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195b91026077425da44510235b488bfbb863bef3
1269 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
LocalAI [bot]
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195b910260 |
chore: ⬆️ Update leejet/stable-diffusion.cpp to 0b8296915c4094090cff6bd2e09a5e98288c3c7d (#9827)
⬆️ Update leejet/stable-diffusion.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> |
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LocalAI [bot]
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7bd1693ad0 |
chore: ⬆️ Update ikawrakow/ik_llama.cpp to 0fcffdb64d21e57f0778f342415754156e01adfa (#9828)
⬆️ Update ikawrakow/ik_llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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b5ac3a7373 |
chore: ⬆️ Update ggml-org/whisper.cpp to 46ca43d6399fdeada1b49fb2126ba373bd9ebc38 (#9829)
⬆️ Update ggml-org/whisper.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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53de474ef5 |
chore: ⬆️ Update antirez/ds4 to 04b6fda2be395094cbf2d20d921e7a705a4166ef (#9830)
⬆️ Update antirez/ds4 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 [bot]
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6e1dbae256 |
feat(llama-cpp): expose 12 missing common_params via options[] (#9814)
The llama.cpp backend already accepts a free-form options: array in the
model config that maps to common_params fields, but a coverage audit
against upstream pin 7f3f843c flagged 12 user-visible knobs that were
neither set via the typed proto fields nor reachable via options:.
Wire them up under the existing if/else chain in params_parse, before
the speculative section. Each new option follows the file's prevailing
patterns (try/catch around numeric parses, the same true/1/yes/on bool
form used elsewhere, hardware_concurrency() fallback for thread counts,
mirror of draft_override_tensor for override_tensor).
Top-level / batching / IO:
- n_ubatch (alias ubatch) -- physical batch size; was previously
force-aliased to n_batch at line 482, blocking embedding/rerank
workloads that need independent control
- threads_batch (alias n_threads_batch) -- main-model batch threads;
mirrors the existing draft_threads_batch
- direct_io (alias use_direct_io) -- O_DIRECT model loads
- verbosity -- llama.cpp log threshold (line 479 had this commented
out)
- override_tensor (alias tensor_buft_overrides) -- per-tensor buffer
overrides for the main model; mirrors draft_override_tensor
Embedding / multimodal:
- pooling_type (alias pooling) -- mean/cls/last/rank/none; previously
only auto-flipped to RANK for rerankers
- embd_normalize (alias embedding_normalize) -- and the embedding
handler now reads params_base.embd_normalize instead of a hardcoded
2 at the previous embd_normalize literal in Embedding()
- mmproj_use_gpu (alias mmproj_offload) -- mmproj on CPU vs GPU
- image_min_tokens / image_max_tokens -- per-image vision token budget
Reasoning surface (the audit-focus three; LocalAI's existing
ReasoningConfig.DisableReasoning only feeds the per-request
chat_template_kwargs.enable_thinking and does not touch any of these):
- reasoning_format -- none/auto/deepseek/deepseek-legacy parser
- enable_reasoning (alias reasoning_budget) -- -1/0/>0 thinking budget
- prefill_assistant -- trailing-assistant-message prefill toggle
All 14 referenced fields exist on both the upstream pin and the
turboquant fork's common.h, so no LOCALAI_LEGACY_LLAMA_CPP_SPEC guard
is needed.
Docs: extend model-configuration.md with new "Reasoning Models",
"Multimodal Backend Options", "Embedding & Reranking Backend Options",
and "Other Backend Tuning Options" subsections; also refresh the
Speculative Type Values table to show the new dash-separated canonical
names alongside the underscore aliases LocalAI still accepts.
Assisted-by: claude-code:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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LocalAI [bot]
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53bdb18d10 |
chore: ⬆️ Update ggml-org/llama.cpp to 7f3f843c31cd32dc4adc10b393342dfee071c332 (#9809)
* ⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(llama-cpp): adapt to upstream COMMON_SPECULATIVE_TYPE_DRAFT rename ggml-org/llama.cpp#22964 ("spec: update CLI arguments for better consistency") renamed the speculative type enum values: COMMON_SPECULATIVE_TYPE_DRAFT -> COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE COMMON_SPECULATIVE_TYPE_EAGLE3 -> COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3 and the registered name strings flipped from underscore- to dash- separated form (e.g. ngram_simple -> ngram-simple), with the bare draft/eagle3 aliases replaced by draft-simple/draft-eagle3. This broke the build with the new LLAMA_VERSION on every variant (vulkan/arm64, darwin and likely all the rest) at grpc-server.cpp:461. Update the upstream branch of the speculative-type fallback to use the new identifier (the LOCALAI_LEGACY_LLAMA_CPP_SPEC fork branch keeps the old name), and normalize spec_type option tokens before passing them to common_speculative_types_from_names so existing model configs that say spec_type:draft / spec_type:ngram_simple keep working. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: claude-code:claude-opus-4-7 --------- 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 [bot]
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0353d3bd77 |
chore: ⬆️ Update ggml-org/whisper.cpp to 3e9b7d0fef3528ee2208da3cdb873a2c53d2ae2f (#9808)
⬆️ Update ggml-org/whisper.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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ec49995190 |
chore: ⬆️ Update ikawrakow/ik_llama.cpp to 949bb8f1d660fc1264c137a6f3dbd619375f6134 (#9807)
⬆️ Update ikawrakow/ik_llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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4430fae779 |
chore: ⬆️ Update antirez/ds4 to 0cba357ca1bc0e7510421cc26888e420ea942123 (#9806)
⬆️ Update antirez/ds4 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 [bot]
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ddbbdf45b9 |
chore: ⬆️ Update TheTom/llama-cpp-turboquant to 5aeb2fdbe26cd4c534c6fa15de73cb5749bd0403 (#9740)
⬆️ Update TheTom/llama-cpp-turboquant 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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Richard Palethorpe
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0245b33eab |
feat(realtime): Add Liquid Audio s2s model and assistant mode on talk page (#9801)
* feat(liquid-audio): add LFM2.5-Audio any-to-any backend + realtime_audio usecase
Wires LiquidAI's LFM2.5-Audio-1.5B as a self-contained Realtime API model:
single engine handles VAD, transcription, LLM, and TTS in one bidirectional
stream — drop-in alternative to a VAD+STT+LLM+TTS pipeline.
Backend
- backend/python/liquid-audio/ — new Python gRPC backend wrapping the
`liquid-audio` package. Modes: chat / asr / tts / s2s, voice presets,
Load/Predict/PredictStream/AudioTranscription/TTS/VAD/AudioToAudioStream/
Free and StartFineTune/FineTuneProgress/StopFineTune. Runtime monkey-patch
on `liquid_audio.utils.snapshot_download` so absolute local paths from
LocalAI's gallery resolve without a HF round-trip. soundfile in place of
torchaudio.load/save (torchcodec drags NVIDIA NPP we don't bundle).
- backend/backend.proto + pkg/grpc/{backend,client,server,base,embed,
interface}.go — new AudioToAudioStream RPC mirroring AudioTransformStream
(config/frame/control oneof in; typed event+pcm+meta out).
- core/services/nodes/{health_mock,inflight}_test.go — add stubs for the
new RPC to the test fakes.
Config + capabilities
- core/config/backend_capabilities.go — UsecaseRealtimeAudio, MethodAudio
ToAudioStream, UsecaseInfoMap entry, liquid-audio BackendCapability row.
- core/config/model_config.go — FLAG_REALTIME_AUDIO bitmask, ModalityGroups
membership in both speech-input and audio-output groups so a lone flag
still reads as multimodal, GetAllModelConfigUsecases entry, GuessUsecases
branch.
