Saved toolsSubmit a tool
FOXAI SEARCH

Search FoxAI

AI Models 183

  • Ling-2.6-flash↗

    inclusionAI

    🤗 Hugging Face    |   🤖 ModelScope    |   🐙 ling.tbox.cn    |    Tech Report Today, we announce the official open-source release of **Ling-2.6-flash**, an **instruct model** with **104B total parameters** and **7.4B active parameters**. As agent capabilities mature, skyrocketing **token consumption** has become a primary barrier to deployment. Unlike standard chat, agent workflows involve massive inputs and complex, multi-step execution, driving up both compute demand and user costs. While the industry is pivoting toward "long-reasoning" to push performance ceilings, a critical question remains: Are these excessive reasoning tokens truly necessary for high-frequency, everyday agent use cases?

  • stormscope-meteosat↗

    NVIDIA

  • docscopeOCR-7B-050425-exp-i1-GGUF↗

    mradermacher

  • SmolLM-1.7B-Instruct-quantized.w4a16↗

    nm-testing

    - **Model Architecture:** SmolLM-135M-Instruct - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** INT4 - **Intended Use Cases:** Intended for commercial and research use in English. Similarly to SmolLM-135M-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:** 8/23/2024 - **Version:** 1.0 - **License(s)**: Apache-2.0 - **Model Developers:** Neural Magic Quantized version of SmolLM-135M-Instruct. It achieves an average score of 31.91 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 31.55. This model was obtained by quantizing the weights of SmolLM-135M-Instruct 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%.

  • Qwen-AgentWorld-35B-A3B-GGUF↗

    unsloth

    Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. 📑 Technical Report | 📖 Blog | 🤗 Hugging Face | 🤖 ModelScope | 💻 GitHub | 🖥️ Demo > [!Note] > This repository contains the model weights and configuration files for **Qwen-AgentWorld-35B-A3B**, a native language world model trained for agentic environment simulation. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc.

  • Qwen3-TTS-12Hz-1.7B-VoiceDesign↗

    阿里巴巴 · Qwen

    &nbsp&nbsp🤗 Hugging Face &nbsp&nbsp | &nbsp&nbsp🤖 ModelScope &nbsp&nbsp | &nbsp&nbsp📑 Blog &nbsp&nbsp | &nbsp&nbsp📑 Paper &nbsp&nbsp | &nbsp&nbsp💻 GitHub We release **Qwen3-TTS**, a series of powerful speech generation models developed by Qwen, offering comprehensive support for voice cloning, voice design, ultra-high-quality human-like speech generation, and natural language-based voice control. Qwen3-TTS covers 10 major languages (Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian) as well as multiple dialectal voice profiles. Key features:

  • Wan2.1-Fun-V1.1-1.3B-InP↗

    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

  • Wan2.1_T2V_14B_LightX2V_StepCfgDistill_VACE-GGUF↗

    QuantStack

    This is a GGUF conversion of an addon of lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill and Wan-AI/Wan2.1-VACE-14B scopes. The process involved extracting VACE scopes and injecting into the target models, using scripts provided by wsbagnsv1. All quantized versions were created from the FP16 model using the conversion scripts provided by city96, available at the ComfyUI-GGUF GitHub repository.

  • Meta-Llama-3.1-8B-Instruct-quantized.w4a16↗

    RedHatAI

    - **Model Architecture:** LlamaForCausalLM - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** INT4 - **Intended Use Cases:** Intended for commercial and research use in English. Similarly to Meta-Llama-3.1-8B-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:** 7/26/2024 - **Version:** 1.0 - **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5 - **License(s):** Llama3.1 - **Model Developers:** Neural Magic This model is a quantized version of Meta-Llama-3.1-8B-Instruct. It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model, including multiple-choice, math reasoning, and open-ended text generation. Meta-Llama-3.1-8B-Instruct-quantized.w4a16 achieves 93.0% recovery for the Arena-Hard evaluation, 98.9% for OpenLLM v1 (using Meta's prompting when available), 96.1% for OpenLLM v2, 99.7% for HumanEval pass@1, and 97.4% for HumanEval+ pass@1. This model was obtained by quantizing the weights of Meta-Llama-3.1-8B-Instruct 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%.

  • Llama-3.2-1B-Instruct-FP8-dynamic↗

    RedHatAI

    - **Model Architecture:** Meta-Llama-3.2 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use in multiple languages. Similarly to Llama-3.2-1B-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:** 9/25/2024 - **Version:** 1.0 - **License(s):** llama3.2 - **Model Developers:** Neural Magic Quantized version of Llama-3.2-1B-Instruct. It achieves an average score of 50.88 on a subset of task from the OpenLLM benchmark (version 1), whereas the unquantized model achieves 51.70. This model was obtained by quantizing the weights and activations of Llama-3.2-1B-Instruct to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.