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secret-filter
A pattern-based PII detector for high-entropy, highly-regular secrets —
API keys, tokens, and private-key blocks — that the NER tier cannot catch
(it has no credential class, so it fragments a key and may leave the secret
part exposed). Detection is bounded restricted-regex compiled to RE2
(linear time, no backtracking); it runs entirely in-process with no model
download, no backend, and zero VRAM.
Install it, then reference it under another model's pii.detectors (or set it
as the instance-wide default detector on the Middleware page) to block leaks
of known credential formats out of the box. Add your own patterns under
pii_detection.patterns in a restricted regex subset (e.g. "tok-\\w{32,}");
each must carry a fixed literal anchor of at least 3 characters, so open-
ended shapes like email addresses are rejected and left to the NER tier.
Repository: localaiLicense: apache-2.0
qwen3.5-27b-claude-4.6-opus-reasoning-distilled-heretic-i1
Repository: localaiLicense: apache-2.0
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
lfm2.5-audio-1.5b-tts
LFM2.5-Audio-1.5B in TTS mode. Four baked voices: us_male, us_female,
uk_male, uk_female — pick the default at load time via `voice:` option,
or override per-request via the OpenAI `/v1/audio/speech` `voice` field.
Repository: localaiLicense: LFM-Open-License-v1.0
allenai_olmo-3.1-32b-think
The **Olmo-3.1-32B-Think** model is a large language model (LLM) optimized for efficient inference using quantized versions. It is a quantized version of the original **allenai/Olmo-3.1-32B-Think** model, developed by **bartowski** using the **imatrix** quantization method.
### Key Features:
- **Base Model**: `allenai/Olmo-3.1-32B-Think` (unquantized version).
- **Quantized Versions**: Available in multiple formats (e.g., `Q6_K_L`, `Q4_1`, `bf16`) with varying precision (e.g., Q8_0, Q6_K_L, Q5_K_M). These are derived from the original model using the **imatrix calibration dataset**.
- **Performance**: Optimized for low-memory usage and efficient inference on GPUs/CPUs. Recommended quantization types include `Q6_K_L` (near-perfect quality) or `Q4_K_M` (default, balanced performance).
- **Downloads**: Available via Hugging Face CLI. Split into multiple files if needed for large models.
- **License**: Apache-2.0.
### Recommended Quantization:
- Use `Q6_K_L` for highest quality (near-perfect performance).
- Use `Q4_K_M` for balanced performance and size.
- Avoid lower-quality options (e.g., `Q3_K_S`) unless specific hardware constraints apply.
This model is ideal for deploying on GPUs/CPUs with limited memory, leveraging efficient quantization for practical use cases.
Repository: localaiLicense: apache-2.0
qwen3-vl-8b-instruct
Qwen3-VL-8B-Instruct is the 8B parameter model of the Qwen3-VL series.
Uses recommended default parameters according to Unsloth documentation for Qwen 3 VL.
Repository: localaiLicense: apache-2.0
qwen3-vl-8b-thinking
Qwen3-VL-8B-Thinking is the 8B parameter model of the Qwen3-VL series that is thinking.
Uses recommended default parameters according to Unsloth documentation for Qwen 3 VL.
Repository: localaiLicense: apache-2.0
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-3-base-q8_0
Depth Anything 3 (base), q8_0 — near-lossless 8-bit quant (~149 MB). Same
depth + camera pose output as the q4_k default at higher fidelity.
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
depth-anything-2-base-q8_0
Depth Anything V2 (base / ViT-B), q8_0 — near-lossless 8-bit quant. Same
relative monocular depth output as the q4_k default at higher fidelity. Use
GenerateImage (src -> depth PNG) or the Depth endpoint.
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
rfdetr-cpp-medium
RF-DETR Medium object detection model (DINOv2-small backbone, 576px input, 4 decoder layers), served
via the native rfdetr.cpp backend. Balanced detection quality vs. CPU latency — recommended when
Base is not accurate enough but Large is too slow. F16 quantization is the recommended default:
identical accuracy to F32, half the size. Drop-in for the /v1/detection endpoint.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-large
RF-DETR Large object detection model (DINOv2-small backbone, 704px input, 4 decoder layers), served
via the native rfdetr.cpp backend. Highest-accuracy detection variant — best for offline workflows
and high-resolution inputs where CPU latency is secondary to recall. F16 quantization is the
recommended default: identical accuracy to F32, half the size. Drop-in for the /v1/detection endpoint.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-seg-nano
RF-DETR Seg-Nano instance segmentation model (DINOv2-small backbone, 312px input, 4 decoder layers,
100 queries), served via the native rfdetr.cpp backend. Smallest segmentation variant — fastest CPU
latency, ideal for edge deployment. Returns both bounding boxes and per-instance masks via the
/v1/detection endpoint. F16 quantization is the recommended default: identical accuracy to F32,
half the size.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-seg-small
RF-DETR Seg-Small instance segmentation model (DINOv2-small backbone, 384px input, 4 decoder layers,
100 queries), served via the native rfdetr.cpp backend. Step up from Seg-Nano in mask quality while
staying CPU-friendly. Returns both bounding boxes and per-instance masks via the /v1/detection
endpoint. F16 quantization is the recommended default: identical accuracy to F32, half the size.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-seg-medium
RF-DETR Seg-Medium instance segmentation model (DINOv2-small backbone, 432px input, 5 decoder layers,
200 queries), served via the native rfdetr.cpp backend. Balanced segmentation quality vs. CPU latency
— recommended for everyday segmentation workloads. Returns both bounding boxes and per-instance masks
via the /v1/detection endpoint. F16 quantization is the recommended default.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-seg-large
RF-DETR Seg-Large instance segmentation model (DINOv2-small backbone, 504px input, 5 decoder layers,
200 queries), served via the native rfdetr.cpp backend. Higher-resolution input than Seg-Medium for
sharper mask boundaries. Returns both bounding boxes and per-instance masks via the /v1/detection
endpoint. F16 quantization is the recommended default: identical accuracy to F32, half the size.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-seg-xlarge
RF-DETR Seg-XLarge instance segmentation model (DINOv2-small backbone, 624px input, 6 decoder layers,
300 queries), served via the native rfdetr.cpp backend. High-capacity segmentation variant with more
queries and deeper decoder — best for dense scenes with many instances. Returns both bounding boxes
and per-instance masks via the /v1/detection endpoint. F16 quantization is the recommended default.
Repository: localaiLicense: apache-2.0
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