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>
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>
* 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>
* 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>
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>
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>
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>
The Q4_K_M quant degraded tool-call reliability for LFM2.5-8B-A1B.
Switch the gallery entry to the Q8_0 GGUF (sha256 verified via HF
x-linked-etag) while keeping the native jinja tool-parsing config.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(voice-detect): add Go purego backend for voice-detect.cpp
Add backend/go/voice-detect implementing the Backend gRPC voice subset
(VoiceEmbed/VoiceVerify/VoiceAnalyze) over libvoicedetect.so via purego,
mirroring the parakeet-cpp / omnivoice-cpp backends.
The flat voicedetect_capi C ABI is dlopen'd cgo-less; malloc'd string and
float-vector returns are owned by Go and released through the matching capi
free functions, with the per-ctx last error surfaced into Go errors. Calls are
serialized via base.SingleThread since the C context is not reentrant.
Proto field mapping:
- VoiceEmbed: VoiceEmbedRequest.audio (path) -> embed_path -> Embedding+Model.
- VoiceVerify: audio1/audio2 + threshold (<=0 falls back to the
verify_threshold option, default 0.25) -> verify_paths -> verified/distance/
threshold/confidence/model/processing_time_ms.
- VoiceAnalyze: audio (path) -> analyze_path_json; the JSON age/gender/emotion
document maps to a single VoiceAnalysis segment (start/end 0; gender "label"
-> dominant_gender with the remaining float scores as the gender map; emotion
label/scores -> dominant_emotion/emotion).
The Makefile pins voice-detect.cpp to 47546430, clones+builds libvoicedetect.so
with ggml static-linked (PIC, GGML_NATIVE off) so dlopen needs no external
libggml/libvoicedetect; ldd on the artifact shows only system libs. Ginkgo
tests cover option parsing and analyze-JSON mapping; embed/verify smoke specs
gate on VOICEDETECT_BACKEND_TEST_MODEL + VOICEDETECT_BACKEND_TEST_WAV.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(voice-detect): wire backend into index, gallery and build
Register the voice-detect.cpp speaker-recognition + voice-analysis
backend (added in Voice-INT-A) into LocalAI's distribution surfaces,
mirroring the ced backend (the closest mudler C++/ggml audio analogue):
- backend/index.yaml: add the &voicedetect meta-backend (capabilities
platform map, no top-level uri) plus the full set of concrete per-arch
image entries (cpu/cuda12/cuda13/metal/rocm/sycl/vulkan/l4t and the
-development variants). Referential integrity audited - every alias
target resolves.
- gallery/index.yaml: add 5 model entries on backend voice-detect -
ECAPA-TDNN, WeSpeaker ResNet34, 3D-Speaker ERes2Net, CAM++ and the
wav2vec2 age/gender/emotion analyze model. The engine architecture is
read from GGUF metadata (voicedetect.arch) at load. GGUF artifacts are
not yet published: each files: entry points at the intended
mudler/voice-detect-gguf location with a TODO to fill sha256 after
upload (no fabricated hashes).
- .github/backend-matrix.yml: add the linux build matrix block + the
darwin metal entry mirroring ced.
- .github/workflows/bump_deps.yaml: track mudler/voice-detect.cpp via
VOICEDETECT_VERSION (pin 47546430, = 4754643).
- core/config/backend_capabilities.go: register voice-detect in the
backend capability map (VoiceVerify/VoiceEmbed/VoiceAnalyze ->
speaker_recognition), mirroring speaker-recognition.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(face-detect): add purego Go backend for face-detect.cpp
Add the LocalAI Go backend that dlopens libfacedetect.so (the flat
facedetect_capi_* C-ABI) via purego, mirroring the sibling voice-detect
backend. Implements the Face subset of the Backend gRPC service:
- Embeddings(PredictOptions): Images[0] base64 -> temp file -> embed_path
-> L2-normalized ArcFace embedding.
- Detect(DetectOptions): src -> detect_path_json -> Detection boxes
(class_name "face", [x1,y1,x2,y2] -> x/y/w/h).
- FaceVerify(FaceVerifyRequest): two images + threshold + anti_spoof ->
verify_paths; best-effort img areas via detect.
- FaceAnalyze(FaceAnalyzeRequest): img -> analyze_path_json -> per-face
age + gender ("M"/"F" normalized to "Man"/"Woman").
The Makefile pins face-detect.cpp to 636a1963 and builds the shared lib
with ggml + vendored libjpeg-turbo static (PIC), so the .so is
ldd-clean (no libggml) and exports only facedetect_capi_* (no jpeg_
symbols). Gated Ginkgo e2e mirrors voice-detect.
