile step The builder-prebuilt path installs gcc-14 with apt directly and ignored the APT_MIRROR/APT_PORTS_MIRROR build args the from-source path already honors, so an ubuntu mirror outage broke every arm64 backend build. Pass the args into the stage and run apt-mirror.sh (already in the build context via COPY . /LocalAI) before the apt step. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): classifier argument slots via constrained completion Hybrid classify-then-complete: a classifier option's canned tool call can declare typed argument slots (number | enum | string, with defaults and prompt hints) referenced as "{{name}}" in the arguments template. When the option wins, the slots are filled by a short grammar-constrained completion that continues the exact scoring prompt — rendered by the same cached ScoreClassifier, so the llama.cpp prompt cache is already warm — with the chosen route JSON re-opened at the first slot field. A GBNF grammar pins the field skeleton and frees only the values; temperature 0, a couple dozen tokens at most (~300ms on a desktop CPU for two slots). Slot declarations and hints ride the option descriptions in the shared system prompt, informing scoring and the fill alike at no per-turn token cost. The localai.classifier.result event carries the final arguments and a fill_latency_ms. On inference failure the slots' defaults apply; a slot without a default fails the response (or falls through with fallback.mode: generate). Slot filling requires completion alongside score in the scoring model's known_usecases. Verified end-to-end on the Pi drone demo: "fly forward three meters" in distance mode classifies forward and infers {"distance": 3, "units": "meters"} in ~310ms, and the drone flies exactly 3 units. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): splice filled slot values into classifier replies A classifier option's spoken reply can now reference its tool's argument slots ("Going forward {{distance}} {{units}}."): the values inferred by the slot-fill completion — or the recovery defaults — are spliced into the reply as plain text before it is emitted, so what the assistant says confirms what it actually inferred. Placeholders without a value stay literal, and options without slots are untouched. FillToolArguments now returns the raw slot values alongside the spliced arguments JSON to make the reply templating possible. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): harden classifier slot completion Reserve context for constrained slot filling, size completions from their encoded output, and encode enum grammar literals as valid JSON. Reject empty enum values and cover the failure modes with regression tests. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): prewarm the classifier scoring prompt on registration Swapping a session's classifier option list (a voice-switched command mode, for instance) made the next turns pay a full re-prefill of the new option-list prompt — measured 2.4s vs 0.3s warm on a desktop CPU, and worse: on hybrid-memory models like LFM2.5, whose state cannot be partially rewound (llama.cpp can only restore checkpoints), *every* probe change re-prefilled from scratch whenever the last checkpoint missed the probe boundary, so even same-list turns intermittently cost full prefills. Registering an option list (pipeline seed or session.update) now fires a best-effort background prewarm: two throwaway scores with distinct probes. The first prefills the new option-list prompt; the second, diverging exactly where per-turn probe text starts, plants the backend's rewind point (KV checkpoint) at the stable-prefix boundary that every real turn reuses. The prewarm hides behind the canned mode-switch reply — by the time it finishes speaking, the cache is warm. Idempotent per option set, detached from the registering request's lifetime. Measured on the drone demo (LFM2.5-1.2B, desktop CPU): first turn after a mode switch 2374ms -> 340ms; intermittent same-list full prefills (1.3-2.1s) all -> under 0.5s. For clients that swap lists frequently, options: [parallel:2] on the scoring model additionally keeps one slot per list via prefix-similarity routing (+26MB RSS, unified KV). Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * perf(llama-cpp): checkpoint scoring at the caller-declared stable prefix Hybrid-memory models (LFM2.5 shortconv, Qwen3.5 deltanet — where new small models are headed) cannot rewind their state, so any prompt-cache reuse that needs a rewind falls back to a full re-prefill. For classifier scoring that meant every probe change re-processed the whole option-list prompt: the server's checkpoints were placed reactively (at wherever the previous task happened to diverge), so a checkpoint past the next divergence was erased rather than restored — measured as intermittent 2-10s turns on prompts with a 95%+ common prefix. The classifier now computes the probe-invariant prompt prefix once (the byte-wise common prefix of two synthetic probe renders) and declares its length with every Score request; the server maps it to a token boundary and forces a KV checkpoint exactly there on each score prefill. That checkpoint sits at or before every future divergence under the same option list, so it always survives and always restores — repeat scoring costs probe+candidates regardless of how the probe changes. Also: - prewarm reruns on every option-list registration instead of memoizing per list: with boundary checkpoints a redundant rewarm costs two probe-sized decodes, while skipping one after a slot eviction (three lists sharing fewer slots evict in LRU cascades) silently moves a full re-prefill onto the user's next turn - new llama.cpp backend option rs_seq:N exposes bounded recurrent-state rollback outside speculative decoding; measured impractical for deltanet-scale states (65GB for 64 snapshots on Qwen3.5-4B) but cheap insurance for small-state models - docs: the multi-list recipe (parallel:N + sps:0.5 — the default slot similarity threshold funnels distinct lists onto one slot) Measured on the drone demo (LFM2.5-1.2B scorer, desktop CPU), steady state: every turn 285-421ms including mode switches, vs 2.4s post-switch and intermittent 1.3-2.9s re-prefills before. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): align classifier cache guidance Document the single-score prewarm behavior and clean the vendored score patch formatting. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(llama-cpp): guard score task for fork backends TurboQuant and Bonsai reuse the primary gRPC server against llama.cpp forks that do not carry LocalAI's slot-based Score patches. Compile the Score integration only for the patched primary backend and return UNIMPLEMENTED from fork builds instead of referencing absent task types and common_params fields. Assisted-by: Codex:gpt-5 [gh] Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(dev): generate gRPC code before commit lint The coverage phase regenerates ignored protobuf bindings, but lint runs first and can fail against missing or stale output. Generate the pinned bindings before lint so the gate always type-checks the current schema. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
||
|
mudler's LocalAI [bot]
|
176000190e |
chore: ⬆️ Update ggml-org/llama.cpp to 1cbfd1988311775425d36c0ce066590f7d3049cf (#11155)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
mudler's LocalAI [bot]
|
0a8a7fbbb4 |
chore(llama-cpp): bump llama.cpp and adapt to the load-mode refactor (#11140)
Bump LLAMA_VERSION to 0d47ea7427463093e69128bf2c2f9cd06b3ee5b3 (73 commits touching common/, src/ and tools/server/). Two upstream changes break the backend: * ggml-org/llama.cpp#20834 folded common_params::use_mmap / use_mlock / use_direct_io into a single `load_mode` enum. LocalAI still exposes the three as independent settings (`mmap`, `mmlock`, and the `direct_io` option), so params_parse folds them once all three have been read, keeping the precedence the separate booleans had: direct I/O bypasses the page cache, mlock implies mmap, everything off is a plain buffered read. turboquant and bonsai compile this same grpc-server.cpp against forks that predate the refactor, so prepare.sh probes the checkout for LLAMA_LOAD_MODE_MMAP and generates llama_compat.h with LOCALAI_LEGACY_LOAD_MODE set accordingly. Probing beats a per-fork build flag here because the fork flavor targets disagree on whether they forward CMAKE_ARGS or EXTRA_CMAKE_ARGS, and it heals itself once a fork rebases past the refactor. * The MiniMax M3 patch no longer applies. Upstream merged the model half of llama.cpp#24523 (LLM_ARCH_MINIMAX_M3, src/models/minimax-m3.cpp, the gguf-py constants and conversion/minimax.py) but not the chat half, so the patch is re-cut to carry only the common/chat.cpp template detection and PEG parser, rebased onto the new pin and onto the thinking_end_tag -> thinking_end_tags rename. Dropping it wholesale (as #11008 did, reverted in #11136) would have silently regressed MiniMax M3 tool calling and thinking. Verified with a CPU docker build of the backend plus LoadModel and Predict against a real GGUF over gRPC in all four load modes. Assisted-by: Claude:claude-opus-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
d9f3007876 |
Revert "chore: ⬆️ Update ggml-org/llama.cpp to d2a818231effb12b7b20b80b3b8c7756a9a33a04" (#11136)
