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Commits
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 dependabot[bot]
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f3f9a8601b |
chore(deps): bump grpcio from 1.84.0 to 1.85.0 in /backend/python/rerankers (#12963)
chore(deps): bump grpcio in /backend/python/rerankers Bumps [grpcio](https://github.com/grpc/grpc) from 1.84.0 to 1.85.0. - [Release notes](https://github.com/grpc/grpc/releases) - [Commits](https://github.com/grpc/grpc/compare/v1.84.0...v1.85.0) --- updated-dependencies: - dependency-name: grpcio dependency-version: 1.85.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> |
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mudler-agentandEttore Di Giacinto
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2cadf319a0 |
feat(realtime): LFM3-Audio speech-to-speech on llama.cpp, remove liquid-audio (#12958)
* chore(backends): remove the liquid-audio Python backend The backend targets liquid-audio, which loads LFM2 checkpoints only. Remove its directory, the gallery file and the LFM2.5-Audio model entries, the library and generated backend gallery entries, the backend matrix and CI job, the Makefile targets and the golden test fixtures that listed it. Assisted-by: Claude:claude-haiku-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(config): drop liquid-audio from capabilities, importers and UI With the liquid-audio backend gone, remove its capability entry, its importer, its artifact-gate entry and the realtime_audio heuristic that only matched that backend. realtime_audio is now set only through known_usecases. Update the Talk and fine-tuning pages, the docs and the swagger text that named the backend. Assisted-by: Claude:claude-haiku-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(llama-cpp): bump llama.cpp to e486a4b5 Move the llama.cpp pin to e486a4b559617dc88bf79978996f6c670b6bbc9d, the master head of 2026-10-28. It contains the LFM3-Audio support (ggml-org/llama.cpp#31257) and the realtime server split (ggml-org/llama.cpp#31269). The score task patch is regenerated against the new pin. It applies with git apply and with patch -p1, with no offset and no fuzz. Its hunks in server-task.h and server-context.cpp were rebased by hand next to SERVER_TASK_TYPE_REALTIME and the control dispatch. The C++ backend was built on CPU for this change: the llama, mtmd and server-context libraries and the grpc-server binary link. Assisted-by: Claude:claude-haiku-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): compile server-realtime.cpp into grpc-server server-context.cpp includes server-realtime.h, and its realtime slot type is defined in server-realtime.cpp. Upstream compiles that file into the server library. The gRPC server is one translation unit, so it must include the file too, or the link fails. The include is guarded by __has_include. Forks that predate the realtime split do not ship the file, so they keep building as before. Assisted-by: Claude:claude-haiku-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(llama-cpp): probe realtime and TTS voice support in prepare.sh prepare.sh writes llama_compat.h from probes on the checkout. Add two: LOCALAI_HAS_REALTIME (SERVER_TASK_TYPE_REALTIME in server-task.h) and LOCALAI_HAS_TTS_VOICE (set_voice in mtmd-helper.h). The probes let the gRPC server choose the realtime and voice code paths per checkout, so forks without them keep compiling. Assisted-by: Claude:claude-haiku-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): advertise speech recognition and speech-to-speech llama-cpp now registers the AudioTranscription and AudioToAudioStream RPCs, with the transcript and realtime_audio usecases. The default usecase stays chat, so a plain GGUF is still a chat model. The backend implements the RPCs in a follow-up change; until then the calls return UNIMPLEMENTED. Assisted-by: Claude:claude-haiku-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(config): add pipeline.any_to_any for speech-to-speech models A realtime_audio model that serves the whole speech loop itself sets pipeline.any_to_any. The flag is off by default, so chained pipelines and existing realtime_audio configs are unchanged. The realtime_audio heuristic stays explicit: a llama-cpp model is never a Talk-page model unless known_usecases says so. Assisted-by: Claude:claude-haiku-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(config): read the audio encoder and speech-to-speech projector flags Add HasAudioEncoderProjector for clip.has_audio_encoder and HasSpeechToSpeechProjector, which requires the LFM3 audio encoder family together with the gen-audio decoder. Qwen3-TTS sets both encoder flags too, so the flags alone cannot tell it apart from LFM3. Assisted-by: Claude:claude-haiku-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(importer): import LFM3-Audio projectors as speech-to-speech An mmproj that carries the LFM3 audio encoder and the gen-audio decoder now imports with known_usecases realtime_audio, chat, transcript and tts, pipeline.any_to_any: true, the parallel:2 option and the tokenizer template. Other gen-audio projectors keep the TTS-only result, Qwen3-TTS included. The probe runs against a local HTTP server in the tests. Assisted-by: Claude:claude-haiku-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): named-voice TTS and the LFM3 ASR prompt Models without a speaker reference (LFM3-Audio) take a named voice through their "tts" chat template instead of a reference file. The request is validated with needs_reference=false, the prompt is rendered with common_chat_get_tts_prompt(), and the voice is set on the gen-audio input. Qwen3-TTS Base keeps the reference-file rule. The branch is guarded by LOCALAI_HAS_TTS_VOICE, so forks without named voices keep the old behaviour. Transcription takes its prompt from common_chat_get_asr_prompt(). LFM3 gets a system turn that names the language and an empty user turn. Qwen3-ASR's preset is unchanged: fixed user text, no system turn, and a caller prompt is still appended after a newline. The preset call is guarded by a new LOCALAI_HAS_ASR_PRESET probe. The pure parts sit in tts_request_options.h and transcription_prompt.h, with standalone tests run by backend/cpp/run-unit-tests.sh. Add the test-extra-backend-llama-cpp-lfm3 target. It runs the tts, ttsstream and transcription specs against LFM3-Audio GGUF weights. It is a manual run and is not wired into CI. Assisted-by: Claude:claude-haiku-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(e2e): mock AudioToAudioStream and pending realtime S2S specs The mock backend serves AudioToAudioStream deterministically: session created, commit with RMS, a tool call on the first turn with tools, then transcript pieces and paced 24 kHz tone frames, with response.cancel ending a reply as cancelled. The realtime S2S specs cover the mock and real-weights variants. They skip until the /v1/realtime S2S path lands, so the mock suite stays green in CI. Assisted-by: Claude:claude-haiku-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): speech-to-speech AudioToAudioStream on the realtime slot Implement AudioToAudioStream on upstream's realtime slot, so a model that answers after the audio (LFM3-Audio) can hold a conversation over the existing duplex RPC. The proto schema does not change: tools travel in the config, tool calls leave as function_call_arguments.done and tool results arrive as a function_call_output item. realtime_session.h keeps what needs no model: the turn text derived from the chat template (with a tool turn), the conversation state, a filter that keeps <tool_call> blocks out of the transcript and drops audio produced inside them, and the translation of slot results into events. grpc-server.cpp wires it to the slot with control tasks and parses calls with the parser the chat template selects. The unit test uses a fake renderer. A ctest-only check renders the real LFM3 template and compares the slot text with the full render. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add the LFM3-Audio-2B entries Two llama-cpp entries for LiquidAI/LFM3-Audio-2B-GGUF, the Q4_K_M default (lfm3-audio-2b) and the Q8_0 build (lfm3-audio-2b-q8), both with the Q8_0 audio projector. Each declares pipeline.any_to_any and the realtime_audio, chat, transcript and tts usecases the importer writes for an LFM3 projector, with parallel:2 and the tokenizer template. The Q4 entry lists the Q8 one as a variant. File sizes and sha256 come from the Hugging Face API for the pinned repo revision. Speaker reference files and the VAD model are not part of the entries: VAD stays optional. Assisted-by: Claude:claude-haiku-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: describe LFM3-Audio speech-to-speech and its gallery entries Adds a speech-to-speech section to the Realtime API page (any_to_any configuration, VAD versus manual commit, tools, transcripts, barge-in, limits), a pipeline.any_to_any row, a llama.cpp speech-to-speech row in the compatibility table, and LFM3-Audio mentions on the TTS and ASR pages. The feature is marked experimental: the behaviour follows the backend session contract and has not been run with real weights. Assisted-by: Claude:claude-haiku-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): run any-to-any models on AudioToAudioStream A realtime_audio model with pipeline.any_to_any now opens one duplex stream to the backend and keeps it for the session, replacing the "any-to-any models not implemented" branch. The chained pipeline is unchanged. The endpoint forwards input audio (with a pre-roll from speech start when a VAD model is set, otherwise every chunk and a client commit), sends response.create, and turns the backend's events into the usual realtime events: audio and transcript deltas, tool calls through the existing function_call items, and a single response.done. A function call output is forwarded before the next response. Barge-in sends response.cancel ahead of the new speech and stops forwarding audio at once. The user's speech is transcribed on the side and reported on the user item; a failure there does not touch the response. Server VAD is on only when pipeline.vad is set. Classifier mode, compaction, streaming.llm, voice gating, sound detection and semantic_vad are rejected for these models. The mock backend now matches the llama.cpp contract (session.updated, the cancel error when idle) and its VAD reports the loud stretches of the audio, so the S2S e2e specs run a VAD turn. The specs are enabled. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(e2e): add the real-weights realtime S2S variant and the gRPC s2s spec The realtime S2S specs now check reply audio on real models: duration, loudness, clipping, and keywords found in the model transcript or in a separate ASR of the output audio. run-realtime-lfm3.sh stages the llama-cpp and sherpa-onnx backends, the LFM3-Audio files and the sherpa VAD, ASR and TTS models, then runs the real variant. It is manual and never part of CI: `make test-realtime` still filters out real-models. The backend suite gains an s2s capability (spoken turn, tool call, tool result, spoken answer over AudioToAudioStream) and optional TTS duration and RMS bounds plus a transcript keyword check, all enabled for test-extra-backend-llama-cpp-lfm3. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(e2e): expect the idle-cancel error from the mock S2S The mock now answers a response.cancel with no reply running with response_cancel_not_active, as the llama.cpp backend does. The cancel spec still treated that cancel as a no-op and read the error as extra data after the stream ended. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(talk): show any-to-any models as one speech-to-speech model /api/pipeline-models lists a pipeline.any_to_any model with any_to_any set and its optional VAD. A realtime_audio model that is not any-to-any is listed only when it has the four pipeline slots, since the session takes the chained path for it. The Talk page shows a speech-to-speech model as one model: no four-slot view, the VAD slot only when a VAD model is set, and the female and male voices offered. Without a VAD model the server does not detect turns, so the page offers Done speaking, which sends input_audio_buffer.commit and then response.create. Text-only replies stream through response.output_text.delta and .done. self_contained stays in the response for clients built before any_to_any. Assisted-by: Claude:claude-haiku-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): refuse a llama-cpp realtime model without pipeline.any_to_any llama.cpp has no VAD, transcription or TTS RPCs, so filling the empty pipeline slots with the model name failed later with UNIMPLEMENTED. Fail at session start and name the missing setting instead. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): hold manual-commit S2S audio until the commit In manual-commit mode the endpoint streamed every appended chunk to the backend, so input_audio_buffer.clear only emptied the Go buffer while the model kept the audio, and idle audio filled its context between turns. Keep the audio in the input buffer and send it, behind pending text and tool-result items, when the client commits or asks for a response. Frames and controls now go through one ordered write queue, so no gRPC send happens under the audio buffer lock. A tool result created while a VAD turn is open is refused with a clear error. When the stream ends outside a response the session reports it and closes, and a bare EOF from Send reports the status that Recv holds. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(e2e): drop the S2S readiness gate and stale comments The gate was always true. Some comments referred to work items that readers of the repository cannot see. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(realtime): add the liquid-audio migration note and the clear semantics Say that the Python liquid-audio backend is gone, which gallery entries replace it and that LFM2.5-Audio has no replacement. Describe what input_audio_buffer.clear drops for speech-to-speech sessions now that manual-commit audio is held back until the commit. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(talk): do not promise turn detection when no VAD model is set The idle note said the server notices when you stop talking. A speech-to-speech model without a VAD model needs Done speaking, so the note now says that. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(e2e): make the mock S2S backend keep the llama-cpp session limits The mock accepted what the real backend refuses and hid two endpoint bugs. It now treats output_modalities from the config as a fixed session mode and picks the default per response from the tools set at that time. A fixed text plus audio mode with tools reads the tool call out loud. After the first commit or message, session.update of instructions, voice or tools is an error that does not belong to a response, committing an empty buffer is an error, and response.create while a reply runs is refused. Errors of a refused response.create carry response_scoped in their meta. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): leave the S2S output mode to the backend unless the client sets one The stream config sent text plus audio when the session had no tools yet. The llama-cpp backend keeps a mode from the config as a fixed session mode, and tools added later by session.update never cleared it. The model then read tool calls out loud. Send output_modalities only when the client chose them. The backend picks the default of each response from the tools it has when the response starts. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): mark the realtime errors that answer a response.create The error event now carries response_scoped: true when it ends the request for a response (a refused response.create, a failed commit made by it, or a stream failure). Other errors, such as a refused session.update or a commit of an empty buffer, are not about a response and one that runs keeps generating. The client needs the difference to know whether a response.done is still coming. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): end an S2S response only on the errors that belong to it Every backend error ended the response in flight, although the backend also reports errors about other things (a refused session.update, a commit of an empty buffer) and goes on generating. The rest of the reply was lost and the next response.create met either a refusal or the leftover events of the old reply. End the response only on an error marked response_scoped, forward the others to the client and keep waiting for response.done. Drop the events buffered by an earlier response when a new one starts, and skip the commit of a turn that forwarded no audio. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): run the S2S input transcription detached from the commit slot The commit waited for the side transcription of the user audio while it held the commit slot, and response.create waits for that slot. The ASR latency was added before every reply, and on a backend with few slots a busy ASR task could stall the reply. The model already has the audio, so run the transcription detached under the session context and release the slot at once. The transcript is still reported on the user item. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(realtime): do not pin the place of the input transcript in the S2S order The transcript of the user audio is reported on the side and may land before or after the first response events. Check the order of the response events without it. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): stop an S2S reader leak and a cancel that waits on the write queue The reader waited on a channel it closes itself when the events buffer was full, so it outlived the session after a turn ended on an error or timed out. It now also stops when the session closes. Stopping a response sent the cancel through the write queue and waited for it before the grace timer started. With backpressure on the stream the wait never ended and nothing read the events meanwhile. The timer now starts first and the cancel is only queued. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): let a chained pipeline run without a VAD model A pipeline that names no pipeline.vad failed to build, because the VAD slot was always loaded, and a new session still started with server_vad. The slot is now optional for chained pipelines as it already is for any-to-any models: the session starts with turn detection off and the client commits each turn. Turning server_vad on for such a session is refused, and a switch to a pipeline without a VAD drops turn detection. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add LFM3-Audio VAD and chained-pipeline entries Add lfm3-audio-2b-vad and -q8-vad (any-to-any with silero-vad-ggml as the turn detector), and lfm3-audio-2b-pipeline, -pipeline-vad and the -q8 forms (the model chained through its own transcription, chat and speech slots, with and without the VAD). The gallery has no dependencies between models, so the VAD entries state that silero-vad-ggml must be installed first. Drop the variants list from lfm3-audio-2b: it let the installer pick the larger Q8 build on any host with enough memory, so a plain install of the Q4 entry did not get Q4. All entries are now independent. Tests cover the entry shapes, the single-build install and their acceptance by the realtime endpoint. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(e2e): run the realtime specs for any-to-any and chained shapes The realtime specs covered one any-to-any model with a VAD. They now run for any-to-any and for a chained pipeline, each with and without a VAD, on the mock backend in CI. VAD-less shapes also check that turn detection starts off and that server_vad is refused. The manual real-weights script writes one config per shape that mirrors the gallery entries and runs the four shapes in turn (LFM3_MATRIX picks a subset). Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(realtime): document the LFM3-Audio VAD and chained-pipeline entries List the eight LFM3-Audio gallery entries, explain that silero-vad-ggml must be installed before the -vad entries, describe the chained pipeline on one model and the behaviour of a pipeline without a VAD, and describe the shape matrix of the e2e specs. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(realtime): wait for the detached input transcription in the S2S specs The input transcription runs in its own goroutine, so the transcription completed and failed events can arrive after the response ends. Poll for them instead of reading the event list right after the response. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): release the S2S stream before running server-side tool calls A response held the stream slot until it returned, and it ran the tool calls from inside that scope. A server-side tool starts a follow-up response on the same stream, which then waited for the slot its parent still held, so the client never got response.done. Run the tool calls once the slot is free; the event order is unchanged. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): end an S2S session whose stream dies, and stop queuing after close A stream that died during a response left the session open: later requests failed one by one and VAD frames kept being forwarded. The response now ends the session through the transport, and enqueue drops requests once the session is closed so the write queue cannot grow. The writer is now the only caller of Send and of CloseSend. close() used to call CloseSend while the writer could be inside Send, which gRPC does not allow. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): close the open S2S turn when the VAD stops Switching turn detection to manual in the middle of a VAD turn left the turn forwarding, and the next client commit sent the whole buffer a second time. Stopping the VAD now commits the forwarded audio on the backend and drops it from the input buffer. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): keep a tool result pending while an S2S user turn is open The check that refuses a tool result during a user turn ran when the item arrived, but the item was forwarded later, and the result was marked as sent before the backend took it. If the user spoke in between, the backend refused it and it was never retried. The result is now claimed and queued under the audio lock only when no turn is open, and otherwise stays pending for the next commit or response. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(talk): empty the input buffer for models without a VAD Without voice detection the microphone fills the server's input buffer for the whole session, so Done speaking committed idle silence and the echo of the previous reply. Clear the buffer when the session starts and when a reply ends. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(importer): keep an existing parallel option on LFM3-Audio imports The speech-to-speech import added parallel:2 even when the options already carried another parallel value. Use the same check as the hardware defaults, which also covers n_parallel. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(talk): list chained pipelines that have no VAD /api/pipeline-models required a VAD for a chained pipeline, so the VAD-less LFM3-Audio pipeline entries never showed up on the Talk page. Any pipeline with transcription, LLM and speech slots now qualifies. The Talk page treats every model without a VAD as manual-turn, and the session sheet says so for chained pipelines too. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(llama-cpp): bump llama.cpp to 0ac1a7fd Move the llama.cpp pin to 0ac1a7fd35c127a18c0cbc6c57d12e256a6a385b, the master head of 2026-10-29. It contains tool calls in the realtime server (ggml-org/llama.cpp#31287). The score task patch is regenerated so that it applies with no offset and no fuzz. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(functions): keep a null tool argument null A tagged parameter whose text was "null" came out as an empty string: unmarshalling JSON null into a string succeeds and leaves it empty, so the value was taken for a string. Check the first byte of the JSON value instead. A parameter with the text "null" now becomes JSON null, as in llama.cpp. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(functions): add an lfm3 XML format preset The LFM3 chat template writes a call as <tool_call>, <function=NAME>, one <parameter=KEY> block per argument, then the closing tags. The qwen3-coder format has the same shape and auto-detection already found it, but xml_format_preset: lfm3 was not a known name. Add the preset, with tests for one call, a call without arguments, two calls, string and non-string values (numbers, booleans, null, arrays, objects), a call between text, and unfinished input. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): parse tool calls with the chat template, as upstream Since ggml-org/llama.cpp#31287 the realtime server handles tools with the parser and grammar of the chat template. Do the same in the speech-to-speech stream instead of a hard-coded tag filter and a parse of the whole text at the end. - Each response gets a common_chat_session with the tools of the session and parallel calls. Its grammar and preserved tokens go into the sampling of the response, so the slot keeps the call tokens in the text and constrains the call. - The text of the response is fed to the session piece by piece (task_result_state::update_chat_msg). Content deltas become the transcript; tool-call deltas are collected into one function_call_arguments.done per call at the end. Audio after the start of a call is dropped. - A tool_choice of auto, none or required is accepted in the params of the config, in session.update and in response.create, with the response overriding the session. - The tool turn is derived from a conversation in which the assistant called a tool, like upstream, not from a plain reply. - response.done carries raw_text, everything the model wrote with the call markup, for a caller that parses it another way. If the parser throws, the rest of the response is content and the raw text is the way out. ToolCallFilter is no longer used and goes in the next commit. The unit tests cover the splitter contract (content, call deltas, the final piece, whole characters only). realtime_chat_check feeds LFM3-format text through the session in pieces of 1, 7 and all bytes and checks the call, the transcript and the grammar. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(realtime): drop the tool-call text filter The parser of the chat template now splits content from tool calls, so ToolCallFilter, which looked for the literal <tool_call> tags, has no caller. Remove it with its tests. Every case it covered (a call between text, split tags, several calls, an unfinished call, text after a call) is handled by the parser, and realtime_chat_check runs the LFM3 format through it in pieces of different sizes. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): fall back to the raw text for S2S tool calls For an any-to-any model, the calls the backend parsed still win. When there are none and the request had tools, parse the raw text that response.done now carries the way the chained pipeline parses the text of its LLM (CleanupLLMResult, ParseFunctionCall, ParseTextContent), so a call format the backend parser does not know can still be served with function.xml_format_preset or the other function settings of the model. The content around the calls replaces the streamed transcript only if the config captures it, as in the chained path. Also pass tool_choice to the backend: the session mode with the stream config and with session.update, and the mode of a response.create override with that event. A named function cannot be forced, so it is not sent. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(e2e): carry raw_text in the mock S2S response.done Match the llama-cpp session: with tools set, response.done has raw_text, the call markup of the LFM3 template for a tool turn and the transcript for any other reply. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(functions): parse tool blocks that have text between them The iterative XML parser stopped at the first scope block that was preceded by text, so "<tool_call>A</tool_call> then <tool_call>B" gave one call. Skip text between blocks once a call was found; text before the first block still ends the parse as before. Blocks that follow each other, or only whitespace, already worked. Tests cover three calls back to back and with text between them, with the lfm3 preset and with auto-detection. They also pin the current reading of argument values: the parser has no tool schema, so a value such as 42 or true is read as JSON even when the schema types it as a string. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): read LFM3-Audio tool calls with the lfm3 preset Set function.xml_format_preset: lfm3 on the eight LFM3-Audio-2B entries and in the config the importer writes for a speech-to-speech projector, so the server parses the call format of the LFM3 chat template when the backend returns no call (and in the chained pipeline entries, which use the regular chat path). gallery/index.yaml is regenerated from the library. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(realtime): describe how S2S tool calls are read Replace the description of the hard-coded tag filter with the parser and grammar of the chat template, tool_choice, and the raw-text fallback in the server. Document function.xml_format_preset, and set it in the real-weights e2e model configs like the gallery entries do. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(functions): keep earlier calls when a later block does not parse After a valid call, text that holds the opening tag again (a prose mention, a markdown example, a malformed second block) made the parser treat it as a new block, fail, and return no calls at all. Remember the state after each complete block and fall back to it when a later one fails: the calls found so far are kept, parsing stops there and the remaining text stays in the content. Text skipped between two valid blocks now goes to the content instead of being dropped, so StripToolCallMarkup keeps it. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): do not drop audio after a tool call started In mixed mode the speech for text written before a call can still be pending when the call starts, so dropping every audio frame after the first call delta cut the speech while its transcript was sent. The llama.cpp server never drops audio when it reads a result; do the same. If a model speaks the call markup aloud, that is model behaviour to observe on real weights, not something to filter here. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): flush held-back text when a response is cancelled The parser got is_partial=true on a cancelled response, so text it held back (a possible start of a tag) never reached the transcript. The llama.cpp server parses a cancelled response with is_partial=false; do the same. A cancelled response still makes no tool call. The path where the parser throws already sends the text as content and needs no change. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(realtime): stop the fake S2S stream from sending after Close The barge-in spec failed now and then with a data race and "send on closed channel": its script pushes audio from a goroutine, and the session closes the stream while that goroutine still runs. A push now does nothing once the stream is closed, and Close waits for a push in flight. The production close order is right: the writer closes the stream only after it stops sending. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): align the S2S tool-call fallback with the backend Four gaps in how the Go side reads the tool calls of an any-to-any reply: - With tool_choice none (the response value wins over the session value) the backend builds no tool parser, yet the raw-text fallback still turned a call the model wrote into a function_call item. Skip it. - Without a capture regex the fallback never cleaned the transcript, so the stored item and transcript.done kept the raw call markup. When the call is XML, use the text left after the markup is cut out; a reply that is only a call now has an empty transcript. - A named function as tool_choice mapped to nothing, so the backend kept the previous mode. Refuse it with an error in session.update and response.create. - A response-level tools list changed what the fallback looked for, but the backend only has the tools of the session. Use those in both. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(realtime): say how S2S tool_choice and response tools behave Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(e2e): use the real llama-cpp backend in the real realtime run The suite always registered the mock backend under the name llama-cpp, so a run with REALTIME_S2S_MODEL set never reached the llama-cpp backend found in REALTIME_BACKENDS_PATH. Register the mock only when that run is off. The real run converts the input audio with ffmpeg. Check for it in the suite and in run-realtime-lfm3.sh before any download or backend start, with a message that says what to install, and list it as a prerequisite in the script header and the Makefile target. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(realtime): cover the tools path of a client session.update to the S2S backend A tool call that never came back from an any-to-any model raised the question whether the tools of the session reach the backend at all. Add a spec that sends the session.update of the real-weights e2e (flat Responses API tool, no session type) and checks that the backend gets the tool in the chat-completions form. Log the number of tools sent when the stream opens and when a session.update carries them, so a run log shows it without reading the wire. The "No tools received" line of the llama-cpp backend does not show it: the server prints it for the input transcription request, which has no tools by design. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(e2e): interrupt the chained pipeline reply while it is in flight With unary stages, the default of the lfm3 pipeline gallery entries, the chained pipeline synthesizes the whole reply and sends it as one audio delta right before response.done. The barge-in spec waited for that delta and then rendered the interruption with the TTS, so the first response was already done when the speech started and ended completed instead of cancelled. Real runs on a GPU showed it with both quantizations. For a pipeline, start the interruption at response.created, which comes before the LLM and TTS stages. Render the interruption audio before the first utterance is sent, so nothing sits between the reply and the interruption. The any-to-any shape still waits for the first audio delta, which that backend streams while it generates. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(e2e): accept a number word and its homophone in the spoken-answer check The check that the reply audio says what the transcript says needs a shared word of 4+ letters. A short answer failed it: the model wrote "Four" and the ASR heard "for". Compare numbers in one spoken form, so digits, number words and their homophones match each other. A homophone only counts when the other text has the number itself, so "for" on both sides still shares nothing and the check does not pass on a stop word. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(llama-cpp): ask LFM3-Audio for function calls when a realtime session has tools The default system prompt of a realtime session was "Respond with interleaved text and audio." whatever the tools. Under that prompt LFM3-Audio answers in words ("I'll check the weather ...") and never writes the call. The model card, and the llama.cpp realtime example client, use "Respond with function calls, and interleaved text and audio." when the session has tools, and the model then calls the tool. Pick the default by the tools of the session, and treat the plain default that the LocalAI server sends back as no choice: it follows the tools too, also when they arrive with a session.update before the first audio. A prompt the client wrote is kept as it is. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): transcribe the input of an any-to-any turn between responses The input transcript of a speech-to-speech turn ran at the same time as the reply, on the same model. llama.cpp serves both from one context: the realtime slot wants embeddings on, the chat slot of the transcript wants them off, and each flip makes it reserve its compute buffers again. In a GB10 run that was two reserves of about 175 ms per step, the reply fell to 2.6 tokens per second and the transcript to 380 ms per token, until the transcript was done. When pipeline.transcription is the model of the stream, run the transcript after the response, or before one that is not started yet, and keep responses out while it runs. A separate transcription model still runs in parallel. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): send the input transcript of an any-to-any turn before its reply The previous change moved the same-model transcript after the response. A client that reads until response.done then never saw conversation.item.input_audio_transcription.completed: the transcript was made (the log shows it) but sent after the end of the window. Run it in line with the commit instead. A VAD turn transcribes and then responds. A client response.create already waits for the commit slot, so it starts once the transcript is out. The transcript event now comes before any output of the response of its turn, for a manual commit and for a VAD turn, and never overlaps the response on the llama.cpp context. A transcription model of its own still runs in parallel. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): transcribe the input of an any-to-any model only when asked The stream answers from the audio, so the transcript of the input is for the client only. On the model of the stream it is costly: llama.cpp serves the transcript request and the realtime stream from one context and rebuilds its compute buffers on every step while both are active. On a GB10 that was 26 to 29 reserves of about 175 ms per turn, against 0 to 1 on the llama.cpp server, and the reply ran at a few tokens per second. An any-to-any model no longer fills pipeline.transcription with its own name. Without it the session does not transcribe the input; with another model it does, in parallel with the reply; with the model itself named it does, before the reply. The reply transcript always comes from the stream. The gallery entries set none, so they get the fast path. The mock configs and run-realtime-lfm3.sh name a transcription model, so the real-weights spec still covers the input transcript. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): end a response as cancelled when a barge-in lands during the LLM call The chained pipeline cancels the response context on a barge-in. When that happens while the model is generating, the backend call fails with the context error, and the response ended as failed: an error event and no response.done. A client that waits for the terminal of the interrupted response then waits forever. On a GB10 the q4 model answered before the barge-in arrived, so only the slower q8 model showed it: the spec read events for 30 s and timed out. When the context is cancelled, a failed Predict call or a failed result is a cancel, as the TTS step and the streamed LLM path already treat it. The response ends with response.done{cancelled} and no error. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(e2e): wait for the input transcript that comes from a model of its own An any-to-any model now gets its input transcript from a separate ASR that runs next to the reply. The reply is often over first, so a spec that stops reading at response.done saw no transcript in the VAD shape. Keep reading, up to 30 s, until the transcript arrives. The contract is that it comes, not when. Add a guard spec for what the input transcription is handed: the whole turn, in a WAV that says the rate it was recorded at (the session rate for a client commit, 16 kHz for a VAD turn). Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(e2e): check the errors of Close in the realtime specs golangci-lint (errcheck) flagged the unchecked Close of the websocket connections, the response body and the fake stream. Use the `defer func() { _ = x.Close() }()` form the other e2e specs use. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(e2e): write the mock S2S specs with Ginkgo and drop the env read forbidigo only allows Ginkgo/Gomega specs and no os.Getenv outside the sanctioned places. Convert the mock S2S tests to Describe/It with a suite bootstrap (the package had none), with the same 11 cases. The mock no longer reads MOCK_S2S_FRAMES: only these specs set it, so the reply length is a package variable they change per spec and restore afterwards. Assisted-by: Claude:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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6a11435f12 |
chore: ⬆️ Update vllm-project/vllm cu130 wheel to 0.32.0 (#12760)
* ⬆️ Update vllm-project/vllm cu130 wheel Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(vllm): use Python 3.12 for CUDA 13 wheels Select the existing portable Python 3.12.12 release for the vLLM cublas13 profile before creating its venv. vLLM 0.32 requires Python >=3.11; keep the cu130 wheel indexes and all other profiles unchanged. Assisted-by: nib:gpt-6.1-sol Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- 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> |
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localai-org-maint-botandmudler
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48caa12333 |
chore: ⬆️ Update OpenNMT/CTranslate2 ROCm wheel to v4.8.4 (#12903)
⬆️ Update OpenNMT/CTranslate2 ROCm wheel Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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pos-ei-don
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ac5a0f3947 |
test: cover config-level disable for xhigh/max and the effort-only load warning
- ApplyReasoningEffort keeps an operator's reasoning.disable for high, xhigh and max
- sglang: _configured_budget() feeds the strict-thinking warning; tested with
effort_budgets alone
Signed-off-by: pos-ei-don <1822533+pos-ei-don@users.noreply.github.com>
(cherry picked from commit
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pos-ei-don
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1c125a8897 |
fix(sglang): review follow-ups for effort_budgets
- warn on an effort_budgets entry without a token count (high=)
- update comments: enable_thinking=false still wins over a listed level
- document that precedence in text-generation.md
- cover xhigh/max and the empty-entry warning in the tests
Signed-off-by: pos-ei-don <1822533+pos-ei-don@users.noreply.github.com>
(cherry picked from commit
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pos-ei-don
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073adc50c9 |
feat(sglang): map reasoning_effort to per-level thinking budgets
The Go side already forwards the request's effort as
Metadata["reasoning_effort"], but the sglang backend only read
enable_thinking: every level got the same model-level thinking_budget,
so low/medium/high produced identical reasoning.
- New model option `effort_budgets:low=…;medium=…;high=…` picks the
budget of the request's level; unlisted levels and requests without an
effort keep `thinking_budget`. Parsing reuses _parse_thinking_budget.
- A level listed in effort_budgets turns reasoning on for that request,
so levels the Go side does not know cannot silently disable it.
- ApplyReasoningEffort now treats `xhigh` and `max` as thinking levels.
Before, they left DisableReasoning unset, so with a model default of
"off" asking for more reasoning produced none.
Signed-off-by: pos-ei-don <1822533+pos-ei-don@users.noreply.github.com>
(cherry picked from commit
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pos-ei-don
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5e63cc1ec1 |
test(vllm): isolate streaming parser fixtures
Mock the request constructor so fake parser tests can run on older
vLLM profiles without ParserManager. Document the streaming version
requirement and fallback behavior.
Signed-off-by: pos-ei-don <1822533+pos-ei-don@users.noreply.github.com>
(cherry picked from commit
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pos-ei-don
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651c8da4d6 |
fix(vllm): keep reasoning out of streamed content
With a reasoning parser configured, the streaming path sent every raw
text delta as ChatDelta.content. LocalAI uses the backend's ChatDeltas as
they are, so with thinking enabled the reasoning arrived as
delta.content, and the final chunk then repeated the whole answer and
the reasoning a second time. Non-streaming requests were split correctly.
Use vLLM's composed parser (ParserManager.get_parser, vLLM >= 0.30), the
one vLLM's own OpenAI server streams with: one instance per request,
built with the request's chat_template_kwargs (enable_thinking), fed
each delta via parse_delta. Reasoning, content and tool calls go out in
their own fields as they are produced; the final chunk carries only
token counts and logprobs.
If the parser raises mid-stream, the rest of the output is buffered and
the final chunk sends what the full-text parse has beyond what was
already streamed (suffixes per field and per tool-call index). Where the
full parse disagrees with what went out, nothing is guessed and the
mismatch is logged.
Only active when a reasoning_parser is set; without one, or on a vLLM
without ParserManager, streaming is unchanged.
Signed-off-by: pos-ei-don <1822533+pos-ei-don@users.noreply.github.com>
(cherry picked from commit
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pos-ei-don
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d254ef5408 |
fix(sglang,vllm): don't cap generation at 200 tokens when max_tokens is omitted
Both Python backends start from a fixed default (sglang max_new_tokens=200,
vLLM max_tokens=200) and only replace it when the request carries Tokens.
OpenAI clients that leave max_tokens out (several agent frameworks do) were
silently truncated after 200 tokens, and the response still said
finish_reason "stop": the Go side only reports "length" against a budget
the client requested, so it cannot see a backend-side cap.
Use None instead, which allows generation up to the remaining context.
That is what sglang's own OpenAI server does when max_tokens is omitted
(the scheduler turns None into the remaining request capacity; dropping
the key would select SamplingParams' own default of 128), and vLLM treats
max_tokens=None the same way. A requested max_tokens still wins.
Tests check both cases and that the None default passes each library's own
parameter validation.
Signed-off-by: pos-ei-don <1822533+pos-ei-don@users.noreply.github.com>
(cherry picked from commit
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pos-ei-don
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50ba04681a |
fix(vllm): build SamplingParams once so stop reaches the engine
_build_sampling_params constructed SamplingParams and then assigned the
request values with setattr. protobuf repeated fields (StopPrompts) stay
RepeatedScalarContainer that way, and __post_init__ never sees them:
- vLLM's argument checks assert `isinstance(self.stop, list)` and the
msgpack encoder that ships the params to the engine core cannot
serialise the container, so every request with `stop` failed
(HTTP 500, EngineGenerateError), streaming or not.
- the stop-string hold-back buffer (output_text_buffer_length) was never
set, so the start of a multi-character stop string could be streamed;
seed -1 was not mapped to None.
Collect the values, turn repeated fields into lists and construct
SamplingParams once, as vLLM's OpenAI server does. Values vLLM rejects
(e.g. a penalty out of range, an empty stop string) now fail with vLLM's
validation error instead of being set unchecked.