Note for the gallery-wiring task: backend registration (index.yaml,
gallery, core/config/backend_capabilities.go) is intentionally not
touched here.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* fix(voice-detect): replace em dashes in net-new descriptions
Project style forbids em/en dashes. Replace the three U+2014 chars
introduced by the voice-detect gallery/index wiring with `-`/`:`.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(face-detect): wire backend into index, gallery and build
Register the face-detect.cpp face detection / embedding / verification /
analysis backend (added in Face-INT-A) into LocalAI's distribution
surfaces, mirroring the voice-detect wiring (the closest mudler C++/ggml
recognition analogue):
- backend/index.yaml: add the &facedetect meta-backend (capabilities
platform map, no top-level uri to avoid the meta-backend gotcha) plus
the full set of concrete per-arch image entries (cpu/cuda12/cuda13/
metal/rocm/sycl-f16/sycl-f32/vulkan/l4t and the -development variants),
22 entries. Referential integrity audited: every alias target resolves.
- gallery/index.yaml: add 4 model entries on backend face-detect -
face-detect-buffalo-l/m/s (insightface SCRFD + ArcFace/MBF, NON-COMMERCIAL)
and face-detect-yunet-sface (OpenCV-Zoo YuNet + SFace, APACHE-2.0, the
commercial-friendly alternative). The detector/embedder architecture is
read from GGUF metadata (facedetect.arch) at load; only the real
verify_threshold option is set (0.35 buffalo, 0.363 sface). GGUF
artifacts are not yet published: each files: entry points at the
intended mudler/face-detect-gguf location with a TODO to fill sha256
after upload (no fabricated hashes).
- core/config/backend_capabilities.go: register face-detect in the
backend capability map (Embedding/Detect/FaceVerify/FaceAnalyze ->
face_recognition), mirroring insightface.
- .github/backend-matrix.yml: add the linux build matrix block + the
darwin metal entry mirroring voice-detect.
- .github/workflows/bump_deps.yaml: track mudler/face-detect.cpp via
FACEDETECT_VERSION (pin 636a1963).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* fix(recon): voice-detect metal build branch + face-detect gallery usecases
Add the missing metal BUILD_TYPE branch to the voice-detect Makefile
forwarding -DVOICEDETECT_GGML_METAL=ON, mirroring face-detect, so the
darwin metal CI artifact is built with the Metal backend instead of
CPU-only.
Expand the 4 face-detect gallery models' known_usecases to
[face_recognition, detection, embeddings] to match the backend
capabilities map and the mirrored insightface-buffalo entries, so
auto-selection for /v1/detect and /embeddings works.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* docs(recon): document voice-detect and face-detect ggml backends
Document the new standalone C++/ggml biometric backends as the
recommended/default option for face and voice recognition, keeping the
existing Python insightface / speaker-recognition backends framed as the
legacy path.
- features/face-recognition.md: add a face-detect (ggml) backend section
with the gallery entries (buffalo-l/m/s non-commercial, yunet-sface
Apache-2.0), licensing, and verify/detect/analyze quickstart.
- features/voice-recognition.md: add a voice-detect (ggml) backend
section with the gallery entries (ecapa-tdnn, wespeaker-resnet34,
eres2net, campplus speaker recognizers; emotion-wav2vec2 non-commercial
analyze head) and quickstart.
- reference/compatibility-table.md: add face-detect.cpp and
voice-detect.cpp rows to the Vision, Detection & Recognition table.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(gallery): publish recon backend GGUF uris + sha256
Fill in the published HuggingFace GGUF uris and verified sha256 for the
9 recon gallery entries (voice-detect-* and face-detect-*), and remove
the TODO publish markers. Correct the eres2net, campplus, and
emotion-wav2vec2 uris to the actual published filenames.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(gallery): re-embed buffalo anti-spoof + add audeering age/gender voice model
Update the 3 buffalo face-detect GGUF sha256 (anti-spoof ensemble now
embedded and re-uploaded under the same filenames/uris) and note the
FaceVerify anti_spoof request flag in each description. Add a new
voice-detect-age-gender-wav2vec2 gallery entry mirroring the emotion
model.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(gallery): add face-detect-buffalo-sc and antelopev2 packs
Add gallery entries for two newly-published insightface face packs on
the face-detect backend: buffalo_sc (smallest pack, SCRFD-500M + small
ArcFace) and antelopev2 (higher-accuracy, SCRFD-10G + ArcFace glint360k
R100, 512-d). Both are non-commercial research-only.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(recon): honor LocalAI per-model threads in voice/face-detect backends
LocalAI spawns one backend process per model and serves requests
concurrently, so the engines' own min(hardware_concurrency, 8) default
can oversubscribe cores. Forward the per-model Threads value from the
gRPC LoadModel options into the engine via VOICEDETECT_THREADS /
FACEDETECT_THREADS (read at backend construction) before the capi load.
A non-positive Threads is treated as unset, leaving the engine default.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump backend pins to CPU-optimized engine commits
voice-detect.cpp -> 0d9c1b3 (radix-2 FFT FBank, threads, flash attn + cached
pos-conv); face-detect.cpp -> 523aee1 (thread-gated direct conv, threads).
