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wemm-embedding-2b
WeMM-Embedding-2B is Tencent's Apache-2.0 multilingual embedding model
built on Qwen3.5. This entry serves the original bfloat16 safetensors with
LocalAI's Transformers backend and produces 2,048-dimensional normalized
embeddings for text retrieval, semantic search, and RAG.
The upstream model can also embed images and videos. LocalAI currently
exposes text input through its embeddings API for this backend.
Repository: localaiLicense: apache-2.0
Attention: Trust Remote Code is required for this model
wemm-embedding-4b
WeMM-Embedding-4B is Tencent's mid-sized Apache-2.0 multilingual embedding
model built on Qwen3.5. This entry serves the original bfloat16 safetensors
with LocalAI's Transformers backend and produces 2,560-dimensional
normalized embeddings for text retrieval, semantic search, and RAG.
The upstream model can also embed images and videos. LocalAI currently
exposes text input through its embeddings API for this backend.
Repository: localaiLicense: apache-2.0
Attention: Trust Remote Code is required for this model
wemm-embedding-9b
WeMM-Embedding-9B is Tencent's largest Apache-2.0 multilingual embedding
model built on Qwen3.5. This entry serves the original bfloat16 safetensors
with LocalAI's Transformers backend and produces 4,096-dimensional
normalized embeddings for text retrieval, semantic search, and RAG.
The upstream model can also embed images and videos. LocalAI currently
exposes text input through its embeddings API for this backend.
Repository: localaiLicense: apache-2.0
mxbai-embed-large-v1-q4
Mixedbread's mxbai-embed-large-v1 is a 335M-parameter English BERT
embedding model for retrieval, semantic search, and RAG. It produces
1,024-dimensional embeddings and supports sequences up to 512 tokens.
Prefix retrieval queries with `Represent this sentence for searching
relevant passages: `. This entry uses the balanced Q4_K_M GGUF.
Repository: localaiLicense: apache-2.0
mxbai-embed-large-v1-q8
Mixedbread's mxbai-embed-large-v1 in the higher-fidelity Q8_0 GGUF
format. This 335M-parameter English BERT model produces
1,024-dimensional embeddings for retrieval, semantic search, and RAG.
Repository: localaiLicense: apache-2.0
mxbai-embed-large-v1-f16
Mixedbread's mxbai-embed-large-v1 in the official full-precision F16
GGUF format. This 335M-parameter English BERT model produces
1,024-dimensional embeddings for retrieval, semantic search, and RAG.
Repository: localaiLicense: apache-2.0
nemotron-3-embed-1b-q4
Nemotron-3-Embed-1B is NVIDIA's multilingual text embedding model for
retrieval, semantic search, and RAG. This compact Q4_K_M GGUF produces
2,048-dimensional normalized embeddings and supports 36 languages. Prefix
retrieval queries with `query: ` and documents with `passage: `.
Repository: localaiLicense: openmdw-1.1
nemotron-3-embed-8b-q4
Nemotron-3-Embed-8B is NVIDIA's larger multilingual text embedding model
for retrieval, semantic search, and RAG. This Q4_K_M GGUF balances retrieval
quality with local resource use and supports 36 languages. Prefix retrieval
queries with `query: ` and documents with `passage: `.
Repository: localaiLicense: openmdw-1.1
parable-granite-4.1-3b-claude-fable-5
# Parable-Granite-4.1-3B-Claude-Fable-5
Granite 4.1 3B fine-tuned on genuine Claude Fable 5 and GPT-5.5 agent
traces (planning, tool use, reasoning from real agent sessions).
Agent-flavored small model: terminal workflows, idiomatic code fixes,
explanations. v2 recipe: completion-masked SFT, replay mix, seed-averaged
weights. Published corpus and eval harness.
Repository: localaiLicense: apache-2.0
bonsai-8b-1bit
Bonsai 8B (PrismML) is an end-to-end 1-bit language model built on the Qwen3-8B dense
architecture (GQA, SwiGLU, RoPE, RMSNorm, 36 layers, 65,536 context). Every weight is a
single sign bit (`-scale` / `+scale`) with one FP16 scale per group of 128 weights, for
an effective 1.125 bits/weight and a ~1.15 GB footprint (14.2x smaller than FP16) while
matching full-precision 8B instruct models at ~70.5 average across 6 benchmark
categories.
The Q1_0 quantization is only decodable by the PrismML llama.cpp fork, so this entry
runs on LocalAI's `bonsai` backend (that fork), not the stock `llama-cpp` backend.
License: Apache 2.0.
Repository: localaiLicense: apache-2.0
ternary-bonsai-8b
Ternary Bonsai 8B (PrismML) is a 1.58-bit ternary language model on the Qwen3-8B dense
architecture. Each weight takes a value from {-1, 0, +1} with one shared FP16 scale per
group of 128 weights (GGUF Q2_0, ~2.18 GB deployed, 7.5x smaller than FP16). The extra
zero state recovers more of the full-precision model than the 1-bit build: it ranks 2nd
among compared 6-9B models at 75.5 average despite being ~1/8th their size.
Q2_0 is the recommended, ternary-lossless variant. The Q2_0 kernels are only in the
PrismML llama.cpp fork, so this runs on LocalAI's `bonsai` backend. License: Apache 2.0.
Repository: localaiLicense: apache-2.0
ternary-bonsai-27b
Ternary Bonsai 27B (PrismML) is the quality-oriented operating point of the Bonsai 27B
family: full 27B-class reasoning in ternary {-1, 0, +1} weights on the Qwen3.6-27B
hybrid-attention backbone (262K context). At a true 1.71 bits/weight it deploys in
~7.2 GB (GGUF Q2_0_g128) and retains 95% of FP16 intelligence (80.49 average across 15
thinking-mode benchmarks) - a higher score than a conventional IQ2_XXS build at less
than two-thirds its footprint. Ships an optional 4-bit vision tower (mmproj), included.
The Q2_0 weights and hybrid-attention kernels are only in the PrismML llama.cpp fork,
so this runs on LocalAI's `bonsai` backend. A GPU is recommended. License: Apache 2.0.
Repository: localaiLicense: apache-2.0
qwopus3.6-27b-coder-mtp
🪐 Qwopus3.6-27B-v2
SFT Release
Reasoning-Enhanced Dense Language Model Fine-Tuned on Qwen3.6-27B
🧬 Trace Inversion & Negentropy
🧠 27B Parameters
🔥 3-Stage Curriculum SFT
🛠️ Vision & Tool-use Support
💡 What is Qwopus3.6-27B-v2?
🪐 Qwopus3.6-27B-v2 is a reasoning-enhanced dense language model built on top of Qwen3.6-27B. By leveraging a multi-stage curriculum learning pipeline and augmented with Trace Inversion datasets (claude-opus-4.6/4.7-traceInversion), it reverse-engineers the compressed "Reasoning Bubbles" of commercial LLMs into structured, step-by-step synthetic reasoning traces, successfully eliminating logical shortcuts and knowledge fractures.
🧩 Structured Reasoning
Injects reconstructed deep CoT chains to eliminate logical shortcuts via Trace Inversion.
🪶 Style Consistency
Enforces strict constraints on the format and convergence of <think> tags.
🔁 Distillation Alignment
Ensures high-quality cross-source SFT data alignment to narrow the capacity gap.
⚡ RL Scalability
Sets up a stable formatting pipeline optimized for downstream Reinforcement Learning (RL).
## 💡 1. Base Model, Training Library & Cooperation
...
Repository: localaiLicense: apache-2.0
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
longcat-video
LongCat-Video served by LocalAI's dedicated CUDA backend. Generates video
from a text prompt or a start image. The SDPA attention path works without
FlashAttention and is suitable for CUDA 13 ARM64 systems such as DGX Spark.
This is a very large checkpoint (roughly 83 GB in Hugging Face storage) and
requires Linux with an NVIDIA CUDA GPU plus substantial memory and disk.
Repository: localaiLicense: mit
z-image-diffusers
Z-Image is the foundation model of the ⚡️-Image family, engineered for good quality, robust generative diversity, broad stylistic coverage, and precise prompt adherence. While Z-Image-Turbo is built for speed, Z-Image is a full-capacity, undistilled transformer designed to be the backbone for creators, researchers, and developers who require the highest level of creative freedom.
Repository: localaiLicense: apache-2.0
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
liquidai_lfm2-1.2b-rag
Based on LFM2-1.2B, LFM2-1.2B-RAG is specialized in answering questions based on provided contextual documents, for use in RAG (Retrieval-Augmented Generation) systems.
Use cases:
Chatbot to ask questions about the documentation of a particular product.
Custom support with an internal knowledge base to provide grounded answers.
Academic research assistant with multi-turn conversations about research papers and course materials.
Repository: localaiLicense: lfm1.0
insightface-buffalo-l
Face recognition using insightface's `buffalo_l` pack
(SCRFD-10GF detector + ResNet50 ArcFace 512-d embedder + genderage head, ~326MB).
Default choice, highest accuracy.
Weights delivered via LocalAI's gallery mechanism (SHA-256 verified,
cached in the models directory like any other managed model).
NON-COMMERCIAL RESEARCH USE ONLY. For commercial use see `insightface-opencv`.
Repository: localaiLicense: insightface-non-commercial
insightface-buffalo-m
Mid-tier insightface pack (SCRFD-2.5GF detector + ResNet50 ArcFace +
genderage, ~313MB). Same recognition accuracy as `buffalo_l` with a
cheaper detector — good balance on mid-range hardware.
NON-COMMERCIAL RESEARCH USE ONLY.
Repository: localaiLicense: insightface-non-commercial
insightface-buffalo-s
Small insightface pack (SCRFD-500MF detector + MBF 512-d embedder +
genderage, ~159MB). Good fit for mid-range CPU deployments.
NON-COMMERCIAL RESEARCH USE ONLY.
Repository: localaiLicense: insightface-non-commercial
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