Realtime endpoint
- core/http/endpoints/openai/realtime.go — extract prepareRealtimeConfig()
so the gate is unit-testable; accept realtime_audio models and self-fill
empty pipeline slots with the model's own name (user-pinned slots win).
- core/http/endpoints/openai/realtime_gate_test.go — six specs covering nil
cfg, empty pipeline, legacy pipeline, self-contained realtime_audio,
user-pinned VAD slot, and partial legacy pipeline.
UI + endpoints
- core/http/routes/ui.go — /api/pipeline-models accepts either a legacy
VAD+STT+LLM+TTS pipeline or a realtime_audio model; surfaces a
self_contained flag so the Talk page can collapse the four cards.
- core/http/routes/ui_api.go — realtime_audio in usecaseFilters.
- core/http/routes/ui_pipeline_models_test.go — covers both code paths.
- core/http/react-ui/src/pages/Talk.jsx — self-contained badge instead of
the four-slot grid; rename Edit Pipeline → Edit Model Config; less
pipeline-specific wording.
- core/http/react-ui/src/pages/Models.jsx + locales/en/models.json — new
realtime_audio filter button + i18n.
- core/http/react-ui/src/utils/capabilities.js — CAP_REALTIME_AUDIO.
- core/http/react-ui/src/pages/FineTune.jsx — voice + validation-dataset
fields, surfaced when backend === liquid-audio, plumbed via
extra_options on submit/export/import.
Gallery + importer
- gallery/liquid-audio.yaml — config template with known_usecases:
[realtime_audio, chat, tts, transcript, vad].
- gallery/index.yaml — four model entries (realtime/chat/asr/tts) keyed by
mode option. Fixed pre-existing `transcribe` typo on the asr entry
(loader silently dropped the unknown string → entry never surfaced as a
transcript model).
- gallery/lfm.yaml — function block for the LFM2 Pythonic tool-call format
`<|tool_call_start|>[name(k="v")]<|tool_call_end|>` matching
common_chat_params_init_lfm2 in vendored llama.cpp.
- core/gallery/importers/{liquid-audio,liquid-audio_test}.go — detector
matches LFM2-Audio HF repos (excludes -gguf mirrors); mode/voice
preferences plumbed through to options.
- core/gallery/importers/importers.go — register LiquidAudioImporter
before LlamaCPPImporter.
- pkg/functions/parse_lfm2_test.go — seven specs for the response/argument
regex pair on the LFM2 pythonic format.
Build matrix
- .github/backend-matrix.yml — seven liquid-audio targets (cuda12, cuda13,
l4t-cuda-13, hipblas, intel, cpu amd64, cpu arm64). Jetpack r36 cuda-12
is skipped (Ubuntu 22.04 / Python 3.10 incompatible with liquid-audio's
3.12 floor).
- backend/index.yaml — anchor + 13 image entries.
- Makefile — .NOTPARALLEL, prepare-test-extra, test-extra,
docker-build-liquid-audio.
Docs
- .agents/plans/liquid-audio-integration.md — phased plan; PR-D (real
any-to-any wiring via AudioToAudioStream), PR-E (mid-audio tool-call
detector), PR-G (GGUF entries once upstream llama.cpp PR #18641 lands)
remain.
- .agents/api-endpoints-and-auth.md — expand the capability-surface
checklist with every place a new FLAG_* needs to be registered.
Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(realtime): function calling + history cap for any-to-any models
Three pieces, all on the realtime_audio path that just landed:
1. liquid-audio backend (backend/python/liquid-audio/backend.py):
- _build_chat_state grows a `tools_prelude` arg.
- new _render_tools_prelude parses request.Tools (the OpenAI Chat
Completions function array realtime.go already serialises) and
emits an LFM2 `<|tool_list_start|>…<|tool_list_end|>` system turn
ahead of the user history. Mirrors gallery/lfm.yaml's `function:`
template so the model sees the same prompt shape whether served
via llama-cpp or here. Without this the backend silently dropped
tools — function calling was wired end-to-end on the Go side but
the model never saw a tool list.
2. Realtime history cap (core/http/endpoints/openai/realtime.go):
- Session grows MaxHistoryItems int; default picked by new
defaultMaxHistoryItems(cfg) — 6 for realtime_audio models (LFM2.5
1.5B degrades quickly past a handful of turns), 0/unlimited for
legacy pipelines composing larger LLMs.
- triggerResponse runs conv.Items through trimRealtimeItems before
building conversationHistory. Helper walks the cut left if it
would orphan a function_call_output, so tool result + call pairs
stay intact.
- realtime_gate_test.go: specs for defaultMaxHistoryItems and
trimRealtimeItems (zero cap, under cap, over cap, tool-call pair
preservation).
3. Talk page (core/http/react-ui/src/pages/Talk.jsx):
- Reuses the chat page's MCP plumbing — useMCPClient hook,
ClientMCPDropdown component, same auto-connect/disconnect effect
pattern. No bespoke tool registry, no new REST endpoints; tools
come from whichever MCP servers the user toggles on, exactly as
on the chat page.
- sendSessionUpdate now passes session.tools=getToolsForLLM(); the
update re-fires when the active server set changes mid-session.
- New response.function_call_arguments.done handler executes via
the hook's executeTool (which round-trips through the MCP client
SDK), then replies with conversation.item.create
{type:function_call_output} + response.create so the model
completes its turn with the tool output. Mirrors chat's
client-side agentic loop, translated to the realtime wire shape.
UI changes require a LocalAI image rebuild (Dockerfile:308-313 bakes
react-ui/dist into the runtime image). Backend.py changes can be
swapped live in /backends/<id>/backend.py + /backend/shutdown.
Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(realtime): LocalAI Assistant ("Manage Mode") for the Talk page
Mirrors the chat-page metadata.localai_assistant flow so users can ask the
realtime model what's loaded / installed / configured. Tools are run
server-side via the same in-process MCP holder that powers the chat
modality — no transport switch, no proxy, no new wire protocol.
Wire:
- core/http/endpoints/openai/realtime.go:
- RealtimeSessionOptions{LocalAIAssistant,IsAdmin}; isCurrentUserAdmin
helper mirrors chat.go's requireAssistantAccess (no-op when auth
disabled, else requires auth.RoleAdmin).
- Session grows AssistantExecutor mcpTools.ToolExecutor.
- runRealtimeSession, when opts.LocalAIAssistant is set: gate on admin,
fail closed if DisableLocalAIAssistant or the holder has no tools,
DiscoverTools and inject into session.Tools, prepend
holder.SystemPrompt() to instructions.
- Tool-call dispatch loop: when AssistantExecutor.IsTool(name), run
ExecuteTool inproc, append a FunctionCallOutput to conv.Items, skip
the function_call_arguments client emit (the client can't execute
these — it doesn't know about them). After the loop, if any
assistant tool ran, trigger another response so the model speaks the
result. Mirrors chat's agentic loop, driven server-side rather than
via client round-trip.
- core/http/endpoints/openai/realtime_webrtc.go: RealtimeCallRequest
gains `localai_assistant` (JSON omitempty). Handshake calls
isCurrentUserAdmin and builds RealtimeSessionOptions.
- core/http/react-ui/src/pages/Talk.jsx: admin-only "Manage Mode"
checkbox under the Tools dropdown; passes localai_assistant: true to
realtimeApi.call's body, captured in the connect callback's deps.
Mirroring chat's pattern means the in-process MCP tools surface "just
works" for the Talk page without exposing a Streamable-HTTP MCP endpoint
(which was the alternative). Clients with their own MCP servers can
still use the existing ClientMCPDropdown path in parallel; the realtime
handler distinguishes them by AssistantExecutor.IsTool() at dispatch
time.
Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(realtime): render Manage Mode tool calls in the Talk transcript
Previously the realtime endpoint only emitted response.output_item.added
for the FunctionCall item, and Talk.jsx's switch ignored the event — so
server-side tool runs were invisible in the UI. The model would speak
the result but the user had no way to see what tool was actually
called.
realtime.go: after executing an assistant tool inproc, emit a second
output_item.added/.done pair for the FunctionCallOutput item. Mirrors
the way the chat page displays tool_call + tool_result blocks.
Talk.jsx: handle both response.output_item.added and .done. Render
FunctionCall (with arguments) and FunctionCallOutput (pretty-printed
JSON when possible) as two transcript entries — `tool_call` with the
wrench icon, `tool_result` with the clipboard icon, both in mono-space
secondary-colour. Resets streamingRef after the result so the next
assistant text delta starts a fresh transcript entry instead of
appending to the previous turn.
Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* refactor(realtime): bound the Manage Mode tool-loop + preserve assistant tools
Fallout from a review pass on the Manage Mode patches:
- Bound the server-side agentic loop. triggerResponse used to recurse on
executedAssistantTool with no cap — a model that kept calling tools
would blow the goroutine stack. New maxAssistantToolTurns = 10 (mirrors
useChat.js's maxToolTurns). Public triggerResponse is now a thin shim
over triggerResponseAtTurn(toolTurn int); recursion increments the
counter and stops at the cap with an xlog.Warn.
- Preserve Manage Mode tools across client session.update. The handler
used to blindly overwrite session.Tools, so toggling a client MCP
server mid-session silently wiped the in-process admin tools. Session
now caches the original AssistantTools slice at session creation and
the session.update handler merges them back in (client names win on
collision — the client is explicit).
- strconv.ParseBool for the localai_assistant query param instead of
hand-rolled "1" || "true". Mirrors LocalAIAssistantFromMetadata.
- Talk.jsx: render both tool_call and tool_result on
response.output_item.done instead of splitting them across .added and
.done. The server's event pairing (added → done) stays correct; the
UI just doesn't need to inspect both phases of the same item. One
switch case instead of two, no behavioural change.
Out of scope (noted for follow-ups): extract a shared assistant-tools
helper between chat.go and realtime.go (duplication is small enough
that two parallel implementations stay readable for now), and an i18n
key for the Manage Mode helper text (Talk.jsx doesn't use i18n
anywhere else yet).
Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* ci(test-extra): wire liquid-audio backend smoke test
The backend ships test.py + a `make test` target and is listed in
backend-matrix.yml, so scripts/changed-backends.js already writes a
`liquid-audio=true|false` output when files under backend/python/liquid-audio/
change. The workflow just wasn't reading it.
- Expose the `liquid-audio` output on the detect-changes job
- Add a tests-liquid-audio job that runs `make` + `make test` in
backend/python/liquid-audio, gated on the per-backend detect flag
The smoke covers Health() and LoadModel(mode:finetune); fine-tune mode
short-circuits before any HuggingFace download (backend.py:192), so the
job needs neither weights nor a GPU. The full-inference path remains
gated on LIQUID_AUDIO_MODEL_ID, which CI doesn't set.
The four new Go test files (core/gallery/importers/liquid-audio_test.go,
core/http/endpoints/openai/realtime_gate_test.go,
core/http/routes/ui_pipeline_models_test.go, pkg/functions/parse_lfm2_test.go)
are already picked up by the existing test.yml workflow via `make test` →
`ginkgo -r ./pkg/... ./core/...`; their packages all carry RunSpecs entries.
Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
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LocalAI [bot]
|
a645c1f4aa |
chore: ⬆️ Update ggml-org/llama.cpp to a9883db8ee021cf16783016a60996d41820b5195 (#9796)
⬆️ 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> |
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LocalAI [bot]
|
957619af53 |
chore: ⬆️ Update ikawrakow/ik_llama.cpp to f9a93c37e2fc021760c3c1aa99cf74c73b7591a7 (#9795)
⬆️ Update ikawrakow/ik_llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
|
0b81e36504 |
chore: ⬆️ Update antirez/ds4 to f8b4ed635d559b3a5b44bf2df6a77e21b3e9178f (#9794)
⬆️ Update antirez/ds4 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 [bot]
|
602866a9d8 |
chore: ⬆️ Update ggml-org/whisper.cpp to 338cce1e58133261753243802a0e7a430118866d (#9793)
⬆️ Update ggml-org/whisper.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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bc4cd3dd85 |
feat(llama-cpp): bump to 1ec7ba0c, adapt grpc-server, expose new spec-decoding options (#9765)
* chore(llama.cpp): bump to 1ec7ba0c14f33f17e980daeeda5f35b225d41994
Picks up the upstream `spec : parallel drafting support` change
(ggml-org/llama.cpp#22838) which reshapes the speculative-decoding API
and `server_context_impl`.
Adapt the grpc-server wrapper accordingly:
* `common_params_speculative::type` (single enum) became `types`
(`std::vector<common_speculative_type>`). Update both the
"default to draft when a draft model is set" branch and the
`spec_type`/`speculative_type` option parser. The parser now also
tolerates comma-separated lists, mirroring the upstream
`common_speculative_types_from_names` semantics.
* `common_params_speculative_draft::n_ctx` is gone (draft now shares
the target context size). Keep the `draft_ctx_size` option name for
backward compatibility and ignore the value rather than failing.
* `server_context_impl::model` was renamed to `model_tgt`; update the
two reranker / model-metadata call sites.
Replaces #9763. Builds cleanly under the linux/amd64 cpu-llama-cpp
target locally.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(llama-cpp): expose new speculative-decoding option keys
Upstream `spec : parallel drafting support` (ggml-org/llama.cpp#22838)
adds the `ngram_mod`, `ngram_map_k`, and `ngram_map_k4v` speculative
families and beefs up the draft-model knobs. The previous bump only
adapted the API; this exposes the new fields through the grpc-server
options dictionary so model configs can drive them.
New `options:` keys (all under `backend: llama-cpp`):
ngram_mod (`ngram_mod` type):
spec_ngram_mod_n_min / spec_ngram_mod_n_max / spec_ngram_mod_n_match
ngram_map_k (`ngram_map_k` type):
spec_ngram_map_k_size_n / spec_ngram_map_k_size_m / spec_ngram_map_k_min_hits
ngram_map_k4v (`ngram_map_k4v` type):
spec_ngram_map_k4v_size_n / spec_ngram_map_k4v_size_m /
spec_ngram_map_k4v_min_hits
ngram lookup caches (`ngram_cache` type):
spec_lookup_cache_static / lookup_cache_static
spec_lookup_cache_dynamic / lookup_cache_dynamic
Draft-model tuning (active when `spec_type` is `draft`):
draft_cache_type_k / spec_draft_cache_type_k
draft_cache_type_v / spec_draft_cache_type_v
draft_threads / spec_draft_threads
draft_threads_batch / spec_draft_threads_batch
draft_cpu_moe / spec_draft_cpu_moe (bool flag)
draft_n_cpu_moe / spec_draft_n_cpu_moe (first N MoE layers on CPU)
draft_override_tensor / spec_draft_override_tensor
(comma-separated <tensor regex>=<buffer type>; re-implements upstream's
static parse_tensor_buffer_overrides since it isn't exported)
`spec_type` already accepted comma-separated lists after the previous
commit, matching upstream's `common_speculative_types_from_names`.
Docs: refresh `docs/content/advanced/model-configuration.md` with
per-family tables and a note about multi-type chaining.
Builds locally with `make docker-build-llama-cpp` (linux/amd64
cpu-llama-cpp AVX variant).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(turboquant): bridge new llama.cpp spec API to the legacy fork layout
The previous commits in this series adapted backend/cpp/llama-cpp/grpc-server.cpp
to the post-#22838 (parallel drafting) llama.cpp API. The turboquant build
reuses the same grpc-server.cpp through backend/cpp/turboquant/Makefile,
which copies it into turboquant-<flavor>-build/ and runs patch-grpc-server.sh
on the copy. The fork branched before the API refactor, so it errors out on:
* `ctx_server.impl->model_tgt` (fork still has `model`)
* `params.speculative.{ngram_mod,ngram_map_k,ngram_map_k4v,ngram_cache}.*`
(none of these sub-structs exist in the fork)
* `params.speculative.draft.{cache_type_k/v, cpuparams[, _batch].n_threads,
tensor_buft_overrides}` (fork uses the pre-#22397 flat layout)
* `params.speculative.types` vector / `common_speculative_types_from_names`
(fork has a scalar `type` and only the singular helper)
Approach:
1. backend/cpp/llama-cpp/grpc-server.cpp: introduce a single feature switch
`LOCALAI_LEGACY_LLAMA_CPP_SPEC`. When defined, the two `speculative.type[s]`
discriminations (the "default to draft when a draft model is set" branch
and the `spec_type` / `speculative_type` option parser) fall back to the
singular scalar form, and the entire new-option block (ngram_mod / map_k
/ map_k4v / ngram_cache / draft.{cache_type_*, cpuparams*,
tensor_buft_overrides}) is preprocessed out. The macro is *not* defined
in the source tree — stock llama-cpp builds get the full new API.
2. backend/cpp/turboquant/patch-grpc-server.sh: two new patch steps applied
to the per-flavor build copy at turboquant-<flavor>-build/grpc-server.cpp:
- substitute `ctx_server.impl->model_tgt` -> `ctx_server.impl->model`
- inject `#define LOCALAI_LEGACY_LLAMA_CPP_SPEC 1` before the first
`#include`, so the guarded blocks above drop out for the fork build.
Both patches are idempotent and follow the existing sed/awk pattern in
this script (KV cache types, `get_media_marker`, flat speculative
renames). Stock llama-cpp's `grpc-server.cpp` is never touched.
Drop both legacy patches once the turboquant fork rebases past
ggml-org/llama.cpp#22397 / #22838.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(turboquant): close draft_ctx_size brace inside legacy guard
The previous turboquant fix wrapped the new option-handler blocks in
`#ifndef LOCALAI_LEGACY_LLAMA_CPP_SPEC ... #endif` but placed the guard
in the middle of an `else if` chain — the `} else if` openings of the
new blocks were responsible for closing the previous block's brace.
With the macro defined the new blocks vanish, draft_ctx_size's `{`
loses its closer, the for-loop's `}` is consumed instead, and the
file ends with a stray opening brace — clang reports it as
`function-definition is not allowed here before '{'` on the next
top-level `int main(...)` and `expected '}' at end of input`.
Move the chain split inside the draft_ctx_size branch:
} else if (... "draft_ctx_size") {
// ...
#ifdef LOCALAI_LEGACY_LLAMA_CPP_SPEC
} // legacy: chain ends here
#else
} else if (... "spec_ngram_mod_n_min") { // modern: chain continues
...
} else if (... "draft_override_tensor") {
...
} // closes last branch
#endif
} // closes for-loop
Brace count is now balanced under both preprocessor branches (verified
with `tr -cd '{' | wc -c` against the patched and unpatched outputs).
Local `make docker-build-turboquant` builds the linux/amd64 cpu-llama-cpp
`turboquant-avx` variant cleanly.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(ci): forward AMDGPU_TARGETS into Dockerfile.turboquant builder-prebuilt
Dockerfile.turboquant's `builder-prebuilt` stage was missing the
`ARG AMDGPU_TARGETS` / `ENV AMDGPU_TARGETS=${AMDGPU_TARGETS}` pair that
`builder-fromsource` already has (and that `Dockerfile.llama-cpp`
mirrors across both stages). When CI uses the prebuilt base image
(quay.io/go-skynet/ci-cache:base-grpc-*, the common path) the build-arg
passed by the workflow never reaches the env inside the compile stage.
backend/cpp/llama-cpp/Makefile:38 (introduced by #9626) errors out on
hipblas builds when AMDGPU_TARGETS is empty, and the turboquant
Makefile reuses backend/cpp/llama-cpp via a sibling build dir, so the
same check fires from turboquant-fallback under BUILD_TYPE=hipblas:
Makefile:38: *** AMDGPU_TARGETS is empty — set it to a comma-separated
list of gfx targets e.g. gfx1100,gfx1101. Stop.
make: *** [Makefile:66: turboquant-fallback] Error 2
The bug is latent on master because the docker layer cache stays warm
across builds — the compile step rarely re-runs from scratch. The
llama.cpp bump in this PR invalidates the cache, so the missing env var
becomes load-bearing and the hipblas turboquant CI job fails.
Mirror the existing pattern from Dockerfile.llama-cpp.
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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dependabot[bot]
|
61c9b187fa |
chore(deps): update charset-normalizer requirement from >=3.4.0 to >=3.4.7 in /backend/python/vllm (#9779)
chore(deps): update charset-normalizer requirement Updates the requirements on [charset-normalizer](https://github.com/jawah/charset_normalizer) to permit the latest version. - [Release notes](https://github.com/jawah/charset_normalizer/releases) - [Changelog](https://github.com/jawah/charset_normalizer/blob/master/CHANGELOG.md) - [Commits](https://github.com/jawah/charset_normalizer/compare/3.4.0...3.4.7) --- updated-dependencies: - dependency-name: charset-normalizer dependency-version: 3.4.7 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]
|
abc2a51641 |
chore(deps): update transformers requirement from >=5.0.0 to >=5.8.0 in /backend/python/transformers (#9775)
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/v5.0.0...v5.8.0) --- updated-dependencies: - dependency-name: transformers dependency-version: 5.8.0 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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LocalAI [bot]
|
78722caedc |
chore: ⬆️ Update ikawrakow/ik_llama.cpp to eb570eb96689c235933b813693ca28ab9d3d26de (#9764)
⬆️ Update ikawrakow/ik_llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
|
621c612b2d |
ci(bump-deps): register ds4 + move version pin into the Makefile (#9761)
* ci(bump-deps): register ds4 + move version pin into the Makefile The initial ds4 PR (#9758) put the upstream commit pin in backend/cpp/ds4/prepare.sh as a shell variable. The auto-bump bot at .github/bump_deps.sh greps for ^$VAR?= in a Makefile, so DS4_VERSION was invisible to it - other backends (llama-cpp, ik-llama-cpp, turboquant, voxtral, etc.) all pin in their Makefile. This change: - Moves DS4_VERSION?= and DS4_REPO?= to the top of backend/cpp/ds4/Makefile. - Inlines the git init/fetch/checkout recipe into the 'ds4:' target (matches llama-cpp's 'llama.cpp:' target pattern). Directory acts as the target so make only re-clones when missing. - Deletes the now-redundant prepare.sh. - Adds antirez/ds4 + DS4_VERSION + main + backend/cpp/ds4/Makefile to the .github/workflows/bump_deps.yaml matrix so the daily bot opens PRs against this pin. - Updates .agents/ds4-backend.md to point at the Makefile. Verified: $ grep -m1 '^DS4_VERSION?=' backend/cpp/ds4/Makefile DS4_VERSION?=ae302c2fa18cc6d9aefc021d0f27ae03c9ad2fc0 $ make -C backend/cpp/ds4 ds4 # clones into ds4/ at the pin $ make -C backend/cpp/ds4 ds4 # no-op on second invocation make: 'ds4' is up to date. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci: route backend/cpp/ds4/ changes through changed-backends.js scripts/changed-backends.js:inferBackendPath has an explicit branch per cpp dockerfile suffix (ik-llama-cpp, turboquant, llama-cpp). Without a matching branch the function returns null, the backend never lands in the path map, and PR change-detection cannot map "backend/cpp/ds4/X changed" -> "rebuild ds4 image". This is why PR #9761 produced zero ds4 jobs even though it directly edits backend/cpp/ds4/Makefile. Adds the missing branch (Dockerfile.ds4 -> backend/cpp/ds4/), placed before the llama-cpp branch (since both share the .cpp ancestry but ds4 is more specific - same ordering rule documented in .agents/adding-backends.md). Verified with a local Node simulation of the script against this PR's diff: the path map now contains 'ds4 -> backend/cpp/ds4/' and a 'backend/cpp/ds4/Makefile' change correctly triggers the ds4 backend in the rebuild set. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(adding-backends): harden the two gotchas that bit ds4 Both omissions are silent at the time you ADD a backend - the failure mode only appears later (the bump bot stays silent forever, or the path filter shows up on the next PR that touches your backend with zero CI jobs and looks broken for unrelated reasons). Expanding the `scripts/changed-backends.js` paragraph from a one-liner to a fully worked example, and adding a new sibling paragraph for the `bump_deps.yaml` + Makefile-pin contract. Both call out the specific mistakes from the ds4 timeline (#9758 → #9761) so future contributors can pattern-match on the cause. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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d892e4af80 |