Brings the CPU optimizations into the LocalAI backend builds. GGUF format and
parity unchanged, so the published HF GGUFs remain valid.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump backend pins to round-2 CPU-optimized engines
voice-detect.cpp -> fe7e6a3 (ERes2Net 1x1->mul_mat, CAM++ layout+context,
wav2vec2 conv-LN, ECAPA capture-drop, AVX512 dispatch opt-in); face-detect.cpp
-> 9c8adb7 (AVX2 Winograd F(2x2,3x3) for SCRFD/ArcFace 3x3 convs, ArcFace
BN-fold). Parity unchanged (cosine=1.0); GGUF format unchanged, HF GGUFs valid.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump backend pins to round-3 Winograd engines
voice-detect.cpp -> 45122ec (Winograd F(2x2,3x3) for WeSpeaker/ERes2Net 3x3
convs, -22%/-20% @8t); face-detect.cpp -> cd5c962 (Winograd F(4x4,3x3) for
SCRFD large maps, -22% @1t on top of F(2x2), more load-stable). Parity held
(cosine=1.0); GGUF format unchanged, HF GGUFs valid.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump backend pins to round-4 Winograd engines (CPU opt complete)
voice-detect.cpp -> d2839ca (CAM++ FCM 2D convs through Winograd, -15.5%/-10.3%);
face-detect.cpp -> c1db23d (AVX2-vectorized Winograd tile transforms, SCRFD
detect -14%/-9.6%). Final CPU optimization round; the conv-kernel lever class is
now exhausted (parity held cosine=1.0; GGUF/parity unchanged, HF GGUFs valid).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump face-detect pin to deep-kernel engine (7ae5c4d)
face-detect.cpp -> 7ae5c4d: register-blocked winograd-domain GEMM microkernel
(2.8x isolated GFLOP/s), AVX-512 zmm evolution behind runtime CPUID dispatch
(ship-safe, AVX2 fallback bit-identical), bias/relu fused into the winograd
output transform, and SFace Conv+BN fold + bias/PReLU fusion. SCRFD detect
~1.4x faster end-to-end vs the round-4 baseline; parity bit-exact; portable
single binary (function-multiversioned, no global -mavx512f).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump voice-detect pin to ECAPA operand-order win (e9c56ae)
voice-detect.cpp -> e9c56ae: weight-as-src0 mul_mat order in ECAPA's F32
conv1d_same (routes through tinyBLAS sgemm); ECAPA embed 1.67x @1t / ~1.3x @8t,
parity cosine=1.0. Isolated to encoder.cpp (ECAPA-only); ERes2Net/CAM++/WeSpeaker
do not call conv1d_same so are provably unaffected.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump pins to FMA-throughput engines (voice f7b9f89, face 2d2d5f0)
face -> 2d2d5f0: route ArcFace 3x3 body convs through the AVX-512 winograd
microkernel (kWinoMinSize 80->14); ArcFace 1.62x @1t, SCRFD detect to 0.966 of
MLAS @1t, no regression. voice -> f7b9f89: runtime-CPUID-dispatched AVX-512
winograd-GEMM microkernel (ship-safe, AVX2 fallback bit-identical); WeSpeaker
1.90x @1t. Parity cosine=1.0 throughout; portable single binaries.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump pins to MLAS-class direct-conv engines (voice 7ecfd07, face be22d67)
Hand-tuned nChw16c AVX-512 register-tiled direct-conv microkernel (~263 GFLOP/s,
within 6-7% of MLAS per-op efficiency), runtime-CPUID-dispatched + AVX2 fallback,
fused bias/relu. voice 7ecfd07: default 3x3-s1 kernel for WeSpeaker (+37%/+32%)
+ ERes2Net, CAM++ pinned to Winograd. face be22d67: shape-gated to the ArcFace
recognizer body (+25-27% @8t); SCRFD detector stays on Winograd (no regression).
Parity cosine=1.0 / detect <=1px on AVX-512 + AVX2 paths. Portable single binaries.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump voice pin to Phase-A blocked backbone (f4e7eef)
WeSpeaker ResNet34 runs as one nChw16c blocked island (2 reorders/forward vs
~60) on AVX-512, default; per-conv directconv fallback on AVX2. +2.9% @1t /
+17-19% @8t vs per-conv directconv, parity cosine=1.0. The conv microkernel is
already FMA-bound near peak (~0.86-0.98x MLAS-implied); residual to MLAS is
sub-peak edge + non-conv tail, documented in docs/cpu-optimization.md.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump pins to breadth blocked-backbone (voice 7f66871, face d80092b)
voice 7f66871: AVX2-vectorized (ymm) blocked island - AVX2-only hosts now run
the blocked backbone for WeSpeaker (2.3x over per-conv-AVX2, cosine=1.0);
ERes2Net stays per-conv (blocked regresses, opt-in only); CAM++ Winograd-pinned.
face d80092b: ArcFace recognizer blocked island, AVX-512 default (-13% @8t, ~0.90x
MLAS, the closest conv result), auto per-conv on AVX2; SCRFD untouched on Winograd
(0 island invocations during detect). Parity cosine=1.0 / detect <=1px throughout.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump pins to small-spatial + stem conv kernels (voice 99b1804, face 47fdab6)
Measured-gap-driven conv kernels: small-spatial (fill the register tile when
output width <= tile width) + small-IC stem + strided-1x1/downsample recovery.