Signed-off-by: pos-ei-don <1822533+pos-ei-don@users.noreply.github.com>
(cherry picked from commit
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G ManandEttore Di Giacinto
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3934ee8e34 |
fix(mlx-vlm): bump mlx-vlm pin to v0.6.12 (#12818)
mlx-vlm v0.4.4 attaches the image placeholder to the last message that is not system or assistant. After a tool call that is the tool result, which the chat template renders as text, so the next turn fails with "Image features and image tokens do not match: tokens: 0". v0.6.12 keeps the placeholder on the user message that carried the image. The v0.4.4 pin (#9568) worked around the mlx-cuda wheels stopping at 0.31.1. They are at 0.32.4 now, and v0.6.12 (mlx>=0.32.0) resolves with mlx[cuda12] and mlx[cuda13]. v0.7.x would need backend changes: it drops mlx_vlm.tool_parsers, which backend.py imports. Signed-off-by: gman <genx7up@users.noreply.github.com> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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localai-org-maint-botandmudler
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aeade22b94 |
chore: ⬆️ Update vllm-metal (darwin) to v0.32.0 (#12811)
⬆️ Update vllm-project/vllm-metal (darwin) Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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mudler-agentandEttore Di Giacinto
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ae8092e1c6 |
feat(sound): expand Simple prompts with chat (#12772)
* feat(sound): expand simple prompts with chat models Allow callers to select an installed chat model for sound prompt expansion. Validate its JSON before audio generation and preserve explicit instrumental and language controls. Keep backend defaults when expansion is omitted, including native Python ACE-Step sampling. Assisted-by: Codex:gpt-6 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(sound): map expansion by loaded audio family Map neutral expanded fields where the resolved audio family is known. Keep unmarked requests unchanged and reject unsupported expansion before inference. Clear YuE2 and HeartMuLa transcripts to prevent lyric fallback. Assisted-by: Codex:gpt-6.1-sol Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(sound): choose a prompt expansion model Require an explicit Simple choice and remember it separately from chat. Clear unavailable choices instead of silently switching language models. Preserve the choice in history and restore it when editing a past result. Keep Backend default and Advanced requests compatible with existing APIs. Document family support and align the published optional request schema. Verify selection, request fields, persistence and history in the browser. Assisted-by: Codex:gpt-6.1-sol Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(sound): authorize both generation models Check the resolved audio model and explicit expansion model before loading either. Permission lookup failures stop generation. Assisted-by: Codex:gpt-6 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(sound): enforce JSON during prompt expansion Constrain expansion output with the existing JSON grammar before decoding. Require a llama-cpp chat model so unsupported backends cannot ignore it. Keep strict field validation and fail before generating audio. Assisted-by: Codex:gpt-6 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Younes Beriane
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e517470554 |
fix(pocket-tts): honour the language option when loading the model (#12766)
Pocket TTS ships one model per language, but LoadModel called TTSModel.load_model() with no arguments, so the library always fell back to its English model whatever the model config said. Read a `language` option and pass it to load_model, keeping the library default when it is not set. Document the option and add unit tests that mock the loader. Fixes #12759 Assisted-by: Claude:claude-opus-5-5 |
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localai-org-maint-botandmudler
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377e71b704 |
chore: ⬆️ Update OpenNMT/CTranslate2 ROCm wheel to v4.8.3 (#12675)
⬆️ Update OpenNMT/CTranslate2 ROCm wheel Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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localai-org-maint-botandmudler
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bee88623d5 |
chore: ⬆️ Update vllm-metal (darwin) to v0.31.0 (#12676)
⬆️ Update vllm-project/vllm-metal (darwin) Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Ettore Di Giacinto
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8dd91dde66 |
fix(diffusers): keep negative_prompt unless negative embeds are set
The sd_embed branch dropped negative_prompt for every pipeline, which discarded the user's negative prompt for Flux, where no negative embeds are produced. Only drop it when negative_prompt_embeds is present, and move the check to a helper that has unit tests. Assisted-by: Claude Code:claude-sonnet-5-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Austen Dicken
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5e53f5aed5 |
fix(diffusers): don't send negative_prompt when using sd_embed
Signed-off-by: Austen Dicken <cvpcsm@gmail.com>
(cherry picked from commit
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localai-org-maint-botandmudler
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5537f4b1ef |
chore: ⬆️ Update vllm-project/vllm cu130 wheel to 0.31.0 (#12502)
⬆️ Update vllm-project/vllm cu130 wheel Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Stefan Walcz
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985df46d24 |
fix(whisperx): keep the transcript when diarization fails, report real errors (#12427)
AudioTranscription caught every exception and returned an empty TranscriptResult. A failed diarization step therefore discarded a transcript that was already finished: with an HF token that has not accepted the terms of the gated pyannote pipeline, the download fails with 403 and every transcription came back as an empty text with HTTP 200. Diarization now degrades: if it fails, the transcript is returned without speaker labels and the reason is logged. Any other failure aborts the call with INTERNAL instead of pretending success with an empty text. Assisted-by: Claude:claude-opus-5-5 Signed-off-by: Stefan Walcz <stefan.walcz@walcz.de> |
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mudler-agent
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53cd6c5716 |
fix(funasr): select Python 3.12 tokenizers
FunASR left Transformers and Hugging Face Hub unconstrained, which let uv backtrack to tokenizers 0.10.3 without Python 3.12 wheels. Keep Transformers on the supported 4.x range, including the Intel upgrade profile.\n\nAssisted-by: nib:gpt-5.6-sol\nSigned-off-by: Ettore Di Giacinto <mudler@localai.io> |
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61f4f67b75 |
sglang backend: pass through thinking_budget + require_reasoning (#12193)
* sglang backend: pass through thinking_budget + require_reasoning sglang's raw Engine.async_generate() API (which this backend calls directly, bypassing sglang's own OpenAI server) supports a precise, tokenizer-derived reasoning-length budget via sampling_params["custom_params"]["thinking_budget"] plus require_reasoning=True, gated behind --enable-strict-thinking. Neither was reachable through LocalAI: this backend built sampling_params only from a fixed field mapping (temperature, top_p, ...) with no custom_params key, and never passed require_reasoning to async_generate at all. - LoadModel now reads a model-level "thinking_budget" option (same mechanism as the existing tool_parser/reasoning_parser options), and _build_sampling_params adds it as custom_params.thinking_budget on every request when configured. - _new_reasoning_parser already derives, from the rendered prompt, whether the model's chat template pre-opened a reasoning block (Qwen3-style templates append <think> to the prompt instead of letting the model emit it) -- the same signal sglang's own OpenAI server computes from per-template config to decide require_reasoning. This backend has no template manager, so it now returns that signal too and _predict forwards it to async_generate(require_reasoning=...). Verified against production (NVFP4, sm_121, Qwen3.6-35B-A3B) via a raw Engine.async_generate() call bypassing this backend: 301 reasoning tokens against a 300-token budget, clean completion, ~27s. Not yet verified through this backend's own gRPC path end-to-end (no local CUDA/sglang environment available here) -- existing + new unit tests in test.py cover the pure-Python merge/passthrough logic only. Scope note: require_reasoning is derived only from the existing prompt-suffix heuristic, not sglang's full per-template _get_reasoning_from_request decision tree (minimax-m3/hunyuan special cases etc.) -- this backend has no template manager to evaluate that tree against, and the prompt-suffix check is the one heuristic already validated in this file (test_reasoning_parser_forced_when_template_prefills_think_tag). Signed-off-by: pos-ei-don <1822533+pos-ei-don@users.noreply.github.com> * sglang backend: honour a model-level reasoning_default A model YAML can already carry "parameters: reasoning_effort:", but that value only reaches this backend when a *caller* sets it per request (the Go side turns it into Metadata["enable_thinking"]). As a model-level default it is silently dropped: a config reading "reasoning_effort: none" still produces full reasoning on every request, so the config says one thing and the model does another. That gap is expensive in practice. On a self-hosted Qwen3.6-35B-A3B the reasoning phase consumed the entire max_tokens budget before any content was produced - 90% of code completions came back empty at max_tokens=768, and the server log filled with "backend produced only reasoning, retrying". The config looked like reasoning was off the whole time. This adds "reasoning_default:off" (or ":on") on the same model-level options: mechanism as thinking_budget. A per-request value always wins; the default only fills in when the request is silent. Measured on the stack above (sglang 0.5.20, NVFP4, GB10/sm_121) after applying it: default (nothing set) -> 0 chars reasoning, 27 tokens "reasoning_effort": "none" -> 0 chars reasoning, 27 tokens metadata enable_thinking=true -> capped at the 512-token thinking_budget, 541 tokens total, finish_reason stop Tests: three cases added to backend/python/sglang/test.py covering the default, per-request override in both directions, and the unconfigured case (which must leave the template untouched). Signed-off-by: pos-ei-don <1822533+pos-ei-don@users.noreply.github.com> * sglang backend: validate thinking_budget instead of crashing LoadModel Addresses the review on this PR: - `int(thinking_budget)` raised on values like "5000.0" or "abc" and took LoadModel down. The option is now parsed by _parse_thinking_budget(): integral numbers in any spelling are accepted, anything else is ignored with a warning on stderr. - Zero and negative budgets are ignored with a warning instead of being passed to sglang, where they have no defined meaning. Turning reasoning off is what reasoning_default:off is for. - A load-time warning when thinking_budget is set but enable_strict_thinking is not in engine_args, since sglang then ignores the budget silently. - Tests for integral spellings, unset, zero, negative, non-integer and the strict-thinking warning. Signed-off-by: pos-ei-don <1822533+pos-ei-don@users.noreply.github.com> * docs(sglang): explain reasoning options Document the reasoning budget, strict-thinking requirement, and precedence of request metadata over the model-level default. Also note that the budget has to stay well below max_tokens (otherwise it never triggers and the reply can end up empty), and that POST /models/reload or a backend-only restart does not pick up changed options; LocalAI itself has to be restarted. Assisted-by: Codex:GPT-6 Signed-off-by: pos-ei-don <1822533+pos-ei-don@users.noreply.github.com> * docs(sglang): clarify configuration reloads Distinguish rereading model configuration from updating a running backend. Keep the full LocalAI restart recommendation for changed reasoning options. Assisted-by: Codex:GPT-6 * sglang backend: only pass require_reasoning when sglang supports it Engine.async_generate() gained the require_reasoning keyword in sglang 0.5.13 and takes no **kwargs. The CPU profile builds v0.5.11 from source and the other profiles only set a >=0.5.11 floor, so passing the keyword unconditionally made every request fail with TypeError. Detect support once at import time, as the file already does for sampling_seed. enable_strict_thinking first appears in sglang 0.5.12; fix the comment. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5-5 [Claude Code] --------- Signed-off-by: pos-ei-don <1822533+pos-ei-don@users.noreply.github.com> Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: localai-org-maint-bot <localai-org-maint-bot@users.noreply.github.com> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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dependabot[bot]
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eb97d5f549 |
chore(deps): bump grpcio from 1.83.1 to 1.84.0 in /backend/python/coqui (#12106)
Bumps [grpcio](https://github.com/grpc/grpc) from 1.83.1 to 1.84.0. - [Release notes](https://github.com/grpc/grpc/releases) - [Commits](https://github.com/grpc/grpc/compare/v1.83.1...v1.84.0) --- updated-dependencies: - dependency-name: grpcio dependency-version: 1.84.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> |
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dependabot[bot]
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e6b2309b3f |
chore(deps): bump grpcio from 1.83.0 to 1.84.0 in /backend/python/transformers (#12102)
chore(deps): bump grpcio in /backend/python/transformers Bumps [grpcio](https://github.com/grpc/grpc) from 1.83.0 to 1.84.0. - [Release notes](https://github.com/grpc/grpc/releases) - [Commits](https://github.com/grpc/grpc/compare/v1.83.0...v1.84.0) --- updated-dependencies: - dependency-name: grpcio dependency-version: 1.84.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> |
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dependabot[bot]
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de203c2fb5 |
chore(deps): update transformers requirement from >=5.15.1 to >=5.17.0 in /backend/python/transformers (#12105)
chore(deps): update transformers requirement Updates the requirements on [transformers](https://github.com/huggingface/transformers) to permit the latest version. - [Release notes](https://github.com/huggingface/transformers/releases) - [Commits](https://github.com/huggingface/transformers/compare/v5.15.1...v5.17.0) --- updated-dependencies: - dependency-name: transformers dependency-version: 5.17.0 dependency-type: direct:production ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> |
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dependabot[bot]
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1cacecc460 |
chore(deps): update numpy requirement from >=2.5.2 to >=2.5.3 in /backend/python/transformers (#12244)
chore(deps): update numpy requirement in /backend/python/transformers Updates the requirements on [numpy](https://github.com/numpy/numpy) to permit the latest version. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v2.5.2...v2.5.3) --- updated-dependencies: - dependency-name: numpy dependency-version: 2.5.3 dependency-type: direct:production ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> |
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dependabot[bot]
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13a01e657a |
chore(deps): bump sentence-transformers from 5.7.0 to 6.1.0 in /backend/python/transformers (#12245)
chore(deps): bump sentence-transformers in /backend/python/transformers Bumps [sentence-transformers](https://github.com/huggingface/sentence-transformers) from 5.7.0 to 6.1.0. - [Release notes](https://github.com/huggingface/sentence-transformers/releases) - [Commits](https://github.com/huggingface/sentence-transformers/compare/v5.7.0...v6.1.0) --- updated-dependencies: - dependency-name: sentence-transformers dependency-version: 6.1.0 dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> |
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localai-org-maint-botandmudler
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3a63699f5c |
chore: ⬆️ Update vllm-project/vllm cu130 wheel to 0.30.0 (#12214)
⬆️ Update vllm-project/vllm cu130 wheel Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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localai-org-maint-botandmudler
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c6a574ef1b |
chore: ⬆️ Update vllm-metal (darwin) to v0.30.0 (#12225)
⬆️ Update vllm-project/vllm-metal (darwin) Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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X
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c5216b36af |
fix(vllm-omni): remove invalid syntax in test.py (#12135)
Signed-off-by: hendrixx-cnc <tjhendrx@icloud.com> |
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pos-ei-don
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0b09cff673 |
fix(sglang): support msgspec-based ServerArgs (sglang >= 0.5.20) (#12155)
sglang 0.5.20 moved its config tier from dataclasses to msgspec.Struct (sgl-project/sglang#38753). _apply_engine_args validates engine_args keys via dataclasses.fields(ServerArgs), which raises TypeError there. That call runs on every LoadModel, so no model loads at all on the sglang backend once sglang >= 0.5.20 is installed, and the error surfaces as a generic "Unexpected <class 'TypeError'>" that does not name the cause. Introspect both shapes: msgspec structs carry their field names in __struct_fields__, so key validation and the close-match suggestion keep working, and older dataclass-based sglang stays supported. Adds a test that pins the msgspec path with a stand-in, so it is covered regardless of which sglang version is installed. Signed-off-by: pos-ei-don <1822533+pos-ei-don@users.noreply.github.com> |
