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AI モデル 85003
- Qwen3.8-35B-A3B-Distill-APEX-GGUF↗
MrFuzzihead
APEX (**A**daptive **P**recision for **EX**pert models) quantizations of empero-ai/Qwen3.8-35B-A3B-Distill, covering every deployment scenario from maximum quality down to ~10 GB. APEX assigns quantization precision per tensor role (routed expert / shared expert / attention / SSM) and per layer position (edge layers high, middle layers compressed), exploiting the sparsity of Mixture-of-Experts models. On the APEX reference model (Qwen3.5-35B-A3B), APEX tiers match or beat Q8_0 quality at ~60% of the size — see the APEX technical report for details. > **Note on the hardware-compatibility widget:** Hugging Face detects quantization variants from standard quant names (`Q4_K_M` etc.) in the filename. APEX tiers are mixed-precision and use APEX names, so the widget cannot classify them (this also affects other APEX repos). Use the table below for sizes; mmproj and MTP files are never classified by the widget by design.
- KURE-Sparse-nano↗
nlpai-lab
- DeepSeek-V3-0324-NVFP4↗
NVIDIA
The NVIDIA DeepSeek V3-0324 FP4 model is the quantized version of DeepSeek AI's DeepSeek V3-0324 model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA DeepSeek V3-0324 FP4 model is quantized with TensorRT Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (DeepSeek V3-0324) Model Card. Developers looking to take off the shelf pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.
- OmniAvatar-14B↗
OmniAvatar
- MiniCPM-V-4.7-1B-gguf↗
openbmb
> **This repository hosts the GGUF (llama.cpp) quantized version of MiniCPM-V 4.7 1B.** For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.7-1B. Two sets of files are provided. **They differ only in the embedded chat template** — the tensor weights are byte-identical. - **Root files** — thinking is disabled inside the embedded template and cannot be turned on. This is the recommended default for this model, which is not trained as a reasoning model. - **`thinking/` files** — the official upstream template is restored, so the model emits a `[Start thinking] … [End thinking]` trace before answering. Useful when you want to see the reasoning steps; note that it multiplies the token count, so set a larger `-n`.
- OpenMed-NER-GenomeDetect-BigMed-560M↗
OpenMed
**Specialized model for Gene/Protein Entity Recognition - Gene and protein mentions** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for gene/protein entity recognition - gene and protein mentions. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as **drug interaction detection**, **medication extraction from patient records**, **adverse event monitoring**, **literature mining for drug discovery**, and **biomedical knowledge graph construction** with **production-ready reliability** for clinical and research applications. - **High Precision**: Optimized for biomedical entity recognition - **Domain-Specific**: Trained on curated BC2GM dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem
- DeepSeek-R1-Distill-Qwen-14B-MLX-4bit↗
Outlier-Ai
- VibeThinker-3B-GSQ-RCO-GGUF↗
pfeifferj
- Qwen3-8B-abliterated-GGUF↗
PocketWeights
- Ternary-Bonsai-2-4B-mlx-2bit↗
prism-ml
> **PrismML-Eng/Bonsai-demo is the source of truth for running these models.** Where anything here disagrees with it, it is right. For an OpenAI-compatible server, run `python -m mlx_vlm.server --model bonsai2-4b-mlx` and send the sampling settings in each request. For CUDA, CPU, and llama.cpp on Metal, use the GGUF packs of the same weights: **Ternary-Bonsai-2-4B-gguf**.