ArcFace recognizer 0.57 -> 0.70x MLAS @1t (the closest conv model), WeSpeaker
0.65 -> 0.79x @1t. Parity cosine=1.0 / detect <=1px. The OC-block-sharing lever
was a measured dead-end (deep stride-1 is L3-weight-bandwidth bound, not
read-port bound) and was NOT shipped. Kernel ceiling reached; further gap needs
an algorithm-class change (cache-blocked weight-stationary GEMM, or q8 weights).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump pins to GPU persistent-graph + multi-model-safe cache (voice 45d2e6b, face 0a4799a)
GPU wins (CUDA/ggml backend, no CPU-path change): persistent per-shape graph+context
cache in Backend::compute() eliminates the per-call cudaGraph re-instantiation churn
-> wav2vec2 emotion+age-gender now AT GPU parity with torch-cuDNN on GB10 (0.97-0.98x),
CAM++ -5.7ms; bit-identical parity. Cache hardened multi-model-safe (invalidate-on-free
keyed by the ModelLoader weights buffer) so LocalAI multi-model hosting cannot stale-hit.
Conv models still trail cuDNN (im2col-materialization-bound) - cuDNN implicit-GEMM lever next.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump pins to cuDNN-conv-capable engines (voice b6e4356, face 6107a24)
Adds the opt-in cuDNN implicit-GEMM conv path (VOICEDETECT_GGML_CUDNN /
FACEDETECT_GGML_CUDNN, DEFAULT OFF -> zero build/runtime dep until enabled).
On GPU it kills the im2col-materialization bottleneck and reaches torch-cuDNN
parity on the spill-bound convs: SCRFD detect 14.8->6.4ms (2.3x, ~parity),
WeSpeaker ~parity, ERes2Net beats torch (1.10x); ArcFace/CAM++ neutral (no
spill). Parity exact (SCRFD <=1px, cosine=1.0). To USE it in LocalAI, the CUDA
backend build must enable the flag AND bundle libcudnn - deferred until a
cuDNN-bundled GPU image; flag stays OFF here.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(recon): enable cuDNN conv path on arm64+CUDA13 recon backends
The voice-detect.cpp / face-detect.cpp engines have an opt-in cuDNN
implicit-GEMM conv path behind VOICEDETECT_GGML_CUDNN / FACEDETECT_GGML_CUDNN
(default OFF) that kills im2col on the GPU and reaches torch-cuDNN parity
(SCRFD 2.3x, WeSpeaker/ERes2Net parity), measured on the GB10
(arm64, CUDA 13, sm_121a).
Enable it for the CUDA build, but only where cuDNN actually ships: the
arm64 + CUDA 13 image (GB10/Jetson/L4T). x86 CUDA images carry no cuDNN,
so flipping it on globally for BUILD_TYPE=cublas would be a link failure.
The Makefiles gate on CUDA_MAJOR_VERSION=13 + arch (TARGETARCH from the
matrix/Docker build, uname -m fallback for local builds).
backend/Dockerfile.golang already installs the runtime libcudnn9-cuda-13
in the arm64+CUDA13 apt block; add the matching libcudnn9-dev-cuda-13 so
the build-time link resolves.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump voice-detect pin to ERes2Net blocked-default (30beecd)
Defaults VD_ERES2NET_BLOCKED ON: routes the ERes2Net Res2Net body through the
blocked nChw16c AVX-512 directconv island instead of the 1x1 mul_mat fast path
(CONT-transpose + skinny low-K GEMM). On the shipped GGML_NATIVE=OFF build (ggml
mul_mat is AVX2-only) this wins ~2x at every thread count (2.07x@1t, 2.2x@4t,
2.05x@8t); pure-AVX2 fallback still 1.3-1.62x. Parity exact (cosine=1.000000 vs
golden), so registered voices + verify/identify thresholds are unaffected. The
prior default-OFF rested on a stale comment whose 23pct regression only held on
the non-shipping GGML_NATIVE=ON build.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* docs(readme): announce native voice-detect + face-detect backends in Latest News
Add a Latest News entry for the new from-scratch C++/ggml biometric backends
(voice-detect.cpp + face-detect.cpp) that replace the Python insightface and
speaker-recognition backends: no Python/onnxruntime at inference, self-contained
GGUF, bit-exact parity, GPU cuDNN parity. Mirrors the parakeet.cpp /
locate-anything.cpp native-backend news entries. Refs PR #10441.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): re-pin to the squashed engine release commits
The voice-detect.cpp and face-detect.cpp histories were squashed to a single
release commit, which orphaned the previous pins (voice 30beecd, face 6107a24).