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mudler-agentandEttore Di Giacinto
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6184a258b3 |
fix: preserve vllm-omni imports after backend relocation (#12137)
Use a regular (non-editable) pip/uv install so the package lands in the venv site-packages. An editable finder records the builder source path, which breaks after the backend is copied out of the image (#9162). Adds a regression test (scripts/build/vllm-omni-install_test.sh) that verifies imports survive relocation with a regular install and fail with an editable install. Supersedes #12040 (DCO not signed by contributor). Assisted-by: MAKI:regolo/glm5.2 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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dependabot[bot]
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eaf04c8b6b |
chore(deps): bump grpcio from 1.83.1 to 1.84.0 in /backend/python/rerankers (#12104)
chore(deps): bump grpcio in /backend/python/rerankers Bumps [grpcio](https://github.com/grpc/grpc) from 1.83.1 to 1.84.0. - [Release notes](https://github.com/grpc/grpc/releases) - [Commits](https://github.com/grpc/grpc/compare/v1.83.1...v1.84.0) --- updated-dependencies: - dependency-name: grpcio dependency-version: 1.84.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> |
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dependabot[bot]
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727360f3da |
chore(deps): bump grpcio from 1.83.1 to 1.84.0 in /backend/python/common/template (#12101)
chore(deps): bump grpcio in /backend/python/common/template Bumps [grpcio](https://github.com/grpc/grpc) from 1.83.1 to 1.84.0. - [Release notes](https://github.com/grpc/grpc/releases) - [Commits](https://github.com/grpc/grpc/compare/v1.83.1...v1.84.0) --- updated-dependencies: - dependency-name: grpcio dependency-version: 1.84.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> |
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dependabot[bot]
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012bc796f2 |
chore(deps): bump grpcio from 1.83.1 to 1.84.0 in /backend/python/vllm (#12099)
Bumps [grpcio](https://github.com/grpc/grpc) from 1.83.1 to 1.84.0. - [Release notes](https://github.com/grpc/grpc/releases) - [Commits](https://github.com/grpc/grpc/compare/v1.83.1...v1.84.0) --- updated-dependencies: - dependency-name: grpcio dependency-version: 1.84.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> |
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dependabot[bot]
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7821dfcd5e |
chore(deps): bump protobuf from 7.35.0 to 7.36.1 in /backend/python/transformers (#12098)
chore(deps): bump protobuf in /backend/python/transformers Bumps [protobuf](https://github.com/protocolbuffers/protobuf) from 7.35.0 to 7.36.1. - [Release notes](https://github.com/protocolbuffers/protobuf/releases) - [Commits](https://github.com/protocolbuffers/protobuf/commits) --- updated-dependencies: - dependency-name: protobuf dependency-version: 7.36.1 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> |
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localai-org-maint-botandEttore Di Giacinto
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5a24c8a2a2 |
fix(qwen-tts): restore CUDA 13 SDPA fallback (#12067)
The CUDA 13 profile resolves PyTorch 2.14. Its headers cannot build the current FlashAttention source release under C++17, so the full backend matrix cannot publish the image. Leave FlashAttention optional on CUDA 13. The backend already retries model loading with SDPA when FlashAttention is unavailable. Assisted-by: Codex:gpt-5 Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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localai-org-maint-botandmudler
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d463316caf |
chore: ⬆️ Update vllm-metal (darwin) to v0.29.0 (#12007)
⬆️ Update vllm-project/vllm-metal (darwin) Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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localai-org-maint-botandmudler
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e11f363a50 |
chore: ⬆️ Update OpenNMT/CTranslate2 ROCm wheel to v4.8.2 (#12004)
⬆️ Update OpenNMT/CTranslate2 ROCm wheel Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Ettore Di Giacinto
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64628da9dd |
chore(deps): bump llvmlite 0.43→0.49, numba 0.60→0.67, numpy>=2.5.2 in transformers
Merge dependabot PRs #11639, #11640, #11641 together: - #11639: llvmlite 0.43.0 → 0.49.0 (alone fails: numba 0.60 requires <0.44) - #11640: numba 0.60.0 → 0.67.0 (alone fails: requires llvmlite >=0.49) - #11641: numpy >=2.0.0 → >=2.5.2 (was blocked by scipy 1.15.1 <2.5, now resolved since scipy was bumped to 1.18.0 in #11399) Also bump outetts backend to match (same numba/llvmlite conflict). Signed-off-by: localai-org-maint-bot <bot@localai.io> Assisted-by: MAKI:GLM5.2 [gh] |
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Ettore Di Giacinto
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1dfa25e681 |
fix(qwen-tts): limit CUDA compiler threads
The CUDA 13 FlashAttention build still exhausts hosted-runner memory with a single ninja worker because nvcc can compile multiple threads internally. Limit nvcc to one thread for that profile and guard the setting in the backend test script. Assisted-by: Codex:gpt-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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dependabot[bot]
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40c4d5990e |
chore(deps): bump scipy in /backend/python/transformers
Bumps [scipy](https://github.com/scipy/scipy) from 1.15.1 to 1.18.0. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.15.1...v1.18.0) --- updated-dependencies: - dependency-name: scipy dependency-version: 1.18.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> |
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localai-org-maint-bot
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7dc617c7c1 |
fix(transformers): use Python 3.12
SciPy 1.18 requires Python 3.12 or newer. Keep this backend on a
compatible portable Python for Linux and macOS builds.
Assisted-by: Codex:gpt-5.6 [Codex]
(cherry picked from commit
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dependabot[bot]
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1f3d89fc4a |
chore(deps): update transformers requirement
Updates the requirements on [transformers](https://github.com/huggingface/transformers) to permit the latest version. - [Release notes](https://github.com/huggingface/transformers/releases) - [Commits](https://github.com/huggingface/transformers/compare/v5.15.0...v5.15.1) --- updated-dependencies: - dependency-name: transformers dependency-version: 5.15.1 dependency-type: direct:production ... Signed-off-by: dependabot[bot] <support@github.com> |
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Ettore Di Giacintoandxingyifeng
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df70ff311d |
feat: add FunASR speech recognition backend (#10090)
Adds FunASR/SenseVoice as a Python backend for speech-to-text with support for CPU, CUDA 12/13, ROCm, Intel SYCL, L4T, and Apple MPS. Co-authored-by: xingyifeng <xingyifeng@users.noreply.github.com> |
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Ettore Di Giacinto
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db09452d54 |
feat(tts): support multi-reference personalities
Saved profiles previously resolved to one audio path and transcript, so cloning backends could not use several examples of one personality. Store ordered audio and transcript pairs while preserving the legacy first-reference fields. Fish Speech and audio.cpp receive all pairs, including on distributed workers. Other backends retain their single-reference behavior. Assisted-by: Codex:gpt-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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cce7a9e441 |
fix(fish-speech): resolve relocated source
The editable install records the backend build path, which does not exist after LocalAI relocates the packaged backend. Add the runtime source directory to PYTHONPATH so inference modules remain importable. Assisted-by: Codex:gpt-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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a15780858e |
feat(diffusers): add AudioLDM2 generation
Expose diffusers audio pipelines through the existing sound-generation RPC. AudioLDM2 can now return PCM WAV output from the model gallery without a separate backend. Assisted-by: Codex:gpt-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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f884d4b456 |
fix(mlx-video): use supported Python on Darwin
The pinned mlx-video package requires Python 3.11 or newer, while an empty PYTHON_VERSION selected the backend helper default of 3.10. Pin the available 3.11.13 portable runtime for the Darwin package build. Assisted-by: Codex:gpt-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |