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AI モデル 183
- DeepSeek V4.1 Flash (batch)↗
DeepSeek
DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on...
- Claude Sonnet 5.5 (batch)↗
Anthropic
Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, succeeding Claude Sonnet 5 as a direct upgrade. It is especially strong at building features, fixing bugs, and producing...
- Wan2.2-TI2V-5B-Diffusers↗
Wan-AI
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Technical Report    |    📑 Blog    |   💬 WeChat Group    |    📖 Discord    We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations: - 👍 **Effective MoE Architecture**: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.
- zeroscope_v2_576w↗
cerspense
- Wan2.2-T2V-A14B↗
Wan-AI
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Technical Report    |    📑 Blog    |   💬 WeChat Group    |    📖 Discord    We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations: This repository contains our T2V-A14B model, which supports generating 5s videos at both 480P and 720P resolutions. Built with a Mixture-of-Experts (MoE) architecture, it delivers outstanding video generation quality. On our new benchmark Wan-Bench 2.0, the model surpasses leading commercial models across most key evaluation dimensions.
- BEYOND_REALITY_Z_IMAGE↗
Nurburgring
更新lora训练适配器，注意，是本淡妆浓抹 3.0 模型的的lora训练专用，重点是diffusion，diffusion是新的训练才是新的，适配器是adapter，Rebuild版底模支持在此适配器上训练，可在aitoolkit中替换并微调自己专属lora。注意，不同于官方底模，是专用于本模型底模的训练适配器，感谢@Laushine制作，请改扩展名为ZIP后解压使用。 https://www.modelscope.cn/models/Nurburgring/BEYOND_REALITY_Z_IMAGE/summary?version=20260206135410 Nunchaku量化版 by tonera(感谢）： https://modelscope.cn/models/tonera/Beyond_Reality_Zimage_v2_svdq/summary NVFP4量化以及采样器搭配 by miccodadada（感谢）： 本人实测，AuraFlow3.5 - 5，首选euler a+FlowMatchEulerDiscreteScheduler,其次是ZImagePowerNodes自带的采样器，然后是DEIS_2M + BONG_TONGENT，追求泛用性的话是Sa_solver+FlowMatchEulerDiscrete,人像的话也可以试试ER_SDE+(sgm_uniform/FlowMatch)。 我自己制作了它的NVFP4混合量化，FEED FORWARD 123层采用NVFP4，其余注意力采用FP8 Per-Tensor量化（目前Comfyui暂时不支持BlockWise量化），质量下降在可接受范围内。在5070上，9步采样时间大概2.8-3.6秒。https://huggingface.co/lieding1994/zimage-serverless/blob/main/BEYOND-REALITY-BF16_learned_fp8mixed_tensor.safetensors
- gemma-scope-2-1b-pt↗
Google
- Qwen-Image↗
gradients-io-tournaments
💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    We are thrilled to release **Qwen-Image**, an image generation foundation model in the Qwen series that achieves significant advances in **complex text rendering** and **precise image editing**. Experiments show strong general capabilities in both image generation and editing, with exceptional performance in text rendering, especially for Chinese. The following contains a code snippet illustrating how to use the model to generate images based on text prompts:
- Qwen-7B↗
阿里巴巴 · Qwen
🤗 Hugging Face    |   🤖 ModelScope    |    📑 Paper    ｜   🖥️ Demo **通义千问-7B（Qwen-7B）**是阿里云研发的通义千问大模型系列的70亿参数规模的模型。Qwen-7B是基于Transformer的大语言模型, 在超大规模的预训练数据上进行训练得到。预训练数据类型多样，覆盖广泛，包括大量网络文本、专业书籍、代码等。同时，在Qwen-7B的基础上，我们使用对齐机制打造了基于大语言模型的AI助手Qwen-7B-Chat。相较于最初开源的Qwen-7B模型，我们现已将预训练模型和Chat模型更新到效果更优的版本。本仓库为Qwen-7B预训练模型的仓库。 1. **大规模高质量训练语料**：使用超过2.4万亿tokens的数据进行预训练，包含高质量中、英、多语言、代码、数学等数据，涵盖通用及专业领域的训练语料。通过大量对比实验对预训练语料分布进行了优化。 2. **强大的性能**：Qwen-7B在多个中英文下游评测任务上（涵盖常识推理、代码、数学、翻译等），效果显著超越现有的相近规模开源模型，甚至在部分指标上相比更大尺寸模型也有较强竞争力。具体评测结果请详见下文。 3. **覆盖更全面的词表**：相比目前以中英词表为主的开源模型，Qwen-7B使用了约15万大小的词表。该词表对多语言更加友好，方便用户在不扩展词表的情况下对部分语种进行能力增强和扩展。
- Unlimited-OCR↗
baidu
- [2026/07/21] 🤝 Thanks to the ms-swift community for their support, our model now supports training with ms-swift. - [2026/07/03] 🤝 Thanks to the Baidu Cloud team for their support. Our model is now available on Baidu Cloud. - [2026/06/28] 🤝 Thanks to the vLLM community and Tianyu Guo for their support, our model now supports vLLM inference. - [2026/06/24] 🤝 Thanks to AK for creating a demo for us. It is now available at Hugging Face Spaces. - [2026/06/23] 📄 Our paper is now available on arXiv. - [2026/06/23] 🤝 Thanks to the ModelScope community for their support. Our model is now available at ModelScope. - [2026/06/22] 🚀 We present Unlimited-OCR, aiming to push Deepseek-OCR one step further. Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.3 + CUDA12.9： **Recipe:** https://recipes.vllm.ai/baidu/Unlimited-OCR