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AI 模型 183
- SegVol↗
BAAI
The SegVol is a universal and interactive model for volumetric medical image segmentation. SegVol accepts point, box, and text prompts while output volumetric segmentation. By training on 90k unlabeled Computed Tomography (CT) volumes and 6k labeled CTs, this foundation model supports the segmentation of over 200 anatomical categories. We have released 25 open source datasets(M3D-Seg) for training SegVol, and these preprocessed data have been uploaded to ModelScope and HuggingFace. You can use the following script to easily load cases and insert them into Test script and Training script. 我们已经发布了用于训练SegVol的25个开源数据集(M3D-Seg),并将预处理后的数据上传到了ModelScope和HuggingFace。 您可以使用下面的script方便地载入，并插入到Test script和Training script中。
- Ling-3.0-tiny↗
inclusionAI
🤗 Hugging Face | 🤖 ModelScope | 🐙 OpenRouter We are introducing **Ling-3.0-tiny**, a lightweight hybrid reasoning MoE model with **7.9B** total parameters and only **1.3B** activated parameters per token. It is designed to deliver strong reasoning and agentic capabilities at low inference cost, making advanced model capabilities more accessible for local and resource-constrained deployment. BF16, FP8, and INT4 weights are provided for a wide range of hardware and deployment settings. + **Efficient Hybrid-Linear Architecture:** Ling-3.0-tiny integrates a 3:1 alternating stacking of KDA and MLA (3 Kimi Delta Attention layers followed by 1 Multi-Head Latent Attention layer per 4-layer block) with a sparse MoE FFN comprising 128 routed experts. Each token activates only 8 routed experts and 1 shared expert, allowing the model to balance long-context modeling capability, parameter efficiency, and computational cost. + **Native Hybrid Reasoning and Agentic Capabilities:** Ling-3.0-tiny supports both fast responses and multi-step reasoning, with thinking mode configurable per request through `enable_thinking`. It delivers balanced performance across general agent tasks, coding, mathematical and scientific reasoning, and instruction following. + **Local and Edge Deployment:** Designed for efficient local deployment, Ling-3.0-tiny has been validated on **NVIDIA DGX Spark, Apple Silicon MacBook, and Mac mini**, enabling capable reasoning and agentic workloads without datacenter-class GPUs. With FP8, Ling-3.0-tiny reaches around **100-105 tokens/s on DGX Spark** and **86-90 tokens/s on an M4 Pro MacBook**, with approximately **8.34 GiB peak memory usage** at an 8K context length.
- Wan2.1-Fun-V1.1-1.3B-Control-Camera↗
alibaba-pai
V1.1: | 名称 | 存储空间 | Hugging Face | Model Scope | 描述 | |--|--|--|--|--| | Wan2.1-Fun-V1.1-1.3B-InP | 19.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-V1.1-1.3B文图生视频权重，以多分辨率训练，支持首尾图预测。 | | Wan2.1-Fun-V1.1-14B-InP | 47.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-V1.1-14B文图生视频权重，以多分辨率训练，支持首尾图预测。 | | Wan2.1-Fun-V1.1-1.3B-Control | 19.0 GB | 🤗Link | 😄Link| Wan2.1-Fun-V1.1-1.3B视频控制权重支持不同的控制条件，如Canny、Depth、Pose、MLSD等，支持参考图 + 控制条件进行控制，支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 | | Wan2.1-Fun-V1.1-14B-Control | 47.0 GB | 🤗Link | 😄Link| Wan2.1-Fun-V1.1-14B视视频控制权重支持不同的控制条件，如Canny、Depth、Pose、MLSD等，支持参考图 + 控制条件进行控制，支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 | | Wan2.1-Fun-V1.1-1.3B-Control-Camera | 19.0 GB | 🤗Link | 😄Link| Wan2.1-Fun-V1.1-1.3B相机镜头控制权重。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 | | Wan2.1-Fun-V1.1-14B-Control | 47.0 GB | 🤗Link | 😄Link| Wan2.1-Fun-V1.1-14B相机镜头控制权重。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 | V1.0: | 名称 | 存储空间 | Hugging Face | Model Scope | 描述 | |--|--|--|--|--| | Wan2.1-Fun-1.3B-InP | 19.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-1.3B文图生视频权重，以多分辨率训练，支持首尾图预测。 | | Wan2.1-Fun-14B-InP | 47.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-14B文图生视频权重，以多分辨率训练，支持首尾图预测。 | | Wan2.1-Fun-1.3B-Control | 19.0 GB | 🤗Link | 😄Link| Wan2.1-Fun-1.3B视频控制权重，支持不同的控制条件，如Canny、Depth、Pose、MLSD等，同时支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 | | Wan2.1-Fun-14B-Control | 47.0 GB | 🤗Link | 😄Link| Wan2.1-Fun-14B视频控制权重，支持不同的控制条件，如Canny、Depth、Pose、MLSD等，同时支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 | Windows 的详细信息： - 操作系统 Windows 10 - python: python3.10 & python3.11 - pytorch: torch2.2.0 - CUDA: 11.8 & 12.1 - CUDNN: 8+ - GPU： Nvidia-3060 12G & Nvidia-3090 24G
- gemma-scope-9b-it-res↗
Google
- Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-AWQ↗
QuantTrio
> **v2 Update:** > - **Accuracy preserved:** Matches base model on HumanEval (**96.91% pass@1**) > > - **Shorter reasoning:** ~**24% reduction** in chain-of-thought length > > - **Higher efficiency:** **+31.6% more correct solutions per token** > > - ⚠️**Trade-off:** −1.24% on HumanEval+ −7.2% on MMLU-Pro (Indicating reduced general knowledge reasoning performance) > > *⚠️Note: Due to the scope of SFT data and training focus, the model may underperform the base model on certain tasks requiring long-context understanding or more complex multi-step reasoning. The efficiency and accuracy results reported here are based solely on the HumanEval and HumanEval+ benchmarks. Thank you for your understanding.* **Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2** is the second iteration of this reasoning-focused Qwen3.5-27B fine-tune, built to drastically improve the *efficiency* of chain-of-thought generation, unlocking highly substantial gains in reasoning speed and cost-reduction while actually increasing absolute accuracy. Compared with the earlier version, **v2 was trained with 14,000 Claude 4.6 Opus-style general reasoning samples**, with a stronger emphasis on transferring concise, reusable reasoning patterns rather than only maximizing raw benchmark scores. The goal of v2 is not simply to make the model "think more," but to help it **think more economically**: reducing unnecessarily long internal chains, avoiding verbose over-analysis on easy problems, and massively improving the reasoning-cost-to-quality ratio while beating the baseline's benchmark correctness.
- Qwen-Image-2.1-FP8↗
unsloth
This is an FP8 / INT8 quantized version of Qwen-Image-2.1, plus a pre-cast FP8 copy of its Qwen3-VL text encoder. - For higher quality, you can also run Dynamic FP8 on just 6GB of VRAM via offloading ( Everything here is safetensors rather than a pickle, so loading runs no arbitrary code. Needs a diffusers main build: Qwen-Image-2.1 support postdates the 0.40 release. 🤖 ModelScope | 🤗 HuggingFace | 📑 Blog | 🖥️ Demo | 🫨 Discord | 💬 WeChat
- Qwen3.8-Flash-Next-NVFP4↗
RadixArk
RadixArk Qwen3.8-Flash-Next-NVFP4 is the quantized version of Qwen/Qwen3.8-Flash-Next, a hybrid-architecture multimodal Mixture-of-Experts model. Quantization was performed with NVIDIA Model Optimizer (snapshot `87c9f8cf`) using the NVFP4 W4A4 recipe, scoped to the routed experts only. See the source model for license terms. Developers evaluating NVFP4-quantized serving of Qwen3.8-Flash-Next for agentic systems, chat, coding, and multimodal reasoning workloads.
- anything-v5↗
genai-archive
This repository is being re-uploaded to HuggingFace in accordance with The CreativeML OpenRAIL-M License under which this repository was originally uploaded, specifically **Section II** which grants: > ...a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model. Note that these files did not come from HuggingFace, but instead from modelscope. As such, some files that were present in the original repository may not be present. File integrity has been verified via checksum.
- Qwen-Image-2.1-PE-T2I↗
阿里巴巴 · Qwen
🤖 ModelScope | 🤗 HuggingFace | 📑 Blog | 🖥️ Demo | 🫨 Discord We are excited to open-source **Qwen-Image-2.1**, a unified text-to-image generation and image editing model in the Qwen family. With just **7B parameters in its visual generation component** (32 Single-Stream DiT layers), Qwen-Image-2.1 balances generation quality, inference efficiency, and versatility. Text-to-image **prompt rewriting model** for Qwen-Image-2.1. A fine-tuned Qwen3.5-VL 9B that turns a brief image request in any language into a detailed English prompt plus a recommended aspect ratio.
- Open-Sora-v2↗
hpcai-tech
We design and implement **Open-Sora**, an initiative dedicated to **efficiently** producing high-quality video. We hope to make the model, tools and all details accessible to all. By embracing **open-source** principles, Open-Sora not only democratizes access to advanced video generation techniques, but also offers a streamlined and user-friendly platform that simplifies the complexities of video generation. With Open-Sora, our goal is to foster innovation, creativity, and inclusivity within the field of content creation. 📍 Since Open-Sora is under active development, we remain different branchs for different versions. The latest version is main. Old versions include: v1.0, v1.1, v1.2, v1.3. Our 11B model supports 256px and 768px resolution. Both T2V and I2V are supported by one model. 🤗 Huggingface 🤖 ModelScope.