feat: add ds4 backend (DeepSeek V4 Flash) with tool calls, thinking, KV cache (#9758)
* test(e2e-backends): allow BACKEND_BINARY for native-built backends
Adds an escape hatch for hardware-gated backends (e.g. ds4) where the
model is too large for Docker build context. When BACKEND_BINARY points
at a run.sh produced by 'make -C backend/cpp/<name> package', the suite
skips docker image extraction and drives the binary directly.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test(e2e-backends): validate BACKEND_BINARY basename + log actual source
Two follow-ups from the
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LocalAI [bot]
|
b9e81dbfd4 |
chore: ⬆️ Update ggml-org/llama.cpp to 389ff61d77b5c71cec0cf92fe4e5d01ace80b797 (#9752)
⬆️ 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> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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LocalAI [bot]
|
19d59102d5 |
feat(whisper-cpp): implement streaming transcription (#9751)
* test(whisper): wire e2e streaming transcription target Adds test-extra-backend-whisper-transcription, mirroring the existing llama-cpp / sherpa-onnx / vibevoice-cpp targets. The generic AudioTranscriptionStream spec at tests/e2e-backends/backend_test.go:644 fails today because backend/go/whisper has no streaming impl - this target is the failing TDD gate that the next phase makes pass. Confirmed RED locally: 3 Passed (health, load, offline transcription), 1 Failed (streaming spec hits its 300s context deadline because the base implementation returns 'unimplemented' but doesn't close the result channel, leaving the gRPC stream open until the client times out). Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(whisper-cpp): expose new_segment_callback to the Go side Adds set_new_segment_callback() and a C-side trampoline that whisper.cpp invokes once per new text segment during whisper_full(). The trampoline dispatches (idx_first, n_new, user_data) to a Go function pointer registered via purego.NewCallback - text and timings are pulled by Go through the existing get_segment_text/get_segment_t0/get_segment_t1 getters. Wires the hook only when streaming is actually requested, to avoid a per-segment function-pointer dispatch on the offline path. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(whisper-cpp): implement AudioTranscriptionStream Wires whisper.cpp's new_segment_callback through purego back to Go so the streaming transcription RPC produces real, time-correlated deltas while whisper_full() is still decoding. Each segment becomes one TranscriptStreamResponse{Delta}; whisper_full's return is the TranscriptStreamResponse{FinalResult} carrying the full segment list, language, and duration. Per-call state is tracked in a sync.Map keyed by an atomic counter; the Go callback registered via purego.NewCallback is a singleton, dispatched through user_data. SingleThread today means only one entry is ever live, but the map shape matches the sherpa-onnx TTS callback pattern. The streaming path's final.Text is the literal concat of every emitted delta (a strings.Builder accumulated by onNewSegment) so the e2e invariant `final.Text == concat(deltas)` holds exactly. The first delta has no leading space; subsequent deltas are space-prefixed. The offline AudioTranscription path is unchanged. Closes the gap with sherpa-onnx, vibevoice-cpp, llama-cpp, and tinygrad, which already implement AudioTranscriptionStream. Verified GREEN locally: make test-extra-backend-whisper-transcription passes 4/4 specs (3 Passed initially under RED, +1 streaming spec now). Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(whisper-cpp): assert progressive multi-segment streaming Drives AudioTranscriptionStream against a real long-audio fixture and asserts len(deltas) >= 2. The generic e2e spec at tests/e2e-backends/backend_test.go:644 only checks len(deltas) >= 1 which is satisfied by both real and faked streaming - this spec is the guardrail that a future "fake" impl can't sneak past. Skipped by default (env-gated, like the cancellation spec); set WHISPER_LIBRARY, WHISPER_MODEL_PATH, and WHISPER_AUDIO_PATH to a 30+ second clip to run. Verified locally with a 55s 5x-JFK concat against ggml-base.en.bin: 1 Passed in 7.3s, deltas >= 2, finalSegmentCount >= 2, concat(deltas) == final.Text. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(whisper-cpp): add transcription gRPC e2e job Mirrors tests-sherpa-onnx-grpc-transcription / tests-llama-cpp-grpc-transcription. Runs make test-extra-backend-whisper-transcription whenever the whisper backend or the run-all switch fires, so a pin-bump or refactor that breaks streaming transcription gets caught before merge. The whisper output on detect-changes is already emitted by scripts/changed-backends.js (it iterates allBackendPaths); this PR just exposes it as a workflow output and consumes it. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(whisper-cpp): silence errcheck on AudioTranscriptionStream defers golangci-lint runs with new-from-merge-base=origin/master, so the identical defer patterns in the existing offline AudioTranscription path are grandfathered while the new ones in AudioTranscriptionStream trip errcheck. Wrap both defers in `func() { _ = ... }()` to match what errcheck wants without altering behavior. The errors from os.RemoveAll and *os.File.Close are not actionable inside a defer here (we're already returning), matching the offline path's contract. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
|
4715a68660 |
chore: ⬆️ Update vllm-project/vllm cu130 wheel to 0.20.2 (#9750)
⬆️ 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 [bot]
|
28f33be48f |
chore: ⬆️ Update ggml-org/whisper.cpp to c33c5618b72bb345df029b730b36bc0e369845a3 (#9749)
⬆️ Update ggml-org/whisper.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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LocalAI [bot]
|
a435f7cc69 |
chore: ⬆️ Update ikawrakow/ik_llama.cpp to 23127139cb6fa314899c3b5f4935b88b3374c56c (#9748)
⬆️ Update ikawrakow/ik_llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
|
f6c9c20911 |
chore: ⬆️ Update ggml-org/llama.cpp to 2b2babd1243c67ca811c0a5852cedf92b1a20024 (#9747)
⬆️ 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> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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LocalAI [bot]
|
6cbf69dc29 |
chore: ⬆️ Update ggml-org/llama.cpp to 1e5ad35d560b90a8ac447d149c8f8447ae1fcaa0 (#9739)
⬆️ 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> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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LocalAI [bot]
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593f3a8648 |
ci: refactor llama-cpp variant Dockerfiles to consume prebuilt base-grpc images (PR 2/2) (#9738)
* ci(backend_build): plumb builder-base-image and BUILDER_TARGET build-args Adds an optional builder-base-image input. When set, BUILDER_BASE_IMAGE is forwarded as a build-arg AND BUILDER_TARGET=builder-prebuilt is set to select the variant Dockerfile's prebuilt-base stage. When empty, BUILDER_TARGET=builder-fromsource (the default) keeps the existing from-source build path. This makes the prebuilt-base optimization opt-in per matrix entry without breaking local `make backends/<name>` invocations or backends whose Dockerfile doesn't have a prebuilt path. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(llama-cpp,ik-llama-cpp,turboquant): multi-target Dockerfiles for prebuilt + from-source Restructure the three llama.cpp-derived Dockerfiles so each supports two builder paths in a single file, selected via the BUILDER_TARGET build-arg: BUILDER_TARGET=builder-fromsource (default) - Standalone build: gRPC stage + apt installs + (conditionally) CUDA/ROCm/Vulkan + compile. - Used by `make backends/llama-cpp` locally and any caller that doesn't supply a prebuilt base. BUILDER_TARGET=builder-prebuilt - FROM \${BUILDER_BASE_IMAGE} (one of quay.io/go-skynet/ci-cache: base-grpc-* shipped in PR #9737). - Skips ~25-35 min of gRPC compile + ~5-10 min of toolchain installs. - Used by CI when the matrix entry sets builder-base-image. Final FROM scratch resolves BUILDER_TARGET via an aliasing FROM stage (BuildKit doesn't support variable expansion directly in COPY --from), then COPY --from=builder pulls package output from the chosen path. BuildKit prunes the unreferenced builder, so each build only does the work for the chosen path. The compile RUN is identical between both builder stages, so it's factored into .docker/<name>-compile.sh and bind-mounted into both. ccache mount + cache-id stay per-arch / per-build-type. Local DX preserved: `make backends/llama-cpp` (no extra args) defaults to BUILDER_TARGET=builder-fromsource and works exactly as before. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(backend.yml,backend_pr.yml): forward builder-base-image from matrix Plumbs the new optional builder-base-image input from matrix into backend_build.yml. backend_build.yml derives BUILDER_TARGET from whether builder-base-image is set, so matrix entries that map to a prebuilt base get the prebuilt path; entries that don't (python/go/ rust backends) fall through to the default builder-fromsource (which their own Dockerfiles don't reference, so it's a no-op for them). Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(backend-matrix): wire builder-base-image to llama-cpp variants For every entry whose Dockerfile is llama-cpp/ik-llama-cpp/turboquant, add a builder-base-image field pointing at the appropriate prebuilt quay.io/go-skynet/ci-cache:base-grpc-* tag. backend_build.yml derives BUILDER_TARGET from this field's presence: non-empty -> builder-prebuilt; empty -> builder-fromsource. So this commit alone activates the prebuilt-base path for these 23 backends in CI, while local `make backends/<name>` (no extra args) keeps the from-source path. Mapping by (build-type, arch): - '' / amd64 -> base-grpc-amd64 - '' / arm64 -> base-grpc-arm64 - cublas-12 / amd64 -> base-grpc-cuda-12-amd64 - cublas-13 / amd64 -> base-grpc-cuda-13-amd64 - cublas-13 / arm64 -> base-grpc-cuda-13-arm64 - hipblas / amd64 -> base-grpc-rocm-amd64 - vulkan / amd64 -> base-grpc-vulkan-amd64 - vulkan / arm64 -> base-grpc-vulkan-arm64 - sycl_* / amd64 -> base-grpc-intel-amd64 - cublas-12 + JetPack r36.4.0 / arm64 -> base-grpc-l4t-cuda-12-arm64 Cold-build savings expected: ~25-35 min per variant (skips the gRPC compile + toolchain install that's now in the base). Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci: add base-grpc-l4t-cuda-12-arm64 variant for legacy JetPack entries Two matrix entries (-nvidia-l4t-arm64-llama-cpp, -nvidia-l4t-arm64- turboquant) build against nvcr.io/nvidia/l4t-jetpack:r36.4.0 + CUDA 12 ARM64. They're distinct from -nvidia-l4t-cuda-13-arm64-* which use Ubuntu 24.04 + CUDA 13 sbsa. Add the missing JetPack-based variant to base-images.yml so those two entries' builder-base-image mapping in the previous commit resolves. Bootstrap order before merging this PR (re-run base-images.yml on this branch — 9 existing variants hit BuildKit cache, only the new l4t-cuda-12-arm64 builds cold): gh workflow run base-images.yml --ref ci/base-images-consumers Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci: extract base-builder install logic into .docker/install-base-deps.sh Pre-extraction, the apt + protoc + cmake + conditional CUDA/ROCm/Vulkan + gRPC install logic was duplicated across four files: - backend/Dockerfile.base-grpc-builder (CI prebuilt-base source of truth) - backend/Dockerfile.llama-cpp (builder-fromsource stage) - backend/Dockerfile.ik-llama-cpp (builder-fromsource stage) - backend/Dockerfile.turboquant (builder-fromsource stage) A bump to e.g. CUDA toolkit packages had to be made in 4 places, and drift between the prebuilt base and the variant-Dockerfile from-source path was a real concern (ik-llama-cpp's hipblas branch was already missing the rocBLAS Kernels echo that llama-cpp / turboquant / base-grpc-builder all had). Factor the install logic into a single .docker/install-base-deps.sh that reads its inputs from env vars and runs conditionally on BUILD_TYPE / CUDA_*_VERSION / TARGETARCH. Each Dockerfile now bind- mounts the script alongside .docker/apt-mirror.sh and invokes it from a single RUN step. The variant Dockerfiles' grpc-source stage is removed entirely — the script handles gRPC compile + install at /opt/grpc, and the builder-fromsource stage mirrors builder-prebuilt by copying /opt/grpc/. to /usr/local/. Result: - install-base-deps.sh: 244 lines (one source of truth) - Dockerfile.base-grpc-builder: 268 -> 98 lines - Dockerfile.llama-cpp: 361 -> 157 lines - Dockerfile.ik-llama-cpp: 348 -> 151 lines - Dockerfile.turboquant: 355 -> 154 lines - Total Dockerfile bytes: 1332 -> 560 lines (58% reduction) Bit-equivalence between prebuilt and from-source paths is now enforced by construction: both invoke the same script with the same inputs. A side-effect is that ik-llama-cpp now also gets the rocBLAS Kernels echo + clblas block parity it was previously missing. Includes the BUILD_TYPE=clblas branch (libclblast-dev) for parity even though no current CI matrix entry uses it. After this commit's force-push, base-images.yml needs to be redispatched on this branch — the Dockerfile.base-grpc-builder content shifts so the existing cache won't apply for the install layer (gRPC layer also rebuilds since it's now in the same RUN step). Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(base-images): skip-drivers on JetPack l4t variant cuda-nvcc-12-0 isn't installable via apt on the JetPack r36.4.0 base image — JetPack ships CUDA preinstalled at /usr/local/cuda and its apt feed doesn't carry the cuda-nvcc-* packages from the public repositories. The original matrix entry for -nvidia-l4t-arm64-llama-cpp on master sets skip-drivers: 'true' for exactly this reason; the new base-grpc-l4t-cuda-12-arm64 base needs to match. Also forwards SKIP_DRIVERS as a build-arg from matrix into the build (was missing entirely before this commit). Caught by run 25612030775 — l4t-cuda-12-arm64 failed at: E: Package 'cuda-nvcc-12-0' has no installation candidate Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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28e29625a2 |
ci: add pre-built base-grpc-builder image infrastructure (PR 1/2) (#9737)
Introduces a parameterized Dockerfile.base-grpc-builder that produces
a fully-prepped builder base image (apt deps + protoc + cmake + gRPC
at /opt/grpc + conditional CUDA/ROCm/Vulkan toolchains) and a
base-images.yml workflow that builds + pushes 9 variants to
quay.io/go-skynet/ci-cache:base-grpc-*:
base-grpc-amd64 (Ubuntu 24.04, CPU-only)
base-grpc-arm64 (Ubuntu 24.04, CPU-only)
base-grpc-cuda-12-amd64 (Ubuntu 24.04 + CUDA 12.8)
base-grpc-cuda-13-amd64 (Ubuntu 22.04 + CUDA 13.0)
base-grpc-cuda-13-arm64 (Ubuntu 24.04 + CUDA 13.0 sbsa)
base-grpc-rocm-amd64 (rocm/dev-ubuntu-24.04:7.2.1 + hipblas)
base-grpc-vulkan-amd64 (Ubuntu 24.04 + Vulkan SDK 1.4.335)
base-grpc-vulkan-arm64 (Ubuntu 24.04 + Vulkan SDK ARM 1.4.335)
base-grpc-intel-amd64 (intel/oneapi-basekit:2025.3.2)
The variant Dockerfiles (Dockerfile.llama-cpp, ik-llama-cpp, turboquant)
are NOT touched in this PR. PR 2 will refactor them to FROM these
prebuilt bases. This PR is intentionally inert - landing it changes no
existing CI behavior. The base images don't exist on quay until
someone manually triggers the workflow.
