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AI モデル 84760
- Noromaid-13B-v0.3-GGUF↗
TheBloke
- SynthIA-70B-v1.5-GGUF↗
TheBloke
- Falcon-H1-Tiny-R-0.6B↗
tiiuae
- mixnet_m.ft_in1k↗
timm
- vit_base_patch16_dinov3.lvd1689m↗
timm
A DINOv3 ViT model image feature encoder. Distilled on LVD-1689M from the DINOv3 ViT-7B model. * The original model weights ended up with all QKV projection biases being zeroes. For `timm`, have disabled the QKV bias (`qkv_bias=False`) for the models and not loaded the zero weights. For some model sizes there are variants with `qkvb` in the name that have the bias enabled (`qkv_bias=True`), but zero, to match the behaviour of `transformers` and original models. * The original models keep RoPE periods as a persistent `bfloat16` buffer. `timm` generates `float32` periods at init. This results in some numerical differences, however the `timm` approach should be less problematic running on devices without bfloat16 support, and appears to work as well if not slightly better for fine-tuning. `model.rope.periods = model.rope.periods.to(torch.bfloat16).to(torch.float32)` will truncate the periods to bfloat16 and result in matching outputs. - **Model Type:** Image Feature Encoder - **Model Stats:** - Params (M): 85.6 - GMACs: 23.6 - Activations (M): 34.1 - Image size: 256 x 256 - **Original:** https://github.com/facebookresearch/dinov3 - **License:** DINOv3 - **Dataset:** LVD-1689M - **Papers:** - DINOv3: https://arxiv.org/abs/2508.10104 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
- ARBERTv2↗
UBC-NLP
**ARBERTv2** is the updated version of ARBERT model described in our **ACL 2021 paper** **"ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic"**. **ARBERTv2** is presented in our paper "ORCA: A Challenging Benchmark for Arabic Language Understanding" **ARBERTv2** is trained on MSA data **243 GB** of text and **27.8B tokens**. If you use our models (ARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated): We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access.
- Fibo-2.0-Dev-and-Edit-Turbo-ComfyUI-GGUF↗
vantagewithai
- Wan2.1-VACE-1.3B↗
Wan-AI
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Technical Report    |    📑 Blog    |   💬 WeChat Group    |    📖 Discord    In this repository, we present **Wan2.1**, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. **Wan2.1** offers these key features: - 👍 **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks. - 👍 **Supports Consumer-grade GPUs**: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models. - 👍 **Multiple Tasks**: **Wan2.1** excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation. - 👍 **Visual Text Generation**: **Wan2.1** is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications. - 👍 **Powerful Video VAE**: **Wan-VAE** delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation. * May 14, 2025: 👋 We introduce **Wan2.1** VACE, an all-in-one model for video creation and editing, along with its inference code, weights, and technical report! * Apr 17, 2025: 👋 We introduce **Wan2.1** FLF2V with its inference code and weights! * Mar 21, 2025: 👋 We are excited to announce the release of the **Wan2.1** technical report. We welcome discussions and feedback! * Mar 3, 2025: 👋 **Wan2.1**'s T2V and I2V have been integrated into Diffusers (T2V | I2V). Feel free to give it a try! * Feb 27, 2025: 👋 **Wan2.1** has been integrated into ComfyUI. Enjoy! * Feb 25, 2025: 👋 We've released the inference code and weights of **Wan2.1**.
- gemma-4-31B-it-abliterated-GGUF↗
wangzhang
- ternary-bonsai-3080-sm86-ninfer↗
wei231
> **本仓 = 开箱即用**：权重 + 引擎二进制 + 一键启动脚本全都在这里。 > 下载整个仓，装好 NVIDIA 驱动，双击 `start.bat` 就能跑。不需要装 CUDA 工具包（cudart 已静态内嵌进引擎 exe）。 **Ternary Bonsai 2 27B**：1.58-bit 三元量化（w ∈ {−1,0,+1} + Hadamard 旋转基）的 27B 模型， 底座 Qwen3.8-27B（64 层：16 纯注意力 + 48 GDN），全精度权重约 18.2 GB → 量化后 **7.74 GB**。 引擎侧把三元格式 + 运行时折叠基变换接进了 **NInfer** 单卡推理引擎，并拼入了同族 **MTP 投机头**（K=2）。 1. 装好 NVIDIA 驱动（本仓引擎编译于 CUDA 13.3，驱动 610+ 即可，**不用装 CUDA Toolkit**） 2. 把整个仓拷到任意目录（建议 SSD，权重 7.7G） 3. 双击 `start.bat` → 服务起在 `http://127.0.0.1:8080/v1`（OpenAI 兼容） - 如果你的 3080 不是 `--device 1`（看 `nvidia-smi` 里的 index），改 `start.bat` 里那一行