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AI 模型 183
- Qwen-Edit-2509-Light-Migration↗
dx8152
This model is trained (code-free!) on ModelScope. Thanks to ModelScope team for providing the training infra: ----- This time, we're bringing you the groundbreaking LoRA light transfer tool. It's the first to solve the problem of Qwen-Edit-2509's inability to perform secondary lighting on images with existing lighting. For communication/cooperation, you can join the discord group to communicate： https://discord.gg/yVAVa43mWk
- Qwen3-30B-A3B-Instruct-2507-quantized.w4a16↗
RedHatAI
- **Model Architecture:** Qwen3ForCausalLM - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** INT4 - **Intended Use Cases:** - Reasoning. - Function calling. - Subject matter experts via fine-tuning. - Multilingual instruction following. - Translation. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). - **Release Date:** 05/05/2025 - **Version:** 1.0 - **Model Developers:** RedHat (Neural Magic) This model was obtained by quantizing the weights of Qwen/Qwen3-30B-A3B-Instruct-2507 to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights of the linear operators within transformers blocks are quantized. Weights are quantized using a symmetric per-group scheme, with group size 128. The GPTQ algorithm is applied for quantization, as implemented in the llm-compressor library.
- Wan2.2-I2V-A14B-Lightning-Diffusers↗
magespace
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Technical Report    |    📑 Blog    |   💬 WeChat Group    |    📖 Discord    🔗 Lightx2v — Distilled & optimized Wan2.2 for fast, high-quality 480P / 720P image-to-video generation 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:
- gemma-scope-2-270m-it↗
Google
- Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic↗
RedHatAI
- **Model Architecture:** Mistral3ForConditionalGeneration - **Input:** Text / Image - **Output:** Text - **Model Optimizations:** - **Activation quantization:** FP8 - **Weight quantization:** FP8 - **Intended Use Cases:** It is ideal for: - Fast-response conversational agents. - Low-latency function calling. - Subject matter experts via fine-tuning. - Local inference for hobbyists and organizations handling sensitive data. - Programming and math reasoning. - Long document understanding. - Visual understanding. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages not officially supported by the model. - **Release Date:** 04/15/2025 - **Version:** 1.0 - **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5 - **Model Developers:** RedHat (Neural Magic) This model was obtained by quantizing activations and weights of Mistral-Small-3.1-24B-Instruct-2503 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.
- ModelScope↗
ckpt
- TurboWan2.1-T2V-1.3B-Diffusers↗
IPostYellow
convert TurboWan2.1-T2V-1.3B-480P(https://modelscope.cn/models/TurboDiffusion/TurboWan2.1-T2V-1.3B-480P/summary) to TurboWan2.1-T2V-1.3B-Diffusers convert script https://github.com/IPostYellow/TurboWan_to_Diffusers/blob/main/convert_turbowan_to_diffusers.py
- Qwen-Image-Edit-2511-GGUF↗
vantagewithai
💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    🖥️ Demo    |   💬 WeChat (微信)    |   🫨 Discord   |    Github    We are excited to introduce Qwen-Image-Edit-2511, an enhanced version over Qwen-Image-Edit-2509, featuring multiple improvements—including notably better consistency. To try out the latest model, please visit Qwen Chat and select the Image Editing feature.
- QwenPaw-Flash-35B-A3B-Q4_K_M↗
agentscope-ai
- Qwen-Image-2512-GGUF↗
Frederic75
💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    🖥️ Demo    |   💬 WeChat (微信)    |   🫨 Discord   |    Github    We are excited to introduce Qwen-Image-2512, the December update of Qwen-Image’s text-to-image foundational model. You are welcome to try the latest model at Qwen Chat. Compared to the base Qwen-Image model released in August, Qwen-Image-2512 features the following key improvements: