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feat/vllm-cpp-darwin-mlx
1495 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Richard Palethorpe
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49ef40a187 |
feat(classifier/VAD): support voice control on low power devices (#10804)
* feat(llama-cpp): route Score through the slot loop Score previously bypassed the slot loop with a direct llama_decode: a conflict guard aborted the whole process if scoring raced generation, the config validator had to reject score alongside chat/completion/embeddings, and every candidate re-decoded the full shared prompt. Add SERVER_TASK_TYPE_SCORE to the (patched) upstream server so score tasks are scheduled like any other slot work: generation and scoring serialize naturally, the shared prompt is decoded once per call, and the slot's prompt cache carries the conversation prefix across calls. Context checkpoints at the score boundary and at the cache-divergence point keep SWA/hybrid/recurrent models (e.g. LFM2.5) from re-prefilling the whole prompt per candidate: warm-turn scoring on a 6-option set drops from ~8s to ~0.5s on a desktop CPU. The conflict guard and the validation split are removed; declaring score with generation usecases on one config is now supported and shares the slot cache. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): classifier wire types and pipeline config Wire types and YAML config for realtime classifier mode: sessions carry a localai_classifier extension (options with canned replies/tool calls, softmax threshold, normalization, history trimming, fallback modes, and a deterministic wake-word address gate), mirrored by pipeline.classifier in the model YAML and surfaced in the config-meta registry. The localai.classifier.result server event reports the full score distribution per turn. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): classifier response flow Classifier-mode responses: instead of autoregressive generation, each user turn is prefill-scored against the option list (router.ScoreClassifier prompt/candidate shapes over the Score primitive) and the winning option's canned reply and tool call are emitted through the existing response machinery. Below-threshold turns take the configured fallback (none / canned reply / generate); empty transcripts and unaddressed turns (wake word not mentioned) skip scoring entirely. The scoring probe defaults to the latest user message only — small scorers echo canned replies from prior turns back as the top option otherwise. Built for hardware that can afford prompt processing but not decode: with slot-based Score the option list stays KV-cached across turns, so a turn costs roughly one forward pass over the new words. session_update_error events now carry the validation cause instead of a generic message. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): bound the VAD tick's scan window and buffer retention The VAD tick loop re-scanned the entire input buffer every 300ms and only trimmed it on zero-segment ticks or commits. Audio that keeps producing segments without a committing pause (steady noise a mic pipeline lets through, music, continuous speech) grew the buffer toward the 100MB cap with each tick rescanning all of it — O(n^2), measured at ~3.3ms of silero per buffered second: past ~90s retained, ticks run back to back and pin ~4 cores until the stream stops. Silero's recurrent state only carries a few hundred ms of context, so rescanning old audio buys nothing. Clip the slice handed to the VAD to the largest silence the commit test can need to measure (server_vad silence window or the semantic eagerness fallback) plus a warm-up margin, and rebase the returned segment times so every downstream consumer keeps whole-buffer coordinates. An open turn whose clipped window is all silence now commits (the silence outran the window) instead of being discarded as no-speech. Independently, retain at most 90s of raw buffer, rebasing the live-feed and EOU cursors on trim — this also bounds the previously unbounded VAD-error path. Turn boundaries are otherwise unchanged: no forced commits, no new coordinator states. pipeline.turn_detection.vad_window_sec can widen the scan window; values below the automatic floor are ignored. The tick body is extracted into vadTick so specs can drive turn detection synchronously (same shape as classifySoundWindow); the babble reproduction that pinned 4 cores now plateaus under 10% of one core. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(backend): let per-model threads override the global default ModelOptions overrode a set per-model threads value with the app-level --threads whenever the latter was non-zero — and WithThreads defaults it to the physical core count, so it always was. The YAML threads: knob has been dead config: a tiny VAD model could never opt down from the global pool size. SetDefaults already fills an unset per-model value from the app config, which is the intended precedence; resolve threads through a helper that honors it (explicit threads: 0 still means unset). Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * chore(gallery): single-thread the silero VAD Silero is a ~2MB recurrent model with no exploitable graph parallelism: measured per-call latency is identical at 1 and 10 ORT threads, while every extra pool thread just spin-waits between the realtime loop's frequent tiny inferences. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * docs(realtime): classifier mode, VAD scan window, threads precedence Document the realtime classifier mode (options, threshold guidance, wake-word address gate, empty-transcript handling), the VAD scan window and 90s buffer retention (pipeline.turn_detection.vad_window_sec), the per-model threads precedence, and the M3 classifier note in the realtime state-machine design doc. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * perf(llama-cpp): score all candidates in one batched decode One scoring call is now a single SERVER_TASK_TYPE_SCORE task: the slot decodes the shared prefix (prompt + longest common candidate token prefix) once, then forks one sequence per candidate off it (metadata-only for the unified KV cache, copy-on-write for recurrent state) and decodes every candidate's unique tail in one llama_decode. Previously each candidate was its own task that restored the boundary checkpoint and re-decoded its full tail sequentially, paying per-candidate task and decode overhead. The context reserves SERVER_SCORE_FORK_SEQS extra sequence ids (and recurrent-state cells) beyond the parallel slots via the new common_params::n_seq_score_forks. Forking requires the unified KV cache (already this backend's default) since per-sequence streams would shrink n_ctx_seq; an explicit kv_unified:false disables forking and Score calls that need it fail cleanly. Candidates beyond the fork/output budget decode in successive chunks. Wire contract and scores are unchanged: per-token logprobs are stitched from the shared region and the forked tails. Verified bitwise deterministic call-to-call and independent of candidate order (no cross-fork leakage via equal-length candidate swap); ranking matches the per-candidate implementation on the drone battery (winner softmax 0.99996 vs 0.99997), and >16-candidate chunking, prefix-of-another and empty candidates all pass. Measured on a desktop CPU: warm /api/score calls 0.52s -> 0.23s; warm realtime classifier turns 196-303ms. The 9-candidate drone turn decodes ~17 unique tail tokens in one batch instead of nine sequential ~220ms checkpoint-restore tasks. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): gate scoring capacity by model usecase Reserve llama.cpp scoring slots only for models that explicitly declare the score usecase, while allowing score to coexist with chat and completion. Reject incompatible unified-KV settings and classifier activation on models without scoring capacity. Propagate application defaults when resolving realtime and preload pipeline stages so unset thread counts are resolved consistently without overriding explicit model settings. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(ci): honor APT mirrors in the prebuilt llama-cpp compile step The builder-prebuilt path installs gcc-14 with apt directly and ignored the APT_MIRROR/APT_PORTS_MIRROR build args the from-source path already honors, so an ubuntu mirror outage broke every arm64 backend build. Pass the args into the stage and run apt-mirror.sh (already in the build context via COPY . /LocalAI) before the apt step. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): classifier argument slots via constrained completion Hybrid classify-then-complete: a classifier option's canned tool call can declare typed argument slots (number | enum | string, with defaults and prompt hints) referenced as "{{name}}" in the arguments template. When the option wins, the slots are filled by a short grammar-constrained completion that continues the exact scoring prompt — rendered by the same cached ScoreClassifier, so the llama.cpp prompt cache is already warm — with the chosen route JSON re-opened at the first slot field. A GBNF grammar pins the field skeleton and frees only the values; temperature 0, a couple dozen tokens at most (~300ms on a desktop CPU for two slots). Slot declarations and hints ride the option descriptions in the shared system prompt, informing scoring and the fill alike at no per-turn token cost. The localai.classifier.result event carries the final arguments and a fill_latency_ms. On inference failure the slots' defaults apply; a slot without a default fails the response (or falls through with fallback.mode: generate). Slot filling requires completion alongside score in the scoring model's known_usecases. Verified end-to-end on the Pi drone demo: "fly forward three meters" in distance mode classifies forward and infers {"distance": 3, "units": "meters"} in ~310ms, and the drone flies exactly 3 units. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): splice filled slot values into classifier replies A classifier option's spoken reply can now reference its tool's argument slots ("Going forward {{distance}} {{units}}."): the values inferred by the slot-fill completion — or the recovery defaults — are spliced into the reply as plain text before it is emitted, so what the assistant says confirms what it actually inferred. Placeholders without a value stay literal, and options without slots are untouched. FillToolArguments now returns the raw slot values alongside the spliced arguments JSON to make the reply templating possible. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): harden classifier slot completion Reserve context for constrained slot filling, size completions from their encoded output, and encode enum grammar literals as valid JSON. Reject empty enum values and cover the failure modes with regression tests. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): prewarm the classifier scoring prompt on registration Swapping a session's classifier option list (a voice-switched command mode, for instance) made the next turns pay a full re-prefill of the new option-list prompt — measured 2.4s vs 0.3s warm on a desktop CPU, and worse: on hybrid-memory models like LFM2.5, whose state cannot be partially rewound (llama.cpp can only restore checkpoints), *every* probe change re-prefilled from scratch whenever the last checkpoint missed the probe boundary, so even same-list turns intermittently cost full prefills. Registering an option list (pipeline seed or session.update) now fires a best-effort background prewarm: two throwaway scores with distinct probes. The first prefills the new option-list prompt; the second, diverging exactly where per-turn probe text starts, plants the backend's rewind point (KV checkpoint) at the stable-prefix boundary that every real turn reuses. The prewarm hides behind the canned mode-switch reply — by the time it finishes speaking, the cache is warm. Idempotent per option set, detached from the registering request's lifetime. Measured on the drone demo (LFM2.5-1.2B, desktop CPU): first turn after a mode switch 2374ms -> 340ms; intermittent same-list full prefills (1.3-2.1s) all -> under 0.5s. For clients that swap lists frequently, options: [parallel:2] on the scoring model additionally keeps one slot per list via prefix-similarity routing (+26MB RSS, unified KV). Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * perf(llama-cpp): checkpoint scoring at the caller-declared stable prefix Hybrid-memory models (LFM2.5 shortconv, Qwen3.5 deltanet — where new small models are headed) cannot rewind their state, so any prompt-cache reuse that needs a rewind falls back to a full re-prefill. For classifier scoring that meant every probe change re-processed the whole option-list prompt: the server's checkpoints were placed reactively (at wherever the previous task happened to diverge), so a checkpoint past the next divergence was erased rather than restored — measured as intermittent 2-10s turns on prompts with a 95%+ common prefix. The classifier now computes the probe-invariant prompt prefix once (the byte-wise common prefix of two synthetic probe renders) and declares its length with every Score request; the server maps it to a token boundary and forces a KV checkpoint exactly there on each score prefill. That checkpoint sits at or before every future divergence under the same option list, so it always survives and always restores — repeat scoring costs probe+candidates regardless of how the probe changes. Also: - prewarm reruns on every option-list registration instead of memoizing per list: with boundary checkpoints a redundant rewarm costs two probe-sized decodes, while skipping one after a slot eviction (three lists sharing fewer slots evict in LRU cascades) silently moves a full re-prefill onto the user's next turn - new llama.cpp backend option rs_seq:N exposes bounded recurrent-state rollback outside speculative decoding; measured impractical for deltanet-scale states (65GB for 64 snapshots on Qwen3.5-4B) but cheap insurance for small-state models - docs: the multi-list recipe (parallel:N + sps:0.5 — the default slot similarity threshold funnels distinct lists onto one slot) Measured on the drone demo (LFM2.5-1.2B scorer, desktop CPU), steady state: every turn 285-421ms including mode switches, vs 2.4s post-switch and intermittent 1.3-2.9s re-prefills before. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): align classifier cache guidance Document the single-score prewarm behavior and clean the vendored score patch formatting. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(llama-cpp): guard score task for fork backends TurboQuant and Bonsai reuse the primary gRPC server against llama.cpp forks that do not carry LocalAI's slot-based Score patches. Compile the Score integration only for the patched primary backend and return UNIMPLEMENTED from fork builds instead of referencing absent task types and common_params fields. Assisted-by: Codex:gpt-5 [gh] Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(dev): generate gRPC code before commit lint The coverage phase regenerates ignored protobuf bindings, but lint runs first and can fail against missing or stale output. Generate the pinned bindings before lint so the gate always type-checks the current schema. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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localai-org-maint-bot
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bc21f832aa |
gallery: add Laguna S 2.1 GGUF variants (#11188)
Add the official Q4_K_M and Q8_0 builds plus the DFlash speculative-decoding pairing for llama.cpp. Assisted-by: Codex:gpt-5 [Hugging Face API] Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
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mudler's LocalAI [bot]
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84972cb745 |
chore(model-gallery): ⬆️ update checksum (#11176)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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mudler's LocalAI [bot]
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e218c7f56a |
chore(model-gallery): ⬆️ update checksum (#11152)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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mudler's LocalAI [bot]
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e9c2754fc2 |
chore(model-gallery): propose variant groupings for review (#11139)
chore(model-gallery): propose variant groupings 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's LocalAI [bot]
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c56373d772 |
chore(model-gallery): ⬆️ update checksum (#11134)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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mudler's LocalAI [bot]
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6dadaea91c |
chore(model-gallery): ⬆️ update checksum (#11129)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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mudler's LocalAI [bot]
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2d889e61a6 |
feat(backend): add magpie-tts-cpp text-to-speech backend (#11115)
* feat(backend): add magpie-tts-cpp text-to-speech backend
Add a Go + purego backend wrapping the magpie-tts.cpp ggml port of NVIDIA's
Magpie TTS Multilingual 357M (encoder + autoregressive decoder over NanoCodec
tokens), producing 22.05 kHz mono audio in 5 baked voices (Aria, Jason, John,
Leo, Sofia; case-insensitive names or indices 0-4) across 9+ languages from a
single self-contained GGUF. Mirrors qwen3-tts-cpp / moss-tts-cpp: dlopen the
static-ggml shared library, bind the flat magpie_tts_capi_* C-API via purego
(no local C shim needed, the upstream .so exports it directly), and serve the
gRPC TTS + TTSStream methods behind base.SingleThread (the C context is not
reentrant across synthesize calls).
The backend CMakeLists translates the Makefile's -DGGML_{CUDA,METAL,VULKAN,HIP}
flags into upstream's MAGPIE_GGML_* toggles (upstream FORCE-overwrites the ggml
cache entries from those), pinned to magpie-tts.cpp v0.1.1
(e3f3dd1ebe22b64e7405f93b519f2d1930712568), which statically links ggml into
libmagpie-tts.so (ldd shows only system libs).
Wires the full registration: backend-matrix.yml (CPU amd64/arm64, CUDA 12/13,
Intel SYCL f16/f32, Vulkan amd64/arm64, ROCm, NVIDIA L4T + L4T CUDA 13, and
Darwin metal), backend/index.yaml metas and image entries, the root Makefile
build targets, the changed-backends backend-filter path mapping, the bump_deps
auto-bump matrix, a test-extra per-backend smoke job, the /backends/known
pref-only importer entry, the backend capabilities map (TTS + TTSStream, no
voice cloning), and the README / compatibility-table docs rows.