Bootstrap after merge:
gh workflow run base-images.yml --ref master
Wait ~30 min for all 9 variants to push, then merge PR 2 (the
consumer-side refactor that uses BUILDER_BASE_IMAGE build-arg to
FROM these tags).
Triggers afterwards:
- Saturdays 05:00 UTC (cron) - picks up upstream security updates,
runs ~24h before the backend.yml Sunday cron so bases are fresh.
- workflow_dispatch - manual ad-hoc rebuild.
- master push touching Dockerfile.base-grpc-builder or this workflow.
Why split into two PRs: the variant Dockerfiles in PR 2 will FROM the
prebuilt bases and have no from-source fallback. Their CI builds fail
if the bases don't exist on quay yet. Landing infrastructure first +
manual bootstrap + then consumer refactor avoids a broken-master window.
Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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Ettore Di Giacinto
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31aa0582a5 |
ci(ik-llama-cpp,turboquant): add BuildKit ccache mount to compile steps
Mirror the ccache mount added to Dockerfile.llama-cpp in
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Ettore Di Giacinto
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9228e5b412 |
ci(llama-cpp): add BuildKit ccache mount to the compile step
The big RUN at line 268 of Dockerfile.llama-cpp re-runs from scratch on every LLAMA_VERSION bump (or any LocalAI source change due to COPY . /LocalAI just before). For CUDA-13 specifically that compile recently hit the GHA 6h hard limit and failed: https://github.com/mudler/LocalAI/actions/runs/25598418931/job/75148244557 Add a BuildKit cache mount on /root/.ccache and thread ccache through CMake (CMAKE_C/CXX/CUDA_COMPILER_LAUNCHER) so most translation units hit cache when their preprocessed source is byte-identical to the previous build. The cache mount is exported to the registry as part of the existing cache-to: type=registry,mode=max in backend_build.yml, so it persists across runs. mount id is keyed on TARGETARCH + BUILD_TYPE so different variants don't thrash the same cache slot; sharing=locked serializes concurrent writes. Cold-build effect (first run after enable, or on LLAMA_VERSION bump that touches every TU): unchanged. Hot-build effect (subsequent runs with the same source, or LLAMA_VERSION bumps that touch a handful of files): ~5-15 min for the llama.cpp compile vs the previous 1-3h cold. For CUDA-13 specifically this should bring rebuilds well under the 6h GHA limit. Does NOT help the *first* post-bump build — that's still cold. For that, follow-up work would be: (a) trim CUDA_DOCKER_ARCH to modern GPUs only, (b) audit which CMake variants the published images actually need, (c) pre-built CUDA+gRPC base image. ccache package is already installed in the builder stage (line 90). Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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a91e718473 |
chore: ⬆️ Update ggml-org/llama.cpp to 00d56b11c3477b99bc18562dc1d1834f0d961778 (#9733)
⬆️ 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> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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LocalAI [bot]
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d1eef05852 |
chore: ⬆️ Update ikawrakow/ik_llama.cpp to ab0f22b819ac57b7e7484f69c00c10fc755d5c6c (#9734)
⬆️ Update ikawrakow/ik_llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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4542833cb4 |
chore: ⬆️ Update ggml-org/llama.cpp to 9f5f0e689c9e977e5f23a27e344aa36082f44738 (#9724)
⬆️ 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> |
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Ettore Di Giacinto
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fe7b27eb66 |
test(ci): trigger faster-whisper rebuild to observe per-arch+merge
The PR that introduced the per-arch + manifest-merge pilot (#9727) only touched CI infrastructure files, so the path filter correctly skipped backend builds on its merge commit. To observe the new backend-merge-jobs flow assemble a real manifest list, this commit touches faster-whisper's Makefile so its two new per-arch entries schedule and the merge job runs. The trailing comment is the smallest possible diff and is harmless to the build. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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14a3275329 |
chore: ⬆️ Update ikawrakow/ik_llama.cpp to 98950267c67fd95937a54ebd6e3c66cf2679b710 (#9725)
⬆️ Update ikawrakow/ik_llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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2be07f61da |
feat(whisper): honor client cancellation via ggml abort_callback (#9710)
* refactor(transcription): propagate request ctx through ModelTranscription* Replaces context.Background() with the HTTP request ctx so client disconnects start cancelling the gRPC call. No backend-side abort wiring yet — that comes in a later commit. Pure plumbing. Assisted-by: Claude:claude-haiku-4-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(cli): pass ctx to backend.ModelTranscription Follow-up to |
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LocalAI [bot]
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806130bbc0 |
chore: ⬆️ Update ggml-org/whisper.cpp to c81b2dabbc45484dee2ca6658cfe39c841df5c70 (#9712)
⬆️ Update ggml-org/whisper.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
|
3b84582567 |
chore: ⬆️ Update ggml-org/llama.cpp to 05ff59cb57860cc992fc6dcede32c696efea711c (#9714)
⬆️ 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> |
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LocalAI [bot]
|
907929ce60 |
chore: ⬆️ Update ikawrakow/ik_llama.cpp to 9a26522af234f8db079ae3735f35ab6c20fe2c66 (#9713)
⬆️ Update ikawrakow/ik_llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Richard Palethorpe
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c894d9c826 |
feat(sglang): wire engine_args, add cuda13 build, ship MTP gallery demos (#9686)
Bring the sglang Python backend up to feature parity with vllm by adding
the same engine_args:-map plumbing the vLLM backend already has. Any
ServerArgs field (~380 in sglang 0.5.11) becomes settable from a model
YAML, including the speculative-decoding flags needed for Multi-Token
Prediction. Validation matches the vllm backend's: keys are checked
against dataclasses.fields(ServerArgs), unknown keys raise ValueError
with a difflib close-match suggestion at LoadModel time, and the typed
ModelOptions fields keep their existing meaning with engine_args
overriding them.
Backend code:
* backend/python/sglang/backend.py: add _apply_engine_args, import
dataclasses/difflib/ServerArgs, call from LoadModel; rename Seed ->
sampling_seed (sglang 0.5.11 renamed the SamplingParams field).
* backend/python/sglang/test.py + test.sh + Makefile: six unit tests
exercising the helper directly (no engine load required).
Build / CI / backend gallery (cuda13 + l4t13 paths are now first-class):
* backend/python/sglang/install.sh: add --prerelease=allow because
sglang 0.5.11 hard-pins flash-attn-4 which only ships beta wheels;
add --index-strategy=unsafe-best-match for cublas12 so the cu128
torch index wins over default-PyPI's cu130; new pyproject.toml-driven
l4t13 install path so [tool.uv.sources] can pin torch/torchvision/
torchaudio/sglang to the jetson-ai-lab index without forcing every
transitive PyPI dep through the L4T mirror's flaky proxy (mirrors the
equivalent fix in backend/python/vllm/install.sh).
* backend/python/sglang/pyproject.toml (new): L4T project spec with
explicit-source jetson-ai-lab index. Replaces requirements-l4t13.txt
for the l4t13 BUILD_PROFILE; other profiles still go through the
requirements-*.txt pipeline via libbackend.sh's installRequirements.
* backend/python/sglang/requirements-l4t13.txt: removed; superseded
by pyproject.toml.
* backend/python/sglang/requirements-cublas{12,13}{,-after}.txt: pin
sglang>=0.5.11 (Gemma 4 floor); add cu130 torch index for cublas13
(new files) and cu128 torch index for cublas12 (default PyPI now
ships cu130 torch wheels by default and breaks cu12 hosts).
* backend/index.yaml: add cuda13-sglang and cuda13-sglang-development
capability mappings + image entries pointing at
quay.io/.../-gpu-nvidia-cuda-13-sglang.
* .github/workflows/backend.yml: new cublas13 sglang matrix entry,
mirroring vllm's cuda13 build.
Model gallery + docs:
* gallery/sglang.yaml: base sglang config template, mirrors vllm.yaml.
* gallery/sglang-gemma-4-{e2b,e4b}-mtp.yaml: Gemma 4 MTP demos
transcribed verbatim from the SGLang Gemma 4 cookbook MTP commands.
* gallery/sglang-mimo-7b-mtp.yaml: MiMo-7B-RL with built-in MTP heads
+ online fp8 weight quantization, verified end-to-end on a 16 GB
RTX 5070 Ti at ~88 tok/s. Uses mem_fraction_static: 0.7 because the
MTP draft worker's vocab embedding is loaded unquantised and OOMs
the static reservation at sglang's 0.85 default.
* gallery/index.yaml: three new entries (gemma-4-e2b-it:sglang-mtp,
gemma-4-e4b-it:sglang-mtp, mimo-7b-mtp:sglang).
* docs/content/features/text-generation.md: new SGLang section with
setup, engine_args reference, MTP demos, version requirements.
* .agents/sglang-backend.md (new): agent one-pager covering the flat
ServerArgs structure, the typed-vs-engine_args precedence, the
speculative-decoding cheatsheet, and the mem_fraction_static gotcha
documented above.
* AGENTS.md: index entry for the new agent doc.
Known limitation: the two Gemma 4 MTP gallery entries ship a recipe
that doesn't yet run on stock libraries. The drafter checkpoints
(google/gemma-4-{E2B,E4B}-it-assistant) declare
model_type: gemma4_assistant / Gemma4AssistantForCausalLM, which
neither transformers (<=5.6.0, including the SGLang cookbook's pinned
commit 91b1ab1f... and main HEAD) nor sglang's own model registry
(<=0.5.11) registers as of 2026-05-06. They will start working when
HF or sglang upstream registers the architecture -- no LocalAI
changes needed. The MiMo MTP demo and the non-MTP Gemma 4 paths work
today on this build (verified on RTX 5070 Ti, 16 GB).
Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Bash] [WebFetch] [WebSearch]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
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Ettore Di Giacinto
|
048daa0cdc |
fix(chatterbox): install chatterbox-tts with --no-deps and pin runtime deps
The previous omegaconf pin only addressed one symptom of a deeper problem: chatterbox-tts upstream depends on `russian-text-stresser` (unpinned git URL), which transitively pins `spacy==3.6.*` and other ancient packages. That cascade forces pip to backtrack through Jinja2/MarkupSafe/omegaconf into Python-2-era sdists that no longer build (e.g. ruamel.yaml<0.15, Jinja2 2.6 importing the long-removed `setuptools.Feature`). Install chatterbox-tts itself with --no-deps in install.sh and list its real runtime deps explicitly in each requirements-*.txt, dropping the optional russian-text-stresser. This unblocks the darwin (and other) builds without playing whack-a-mole on each newly-discovered transitive pin. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-7 [Claude Code] |
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Ettore Di Giacinto
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7c77d3506a |
fix(chatterbox): pin omegaconf in every profile requirements file
The previous pin in requirements.txt was ineffective: installRequirements runs a separate `pip install --requirement` per file, so resolution does not carry over to the per-profile file where chatterbox-tts is declared. With chatterbox-tts's unpinned `omegaconf` dep, pip backtracked through 1.x sdists into ruamel.yaml<0.15, whose Python-2-era setup.py fails on Python 3.10+. Pin omegaconf==2.3.0 next to chatterbox-tts in every profile file (matches what upstream chatterbox uses). Drop the dead pin from requirements.txt. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-7 [Claude Code] |
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dependabot[bot]
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c96ce99742 |
chore(deps): bump openssl from 0.10.76 to 0.10.79 in /backend/rust/kokoros in the cargo group across 1 directory (#9694)
chore(deps): bump openssl Bumps the cargo group with 1 update in the /backend/rust/kokoros directory: [openssl](https://github.com/rust-openssl/rust-openssl). Updates `openssl` from 0.10.76 to 0.10.79 - [Release notes](https://github.com/rust-openssl/rust-openssl/releases) - [Commits](https://github.com/rust-openssl/rust-openssl/compare/openssl-v0.10.76...openssl-v0.10.79) --- updated-dependencies: - dependency-name: openssl dependency-version: 0.10.79 dependency-type: indirect dependency-group: cargo ... 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 [bot]
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0b9344ef3d |
chore: ⬆️ Update leejet/stable-diffusion.cpp to 90e87bc846f17059771efb8aaa31e9ef0cab6f78 (#9701)
⬆️ Update leejet/stable-diffusion.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> |
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LocalAI [bot]
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151d6c9cf0 |
chore: ⬆️ Update ggml-org/llama.cpp to 2496f9c14965c39589f53eea31bdb6d762b1d360 (#9698)
⬆️ 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> |
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LocalAI [bot]
|
659939db9b |
chore: ⬆️ Update ikawrakow/ik_llama.cpp to b93721902b4662f9b973b1c412006081c958d085 (#9697)
⬆️ Update ikawrakow/ik_llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Ettore Di Giacinto
|
b2be9729ef |
fix(chatterbox): pin omegaconf>=2.0 to prevent resolver backtracking
Without an upper-floor pin, pip's resolver backtracks through omegaconf 1.x sdists when installing chatterbox-tts. Old 1.x setups depend on ruamel.yaml<0.15, whose setup.py uses Python-2-era names (Str, Bytes) and fails to build on Python 3.10+, breaking the darwin python backend build. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-7 [Claude Code] |
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LocalAI [bot]
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4e154b59e5 |
fix(ci): unbreak rerankers (torch bump) and vllm-omni on aarch64 (#9688)
Two unrelated CI breakages bundled together since both are one-liners: - rerankers: bump torch 2.4.1 -> 2.7.1 on cpu/cublas12. The unpinned transformers resolves to 5.x, whose moe.py registers a custom_op with string-typed `'torch.Tensor'` annotations that torch 2.4.1's infer_schema rejects, blocking the gRPC server from starting and failing all 5 backend tests with "Connection refused" on :50051. Matches the version used by the transformers backend. - vllm-omni: strip fa3-fwd from the upstream requirements/cuda.txt before resolving on aarch64. fa3-fwd 0.0.3 ships only an x86_64 wheel and has no sdist, making the cuda profile unsatisfiable on Jetson/SBSA. fa3-fwd is a soft runtime dep — vllm-omni's attention backends fall back to FA2 then SDPA when it's missing. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |