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AI モデル 183
- reformer-enwik8↗
Google
*enwik8* is a dataset based on Wikipedia and is often used to measure the model's ability to *compress* data, *e.g.* in the scope of the *Hutter prize*: https://en.wikipedia.org/wiki/Hutter_Prize. `reformer-enwik8` was pretrained on the first 90M chars of *enwik8* whereas the text was chunked into batches of size 65536 chars (=2^16). The model's weights were taken from https://console.cloud.google.com/storage/browser/trax-ml/reformer/enwik8 and converted to Hugging Face's PyTorch ReformerLM model `ReformerModelWithLMHead`. The model is a language model that operates on characters. Therefore, this model does not need a tokenizer. The following function can instead be used for **encoding** and **decoding**:
- Qwen-Image-2.1-Turbo↗
阿里巴巴 · Qwen
🤖 ModelScope | 🤗 HuggingFace | 💻 GitHub | 📑 Qwen-Image-2.1 Blog | 🫨 Discord | 💬 WeChat **Qwen-Image-2.1-Turbo** is an accelerated checkpoint of Qwen-Image-2.1 for text-to-image generation and image editing with **8 denoising steps**. It uses the same 7B visual generation architecture and loads directly with `QwenImage21Pipeline` in Diffusers. The checkpoint includes its recommended sampling schedule, so it is ready to use without manually configuring the scheduler. Generation uses CFG=1 by default, and prefix KV caching reuses the text and reference-image context across denoising steps.
- Wan2.2-I2V-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 also includes our I2V-A14B model, designed for image-to-video generation, supporting both 480P and 720P resolutions. Built with a Mixture-of-Experts (MoE) architecture, it achieves more stable video synthesis with reduced unrealistic camera movements and offers enhanced support for diverse stylized scenes.
- Qwen2.5-7B-Instruct-FP8-dynamic↗
RedHatAI
- **Model Architecture:** Qwen2 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Activation quantization:** FP8 - **Weight quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use multiple languages. Similarly to Qwen2.5-7B, 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). - **Release Date:** 11/27/2024 - **Version:** 1.0 - **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5 - **License(s):** apache-2.0 - **Model Developers:** Neural Magic This model was obtained by quantizing activations and weights of Qwen2.5-7B-Instruct to FP8 data type. This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x). Weight quantization also reduces disk size requirements by approximately 50%. Only weights and activations of the linear operators within transformers blocks are quantized. Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme. The llm-compressor library is used for quantization.
- DeepSeek-Coder-V2-Lite-Instruct-FP8↗
RedHatAI
- **Model Architecture:** DeepSeek-Coder-V2-Lite-Instruct - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use in English. Similarly to Meta-Llama-3-7B-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:** 7/18/2024 - **Version:** 1.0 - **License(s):** deepseek-license - **Model Developers:** Neural Magic Quantized version of DeepSeek-Coder-V2-Lite-Instruct. It achieves an average score of 79.60 on the HumanEval+ benchmark, whereas the unquantized model achieves 79.33.
- RynnBrain1.1-2B↗
Alibaba-DAMO-Academy
💫 Project Page | 🤗 Hugging Face | 🤖 ModelScope | 📚 Cookbooks | 📄 arXiv * **[2026.07.16]** ✨✨ Release **RynnBrain 1.1** checkpoints (**2B / 9B / 122B-A10B**) on Hugging Face & ModelScope ! * **[2026.07.16]** ✨✨ Release the **RynnBrain 1.1** Technical Report on arXiv ! We present **RynnBrain 1.1**, a systematic upgrade of RynnBrain for embodied intelligence. RynnBrain 1.1 is released in three scales: **2B**, **9B**, and **122B-A10B**, extending the model family from compact dense models to its first 122B-level sparse-MoE model.
- Wan2.1-I2V-14B-720P-Diffusers↗
Wan-AI
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Paper (Coming soon)    |    📑 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. This repository contains our I2V-14B model, which is capable of generating 720P high-definition videos. After thousands of rounds of human evaluations, this model has outperformed both closed-source and open-source alternatives, achieving state-of-the-art performance.
- Mistral-Small-3.2-24B-Instruct-2506-NVFP4↗
RedHatAI
- **Model Architecture:** unsloth/Mistral-Small-3.2-24B-Instruct-2506 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP4 - **Activation quantization:** FP4 - **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:** 10/29/2025 - **Version:** 1.0 - **Model Developers:** RedHatAI This model is a quantized version of unsloth/Mistral-Small-3.2-24B-Instruct-2506. It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model. This model was obtained by quantizing the weights and activations of unsloth/Mistral-Small-3.2-24B-Instruct-2506 to FP4 data type, ready for inference with vLLM>=0.9.1 This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.
- Wan2.1-I2V-14B-480P-Diffusers↗
Wan-AI
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Paper (Coming soon)    |    📑 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. This repo contains our I2V-14B model, which is capable of generating 480P videos, offering advantages in terms of fast generation and excellent quality.
- Wan2.1-VACE-1.3B-diffusers↗
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. If your work has improved **Wan2.1** and you would like more people to see it, please inform us. - Phantom has developed a unified video generation framework for single and multi-subject references based on **Wan2.1-T2V-1.3B**. Please refer to their examples. - UniAnimate-DiT, based on **Wan2.1-14B-I2V**, has trained a Human image animation model and has open-sourced the inference and training code. Feel free to enjoy it! - CFG-Zero enhances **Wan2.1** (covering both T2V and I2V models) from the perspective of CFG. - TeaCache now supports **Wan2.1** acceleration, capable of increasing speed by approximately 2x. Feel free to gi