Revert "chore: ⬆️ Update ggml-org/llama.cpp to `d2a818231effb12b7b20b…"
This reverts commit
|
||
|
mudler's LocalAI [bot]
|
6e69dbd617 |
chore: ⬆️ Update ggml-org/llama.cpp to d2a818231effb12b7b20b80b3b8c7756a9a33a04 (#11008)
* ⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(llama-cpp): drop upstreamed MiniMax M3 patch The pinned llama.cpp revision already contains MiniMax M3 support, so the downstream patch rejects during backend preparation on every platform. 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: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
||
|
mudler's LocalAI [bot]
|
1cd7d63c7b |
fix(distributed): reject wrong-model requests on the remaining modalities (#10990)
#10970 gave the four PredictOptions RPCs a model-identity check so a backend reached through a stale distributed route rejects the request instead of answering from whatever model it holds (#10952). Every other modality shares that exposure: the route is cached by host:port, a worker can recycle a stopped backend's port for another model's backend, and a liveness-only probe cannot tell a stale row from a valid one. Extends the same mechanism to the 21 remaining request messages that reach a backend through the router, using the pattern #10970 established rather than a parallel one: - proto: ModelIdentity on each modality request message. - controller: populated from ModelConfig.Model at the call site that also builds ModelOptions, so load-time and request-time values are equal by construction. - backends: one generic guard in pkg/grpc/server.go (27 Go backends), the method set in backend/python/common (36 Python backends), llama-cpp (AudioTranscription/Stream, Rerank, Score) and privacy-filter (TokenClassify). - reconcile already drops the stale row on IsModelMismatch; no change. TTSRequest and SoundGenerationRequest get a SEPARATE ModelIdentity field rather than reusing their existing `model`: FileStagingClient rewrites `model` to a worker-local path, so comparing it would reject valid requests in exactly the configuration this guards. AudioEncode/AudioDecode are deliberately left unguarded: the opus codec backend is loaded from a literal rather than a ModelConfig, so no value carries the equality guarantee the comparison depends on. The four bidirectional stream RPCs are out of scope; they bypass reconcile. Empty means skip on both sides, so an old controller, an old backend, and the bare request structs in tests/e2e-backends all keep working. Assisted-by: Claude Code:claude-opus-4-8 [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
mudler's LocalAI [bot]
|
465d488c90 |
fix(distributed): reject wrong-model requests at the backend (#10970)
fix(distributed): reject wrong-model requests at the backend (#10952) In distributed mode the controller caches a NodeModel row naming a backend's host:port. A worker can recycle a stopped backend's gRPC port for a different model's backend, and probeHealth verifies liveness rather than identity, so the probe succeeds against whatever now occupies the port and the request is dispatched to the wrong backend. The caller gets a silent wrong-model answer. Nothing in the request could catch this: PredictOptions had no model field, so model identity crossed the wire only in ModelOptions.Model at LoadModel time, and the cached-hit path issues no LoadModel. Every backend's "model not loaded" guard checks a nil handle, which a process holding a different model passes, so the stale row was never dropped either. Add PredictOptions.ModelIdentity and enforce it at the point of use: - The controller populates it in gRPCPredictOpts from ModelConfig.Model, the same expression ModelOptions feeds to model.WithModel and therefore the same value the backend received as ModelOptions.Model. Both are read from one config value in one function, so they are equal by construction and the comparison cannot false-reject. - Backends compare it against what they loaded and return NOT_FOUND with a fixed sentinel. Enforced in pkg/grpc/server.go (27 Go backends), an interceptor in backend/python/common (all 36 Python backends, no per-backend change), and the llama-cpp / ik-llama-cpp / ds4 C++ servers. That is every backend with real exposure: kokoros answers all four RPCs with unimplemented and privacy-filter implements none of them. - The router's reconcile drops the stale replica row on a mismatch, so the next request reloads somewhere correct. Empty means "skip the check" on both sides: a controller that predates the field sends nothing, a backend loaded by such a controller has nothing to compare, and the C++ server synthesizes PredictOptions internally for ASR. That keeps upgrades working in both directions. Scoped to the four PredictOptions RPCs. TTSRequest.model and SoundGenerationRequest.model are deliberately NOT validated: FileStagingClient already rewrites them to worker-local absolute paths, so in distributed mode they already differ from the load-time value and comparing them would reject valid requests. IsModelMismatch requires both the NOT_FOUND code and the sentinel, unlike the neighbouring helpers which accept either. insightface's Embedding returns NOT_FOUND "no face detected" on a PredictOptions RPC, and a code-only check would drop a healthy replica row on every faceless image. Assisted-by: Claude Code:claude-opus-4-8[1m] [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Nandana Dileep
|
b5e4413eab |
feat: add MiniMax-M3 model support (#10837)
Adds inference parameter defaults for the minimax-m3 model family and includes a vendored patch of upstream llama.cpp PR #24523 to recognize the minimax-m3 architecture. Once the upstream PR merges, the patch can be removed and LLAMA_VERSION bumped normally. Changes: - backend/cpp/llama-cpp/patches/0001-add-minimax-m3-support.patch: vendored patch from ggml-org/llama.cpp#24523 (Preliminary MiniMax-M3 support). Applied by prepare.sh during the build; keeps the pinned LLAMA_VERSION pointing at the latest upstream tag. - core/config/inference_defaults.json: add minimax-m3 family entry (temperature=1.0, top_p=0.95, top_k=40, min_p=0.01, repeat_penalty=1.0, matching the existing minimax defaults) and register it in the patterns list before the shorter minimax-m2.7 entry for correct longest-match-first ordering. Upstream: depends on ggml-org/llama.cpp#24523 Closes: https://github.com/mudler/LocalAI/issues/10820 Signed-off-by: Nandana Dileep <110280757+nandanadileep@users.noreply.github.com> |
||
|
mudler's LocalAI [bot]
|
139470cca0 |
chore: ⬆️ Update ggml-org/llama.cpp to 571d0d540df04f25298d0e159e520d9fc62ed121 (#10935)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
mudler's LocalAI [bot]
|
911fb754a6 |
chore: ⬆️ Update ggml-org/llama.cpp to 6bdd77f13cf11b264b4231d320afc404f48d576e (#10898)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
6dfda9c4b6 |
chore: ⬆️ Update ggml-org/llama.cpp to e8f19cc0ad70a243c8012bf17b4be601abfc8ea2 (#10870)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
1f53dff436 |
fix(turboquant,bonsai): do not apply vendored llama.cpp patches to fork trees (#10866)
The turboquant and bonsai backends copy backend/cpp/llama-cpp/ wholesale into their build directories and reuse its Makefile/prepare.sh against their own llama.cpp forks. When PR #10837 added backend/cpp/llama-cpp/patches/0001-add-minimax-m3-support.patch, the copied patches/ directory was mis-applied to the fork checkouts: the fork trees diverge from upstream, hunks rejected, and because the patch-apply loop in prepare.sh ran before set -e took effect the build kept going and died much later with a confusing compile error ("'LLM_ARCH_MINIMAX_M3' was not declared in this scope"). This broke tests-turboquant-grpc on that PR. Two hardening changes: - turboquant/bonsai Makefiles: delete the copied patches/ directory right after the cp -rf of backend/cpp/llama-cpp/. Patches vendored for upstream llama.cpp must never be applied to the forks; each fork carries its own patch series under backend/cpp/<backend>/patches/, applied by its apply-patches.sh. - llama-cpp prepare.sh: run the patch-apply loop under set -e so a rejecting patch fails fast and loudly at apply time instead of surfacing as a downstream compile error. A missing or empty patches/ directory remains a no-op success, so all existing callers (the llama-cpp Makefile targets and the turboquant/bonsai copies) are unaffected when no patches ship. Exposed by PR #10837. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]
|
e488884b20 |
chore: ⬆️ Update ggml-org/llama.cpp to 505b1ed15ca80e2a19f12ff4ac365e40fb374053 (#10848)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
d19c9875ed |
chore: ⬆️ Update ggml-org/llama.cpp to 00fa7cb284cbf133fc426733bd64238a3588a33e (#10814)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
b90e1cae73 |
chore: ⬆️ Update ggml-org/llama.cpp to 6b4dc2116a92c5c8f2782bfe51fabe5ee66fb5ef (#10797)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
5013d53a1c |
chore: ⬆️ Update ggml-org/llama.cpp to e3546c7948e3af463d0b401e6421d5a4c2faf565 (#10787)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
6084497da1 |
chore: ⬆️ Update ggml-org/llama.cpp to 4f37f519722aa3242eecb7649466b4a4a2d6d6da (#10772)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
c46a224b44 |
chore: ⬆️ Update ggml-org/llama.cpp to 049326a00025d00b08cc188ed716b681e984a3f8 (#10757)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
70e15679cd |
chore: ⬆️ Update ggml-org/llama.cpp to a646006f09d2f76f2d62d6c0d5e8e8490d570720 (#10747)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
d829e818d0 |
chore: ⬆️ Update ggml-org/llama.cpp to bec4772f6a2527d371557b5d2032641e5ff7619c (#10739)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
Roman Mazurenko
|
a3fdfbc0d1 |
feat(llama-cpp): add device selection option (#10724)
Allow llama.cpp model configs to select the backend devices used for offload, matching upstream --device behavior so users can exclude a display or debug GPU. Signed-off-by: rvmzes <rvmzes@rvmzess-MacBook-Pro.local> Co-authored-by: rvmzes <rvmzes@rvmzess-MacBook-Pro.local> |
||
|
LocalAI [bot]
|
c1fd12a506 |
chore: ⬆️ Update ggml-org/llama.cpp to f36e5c348bc8795c34f9a038e58876e7a8423d4d (#10710)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
2348bdc16d |
chore: ⬆️ Update ggml-org/llama.cpp to 2da668617612d2df773f966e3b0ee22dc2beef7b (#10694)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
33869da527 |
chore: ⬆️ Update ggml-org/llama.cpp to 665892536dfb1b7532161e3182304bd35c33e768 (#10681)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
8396ce1388 |
chore: ⬆️ Update ggml-org/llama.cpp to d4cff114c0084f1fbc9b4c62717eca8fb2ae494a (#10671)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
348f3c87c0 |
fix(gpu-libs): bundle hipBLASLt TensileLibrary data so ROCm backends stop falling back (#10660) (#10672) the
The ROCm packager copied rocBLAS kernel data (rocblas/library/*.dat) into the
bundled lib/ dir and run.sh pointed ROCBLAS_TENSILE_LIBPATH at it, but the
parallel hipBLASLt data dir (hipblaslt/library/TensileLibrary_lazy_gfx*.dat)
was never packaged and no HIPBLASLT_TENSILE_LIBPATH was set. The bundled
libhipblaslt.so therefore resolved its per-arch kernel data relative to itself,
found nothing, and silently fell back to slow generic kernels, logging:
rocblaslt error: Cannot read "TensileLibrary_lazy_gfx1201.dat": No such file or directory
rocblaslt error: Could not load "TensileLibrary_lazy_gfx1201.dat"
Fix, mirroring the existing rocBLAS handling:
- package-gpu-libs.sh: extract the rocblas data-dir copy into a reusable
copy_rocm_data_dir helper and call it for both rocblas and hipblaslt.
- llama-cpp/turboquant run.sh: export HIPBLASLT_TENSILE_LIBPATH when the
bundled hipblaslt/library dir exists.
The helper takes an optional ROCM_BASE_DIRS override so the copy is unit
testable without a real ROCm install; add a regression test that runs
package_rocm_libs against a fabricated ROCm tree and asserts both data dirs
are bundled.
Note: this bundles whatever gfx*.dat the build image's ROCm provides. If a
given arch's tensile data is absent from the shipped ROCm, that arch still
needs a ROCm bump; the packaging gap itself is fixed for every supported arch.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
|
||
|
LocalAI [bot]
|
715d4ed8e5 |
chore: ⬆️ Update ggml-org/llama.cpp to fdb1db877c526ec90f668eca1b858da5dba85560 (#10647)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
a4e6e01e4d |
fix(process): give backend workers a parent-death safety net (#10639)
* fix(grpc): self-terminate backend workers when LocalAI dies non-gracefully Symptom: a backend model-worker subprocess (the per-model gRPC server LocalAI spawns) can be orphaned and linger — holding VRAM and its listen port — if the LocalAI process is killed non-gracefully (e.g. a supervisor's graceful-shutdown grace period elapses and LocalAI is SIGKILLed) before its own teardown runs. Root cause: LocalAI's graceful teardown (pkg/signals/handler.go installs the SIGINT/SIGTERM handler; core/cli/run.go registers app.Shutdown -> ModelLoader.StopAllGRPC -> process.Stop in pkg/model/process.go) only runs when LocalAI receives a catchable signal and survives long enough to run its handlers. Backends are spawned via github.com/mudler/go-processmanager v0.1.1, whose getSysProcAttr() sets Setpgid:true (own process group, so the group can be signalled) but never PR_SET_PDEATHSIG/Pdeathsig, and exposes no Config field or option for a caller to inject/extend SysProcAttr. LocalAI fully delegates spawning to that library (it never builds the exec.Cmd itself), so it cannot set a kernel parent-death signal at the spawn site. If LocalAI is SIGKILLed, nothing tells the backend to exit and it is reparented to init. Fix: add a best-effort, backend-side safety net at the one shared choke point every out-of-process Go backend routes through — grpc.StartServer / RunServer in pkg/grpc. On startup it captures getppid() and polls; when the process is reparented (getppid changes / becomes 1 — the standard POSIX signal the original parent died) it logs and self-terminates. getppid() reparent detection is portable (Linux + macOS), unlike Linux-only PR_SET_PDEATHSIG. Toggle via LOCALAI_BACKEND_PARENT_WATCH (default on; off on Windows) and LOCALAI_BACKEND_PARENT_WATCH_INTERVAL. This is strictly a backstop alongside the existing graceful SIGTERM->grace->SIGKILL teardown, which is unchanged. Scope/limitations: covers Go-based backends (everything using pkg/grpc). The C++ backends (e.g. llama-cpp) and Python backends do not route through pkg/grpc and are not covered by this mechanism — they would each need an equivalent parent-death check (follow-up). The fully general fix is for go-processmanager to expose SysProcAttr injection so LocalAI can set Pdeathsig at spawn for every backend regardless of language (suggested upstream follow-up; out of scope for this LocalAI-only PR). Test: pkg/grpc/parentwatch_test.go builds a real test -> middle -> grandchild process tree, lets the middle process exit to orphan the grandchild running the real watchParentDeath, and asserts it detects the reparent and self-terminates. Unix-only (build-tagged), runs in CI (Linux). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(process): extend parent-death backstop to C++ and Python backends The Go parent-death watcher (pkg/grpc/parentwatch.go, commit 772b435d5) only protects backends that route through pkg/grpc. C++ and Python backends don't, so the originally-reported case — the llama.cpp gRPC worker surviving a non-graceful LocalAI death — was still uncovered. Extend the same best-effort backstop to both languages, reusing the exact mechanism and semantics: - capture getppid() at startup, skip if already orphaned (<=1) - a background thread polls getppid() and self-exits on reparenting (getppid() != orig || == 1), portable across Linux/macOS, no-op on Windows - same env vars: LOCALAI_BACKEND_PARENT_WATCH (default on; falsy false/0/no/off disable) and LOCALAI_BACKEND_PARENT_WATCH_INTERVAL (default 2s; accepts Go-style durations like 500ms/2s/1m) C++: implemented in backend/cpp/llama-cpp (the reported, most-used C++ backend) as a dependency-free header parent_watch.h, wired into grpc-server.cpp's main() and copied at build time via prepare.sh. C++ backends have no shared server scaffolding, so other C++ backends (ds4, ik-llama-cpp, privacy-filter, ...) are not yet covered and would each need the same one-line include+call as follow-ups. Python: implemented once in the shared common/parent_watch.py and armed from common/grpc_auth.py's get_auth_interceptors() — the single helper every one of the 35 Python backends invokes while building its gRPC server — so all Python backends (and future ones) are covered with no per-backend edits and no duplicated implementation. Tests (real process-tree reparent detection, mirroring the Go test): - backend/cpp/llama-cpp/parent_watch_test.cpp (via run-unit-tests.sh) - backend/python/common/parent_watch_test.py (python -m unittest) Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> 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> Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com> |
||
|
LocalAI [bot]
|
006310d746 |
chore: ⬆️ Update ggml-org/llama.cpp to 4fc4ec5541b243957ae5099edb67372f8f3b550e (#10630)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
0d8adfc59a |
chore: ⬆️ Update ggml-org/llama.cpp to 0eca4d490e591d4e93058d07540cf47278a72577 (#10617)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
605348925d |
chore: ⬆️ Update ggml-org/llama.cpp to 6f4f53f2b7da54fcdbbecaaa734337c337ad6176 (#10595)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
bd1ec8f2c2 |
chore: ⬆️ Update ggml-org/llama.cpp to dbdaece23de9ac63f2e7ca9e6bfcdc4fc156a3fa (#10582)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
13b1ae53bc |
chore: ⬆️ Update ggml-org/llama.cpp to 0ed235ea2c17a19fc8238668653946721ed136fd (#10536)
* ⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(llama-cpp): link server-stream.cpp TU into grpc-server for upstream 0ed235ea (#10536) Upstream llama.cpp 0ed235ea added an SSE stream-resumption layer in a new translation unit tools/server/server-stream.cpp, which defines stream_session, stream_pipe_producer and the g_stream_sessions manager. server-context.cpp (already #included into grpc-server.cpp) now calls into it via spipe->cleanup(), stream_aware_should_stop() and stream_session_attach_pipe(), so without the new TU the grpc-server link fails on every arch with: undefined reference to `stream_pipe_producer::cleanup()' prepare.sh already copies every tools/server/* file into tools/grpc-server/, so the source is present; the only missing piece was including its definitions. Add an __has_include-guarded #include "server-stream.cpp" before server-context.cpp, mirroring the existing server-chat.cpp and server-schema.cpp guards, keeping the source compatible with older pins/forks that predate the split. The file is self-contained (its only external symbols come from server-common, already in the TU) so it adds no new undefined references; the http route-handler factories it also defines are unused in the grpc path but harmless. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(llama-cpp): build renamed ggml-rpc-server target for upstream 0ed235ea (#10536) Upstream renamed the RPC server CMake target and binary from `rpc-server` to `ggml-rpc-server` (tools/rpc/CMakeLists.txt: `set(TARGET ggml-rpc-server)`), so the RPC-enabled grpc build failed with "No rule to make target 'rpc-server'". The grpc-server itself links fine after the server-stream.cpp fix; this only updates the RPC target name and the binary path copied to llama-cpp-rpc-server. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] --------- Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]
|
f0d0bff232 |
fix(llama-cpp): stop reinterpreting plain-string message content as JSON (#10524) (#10538)
build container images / core-image-build (intel/oneapi-basekit:2025.3.2-0-devel-ubuntu24.04, intel, --jobs=3 --output-sync=target, linux/amd64, ubuntu-latest, auto, -gpu-intel, noble, 2404) (push) Has been skipped
build container images / core-image-build (ubuntu:24.04, vulkan, --jobs=4 --output-sync=target, amd64, linux/amd64, ubuntu-latest, false, auto, -gpu-vulkan, noble, 2404) (push) Has been skipped
build container images / core-image-build (ubuntu:24.04, vulkan, --jobs=4 --output-sync=target, arm64, linux/arm64, ubuntu-24.04-arm, false, auto, -gpu-vulkan, noble, 2404) (push) Has been skipped
build container images / hipblas-jobs (rocm/dev-ubuntu-24.04:7.2.1, hipblas, --jobs=3 --output-sync=target, linux/amd64, ubuntu-latest, auto, -gpu-hipblas, noble, 2404) (push) Has been skipped
build container images / core-image-build (ubuntu:22.04, cublas, 13, 0, --jobs=4 --output-sync=target, linux/amd64, ubuntu-latest, false, auto, -gpu-nvidia-cuda-13, noble, 2404) (push) Has been skipped
build container images / core-image-build (ubuntu:24.04, , --jobs=4 --output-sync=target, amd64, linux/amd64, ubuntu-latest, false, auto, , noble, 2404) (push) Has been skipped
build container images / core-image-build (ubuntu:24.04, , --jobs=4 --output-sync=target, arm64, linux/arm64, ubuntu-24.04-arm, false, auto, , noble, 2404) (push) Has been skipped
build container images / core-image-build (ubuntu:24.04, cublas, 12, 8, --jobs=4 --output-sync=target, linux/amd64, ubuntu-latest, false, auto, -gpu-nvidia-cuda-12, noble, 2404) (push) Has been skipped
build container images / gh-runner (nvcr.io/nvidia/l4t-jetpack:r36.4.0, cublas, 12, 0, --jobs=4 --output-sync=target, linux/arm64, ubuntu-24.04-arm, true, auto, -nvidia-l4t-arm64, jammy, 2204) (push) Has been skipped
build container images / gh-runner (ubuntu:24.04, cublas, 13, 0, --jobs=4 --output-sync=target, linux/arm64, ubuntu-24.04-arm, false, auto, -nvidia-l4t-arm64-cuda-13, noble, 2404) (push) Has been skipped
build container images / gpu-hipblas-image-merge (push) Has been skipped
build container images / core-image-merge (push) Has been skipped
build container images / gpu-vulkan-image-merge (push) Has been skipped
build container images / gpu-nvidia-cuda-12-image-merge (push) Has been skipped
build container images / nvidia-l4t-arm64-cuda-13-image-merge (push) Has been skipped
build container images / gpu-intel-image-merge (push) Has been skipped
build container images / nvidia-l4t-arm64-image-merge (push) Has been skipped
build container images / gpu-nvidia-cuda-13-image-merge (push) Has been skipped
Security Scan / tests (push) Has been cancelled
goreleaser / goreleaser (push) Has been cancelled
Explorer deployment / build-linux (push) Has been cancelled
GPU tests / ubuntu-latest (1.21.x) (push) Has been cancelled
goreleaser / launcher-build-darwin (push) Has been cancelled
goreleaser / launcher-build-linux (push) Has been cancelled
Tests extras backends / detect-changes (push) Has been cancelled
Tests extras backends / tests-llama-cpp-smoke (push) Has been cancelled
tests / tests-linux (1.26.x) (push) Has been cancelled
tests / tests-apple (1.26.x) (push) Has been cancelled
tests / tests-backend-cpp (push) Has been cancelled
build backend container images / generate-matrix (push) Has been cancelled
E2E Backend Tests / tests-e2e-backend (1.25.x) (push) Has been cancelled
tests-aio / tests-aio (push) Has been cancelled
Tests extras backends / tests-vibevoice-cpp-grpc-tts (push) Has been cancelled
build backend container images / backend-jobs-multiarch (push) Has been cancelled
build backend container images / backend-jobs-singlearch (push) Has been cancelled
Tests extras backends / tests-vibevoice-cpp (push) Has been cancelled
Tests extras backends / tests-vibevoice-cpp-grpc-transcription (push) Has been cancelled
Tests extras backends / tests-localvqe-grpc-transform (push) Has been cancelled
Tests extras backends / tests-voxtral (push) Has been cancelled
Tests extras backends / tests-kokoros (push) Has been cancelled
Tests extras backends / tests-insightface-grpc (push) Has been cancelled
Tests extras backends / tests-speaker-recognition-grpc (push) Has been cancelled
build backend container images / backend-merge-jobs-multiarch (push) Has been cancelled
build backend container images / backend-merge-jobs-singlearch (push) Has been cancelled
build backend container images / backend-jobs-darwin (push) Has been cancelled
Tests extras backends / tests-transformers (push) Has been cancelled
Tests extras backends / tests-rerankers (push) Has been cancelled
Tests extras backends / tests-diffusers (push) Has been cancelled
Tests extras backends / tests-coqui (push) Has been cancelled
Tests extras backends / tests-moonshine (push) Has been cancelled
Tests extras backends / tests-pocket-tts (push) Has been cancelled
Tests extras backends / tests-qwen-tts (push) Has been cancelled
Tests extras backends / tests-qwen-asr (push) Has been cancelled
Tests extras backends / tests-nemo (push) Has been cancelled
Tests extras backends / tests-voxcpm (push) Has been cancelled
Tests extras backends / tests-liquid-audio (push) Has been cancelled
Tests extras backends / tests-llama-cpp-quantization (push) Has been cancelled
Tests extras backends / tests-llama-cpp-grpc (push) Has been cancelled
Tests extras backends / tests-llama-cpp-grpc-transcription (push) Has been cancelled
Tests extras backends / tests-sherpa-onnx-realtime (push) Has been cancelled
Tests extras backends / tests-sherpa-onnx-grpc-transcription (push) Has been cancelled
Tests extras backends / tests-whisper-grpc-transcription (push) Has been cancelled
Tests extras backends / tests-parakeet-cpp-grpc-transcription (push) Has been cancelled
Tests extras backends / tests-sherpa-onnx-grpc-tts (push) Has been cancelled
Tests extras backends / tests-ik-llama-cpp-grpc (push) Has been cancelled
Tests extras backends / tests-turboquant-grpc (push) Has been cancelled
Tests extras backends / tests-acestep-cpp (push) Has been cancelled
Tests extras backends / tests-qwen3-tts-cpp (push) Has been cancelled
Tests extras backends / tests-rfdetr-cpp (push) Has been cancelled
Tests extras backends / tests-locate-anything-cpp (push) Has been cancelled
The llama-cpp gRPC backend reconstructs OpenAI messages from proto for the tokenizer-template path and blindly json::parse'd each message's content string. LocalAI's Go layer always flattens content to a plain string, so a user prompt that merely looks like JSON (e.g. mealie's ingredient array ["1/4 cup brown sugar", ...]) was reinterpreted as structured content parts and rejected by oaicompat_chat_params_parse with "unsupported content[].type". Normalize content per role instead: user/system/developer content is opaque text and is never JSON-sniffed; assistant/tool content still collapses a literal JSON null/object (tool-call bookkeeping) to a string, but a plain string is never turned into an array/scalar. The array defense is role-independent, so the role gate only governs the benign null/object case. While here, extract the duplicated per-message reconstruction and the pre-template content sanitization into shared, unit-tested helpers (message_content.h) so the streaming (PredictStream) and non-streaming (Predict) paths cannot drift. This removes ~490 lines of copy-pasted defensive code, the dead tool-role parse branches, and the redundant Predict-only tool_calls branch, while preserving the prior #7324 (null content -> "") and #7528 (tool array content -> string) fixes. Tests: - backend/cpp/llama-cpp/message_content_test.cpp: standalone C++ unit tests for all three helpers (#10524, #7324, #7528, multimodal), discovered and run by `make test-backend-cpp` and a new generic tests-backend-cpp CI job. Also wired as an opt-in CMake/ctest target (-DLLAMA_GRPC_BUILD_TESTS=ON). - core/schema/message_test.go: Go regression pinning that ToProto flattens a JSON-array-looking text part to the verbatim string. - prepare.sh now copies message_content.h into the build tree. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |