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AI 모델 183

  • Meta-Llama-3.1-70B-Instruct-FP8↗

    RedHatAI

    - **Model Architecture:** Meta-Llama-3.1 - **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 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:** 7/23/2024 - **Version:** 1.0 - **License(s):** llama3.1 - **Model Developers:** Neural Magic Quantized version of Meta-Llama-3.1-70B-Instruct. It achieves an average score of 84.29 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 84.40. This model was obtained by quantizing the weights and activations of Meta-Llama-3.1-70B-Instruct to FP8 data type. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.

  • acestep-5Hz-lm-4B↗

    ACE-Step

    Project | Hugging Face | ModelScope | Space Demo | Discord Tech Report 🚀 **ACE-Step v1.5** is a highly efficient open-source music foundation model designed to bring commercial-grade music generation to consumer hardware. * **💰 Commercial-Ready:** Unlike many models trained on ambiguous datasets, ACE-Step v1.5 is designed for creators. You can strictly use the generated music for **commercial purposes**. * **📚 Safe & Robust Training Data:** The model is trained on a massive, legally compliant dataset consisting of: * **Licensed Data:** Professionally licensed music tracks. * **Royalty-Free / No-Copyright Data:** A vast collection of public domain and royalty-free music. * **Synthetic Data:** High-quality audio generated via advanced MIDI-to-Audio conversion. * **⚡ Extreme Speed:** Generates a full song in under 2 seconds on an A100 and under 10 seconds on an RTX 3090. * **🖥️ Consumer Hardware Friendly:** Runs locally with less than 4GB of VRAM.

  • Llama-3.3-70B-Instruct-FP8-dynamic↗

    RedHatAI

    - **Model Architecture:** Meta-Llama-3.1 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.3 model also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.3 Community License allows for these use cases. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.3 Community License. Use in languages beyond English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. - **Release Date:** 12/11/2024 - **Version:** 1.0 - **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5 - **License(s):** llama3.3 - **Model Developers:** RedHat (Neural Magic) This model was obtained by quantizing activation and weights of Llama-3.3-70B-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.

  • gemma-scope-2-27b-pt↗

    Google

  • Imgscope-OCR-2B-0527-i1-GGUF↗

    mradermacher

  • StormScope-NSRDB↗

    NVIDIA

  • Jan-code-4b-gguf↗

    janhq

    **Jan-Code-4B** is a **code-tuned** model built on top of Jan-v3-4B-base-instruct. It’s designed to be a practical coding model you can run locally and iterate on quickly—useful for everyday code tasks and as a lightweight “worker” model in agentic workflows. Compared to larger coding models, Jan-Code focuses on handling **well-scoped subtasks** reliably while keeping latency and compute requirements small. Jan-code is optimized for direct integration with Jan Desktop, select the model in the app to start using it.

  • Ace-Step1.5↗

    ACE-Step

    Project | Hugging Face | ModelScope | Space Demo | Discord Tech Report 🚀 **ACE-Step v1.5** is a highly efficient open-source music foundation model designed to bring commercial-grade music generation to consumer hardware. * **💰 Commercial-Ready:** Unlike many models trained on ambiguous datasets, ACE-Step v1.5 is designed for creators. You can strictly use the generated music for **commercial purposes**. * **📚 Safe & Robust Training Data:** The model is trained on a massive, legally compliant dataset consisting of: * **Licensed Data:** Professionally licensed music tracks. * **Royalty-Free / No-Copyright Data:** A vast collection of public domain and royalty-free music. * **Synthetic Data:** High-quality audio generated via advanced MIDI-to-Audio conversion. * **⚡ Extreme Speed:** Generates a full song in under 2 seconds on an A100 and under 10 seconds on an RTX 3090. * **🖥️ Consumer Hardware Friendly:** Runs locally with less than 4GB of VRAM.

  • Qwen3-1.7B_eagle3↗

    AngelSlim

    Dedicated to building a more intuitive, comprehensive, and efficient LLMs compression toolkit. 📖 Documentation &nbsp&nbsp | &nbsp&nbsp🤗 Hugging Face &nbsp&nbsp | &nbsp&nbsp🤖 ModelScope &nbsp&nbsp | &nbsp&nbsp💬 WeChat - [26/01/13] We have released v0.3. We support the training and deployment of Eagle3 for all-scale LLMs/VLMs/Audio models, as detailed in the guidance documentation. And We released **Sherry**, the hardware-efficient 1.25 bit quantization algorithm [Paper Comming soon] | [[Code]](https://github.com/Tencent/AngelSlim/tree/sherry/Sherry)🔥🔥🔥 - [25/11/05] We have released v0.2. Quantization support for new models, such as `GLM-4.6`, `Qwen3-VL` and `Qwen3-Omni`, open-sources the Eagle3 speculative decoding training framework, and updates the Diffusion model quantization tools. - [25/09/30] We have released **SpecExit**, the reasoning early-exit algorithm: [[Paper]](http://arxiv.org/abs/2509.24248) | [[Docs]](https://angelslim.readthedocs.io/zh-cn/latest/features/speculative_decoding/spec_exit.html) | [[vLLM Code]](https://github.com/vllm-project/vllm/pull/27192) - [25/09/26] We have released **TEQUILA**, the ternary quantization algorithm [[Paper]](https://arxiv.org/abs/2509.23809) | [[Code]](https://github.com/Tencent/AngelSlim/tree/tequila/TernaryQuant) - [25/09/24] We now support the PTQ quantization of NVFP4 for the Qwen3 series models. We also opensource Qwen3-32B-NVFP4 and Qwen3-235B-A22B-NVFP4 weights.

  • Qwen-Image-GGUF↗

    unsloth

    > [!NOTE] > This is a GGUF quantized version of Qwen-Image. > unsloth/Qwen-Image-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. Important layers are upcasted to higher precision. 💜 Qwen Chat &nbsp&nbsp | &nbsp&nbsp🤗 Hugging Face &nbsp&nbsp | &nbsp&nbsp🤖 ModelScope &nbsp&nbsp | &nbsp&nbsp 📑 Tech Report &nbsp&nbsp | &nbsp&nbsp 📑 Blog &nbsp&nbsp 🖥️ Demo &nbsp&nbsp | &nbsp&nbsp💬 WeChat (微信) &nbsp&nbsp | &nbsp&nbsp🫨 Discord &nbsp&nbsp