Verified locally: unit + e2e Ginkgo suites pass against the real q8_0 GGUF
(22.05 kHz mono WAV, RMS > 0.01), a live gRPC LoadModel + TTS round-trip
returns valid non-silent audio, and the pre-commit gates (make lint,
make test-coverage-check) pass, run manually with LOCALAI_TEST_HTTP_PORT
overriding the locally-occupied 9090.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* gallery: add magpie-tts-cpp model entries (q8_0 + f16)
Add the Magpie TTS Multilingual 357M GGUFs from mudler/magpie-tts.cpp-gguf to
the model gallery: q8_0 (~624 MB, near-lossless, fastest decode, recommended)
with an f16 (~784 MB) variant, both served by the magpie-tts-cpp backend.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* magpie-tts-cpp: bump pin to rewritten upstream v0.1.1 SHA
Upstream history was rewritten to purge accidentally committed build
artifacts; v0.1.1 now resolves to 6f7696cf.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---------
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
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mudler's LocalAI [bot]
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cda67dfb87 |
chore(model-gallery): ⬆️ update checksum (#11108)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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mudler's LocalAI [bot]
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ce6c42d677 |
chore(model-gallery): ⬆️ update checksum (#11092)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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mudler's LocalAI [bot]
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95afddd936 |
chore(model-gallery): ⬆️ update checksum (#11061)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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mudler's LocalAI [bot]
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3154bec357 |
chore(model-gallery): ⬆️ update checksum (#11036)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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mudler's LocalAI [bot]
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5c96e097ba |
feat(gallery): fix stale DFlash drafters and add the APEX families as variant ladders (#11027)
* fix(gallery): repoint qwen3-4b/qwen3.5-9b dflash drafters at post-rename GGUFs The drafters both entries referenced were converted from the pre-merge DFlash PR branch and carry dflash.target_layer_ids. llama.cpp reads dflash.target_layers and refuses the load. The stored values are offset by +1 relative to the HF-side field, so the files cannot be repaired by renaming the key and must be replaced. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ci): add apexentries HuggingFace client Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(apexentries): build the HF client via pkg/httpclient The apexentries HuggingFace client was constructed as a raw &http.Client{Timeout: 60s}. The repo convention (documented in .golangci.yml, which cannot express this as a forbidigo pattern) is that all outbound HTTP goes through pkg/httpclient, which refuses redirects by default and sets a TLS 1.2 floor. The std client follows redirects and forwards custom credential headers to the redirect target on a cross-host hop (GHSA-3mj3-57v2-4636). Only a User-Agent is sent today, but this calls an external API and an HF_TOKEN header added later would leak. Switch to httpclient.NewWithTimeout, preserving the 60 second timeout. No behaviour change for the current header set. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ci): discover APEX tiers by filename suffix Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ci): resolve unsloth counterparts and sharded quants Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ci): render APEX child entries with the dflash/mtp tag rule Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(apexentries): set backend, known_usecases and cross-repo drafters RenderChild left three gaps against the hand-written gallery entries. The generated entries reference gallery/virtual.yaml, which supplies no backend, so every generated entry named no engine at all. All comparable hand-written entries set backend: llama-cpp in overrides; do the same. Set known_usecases to [chat] alongside it: LocalAI falls back to the backend defaults when it is absent, so this is convention rather than breakage, but generated entries should not read differently from their neighbours. The drafter was also assumed to live in the repo publishing the weights. Speculative pairings routinely cross repos, and a drafter URI built from the weights repo 404s at install time. Add ChildInput.DraftRepo, used for both the drafter URI and its local path, falling back to Repo when empty so pairings that do ship the drafter alongside the weights are unchanged. The dflash/mtp tagging rule is untouched: the tag still follows SpecType and nothing else. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ci): dedupe generated entries against the existing gallery Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * apexentries: canonicalize HF URIs and dedup the generated batch Merge exists to stop a second gallery entry being added for weights the gallery already ships, but two gaps let duplicates through on a bulk run. The URI key was compared as an exact string while render.go only ever emits https://huggingface.co/{repo}/resolve/main/{file} and the gallery records 1038 of its URIs in huggingface://{repo}/{file} shorthand. A generated unsloth rung whose weights are already shipped in shorthand was therefore not recognised. canonicalURI reduces both spellings to one key and is applied on both sides, taking care that the repo is exactly the first two path segments so sharded quants in a subdirectory still match. A URI in neither form is returned untouched so other hosts dedup on their literal string. Merge also never accounted for entries it had just accepted, so two generated entries sharing a name or a primary URI both landed in add. Several APEX repos share one base model and resolve to the same unsloth counterpart, so the identical rungs are generated twice under the same name. Batch state is tracked locally rather than written back into the caller's ExistingIndex, which a caller may reasonably reuse. Name is still checked before URI: a name collision must block the add regardless of the weights. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ci): verify variant and tagging invariants in the gallery index Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ci): scope the apex-entries verifier to what it can actually judge The verifier reported 60 problems against the real gallery, 57 of which were llama.cpp assumptions meeting entries from other backends. A gate that is wrong 57 times out of 60 cannot gate anything. - The weight-count check catches a quant label collision in llama-cpp quant discovery, so it now runs only for overrides.backend: llama-cpp. Entries with no declared backend are skipped because their weights are declared in the referenced url: template, which the verifier never reads. - The dflash/mtp tag check now implements the per-backend table in .agents/adding-gallery-models.md instead of assuming llama.cpp's spec_type: vocabulary. ds4 declares mtp_path:/mtp_draft:; sglang declares speculative_algorithm: in a file this verifier cannot follow, so sglang entries are not judged in either direction. The check stays bidirectional within the backends it does judge. - sha256 is now required on .gguf files only, since every non-GGUF asset in the index belongs to a hand-curated entry outside this generator's scope. Against the current gallery this leaves exactly the three genuine problems: two entries setting spec_type:draft-mtp without the mtp tag, and one entry whose overrides.mmproj names a file it does not download. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(apexentries): anchor quant matching and invert the sha256 rule UnaccountedQuants matched files to wanted quants with strings.Contains, which reproduces the substring collision it was written to warn about: Q8_0 is a substring of UD-Q8_0, so a repo publishing only UD-Q8_0 was reported as publishing an unbuilt Q8_0. Subdirectory-sharded UD quants are the normal unsloth layout for large repos, so this fired on realistic input. Match on the quant label as an anchored token instead, the way DiscoverUnslothQuants does, so the diagnostic and the discovery it audits cannot disagree about what a file is. Root-level shards, the layout the diagnostic mainly exists to catch, stay detected. The sha256 requirement was scoped to .gguf, which exempted seven real model weights: wan_2.1_vae.safetensors and clip_vision_h.safetensors across the wan-2.1-*-ggml entries, both load-bearing weights named by gallery/wan-ggml.yaml. Invert the rule so a checksum is required on everything except metadata extensions, which keeps a future weight format covered by default rather than silently exempt. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ci): wire the apexentries command Adds the generation path to the apexentries command: list the mudler APEX repos, discover each one's quality ladder and its unsloth counterpart's quant rungs from the filenames actually published, render a child entry per build plus a family parent carrying the variants list, dedup against the gallery, and write the additions to -out or append them with -apply. Discovery shortfalls are reported at discovery time rather than left to the verifier. A quant or a tier that discovery drops leaves no trace in a finished gallery file, and because an empty imatrix ladder falls back to the plain one, a repo whose imatrix filenames all fail to match downgrades the whole family silently instead of erroring. Merge's single reused map is split into two reported categories. A URI match means the gallery already ships exactly these weights and referencing the existing entry is correct; a name collision means an unrelated entry owns the name and referencing it would substitute a different build. Multimodal children now declare known_usecases [chat, vision]. An explicit known_usecases suppresses the backend-default fallback, so a chat-only entry carrying an mmproj never matches the vision or multimodal gallery filters. .github/ci is invisible to go list ./..., so a workflow names both generator packages explicitly and their specs finally run on pull requests. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ci): gather APEX builds under the base model entry The hub for a family is the BASE model entry, never a generated *-apex parent. Somebody looking for qwen3.6-35b-a3b has to find every build of those weights under that one name, so a competing qwen3.6-35b-a3b-apex hub would split the family and leave half of it invisible. When the gallery already ships the base entry, a variants block is spliced into it textually, leaving its description, icon, tags, overrides and files untouched. Only a family whose base model the gallery does not ship gets a new hub, still named for the base model and carrying one of the discovered builds as its own payload so it declares a backend the verifier can judge. The line editing is factored into .github/ci/galleryedit, shared with the variantproposals job, so the two cannot drift apart on where a variants block belongs. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(apexentries): treat an unreadable optional counterpart repo as absent HuggingFace answers 401 Unauthorized, not 404, for a repository that does not exist when the request carries no credentials. FetchRepoFiles treated only 404 as absence, so probing for the OPTIONAL unsloth counterpart hard failed for every family that legitimately has none: 27 of the 45 APEX families are community merges that will never have an unsloth build, and a full run failed all of them. Split the fetch so the two call sites can apply different policies to the same response. The APEX repo itself stays strict: a 401 or 403 on a repo the run requires is a real failure and still errors. Only the optional probe tolerates it, because without a token 401 cannot be told apart from absence. That collapse is lossy in one direction, since a private or gated repo also answers 401, so the skipped candidates are named in the run summary alongside the other silent-shortfall counters instead of being dropped in silence. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(apexentries): report full-precision sources as a known exclusion The 45 APEX repos publish their unquantized F16 sources next to the imatrix ladder, flat or sharded. Discovery correctly emits nothing for them, but they were landing in the unclassified total, leaving a permanent baseline of 24 benign lines on every run. That baseline is what the unclassified check exists to prevent: a standing count of known-benign files is exactly what hides the one file that ever genuinely matters. Count full-precision sources separately and give them their own summary line, so unclassified returns to 0 and stays loud when something really is an unknown shape. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(apexentries): namespace local paths by owner and enable MTP builds localPath namespaced downloads by the repo basename alone, so two repos publishing the same filename under different owners collapsed to one local path. LiquidAI/LFM2.5-8B-A1B-GGUF and unsloth/LFM2.5-8B-A1B-GGUF collided that way, and both were offered from the same hub, so installing the second either overwrote the first model's weights or was skipped as already present while recording a sha256 that did not match the bytes on disk. The owner is now its own path segment: owner/repo is globally unique on HuggingFace and neither half can contain a separator, so uniqueness holds by construction. Verify gains a check for the whole class, that no local filename may map to two different upstream URIs. It surfaces seven pre-existing collisions in the gallery, which are left alone here. Entries built from the *-APEX-MTP-GGUF repos now configure MTP rather than shipping the heads inert, matching the pattern the hand-written MTP entries already use: spec_type:draft-mtp with spec_n_max and spec_p_min, tagged mtp, and no draft_model because the heads live in the weights. RenderChild no longer requires a separate drafter file before it will configure a spec type, while the cross-repo drafter path is unchanged. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add the APEX GGUF families as variant ladders Adds the imatrix quality ladder from each mudler/*-APEX-GGUF repo, a fixed subset of unsloth quant rungs where a counterpart repo exists, and the MTP builds, then attaches them to the base model entry so one entry offers every build of the same weights and LocalAI picks the one that fits the hardware. Ten existing base model entries gain a variants list; twenty-seven families that the gallery had no base entry for get one. Builds are discovered from the filenames each repo actually publishes rather than derived from its name, since six repos ship a stem that differs from their repo name. Every file carries a sha256 taken from the HuggingFace API. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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mudler's LocalAI [bot]
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9c8f510021 |
chore(model gallery): 🤖 add 1 new models via gallery agent (#11013)
chore(model gallery): 🤖 add new models via gallery agent 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's LocalAI [bot]
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a2c87947a9 |
chore(model-gallery): ⬆️ update checksum (#11005)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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mudler's LocalAI [bot]
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0cdd781c2d |
ci(gallery): propose variant groupings for review instead of letting them decay (#10992)
ci(gallery): propose variant groupings for review on a schedule A gallery entry may declare `variants:`, references to other entries that are alternative builds of the same weights, and auto-selection then installs the best build for the host. Those families exist only because humans curated them in two manual sweeps. The gallery agent dedupes on the HuggingFace repo URL and picks one quantization per model, so it never adds a second build of a repo it already has, and consequently never creates a family and never joins one. A model published across two repos lands as two unrelated standalone entries. The grouping decays as the gallery grows and nothing notices. Add a scheduled job, in the same shape as the checksum checker: compute offline, edit the index textually, open a pull request against ci-forks. It proposes and never decides. Grouping is a judgement call that has gone wrong in both directions, so the value is catching drift and surfacing candidates with their evidence. Three grouping signals, taken from the manual sweeps: same name once quantization markers are stripped, the `:` config-suffix convention, and the same primary weight filename once quantization markers are stripped. The third requires the same upstream repository. Excluding auxiliary files is not enough on its own: bert-embeddings, an ultravox audio model and a roleplay finetune all declare a primary file called llama-3.2-1b-instruct-q4_k_m.gguf, and grouping on that is the same error that linked four wan-2.1 entries and Z-Image-Turbo to qwen3-4b. Add gallery/variant-exclusions.yaml, a checked-in rejection ledger. A job that re-proposes declined candidates every night becomes noise and gets ignored. Declining a proposal is one flow-mapping line a reviewer adds inside the proposal pull request itself. Seeded with the six -abliterated pairs whose base is also in the gallery, the mistral-small multimodal pair, the whisper-1 alias, the kokoros language set, and the recurring finetune tokens. qat and apex are deliberately not on it: they are quantization techniques. Proposals refuse to nest, to let two parents claim one target, to target an entry that installs nothing, and to touch a merge anchor, since a variants key added to an anchor is inherited by every merging child. The anchor refusal names every entry that would inherit, which is the worklist a human needs. Run against the pre-sweep gallery, the job rediscovers 12 of the 19 groupings the second manual sweep made, with no false positives. The rest it reports as refusals or ledger declines rather than missing silently. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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mudler's LocalAI [bot]
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a4bab71f27 |
gallery: remove duplicated entries and lint against them recurring (#10996)
gallery: remove duplicated entries and lint against them coming back gallery/index.yaml declared eight names twice: deepseek-r1-distill-llama-8b, llama3.2-3b-enigma, qwen3-asr-0.6b, qwen3-asr-1.7b, qwopus-glm-18b-merged, voice-en-us-kathleen-low, whisper-large-q5_0 and whisper-small-q5_1. FindGalleryElement resolves a reference by returning the first match, so in every pair the second copy was unreachable: it could not be installed, could not be selected as a variant target, and could not be corrected, because any edit to it went to a copy nobody reads. A reference to such a name is also ambiguous to anything reasoning over the catalog, which is why the variant proposal job refuses to propose against them. Each pair was compared both as parsed entries and as raw text, and all eight were byte-identical apart from position. None of the sixteen blocks defines a YAML anchor or pulls one in with a merge key, so nothing was reachable only through a deleted block, and no entry named a removed copy as a variant target. Removing the second copy of each therefore changes no behaviour: the parsed set loses exactly eight entries and every surviving entry is field-for-field unchanged. The removal is textual, by line range, so the diff is pure deletions rather than a reflow of forty thousand lines. checkNoDuplicateEntryNames is the rule that keeps them out, added beside the existing gallery invariants and reporting in the same style. checkSingleVariantClaim closes the adjacent gap in the same place. VariantParents resolves a build claimed by two parents by taking the first in gallery order and calls that deterministic "for a gallery the linter would reject", but nothing rejected it: the invariant was held by curation alone. Now it is a rule, and the comment describes something real. No target is doubly claimed today, so the rule is green on arrival. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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mudler's LocalAI [bot]
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f381844403 |
gallery: group QAT, APEX and cross-backend builds under their base entry (#10983)
feat(gallery): group 21 more model families under variants Second variant-grouping sweep. QAT and APEX builds are now treated as quantization techniques rather than distinct weights, per maintainer ruling, so they group with their base entry instead of standing alone. Adds 15 new families: quantization and serving-config pairs for llama-3.2-1b/3b-instruct, dolphin-2.9-llama3-8b, phi-2-chat, ideogram-4, meta-llama-3.1-8b-instruct, omnivoice-cpp and qwen3-tts-cpp; the gemma-3 4b/12b/27b QAT families; and three cross-backend pairs (silero-vad plus its sherpa-onnx build, and the vibevoice TTS and ASR builds shared between the vibevoice-cpp and crispasr backends). The cross-backend pairs are the first entries that meaningfully exercise engine-preference ranking during auto-selection. Restructures four gemma-4 families (31b-it, 26b-a4b-it, e2b-it, e4b-it). Those bare entries were skipped by the first sweep, which left a QAT build as parent by default. The bare entry is what installs when nothing else fits, so it reclaims the parent slot and the former parent becomes a plain target. Every pre-existing relationship is preserved; nothing is dropped and nothing nests. gemma-4-12b-it has no bare entry, so it is left as is. qwen3-tts-cpp is a YAML anchor with nine merging children, so the five children that did not already override variants get an explicit empty list to stop them inheriting the parent's. Abliterated builds stay excluded: abliteration edits the weights to remove refusal behaviour, which makes them a different model rather than another build of the same one. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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mudler's LocalAI [bot]
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83a0f16a21 |
feat(gallery): let one gallery entry offer several builds of the same model (#10943)
* feat(system): expose raw detected capability for model meta resolution Model meta gallery entries express hardware fallback through candidate ordering rather than a capability map, so they need the undecorated detected capability string without Capability's default/cpu fallback chain. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(system): drop duplicate capability accessor, cover DetectedCapability ReportedCapability was added with a body identical to the existing DetectedCapability. Keep one accessor and move the specs onto it, since DetectedCapability had no direct coverage of its no-fallback behavior. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): parse IEC binary size suffixes (KiB..PiB) ParseSizeString accepted only SI suffixes, so a "20GiB" floor was rejected outright. Model and VRAM sizes are conventionally quoted in IEC units, and silently reading GiB as GB would understate a floor by about 7%. Purely additive: these inputs previously returned an unknown-suffix error. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add Candidate type for meta model entries Candidate is one option in a meta entry's ordered variant list. It names a concrete gallery entry and declares when that entry suits the host. EffectiveMinVRAM resolves the VRAM floor, letting an authored min_vram win over a nightly-inferred one. An unparseable floor errors instead of being treated as absent: swallowing a typo would turn a constrained candidate into an unconstrained one and select a too-large variant rather than fail loudly. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add hardware-aware model variant resolver Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): allow gallery model entries to declare variant candidates A gallery entry with a non-empty candidates list is a meta entry: it names an ordered list of concrete entries and resolves to the first one the host can satisfy, instead of describing model files directly. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): resolve meta model entries to hardware-appropriate variants at install Meta gallery entries carry an ordered candidate list; at install time the first candidate the host satisfies is resolved and its payload installed under the meta's name, so the model keeps a stable name regardless of which variant backs it. The resolution is recorded in the installed gallery config so a reinstall honors a prior pin and operators can see the backing variant. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(gallery): key meta pin recall on the installed name and detach resolved entries Six review findings on the meta-entry install path. Pin recall was keyed on the gallery entry name while applyModel writes the record under the install name (req.Name when supplied), so a meta installed under a custom name with a pin lost that pin on reinstall and was silently re-resolved onto a different variant, possibly swapping its backend. Compute the install name with applyModel's own precedence before the recall. ResolveMetaModel returned a shallow struct copy, so the resolved entry's Overrides aliased the gallery entry's map and the install path's in-place mergo merge wrote the caller's request into the shared catalog. Detach Overrides, ConfigFile, AdditionalFiles, URLs and Tags. Not exploitable today only because this path re-unmarshals the gallery per call, which is a property nobody should have to rely on. Also: overlay the meta's name onto the persisted config for meta installs so the gallery file no longer records the variant's name; move the pinned-VRAM warning below the variant validation so a pin naming a nonexistent entry does not warn about VRAM before failing for an unrelated reason; and stop seeding config.URLs in the config_file branch, which duplicated every declared URL. Add seven network-free specs driving InstallModelFromGallery with a meta entry: variant payload wins over the meta's legacy url fallback, the resolution record round-trips to disk, a pin is recorded and honored on reinstall including under a custom install name, and the resolved entry does not alias the gallery's maps. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(gallery): deep-copy meta overrides and make two specs functional ResolveMetaModel detached the resolved entry's Overrides and ConfigFile with maps.Clone, which only copies the top level. Gallery overrides are nested in practice (parameters.model is near-universal) and the install path merges the caller's request with mergo.WithOverride, which recurses into nested maps and overwrites them in place, so the gallery entry's own inner maps were still reachable and still got rewritten by the last caller to install. Copy both maps all the way down instead, recursing through the container shapes a YAML decoder produces. ConfigFile is not mutated on the install path today, but it carries the same kind of nested payload and leaving it shallowly cloned would invite the bug back. Also fix two specs that passed whether or not their target fix was present: - "does not write the caller's overrides back into the gallery entry" re-read the catalog from disk, which re-unmarshals fresh structs and so cannot observe in-memory aliasing. It now asserts against the in-memory gallery entry and drives the real mergo merge. - "round-trips the resolution record to disk under the meta's name" asserted a name that is already correct in the config_file branch. It now drives the url branch via a file:// fixture, where the meta-name overlay actually applies. Both were verified red by reverting their fix. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(gallery): lint meta model entry invariants in index.yaml Adds Ginkgo specs that parse the shipped gallery/index.yaml and enforce the invariants that keep meta entries safe: a legacy url fallback equal to the final candidate's url, references only to existing non-meta entries, a min_vram floor on every candidate but the last-resort one, a capability drawn only from the vocabulary the system can report, and descending VRAM floors within a capability group. The capability check is the only compensating control for a typo there. Candidate matching is a case-sensitive exact comparison against SystemState.DetectedCapability(), so an unknown value never matches and falls through silently instead of erroring. The vocabulary therefore mirrors the raw return set of getSystemCapabilities(), which notably excludes "cpu": that is a fallback key inside Capability(capMap) on the meta backend path, never a reported capability. A CPU-only host reports "default". These pass vacuously until the pilot meta entry lands; the guard is intentionally in place before the thing it guards. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(gallery): close coverage gaps in the meta entry lint The ordering invariant grouped candidates by capability and asserted floors descend within a group. A candidate with an EMPTY capability matches every host, so it does not belong in its own group: it dominates every later candidate whose floor is at or above its own, across capability groups. Track a running minimum floor over the unconditional candidates instead, which subsumes the old same-group check for the empty capability. Every spec skipped non-meta entries, so with zero meta entries in the index all five bodies were no-ops. Aligning GalleryModel.IsMeta() with GalleryBackend.IsMeta(), whose semantics are deliberately opposite, would have made all of them pass while checking nothing. Extract each invariant into a helper over a slice of entries returning the violations it finds, and cover those helpers with synthetic fixtures so the logic stays tested at zero meta entries. The index-driven specs are now a thin application of already proven logic. Also assert the index parses non-empty, report every violation in one run rather than aborting on the first, and parse the index once for the suite. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(gallery): add nightly denormalization of meta model candidates Fills the read-only backend, quantization and inferred_min_vram fields on meta gallery candidates and opens a PR, modeled on the existing checksum_checker job. Computing these needs network access, so it happens nightly rather than at install time. An authored min_vram is never modified: a human who measured a real load knows more than a pre-download estimate does. The index is rewritten via yaml.Node rather than a document round-trip. A full round-trip reflows all ~26k lines of gallery/index.yaml, which would bury the computed values and make the nightly PR unreviewable. The rewrite touches only the three derived keys, so authored styling survives and a run that computes nothing leaves the file untouched. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ci): keep the gallery denormalize diff reviewable and self-healing The nightly denormalization job edits YAML nodes instead of round-tripping structs so its PR stays small enough for a human to review, but the write path undid that: yaml.Marshal re-encoded the node tree at yaml.v3's default 4-space indent and dropped the leading document marker, reflowing roughly 6000 lines around the handful of real changes. Encode through yaml.NewEncoder at the index's authored 2-space indent and restore the header. A write that changes three fields now changes three lines. Stale inferred_min_vram values were also never cleared. Both skip paths (an authored min_vram is present, or the candidate is the last resort) returned before touching the field, so a candidate that gained a floor or became the last resort after a reorder kept an inferred value that EffectiveMinVRAM reported as a real constraint, failing the meta lint with no way for the job to self-heal. Clear the field before both skips. The workflow discarded a whole night's work on any single failure: the program exits 1 when a candidate cannot be estimated, which aborted the job before the PR step, so one unreachable candidate blocked every other refresh indefinitely. Capture the status, open the PR with what was computed, mark the PR body as partial, and fail the run afterwards so the problem still surfaces. Also preserve the index's existing file mode instead of forcing 0644, and drop the redundant //go:build ignore tag, since Go already skips dot directories and the sibling modelslist.go carries no tag. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add nanbeige4.1-3b meta entry with hardware-resolved variants Adds the first real meta entry to the gallery index. It resolves to the Q8_0 build on hosts with at least 6GiB of VRAM and to the Q4_K_M build everywhere else, installing either payload under the stable name nanbeige4.1-3b. The entry carries a url equal to its final candidate's url. LocalAI releases that predate candidates support parse the index non-strictly and drop the key silently, so without that url they would list the entry and install nothing. A regression spec parses the index the way those releases do and asserts every meta entry stays installable for them. Also teaches core/schema/gallery-model.schema.json about candidates. The schema sets additionalProperties: false at the top level, so an author following CONTRIBUTING.md and adding the yaml-language-server comment would otherwise get a validation error on this entry. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): make candidate entries complete, installable entries Reworks hardware-resolved gallery variants after a design pivot. There is no longer a separate "meta" entry kind. A gallery entry is a normal, complete entry that may additionally carry candidates:, a list of hardware-gated upgrades over itself, and the entry is itself the last-resort candidate. The previous design relied on a bare url: as the fallback for LocalAI releases that predate candidates support. That fallback is empty in practice: none of the 80 gallery/*.yaml files carry a top-level files:, and 1216 of 1281 index entries carry their payload in the index entry itself, so a url alone yields a config template with nothing to download. Since every released LocalAI reads gallery/index.yaml live from master, merging a payload-less entry would have shown every existing user a model that installs to a broken state. Making the entry its own base candidate removes the problem at the root: old clients drop the candidates key and install the entry exactly as they do today. Resolution order is now explicit pin, then capability plus VRAM over the declared upgrades, then the entry itself. The entry ALWAYS installs: when its own min_vram or capability is unmet the installer warns and installs it anyway, because there is nothing below it and refusing would make the gallery behave worse the newer the client is. A pin naming the entry's own name is valid and is how an operator declines an upgrade. IsMeta() becomes HasCandidates(), ResolveMetaModel becomes ResolveVariant, and the persisted meta_name record key becomes entry_name. GalleryBackend.IsMeta() is a separate concept and is untouched. The lint drops the three rules the pivot makes wrong (url equality with the final candidate, no inline payload, unconstrained final candidate) and gains one: the entry's own floor must sit strictly below every candidate's, since a base that outranks a candidate makes that candidate unreachable. The pilot entry is now the existing nanbeige4.1-3b-q4, which gains a 2GiB floor of its own and a single 6GiB upgrade to nanbeige4.1-3b-q8, replacing the separate nanbeige4.1-3b entry added in |
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mudler's LocalAI [bot]
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1618c2e445 |
chore(model gallery): 🤖 add 1 new models via gallery agent (#10971)
chore(model gallery): 🤖 add new models via gallery agent 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's LocalAI [bot]
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92dc326606 |
chore(model-gallery): ⬆️ update checksum (#10965)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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mudler's LocalAI [bot]
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626ae4d51e |
fix(model-artifacts): materialize longcat-video on the controller, and support companion repos (#10949)
* fix(model-artifacts): materialize longcat-video checkpoints on the controller longcat-video loads a checkpoint directory: its backend.py takes request.ModelFile when os.path.isdir(request.ModelFile) and otherwise falls back to snapshot_download. That places it in the same class as transformers/vllm/diffusers/sglang, but the allow-list added in #10910 did not enumerate it, so PrimaryArtifactSpec returned no managed artifact for a bare HuggingFace repo id. The consequence in distributed mode: nothing was acquired on the controller, ModelFileName fell through to the raw repo id, and staging skipped the resulting phantom /models/<owner>/<repo> path. The worker received a blank ModelFile, fell back to request.Model, and downloaded ~83GB from HuggingFace inside the remote LoadModel deadline - so the load could only ever fail with DeadlineExceeded while an abandoned backend process kept downloading. Note this materializes the full repository. The backend restricts its own snapshot_download with allow_patterns, and the avatar repo ships both base_model/ and base_model_int8/ where only one is ever loaded; inferred specs have no way to carry patterns today. Tracked separately. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): warn when staging skips a non-existent model path stageModelFiles logs "Staging model files for remote node" up front, then silently drops any path field that does not exist on the controller. The skip itself is legitimate and must stay: a backend outside managedArtifactBackends that takes a bare HuggingFace repo id gets an optimistically constructed path (ModelFileName falls through to the raw model reference) that was never materialized, and sources its own weights on the worker. Erroring would break those configs. But at debug level the operator is left with a reassuring staging line and no trace of the skip, so a genuine controller-side acquisition gap is indistinguishable from a healthy pass-through - it surfaces much later as a remote LoadModel timeout, on a worker that is quietly downloading tens of gigabytes. Raise the skip to warn and name the field, path, node and tracking key. Behavior is unchanged. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(model-artifacts): allow a config to declare companion artifacts A composed pipeline needs more than one HuggingFace snapshot. LongCat-Video-Avatar-1.5 loads its own transformer but takes the tokenizer, text encoder and VAE from the separate LongCat-Video base repo, so a single-artifact config cannot express it and the backend is left to fetch the second repo itself at load time. Widen the artifact model to target: model plus any number of named target: companion entries. Normalize accepts the new target and constrains a companion name to [a-z0-9][a-z0-9_-]{0,63} because that name is the option key the backend later receives; a companion may not claim primary_file, which only means anything for a load target. ModelConfig.Validate requires exactly one primary and requires it first, since Artifacts[0] is what ModelFileName, size estimation and staging all resolve from. Both acquisition paths now loop instead of touching index 0 alone: preloadOne for an already-installed config, bindPrimaryArtifact for a gallery install. Failure policy differs by provenance. An inferred primary keeps its warn-and-fall-back, because the legacy download path still exists for it. Companions are explicit by construction, so they are all-or-nothing: a config naming one is asserting the backend needs it, and failing at the acquisition boundary is far more legible than a missing-weights error surfacing later inside the backend. The cache key is deliberately unchanged. It hashes source identity only, never name or target, so every already-installed managed model still hits its existing snapshot instead of silently re-downloading. Two specs pin that: one proving a companion and a primary with identical sources agree on the key, and one pinning the digest of a known primary outright. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(model-artifacts): hand resolved companion snapshots to the backend A materialized companion is useless until the backend can find it, and its location is a content-addressed cache key that does not exist until the artifact resolves. A static gallery override cannot carry that, and persisting it into the config YAML would rot the moment a re-resolve produced a new key. Synthesize it instead at load time: each resolved companion becomes "<artifact name>:<snapshot path>" in ModelOptions.Options, reusing the key:value convention backends already parse for options like attention_backend. The value stays relative to the models directory so a remote worker can resolve it under its own ModelPath once staging has rewritten the model root. An option the author set explicitly always wins, so pinning a companion to a local checkout still beats the managed snapshot. longcat-video resolves base_model through ModelPath, the same convention qwen-tts, voxcpm, outetts and ace-step already use for companion assets. Its sibling-directory heuristic is deleted: it looked for a LongCat-Video directory next to the model, which cannot exist under the content addressed .artifacts/huggingface/<key>/snapshot layout, so it was dead code the moment the model became managed. The gallery entry declares both repositories and restricts each with allow_patterns. The avatar repo ships base_model/ and base_model_int8/ and only ever loads one, so fetching the whole repo would roughly double the download. The patterns match the entry's own options (use_distill true, use_int8 default false); enabling use_int8 here also requires adding base_model_int8/**, which is called out in the entry. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): stage managed artifact trees from the models root Staging anchored the worker's models directory on the primary snapshot whenever a model was managed, so a companion snapshot could not reach the worker at all. frontendModelsDir was derived by stripping the Model relative path off the end of ModelFile. For a managed artifact nothing matches: ModelFile is .artifacts/huggingface/<key>/snapshot while Model stays a bare HuggingFace repo id, so the strip was a no-op and the "models directory" came out as the snapshot itself. Two consequences, both silent. Staging keys lost the .artifacts/huggingface/<key>/snapshot prefix, so two snapshots of one model were indistinguishable on the worker. And a companion, which lives in a sibling snapshot directory outside the primary, fell outside that directory entirely: StagingKeyMapper.Key collapsed its files to bare basenames and resolveOptionPath could not resolve the relative option at all, so it was skipped without a word. Derive the models root from the artifact tree instead when the path runs through it, and compute the worker's ModelPath from the file's path relative to that root rather than from the Model field. The legacy layout is unaffected: where Model really is the relative path, the new derivation reduces to the old one, which a regression spec pins. This deliberately changes an invariant that router_dirstage_test.go pinned: for a managed primary, ModelFile and ModelPath were both the snapshot directory, and staging keys were relative to it. Now ModelFile is the snapshot, ModelPath is the models root above it, and keys keep the full relative path. That spec is updated rather than accommodated, with the reasoning recorded inline, because the old invariant is exactly what made a sibling companion unreachable. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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mudler's LocalAI [bot]
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10211948b5 |
chore(model gallery): 🤖 add 1 new models via gallery agent (#10942)
chore(model gallery): 🤖 add new models via gallery agent 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's LocalAI [bot]
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81c407bc40 |
chore(model-gallery): ⬆️ update checksum (#10938)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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mudler's LocalAI [bot]
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55e2726958 |
chore(model-gallery): ⬆️ update checksum (#10906)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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mudler's LocalAI [bot]
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4f592c8734 |
chore(model-gallery): ⬆️ update checksum (#10891)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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3f8806b0b2 |
chore(model gallery): 🤖 add 1 new models via gallery agent (#10881)
chore(model gallery): 🤖 add new models via gallery agent 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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14c7c04feb |
chore(model gallery): 🤖 add 1 new models via gallery agent (#10874)
chore(model gallery): 🤖 add new models via gallery agent 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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e9056399a7 |
feat(gallery): add MOSS-TTS-Local v1.5 models for the moss-tts-cpp backend (#10877)
Add the q8_0 (default) and f16 gallery entries for the moss-tts-cpp backend, each pulling the MOSS-TTS-Local v1.5 GGUF plus the MOSS-Audio-Tokenizer-v2 codec and the text tokenizer from mudler/MOSS-TTS-Local-Transformer-v1.5-GGUF. The backend auto-discovers the codec and tokenizer siblings; output is 48 kHz stereo with reference-audio voice cloning. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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0bd7a29f31 |
feat(gallery): add Gemma 4 llama.cpp MTP variants; fix gemmable-4-12b-mtp (#10876)
Google shipped the Gemma 4 MTP drafter heads and llama.cpp merged native support in ggml-org/llama.cpp#23398. LocalAI's pinned llama.cpp already carries it, and the config plumbing (draft_model + core/config/mtp.go) was built for exactly this path, but no official Gemma 4 gallery entry wired it up. Add llama.cpp draft-mtp speculative-decoding variants for the dense sizes, sourced from the unsloth QAT GGUF repos (target UD-Q4_K_XL + mtp-*.gguf drafter + BF16 mmproj): - gemma-4-e2b-it-qat-mtp - gemma-4-e4b-it-qat-mtp - gemma-4-12b-it-qat-mtp - gemma-4-31b-it-qat-mtp These replace the previously commented-out attempts, which were disabled because the Janvitos/boxwrench drafter GGUFs declared the architecture as `gemma4_assistant` (underscore) and failed to load on stock llama.cpp. The unsloth drafters use the upstream `gemma4-assistant` (hyphen) spelling that mtp.go's isDraftOnlyAssistantArch expects, so they load without any backend patch. The 26B-A4B MoE is intentionally omitted (the upstream PR reports no meaningful MTP speedup for it). Also fix gemmable-4-12b-mtp: it loaded the draft-only `-mtp` GGUF as the main model with no draft_model set, which cannot run standalone. It now loads the target as the model, wires the drafter via draft_model, enables spec_type:draft-mtp, and downloads both files. All sha256 pins were taken from the HuggingFace API lfs.oid (reliable content hash even for Xet-backed repos). Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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dffcbd7e5d |
chore(model-gallery): ⬆️ update checksum (#10871)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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bbe018c1a0 |
feat(bonsai): PrismML llama.cpp fork backend + Bonsai/Ternary-Bonsai gallery models (#10834)
feat(bonsai): add PrismML llama.cpp fork backend + Bonsai gallery models Adds a new `bonsai` backend that runs the PrismML fork of llama.cpp (github.com/PrismML-Eng/llama.cpp, `prism` branch), which ships the Q1_0 (1-bit) and Q2_0 (ternary / 1.58-bit) weight-quantization kernels used by the Bonsai and Ternary-Bonsai models. Stock llama.cpp cannot decode these quants. Modeled on the turboquant backend: reuses backend/cpp/llama-cpp/grpc-server.cpp against the fork's libllama via a thin wrapper Makefile, so the sub-2-bit models are served with the same OpenAI-compatible API. No grpc-server allow-list patch is needed (bonsai adds weight quants, transparent to the server, not KV-cache types), and the reused server compiles cleanly against the fork with no skew patches (validated locally via a CPU docker build; patches/ is present but empty for any future re-pin skew). Backend wiring: backend/cpp/bonsai/, .docker/bonsai-compile.sh, backend/Dockerfile.bonsai, top-level Makefile targets, backend-matrix.yml build rows (CPU, CUDA 12/13, L4T, SYCL f32/f16, Vulkan, ROCm/hipblas), backend/index.yaml meta-backend + per-platform images, and a nightly bump_deps entry tracking the `prism` branch. Gallery: 8 entries across 4 families - bonsai-8b-1bit, ternary-bonsai-8b (+g64, +pq2), bonsai-27b-1bit (vision), ternary-bonsai-27b (+pq2, +g64, vision). The 27B models wire the mmproj vision tower; the DSpark speculative drafter GGUFs are not wired (custom semi-autoregressive drafter, not a standard llama.cpp draft model). Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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8c9b3b2e33 |
chore(model-gallery): ⬆️ update checksum (#10854)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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a23fcc90c3 |
feat(gallery): add Qwen3.5-4B DFlash speculative-decoding model (#10842)
Pairs unsloth/Qwen3.5-4B-GGUF (Q4_K_M target) with the AtomicChat/Qwen3.5-4B-DFlash-GGUF Q8_0 drafter (quantized from z-lab/Qwen3.5-4B-DFlash, upstream GGUF arch `dflash`), same shape as the existing DFlash entries. Assisted-by: Claude Code:claude-fable-5 [Bash] [Read] [Edit] 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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bcc41219f7 |
feat: materialize Hugging Face model artifacts (#10825)
* feat(config): add model artifact source contract Assisted-by: Codex:GPT-5 [Codex] * feat(downloader): add authenticated raw-byte progress Assisted-by: Codex:GPT-5 [Codex] * feat(huggingface): resolve immutable snapshot manifests Assisted-by: Codex:GPT-5 [Codex] * feat(models): add artifact storage primitives Assisted-by: Codex:GPT-5 [Codex] * feat(models): materialize pinned Hugging Face snapshots Assisted-by: Codex:GPT-5 [Codex] * feat(models): bind managed snapshots at runtime Assisted-by: Codex:GPT-5 [Codex] * feat(gallery): materialize model artifacts during install Assisted-by: Codex:GPT-5 [Codex] * feat(gallery): declare managed Hugging Face artifacts Assisted-by: Codex:GPT-5 [Codex] * feat(models): preload managed model artifacts Assisted-by: Codex:GPT-5 [Codex] * fix(gallery): retain shared artifact caches on delete Assisted-by: Codex:GPT-5 [Codex] * feat(models): report artifact acquisition progress Assisted-by: Codex:GPT-5 [Codex] * refactor(backends): load managed models from ModelFile Assisted-by: Codex:GPT-5 [Codex] * refactor(backends): load staged speech model snapshots Assisted-by: Codex:GPT-5 [Codex] * refactor(backends): use staged snapshots in engine backends Assisted-by: Codex:GPT-5 [Codex] * test(distributed): cover staged artifact snapshots Assisted-by: Codex:GPT-5 [Codex] * docs: explain managed model artifacts Assisted-by: Codex:GPT-5 [Codex] * docs: add product design context Assisted-by: Codex:GPT-5 [Codex] * feat(ui): show model artifact download progress Assisted-by: Codex:GPT-5 [Codex] * Eagerly materialize Hugging Face artifacts Materialize HF-backed model references as managed GGUF artifacts during load, with lazy download retained only as fallback. Assisted-by: Codex:GPT-5 [shell] * Refactor HF downloads through a shared executor Assisted-by: Codex:GPT-5 [shell] * drop 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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88cc80ee3d |
chore(model-gallery): ⬆️ update checksum (#10831)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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9f14571397 |
chore(model-gallery): ⬆️ update checksum (#10816)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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4056283aa4 |
[voice] feat: add managed voice cloning profiles (#10799)
* feat(ui): add voice library workflow Give administrators a production-ready flow to record or upload consented reference audio, manage reusable profiles, inspect API usage, discover compatible models, and hand a saved voice directly to text-to-speech. Assisted-by: Codex:gpt-5 * feat(voice): add managed voice cloning profiles Make reusable reference voices manageable through the admin API instead of requiring model-directory and YAML edits. Discover compatible installed and gallery models from server-side backend capabilities, retain explicit model configuration controls, and stage saved references for supported backends. Expose profile management through REST and MCP, document backend-specific behavior, and cover the workflow from profile creation through real Qwen3-TTS synthesis. Harden the agent-job HTTP test against completion racing cancellation. Assisted-by: Codex:gpt-5 --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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67c14e1b7e |
chore(model-gallery): ⬆️ update checksum (#10798)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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b00422e45f |
feat(backends): add LongCat video and avatar generation (#10792)
* feat(backends): add LongCat video and avatar generation Assisted-by: Codex:GPT-5 [apply_patch] [exec_command] [web] * refactor(config): declare model I/O modalities Make model configs declare input and output modalities so capability discovery no longer branches on backend or checkpoint names. Complete the LongCat gallery and user documentation, make the SDPA patch apply to the pinned upstream revision, and stabilize the Agent Jobs race exposed by the required hook. Assisted-by: Codex:GPT-5 [web] --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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fb0f5e4bdd |
feat(gallery): add Qwen DFlash speculative-decoding models (#10791)
Add four ready-to-run DFlash speculative-decoding entries for the llama.cpp backend, now that upstream DFlash support (draft-dflash) is in the pinned llama.cpp. Each entry bundles a full target model with its small z-lab block-diffusion drafter and sets spec_type:draft-dflash, spec_n_max:15, and flash attention (required by DFlash): - qwen3-4b-dflash (Qwen3-4B + Qwen3-4B-DFlash drafter) - qwen3.5-9b-dflash (Qwen3.5-9B + Qwen3.5-9B-DFlash drafter) - qwen3.6-27b-dflash (Qwen3.6-27B dense + drafter) - qwen3.6-35b-a3b-dflash (Qwen3.6-35B-A3B MoE + drafter) The 4B pair uses the base Qwen3-4B target (not Qwen3.5-4B): its drafter reports general.name "Qwen3 4B DFlash" and is the canonical pairing documented upstream. All drafters were downloaded and verified to carry GGUF architecture "dflash" (not the fork-only "dflash-draft" / "DFlashDraftModel") so they load in the upstream backend, and every drafter SHA256 was confirmed against the downloaded bytes. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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cad07be2fc |
chore(model-gallery): ⬆️ update checksum (#10789)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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9b4f373bc4 |
chore(model-gallery): ⬆️ update checksum (#10778)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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6ceb2f86a7 |
chore(model-gallery): ⬆️ update checksum (#10763)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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c5b36639d4 |
chore(model gallery): 🤖 add 1 new models via gallery agent (#10755)
chore(model gallery): 🤖 add new models via gallery agent 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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94bdc825dc |
feat(backend): add moss-transcribe-cpp backend (MOSS-Transcribe-Diarize) (#10756)
C++/ggml transcription + speaker diarization + timestamps backend. Purego dlopens libmoss-transcribe.so (ggml statically linked) from moss-transcribe.cpp and serves offline AudioTranscription, parsing the [start][Sxx]text[end] output into segments with nanosecond timestamps. Adds the importer (surfaces in GET /backends/known), backend-matrix (Linux + Darwin/metal), backend/index.yaml, and a gallery entry (default q5_k GGUF from mudler/moss-transcribe.cpp-gguf). Local L0 smoke (go build + go test ./... = 16 pass, golangci-lint 0 issues) passed against the real libmoss-transcribe.so. The pre-commit coverage gate (full pkg/core + tests/e2e) could not run in the authoring sandbox (no live models, port 9090 held); CI must enforce it before merge. Assisted-by: Claude:claude-opus-4-8 golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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35024338a6 |
feat(crispasr): add F5-TTS support and gallery model (#10753)
Link the f5-tts library into the crispasr backend so CrispASR's native F5-TTS runtime (SWivid F5-TTS, 22-layer DiT flow-matching + built-in Vocos vocoder) is compiled in. The single self-contained GGUF auto-detects as f5-tts through the session router, so no explicit backend selector is needed. Add the f5-tts-crispasr gallery entry (cstr/f5-tts-GGUF) and an env-gated e2e synthesis spec. F5-TTS is voice-cloning only and has no baked speaker: it clones from a reference WAV plus its transcript, supplied via the voice/voice_text options. The gallery description documents this bring-your-own-reference requirement. Verified e2e on the pinned engine (278fb79): the GGUF auto-detects as f5-tts, the reference voice loads, and synthesis produces a valid 24 kHz mono WAV. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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e948f27965 |
chore(model-gallery): ⬆️ update checksum (#10749)
⬆️ Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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8671c8adac |
chore(model gallery): 🤖 add 1 new models via gallery agent (#10743)
chore(model gallery): 🤖 add new models via gallery agent 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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Dennis Huang
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ff5758113b |
chore(model gallery): add MiniCPM series models (#10699)
Add 9 MiniCPM models to the gallery: - MiniCPM-V 4.6 (1.3B multimodal, edge-optimized) - MiniCPM-V 4.6 Thinking (1.3B multimodal with reasoning) - MiniCPM-V 4 (multimodal) - MiniCPM-o 4.5 (8B omni-modal, vision+speech) - MiniCPM-o 2.6 (7.6B omni-modal) - MiniCPM5-1B (text) - MiniCPM4.1-8B (text) - MiniCPM4-8B (text) - MiniCPM3-4B (text) All sha256 checksums sourced from HuggingFace LFS metadata. Signed-off-by: Dennis Huang <huangsiyuan20060408@hotmail.com> |