Re-pin to the new single-commit SHAs (voice 3d51077, face 06914b0); the tree is
identical, so the backend build is unchanged.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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>
The GGUF metadata parser (gpustack/gguf-parser-go) cannot read NVFP4-quantized
GGUFs at all: it errors with "read tensor info 0: This quantized type is
currently unsupported" because NVFP4 is a ggml tensor type it does not know.
When ParseGGUFFile errors, the llama-cpp defaults hook skips guessGGUFFromFile
entirely and the deferred fallback sets the context window to the conservative
GGUFFallbackContextSize (1024). The result: a model that trains to 262144
tokens runs with n_ctx=1024, and every prompt over ~1k tokens fails with
"request (N tokens) exceeds the available context size (1024 tokens)".
Two changes:
- Drop GGUFFallbackContextSize (1024) and fall back to DefaultContextSize
(4096) in both the GGUF run-estimate path (gguf.go) and the deferred hook
fallback (hooks_llamacpp.go). 1024 is a sensible floor for a tiny CPU GGUF
but a footgun for a large, long-context model whose header simply cannot be
parsed. Strengthen the existing "GGUF unreadable" test to assert the value.
- Set context_size explicitly on the four NVFP4 gallery entries
(qwen3.6-35b-a3b-nvfp4-mtp, qwopus3.6-27b-v2-mtp-nvfp4,
qwopus3.6-27b-coder-mtp-nvfp4, qwen3.6-27b-nvfp4-mtp) so the parser failure
is irrelevant for them. 32768 matches sibling Qwen entries and is safe on
memory; operators can raise it toward the 262144 train length.
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>
* fix(pii): post-merge review fixes + live NER e2e for the privacy-filter tier
Follow-up to the NER tier engine (#10360), already on master. This carries
only the incremental review fixes and tests that postdate that merge — the
feature itself is not re-introduced.
Review fixes:
- openai_completion.go: remove the dead `elem >= 0` conjunct in applyAnyText
(the `elem < 0` guard above already returns).
- application.go: collapse ResolvePIIPolicy's inline re-implementation of
PIIIsEnabled to a single cfg.PIIIsEnabled() call (sole source of the
"explicit pii.enabled wins, else cloud-proxy default" rule) and return true
past the !enabled guard where it is provable.
- pattern.go: hoist the triple `appConfig != nil && EnableTracing` check in
patternDetector.Detect into one local.
- grammar.go: MaxQuantifier was 4096, but Go's regexp/syntax rejects repeat
bounds above 1000 at Parse time, so walk()'s {n,m} guard could never fire —
dead code shadowed by the parser. Lower it to 512 so a bound in (512,1000]
is rejected here with an actionable error; >1000 still fails closed via
Parse. Specs pin the relationship so the guard can't silently revert.
- PatternListEditor.jsx: clamp a directly-typed negative min_len to >=0 and
force the DOM value back when clamping (min={0} only constrained the spinner,
so a negative reached saved config and silently disabled the length filter).
Tests:
- piipattern_test.go: MaxQuantifier guard specs (must stay live, not dead).
- model-config.spec.js: assert the min_len clamp, and that entity_actions
collapses a duplicate group to a single row (map semantics; regression guard
against emitting an array that drops a row on save).
- tests/e2e-backends: token_classify capability driving the TokenClassify gRPC
RPC against the backend image, asserting byte-correct, UTF-8 rune-aligned
spans (entity.Text == text[start:end]) at threshold 0. Verified on CPU via
`make test-extra-backend-privacy-filter` (3/3 specs).
- Makefile: test-extra-backend-privacy-filter wrapper.
- tests/e2e: e2e_pii_ner_test.go drives /api/pii/analyze + /api/pii/redact
(mask + block) through the full HTTP -> detector -> redactor path; gated on
PII_NER_MODEL_GGUF so the default suite is unaffected.
- .github/workflows/tests-pii-ner-e2e.yml: path-filtered / nightly CI job
running the container harness on CPU.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(gallery): add privacy-filter-nemotron (f16 + q8)
GGUF conversions of OpenMed/privacy-filter-nemotron — a fine-grained English
PII token-classifier (55 categories / 221 BIOES classes), fine-tuned from
openai/privacy-filter on NVIDIA's Nemotron-PII dataset. Sibling to the existing
privacy-filter-multilingual entry, trading language breadth for category depth.
- privacy-filter-nemotron: F16 reference artifact (~2.8 GB).
- privacy-filter-nemotron-q8: Q8_0 quant (~1.64 GB) for RAM-constrained / edge
use; description notes the size/speed tradeoff and to validate on your own
data (a single dropped span is a PII leak).
Both run on the privacy-filter backend with known_usecases [token_classify] and
a default mask policy (min_score 0.5); operators add per-category entity_actions
as needed. sha256s taken from the HF repo's LFS object ids.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(ced): sketch sound-classification backend (CED audio tagger)
Wires ced.cpp (CED, 527-class AudioSet sound-event tagger; baby cry,
footsteps, glass, alarms, dog bark) into LocalAI as a Go/purego backend.
SKETCH (backend skeleton real; core REST wiring + CI/gallery is a checklist
in DESIGN.md):
- backend/backend.proto: new SoundDetection rpc + SoundClass messages
(run `make protogen-go` to regenerate pkg/grpc/proto).
- backend/go/ced: main.go (purego dlopen libced.so + ced_capi.h),
goced.go (Ced gRPC backend: Load + SoundDetection), Makefile
(clone-at-pin CED_VERSION, ggml static-PIC shared build), run.sh,
package.sh, .gitignore.
- DESIGN.md: REST /v1/audio/classification wiring (handler/route/capability
registration checklist), gallery/index + CI registration, and a scoping
note for the realtime/websocket live-recognition path (sliding-window
classify over the existing ws transport + voicegate; the ced C-API
per-PCM entry point is already window-friendly).
Backend code does not compile until protogen-go regenerates the pb types
and a libced.so is built (Makefile clones+builds it).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ced): REST /v1/audio/classification endpoint + capability registration
Wires the ced sound-event classification backend (AudioSet audio tagger)
end to end through the REST surface, mirroring the transcription path.
- Handler: core/http/endpoints/openai/sound_classification.go parses the
multipart audio upload, temp-files it, resolves the model config and
calls the SoundDetection RPC; returns {model, detections[]} JSON.
- Backend wrapper: core/backend/sound_classification.go (ModelSoundDetection)
loads the model and normalizes the proto response into schema types.
- Schema: core/schema/sound_classification.go (SoundClassificationResult).
- gRPC layer: SoundDetection wired through the LocalAI wrapper (interface,
Backend client, Client, embed, server, base default) so the loader-typed
client exposes the RPC; proto regenerated via make protogen-go.
- Route: POST /v1/audio/classification (+ /audio/classification alias) with
the audio/multipart default-model middleware in routes/openai.go.
- Capability surfaces: swagger @Tags/@Router on the handler; FLAG_SOUND_
CLASSIFICATION usecase flag + UsecaseSoundClassification + UsecaseInfoMap +
GuessUsecases + ModalityGroups + GetAllModelConfigUsecases; meta usecase
option; /api/instructions audio area updated; auth RouteFeatureRegistry +
FeatureAudioClassification (APIFeatures, default ON) + FeatureMetas; UI
usecaseFilters, capabilities.js CAP_SOUND_CLASSIFICATION, Models.jsx filter
+ i18n; docs page features/audio-classification.md + whats-new + crosslink.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ced): realtime sound-event detection over the websocket API
When a realtime pipeline configures a sound-classification model, each
VAD-committed utterance (the same window the transcription path produces)
is also run through the CED sound-event classifier and the scored AudioSet
tags are emitted as a new server event. No new backend rpc is needed: the
SoundDetection gRPC method already exists on this branch.
- config: add Pipeline.SoundDetection (yaml/json sound_detection,omitempty)
beside Transcription/VAD.
- realtime: add Model.SoundDetection(ctx, audio, topK, threshold) to the
ModelInterface; implement it on wrappedModel and transcriptOnlyModel by
calling backend.ModelSoundDetection with the session's sound-classification
model config (mirrors how Transcribe dispatches). Load the optional config
in newModel / newTranscriptionOnlyModel; nil config keeps it additive.
- types: add ConversationItemSoundDetectionEvent (item_id, content_index,
detections[]{label,score,index}) with type conversation.item.sound_detection,
its ServerEventType constant and MarshalJSON, mirroring the transcription
completed event.
- realtime: add emitSoundDetection (unary path: classify the committed window,
build the event, t.SendEvent) and wire it at the utterance-commit hook right
after emitTranscription; gated on session.SoundDetectionEnabled (resolved
from Pipeline.SoundDetection at session setup, defaults top_k=5, threshold=0).
Its error is logged via xlog but never aborts the turn.
- test: Ginkgo specs for emitSoundDetection (tags emitted, empty detections,
classifier error) plus a SoundDetection method on the fakeModel double.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(ced): implement SoundDetection in nodes backend test doubles
The SoundDetection method added to the grpc backend interface left two
test doubles (fakeBackendClient, fakeGRPCBackend) incomplete, so
core/services/nodes failed to compile under `go vet`/`go test` (go build
missed it: the doubles live in _test.go). Add the method to both,
mirroring their existing Detect mock. Repairs CI for the nodes package.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ced): decouple realtime sound detection from VAD (sound-only sessions)
Sound-event detection must activate on sounds, not speech, so it no longer
runs through the voice VAD/transcription path. A sound-detection-only
pipeline (sound_detection set, no transcription/LLM) now:
- is accepted by prepareRealtimeConfig (sound_detection counts as a pipeline
stage),
- builds a lightweight model via newSoundDetectionOnlyModel (no VAD/STT/LLM/TTS
loaded), and
- defaults the session to turn_detection none (no VAD) with no transcription
stage, so the client drives windowing via input_audio_buffer.commit
(option A: client-side sliding window). The per-PCM C-API already supports
arbitrary windows.
commitUtterance gains a sound-only branch: it emits the
conversation.item.sound_detection event (scored AudioSet tags) and stops -
no transcription, no LLM response. generateResponse is now guarded on a
transcription stage being present, so a sound-only turn never invokes the LLM.
Existing transcription/VAD sessions are unchanged (additive). Added a
commitUtterance sound-only Ginkgo spec asserting it emits the sound event and
neither transcribes nor generates a response. go vet + golangci-lint
(new-from-merge-base) clean; openai suite green.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ced): register sound-classification backend in gallery + CI
Mechanical backend-image registration for the ced sound-event classifier,
mirroring the parakeet-cpp Go/purego backend everywhere it is wired up.
- .github/backend-matrix.yml: add the ced build matrix, field-for-field copies
of the parakeet-cpp entries (cpu amd64/arm64, cublas cuda 12/13 amd64,
l4t cuda-13 arm64, l4t-jetpack cuda-12 arm64, sycl f32/f16, vulkan
amd64/arm64, rocm hipblas, and the metal darwin entry), changing only
backend and tag-suffix. dockerfile stays ./backend/Dockerfile.golang.
- backend/index.yaml: add the &ced meta anchor (capabilities map per platform)
plus ced-development and the per-arch image entries, each uri/mirror
tag-suffix matching the matrix exactly. The model gallery (GGUF) entry is
intentionally deferred pending the HuggingFace publish (TODO note inline).
- scripts/changed-backends.js: add an explicit item.backend === "ced" branch in
inferBackendPath mapping to backend/go/ced/, same mechanism and ordering as
the parakeet-cpp branch (before the generic golang fallthrough).
- .github/workflows/bump_deps.yaml: register mudler/ced.cpp -> CED_VERSION in
backend/go/ced/Makefile so the daily bot bumps the pin.
- swagger/{docs.go,swagger.json,swagger.yaml}: regenerated via make swagger so
the existing /v1/audio/classification annotations land in the generated spec.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ced): server-side windowing for realtime sound detection (option B)
Adds an optional server-driven sliding-window classifier so a sound-only
realtime client only has to stream audio (no input_audio_buffer.commit):
- Pipeline.sound_detection_window_ms / sound_detection_hop_ms config knobs.
When both > 0 on a sound-only session, the server classifies the last
window of streamed audio every hop and emits a conversation.item.sound_
detection event; the input buffer is trimmed to one window so a long
stream stays bounded. When unset, the session stays client-driven
(option A). Runs independent of VAD (sound events are not speech).
- handleSoundWindow (ticker) + classifySoundWindow (one tick, extracted so
it is unit-testable) + writeWindowWAV, which declares the true
InputSampleRate (NewWAVHeaderWithRate) so the classifier resamples
correctly. Goroutine is started after toggleVAD and torn down with the
session (close + wg.Wait).
- Register pipeline.sound_detection (+window_ms/hop_ms) in the config meta
registry; the earlier realtime commit added pipeline.sound_detection
without a registry entry, failing TestAllFieldsHaveRegistryEntries. This
fixes that and covers the two new knobs.
Tests: classifySoundWindow emits an event + trims the buffer to one window,
no-ops on too-little audio; writeWindowWAV declares the given sample rate.
go build/vet + golangci-lint (new-from-merge-base) clean; config + openai
suites green.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ced): add ced-base GGUF model gallery entries (f16 + q8_0)
The ced-base weights are now published at mudler/ced-base-gguf (Apache-2.0,
converted from mispeech/ced-base). Adds gallery/ced.yaml (backend: ced +
known_usecases: sound_classification) and two gallery/index.yaml entries
(ced-base-f16 default, ced-base-q8 smallest) with sha256-pinned files, and
removes the now-resolved TODO from backend/index.yaml.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ced): add tiny/mini/small GGUF model gallery entries
Publishes the rest of the CED family (same architecture, metadata-driven port
verified end-to-end on ced-tiny) to mudler/ced-{tiny,mini,small}-gguf and adds
their f16 + q8_0 gallery entries:
ced-tiny (5.5M, edge/Pi-class) f16 11MB / q8_0 6MB
ced-mini (9.6M) f16 19MB / q8_0 11MB
ced-small (22M) f16 42MB / q8_0 23MB
All sha256-pinned. ced-base remains the accuracy default.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(ced): point gallery entries at the consolidated mudler/ced-gguf repo
All CED quantizations (tiny/mini/small/base, f16/q8_0) now live in a single
HuggingFace repo, mudler/ced-gguf, instead of per-model repos. Repoint the 8
gallery model entries' urls + file uris accordingly. sha256 and filenames are
unchanged.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(ced): bump CED_VERSION to the short-clip fix
Pin the ced backend to ced.cpp 99c6ed3, which fixes a crash on any clip
shorter than target_length (~10.11s): time_pos_embed was added at its full
63-frame grid instead of being sliced to the clip's actual time grid, tripping
ggml_can_repeat in ggml_add. Surfaced by the live realtime e2e (sub-10s
windows) and gated with a short-clip parity test upstream.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* docs(ced): list ced.cpp as a LocalAI-team engine + backend-guide directive
- README.md: add ced.cpp to the "native C/C++/GGML engines developed and
maintained by the LocalAI project" table.
