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AI モデル 85003
- drama3-small-GGUF↗
pentacoxian-dev
**Built with Fish Audio.** This is a compressed derivative of Fish Audio Drama 3. Fish Audio does not sponsor or endorse it. drama3-small keeps Drama 3's expressive Japanese and English voice cloning: inline tags, English style directions and director's notes. It should fit in 12 GB of VRAM. The 35B-A3B mixture-of-experts backbone was pruned to 45% of its expert slots and quantized to about 3 bits. It was then distilled back towards the original on Japanese and English speech. All samples come from this GGUF in one take, cloned from a 29-second Japanese reference recording of the repo owner's voice (used with consent).
- Ternary-Bonsai-4B-gguf↗
prism-ml
Q2_0 encodes each weight as a 2-bit code `q in {0, 1, 2, 3}`, dequantized via `w = (q - 1) * scale`. One 128-element block is 34 bytes (2 bytes FP16 scale + 32 bytes of packed 2-bit codes) for an effective **2.125 bits/weight**. The fourth code point (`q = 3`, reconstructing to `+2 * scale`) is reserved for future extensions; for ternary weights it is unused. Q2_0 is effectively lossless for ternary weights — the ternary values land exactly on three of the four 2-bit code points, so quantize/dequantize is bit-exact in the absence of FP16 scale rounding.
- Canopus-LoRA-Flux-FaceRealism↗
prithivMLmods
- TranslatePsy-AfriSLM-0.8B-Q4-GGUF↗
qvac
- Meta-Llama-3.1-8B-Instruct-FP8↗
RedHatAI
- **Model Architecture:** Meta-Llama-3.1 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use in multiple languages. Similarly to Meta-Llama-3.1-8B-Instruct, this models is intended for assistant-like chat. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. - **Release Date:** 7/23/2024 - **Version:** 1.0 - **License(s):** llama3.1 - **Model Developers:** Neural Magic Quantized version of Meta-Llama-3.1-8B-Instruct. It achieves an average score of 73.44 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 73.79. This model was obtained by quantizing the weights and activations of Meta-Llama-3.1-8B-Instruct to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
- Qwen3.6-35B-A3B-FP8↗
RedHatAI
> [!Note] > This repository contains FP8-quantized model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. > > The quantization method is fine-grained fp8 quantization with block size of 128, and its performance metrics are nearly identical to those of the original model. Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience. If you have hardware with more compute than memory bandwidth, you may prefer this MoE variant for performance reasons.
- Qwen3.8-2.4T-A95B-NVFP4-REAP-25↗
RedHatAI
- replit-code-v1-3b↗
replit
- roadz_-_dv-finetuned-211124-gguf↗
RichardErkhov
- Agents-A1-4B-heretic-GGUF↗
richardyoung