Unsloth AI· @UnslothAI · X·· 3 小时前精选AI 评分77
AI 导读
Unsloth 发布 Mistral Le Chonk（Mistral Large 4，1T 参数 MoE，52B 激活）的 Dynamic 1-bit GGUF，可在 Mac Studio 上本地运行，保留 91% top-1 准确率，体积约 233GB（较 1.1TB FP8 小 80%）。图为各量化档位给出硬件需求，1-bit 需 256-270GB 内存，最小 UD-IQ1_S 约占 233GB 磁盘，并支持文本与图像输入、1M 上下文和 160+ 语言。GGUF 与指南见 huggingface.co/unsloth/Mistral-Large-4-1T-A52B-GGUF 和 unsloth.ai/docs/models/mistral-large-4。
推荐理由
原文给出在 Mac Studio 上本地运行 1T 参数模型的量化方案和硬件需求表，读者可直接照做部署。
正文 · 原文
Mistral Le Chonk can now run locally!
The 1 trillion parameter model runs on a Mac Studio with our 1-bit GGUF which retains 91% top-1 accuracy.
Le Chonk is the strongest Western open model to date.
GGUF: https://huggingface.co/unsloth/Mistral-Large-4-1T-A52B-GGUF
Guide: https://unsloth.ai/docs/models/mistral-large-4
Today, we release the weights of a new and improved Le Chonk, advancing the open frontier for agentic coding and cybersecurity. 💻 Built to build: Le Chonk ranks #1 on @cognition’s FrontierCode among open models, and sets a new benchmark for Pareto efficiency on @artificialanalysis. 🛡 Built to defend: State-of-the-art in cyberdefense, put through the paces by the world's leading cyber vendors, including @CrowdStrike, @cloudflare, @rubrik, @zscaler, and others. Built for sovereignty: Make it yours. Download the weights on @huggingface , customize it with Mistral Forge, and deploy it wherever you need it. Read more from our team and customers 👇:在 X 查看被引用的帖子
来源：Unsloth AI · x.com