- docs/content/features/backends.md: add a Sound Classification backend
category (sound-event classification / audio tagging) listing ced.cpp.
- .agents/adding-backends.md: add a "Documenting the backend" section and two
verification-checklist items requiring new backends to be documented in the
backends.md category list, and in-house native engines to be added to the
README maintained-engines table. This directive was missing.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(ced): repin CED_VERSION to the v0.1.0 release commit
ced.cpp history was squashed into a single release commit (tagged v0.1.0), so
the previous pin (99c6ed3) no longer exists upstream. Pin to c04ac14, the
v0.1.0 release commit, so the backend builds against a commit that exists.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(ced): silence gosec G304/G103 + govet unsafeptr on audited paths
- sound_classification.go: os.Create(dst) where dst = temp dir + path.Base of
the upload (no traversal). #nosec G304, matching the depth-anything-cpp handler.
- goced.go: reading a NUL-terminated C string from a libced-owned buffer.
#nosec G103 (gosec) + //nolint:govet (golangci-lint's unsafeptr check), since
the uintptr is a C-owned malloc'd buffer, not Go-GC memory.
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>
* feat(gallery): add Depth Anything V2 models + bump native version
Add Depth Anything V2 (DA2) support to the depth-anything backend. DA2 is
depth-only (no camera pose, no confidence) and ships both relative
(relative inverse depth) and metric (depth in metres) variants. The Go
backend is model-agnostic, so no backend code changes are required — only
a native version bump and new gallery entries.
- backend/go/depth-anything-cpp/Makefile: pin DEPTHANYTHING_VERSION to the
depth-anything.cpp commit that adds the DA2 engine + C-API routing
(e3dec57f13a52366bbc4f279ef44804915960a6b, kept alive by the upstream tag
da2-support so it survives a squash-merge).
- gallery/index.yaml: add 12 DA2 entries (4 base quants, small, large, plus
Hypersim indoor and VKITTI outdoor metric models in S/B/L). Metric models
carry the metric-depth tag; none carry camera-pose.
Assisted-by: Claude:claude-opus-4-8
* chore(depth-anything-cpp): pin to merged DA2 master commit
PR #1 (mudler/depth-anything.cpp) merged to master as f4e17de (squash); repoint
the pin from the pre-merge commit to the canonical master commit.
Assisted-by: Claude:claude-opus-4-8
---------
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Squashed feat/pii-ner-tier-engine rebased onto master (was 45 commits; see
backup/pii-ner-tier-engine-prerebase). Net change:
- privacy-filter.cpp: standalone GGML engine for the openai-privacy-filter
PII/NER token classifier, wired as a LocalAI gRPC backend (CPU/CUDA/Vulkan).
TokenClassify moves off the patched llama.cpp path onto this backend.
- PII filter reworked to be NER-centric (encoder/NER detection tier scanning
whole conversations as one document), with a recreated bounded restricted-
regex secret-matching pattern detector tier alongside it (per-model
pii_detection.builtins / .patterns + core/services/routing/piipattern).
- Detection labelled by source (ner vs pattern); backend trace / confidence /
debug observability; analyze/redact exposed as a synchronous API.
- Instance-wide default detector policy + per-usecase default-on; request
filtering extended to completions, embeddings, edits & Ollama.
- React UI: NER-centric PII editor, detector-models table, pattern/builtins
editor, middleware default-policy UI.
- Gallery: privacy-filter-multilingual token-classify model + NER install
filter; token_classify known_usecase; batch sized to context for NER models.
privacy-filter backend registered in the backend gallery (cpu/vulkan/cuda-13
meta + image entries with a capabilities map) matching its CI matrix jobs,
and an /import-model auto-detect importer (PrivacyFilterImporter, narrow
privacy-filter GGUF detection) replacing the prior pref-only registration.
Reconciled against master's independent evolution:
- Dropped master's PIIPatternOverrides feature (global-pattern runtime
overrides + /api/pii/patterns API + runtime_settings.json persistence). The
per-model NER + pattern-detector design supersedes it; it was built on the
global redactor pattern set this branch replaced.
- Reverted the llama.cpp Score carry-patch (0006-server-task-type-score):
removed the patch and restored master's grpc-server.cpp Score RPC (direct
llama_decode, slot-loop bypass) and LLAMA_VERSION pin, plus master's
model_config validation forbidding score + chat/completion/embeddings on
llama-cpp. token_classify is unaffected (it runs on the privacy-filter
backend, not llama-cpp).
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
