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ced-base-f16
CED (Consistent Ensemble Distillation, Xiaomi) is a sound-event classifier that tags everyday sounds (baby cry, footsteps, glass breaking, alarms, dog bark, ...) into the 527-class AudioSet ontology. This is the f16 GGUF for the ced backend (a standalone C++/ggml port). Recommended default: fastest on CPU and near-lossless. Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
vibevoice-cpp
VibeVoice Realtime 0.5B (C++ / GGML, Q8_0) - native C++ port of Microsoft VibeVoice
via the vibevoice-cpp backend. 24kHz mono TTS with voice cloning from a single
reference voice prompt. Default voice prompt: en-Carter_man.
Repository: localaiLicense: mit
vibevoice-cpp-asr
VibeVoice ASR 7B (C++ / GGML, Q4_K) - long-form speech-to-text with speaker
diarization. Returns per-speaker JSON segments with start/end timestamps.
English-only. ~10 GB download.
Repository: localaiLicense: mit
face-detect-buffalo-l
Face recognition with insightface's `buffalo_l` pack (SCRFD-10GF
detector + ResNet50 ArcFace 512-d embedder), ported to C++/ggml and
shipped as a single GGUF for the `face-detect` backend. Highest
accuracy of the buffalo line.
No Python / onnxruntime / torch runtime: face-detect.cpp reads the
detector and embedder architecture (`facedetect.arch`) directly from
the GGUF metadata, so installing this entry is all that is needed to
select buffalo_l. Drives the Embedding / Detect / FaceVerify /
FaceAnalyze gRPC rpcs and the /v1/face/{verify,analyze,embed,detect}
REST endpoints. This GGUF also embeds the MiniFASNet anti-spoof
ensemble, available via the FaceVerify `anti_spoof` request flag.
NON-COMMERCIAL RESEARCH USE ONLY: for commercial use see
`face-detect-yunet-sface`.
Repository: localaiLicense: insightface-non-commercial
face-detect-buffalo-m
Face recognition with insightface's `buffalo_m` pack (SCRFD-2.5GF
detector + ResNet50 ArcFace embedder), converted to a C++/ggml GGUF
for the `face-detect` backend. Same recognition accuracy as
`buffalo_l` with a cheaper detector: a good balance on mid-range
hardware.
The architecture (`facedetect.arch`) is read from the GGUF metadata,
so this entry alone selects the buffalo_m engine. This GGUF also
embeds the MiniFASNet anti-spoof ensemble, available via the
FaceVerify `anti_spoof` request flag. NON-COMMERCIAL RESEARCH USE
ONLY.
Repository: localaiLicense: insightface-non-commercial
face-detect-buffalo-s
Face recognition with insightface's `buffalo_s` pack (SCRFD-500MF
detector + MBF 512-d embedder), converted to a C++/ggml GGUF for the
`face-detect` backend. Small and CPU-friendly: a good fit for
mid-range and edge deployments.
The architecture (`facedetect.arch`) is read from the GGUF metadata,
so this entry alone selects the buffalo_s engine. This GGUF also
embeds the MiniFASNet anti-spoof ensemble, available via the
FaceVerify `anti_spoof` request flag. NON-COMMERCIAL RESEARCH USE
ONLY.
Repository: localaiLicense: insightface-non-commercial
face-detect-buffalo-sc
Face recognition with insightface's `buffalo_sc` pack (SCRFD-500M
detector + a small ArcFace embedder), converted to a C++/ggml GGUF
for the `face-detect` backend. This is the smallest insightface
pack: the lightest option for low-resource and edge deployments.
The architecture (`facedetect.arch`) is read from the GGUF metadata,
so this entry alone selects the buffalo_sc engine. If this GGUF
embeds the MiniFASNet anti-spoof ensemble, it is available via the
FaceVerify `anti_spoof` request flag. NON-COMMERCIAL RESEARCH USE
ONLY.
Repository: localaiLicense: insightface-non-commercial
face-detect-antelopev2
Face recognition with insightface's `antelopev2` pack (SCRFD-10G
detector + ArcFace glint360k R100, 512-d embedder), converted to a
C++/ggml GGUF for the `face-detect` backend. The higher-accuracy
insightface pack: heavier, but the best fit when recognition
quality matters more than speed.
The architecture (`facedetect.arch`) is read from the GGUF metadata,
so this entry alone selects the antelopev2 engine. If this GGUF
embeds the MiniFASNet anti-spoof ensemble, it is available via the
FaceVerify `anti_spoof` request flag. NON-COMMERCIAL RESEARCH USE
ONLY.
Repository: localaiLicense: insightface-non-commercial
face-detect-yunet-sface
Face recognition with OpenCV Zoo weights: YuNet detector + SFace
128-d recognizer, converted to a C++/ggml GGUF for the `face-detect`
backend. APACHE 2.0: safe for commercial use. Lower accuracy than the
buffalo packs and no demographic head, but the commercial-friendly
alternative to the insightface buffalo line.
The architecture (`facedetect.arch`) is read from the GGUF metadata,
so this entry alone selects the YuNet + SFace engine.
Repository: localaiLicense: apache-2.0
voice-detect-ecapa-tdnn
Speaker (voice) recognition with SpeechBrain's ECAPA-TDNN trained
on VoxCeleb, ported to C++/ggml and shipped as a single GGUF for the
`voice-detect` backend. 192-d L2-normalised embeddings, ~1.9% Equal
Error Rate on VoxCeleb1-O. APACHE 2.0 - commercial-safe.
No Python / torch runtime: voice-detect.cpp reads the embedding
architecture (`voicedetect.arch`) directly from the GGUF metadata,
so installing this entry is all that is needed to select ECAPA-TDNN.
Drives the VoiceVerify / VoiceEmbed gRPC rpcs and the
/v1/voice/{verify,embed,register,identify,forget} REST endpoints.
Repository: localaiLicense: apache-2.0
voice-detect-wespeaker-resnet34
Speaker recognition with WeSpeaker's ResNet34 trained on VoxCeleb,
converted to a C++/ggml GGUF for the `voice-detect` backend. 256-d
embeddings, CPU-friendly and runtime-free (no onnxruntime or torch).
CC-BY-4.0.
Use when you want WeSpeaker's ResNet34 topology instead of
ECAPA-TDNN. The embedding architecture (`voicedetect.arch`) is read
from the GGUF metadata, so this entry alone selects the engine.
Repository: localaiLicense: cc-by-4.0
voice-detect-eres2net
Speaker recognition with 3D-Speaker's ERes2Net trained on VoxCeleb,
converted to a C++/ggml GGUF for the `voice-detect` backend.
192-d embeddings with strong verification accuracy. APACHE 2.0.
The embedding architecture (`voicedetect.arch`) is read from the
GGUF metadata, so this entry alone selects the ERes2Net engine.
Repository: localaiLicense: apache-2.0
voice-detect-campplus
Speaker recognition with 3D-Speaker's CAM++ trained on VoxCeleb,
converted to a C++/ggml GGUF for the `voice-detect` backend. 192-d
embeddings, a fast context-aware masking topology well-suited to
CPU and edge deployments. APACHE 2.0.
The embedding architecture (`voicedetect.arch`) is read from the
GGUF metadata, so this entry alone selects the CAM++ engine.
Repository: localaiLicense: apache-2.0
voice-detect-emotion-wav2vec2
Voice analysis (age / gender / emotion) with audEERING's wav2vec2
model, converted to a C++/ggml GGUF for the `voice-detect` backend.
Drives the VoiceAnalyze gRPC rpc and the /v1/voice/analyze REST
endpoint, returning a continuous age estimate plus gender and
emotion class scores for a single utterance. CC-BY-NC-SA-4.0 -
research / non-commercial use only.
The analysis architecture (`voicedetect.arch`) is read from the
GGUF metadata, so this entry alone selects the wav2vec2 analyze head.
Repository: localaiLicense: cc-by-nc-sa-4.0
voice-detect-age-gender-wav2vec2
wav2vec2-large-robust age + gender analysis head
(audeering/wav2vec2-large-robust-24-ft-age-gender), converted to a
C++/ggml GGUF for the `voice-detect` backend. Drives the VoiceAnalyze
gRPC rpc and the /v1/voice/analyze REST endpoint, returning a
continuous age estimate plus gender class scores for a single
utterance. CC-BY-NC-SA-4.0 - research / non-commercial use only.
The analysis architecture (`voicedetect.arch`) is read from the
GGUF metadata, so this entry alone selects the wav2vec2 analyze head.
Repository: localaiLicense: cc-by-nc-sa-4.0
rfdetr-cpp-nano
RF-DETR Nano object detection model, served via the native rfdetr.cpp backend (ggml + purego, no Python).
Q8_0 quantization is the recommended default for CPU: same accuracy as F16/F32, ~20MB on disk, fastest CPU latency.
Pure C++/ggml runtime; no Python dependencies. Drop-in for the /v1/detection endpoint.
Repository: localaiLicense: apache-2.0
locate-anything-3b
NVIDIA LocateAnything-3B open-vocabulary object detection (visual grounding), served via the native
locate-anything.cpp backend (C++/ggml + purego, no Python). Describe what to find in a text prompt and
get labeled boxes back; separate multiple categories with . Q8_0 is the recommended default:
box-identical to F16/F32, ~6.3GB, fastest CPU latency. Drop-in for the /v1/detection endpoint (pass the
prompt).
Repository: localaiLicense: other
depth-anything-3-base
Depth Anything 3 (base) monocular metric depth + camera pose, served via the
native depth-anything.cpp backend (C++/ggml + purego, no Python at inference).
Given an image it returns a dense depth map plus the recovered camera
extrinsics (3x4) and intrinsics (3x3). Use GenerateImage (src -> normalized
depth PNG at dst) or Predict (JSON depth stats + pose). q4_k is the
recommended CPU default.
Repository: localaiLicense: apache-2.0
depth-anything-2-base
Depth Anything V2 (base / ViT-B) monocular depth, served via the native
depth-anything.cpp backend (C++/ggml + purego, no Python at inference).
Given an image it returns a dense monocular depth map only — no camera pose,
no confidence. This is the relative variant (relative inverse depth). Use
GenerateImage (src -> normalized depth PNG at dst) or the Depth endpoint.
q4_k is the recommended CPU default.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-small
RF-DETR Small object detection model (DINOv2-small backbone, 512px input, 3 decoder layers), served
via the native rfdetr.cpp backend (ggml + purego, no Python). A step up from Nano in accuracy while
staying lightweight on CPU. F16 quantization is the recommended default: identical accuracy to F32
at roughly half the size. Drop-in for the /v1/detection endpoint.
Repository: localaiLicense: apache-2.0
wan-2.1-t2v-1.3b-ggml
Wan 2.1 T2V 1.3B — text-to-video diffusion model, GGUF-quantized for the
stable-diffusion.cpp backend. Generates short (33-frame) 832x480 clips
from a text prompt. Cheapest Wan variant, suitable for CPU-offloaded
inference with ~10 GB of usable RAM.
Repository: localaiLicense: apache-2.0
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