Unsloth AI· @UnslothAI · X·· 2 小时前精选AI 评分70
AI 导读
Unsloth 发布 MiniMax-M3.1-Flash 的量化 GGUF，2-bit 版在缩小 45% 体积的同时保留 91.5% top 1 精度，可在 150GB 内存上本地运行，1-bit 版约 89.9%，4-bit UD-Q4_K_XL 达 98.0%。作者称其为同尺寸最强开源模型之一，并给出 Hugging Face 权重链接与本地运行指南。
推荐理由
原文给出自建 GGUF 量化在保留精度与压缩体积间的实测数据，以及各档位所需内存，读者可据此判断能否本地部署。
正文 · 原文
MiniMax-M3.1-Flash can now run locally!
We made 2-bit GGUF retain 91.5% top 1 accuracy while being 45% smaller. Run on 150GB RAM.
MiniMax-M3.1-Flash is the strongest open model for its size & among the best overall.
GGUF: https://huggingface.co/unsloth/MiniMax-M3.1-Flash-GGUF
Guide: https://unsloth.ai/docs/models/minimax-m3.1-flash
MiniMax M3.1 Flash: Space Performance + Bunny Speed 🚀🐰 Yes, Space Bunny - which has topped global leaderboards with 200T+ tokens - was an earlier version of M3.1 Flash. 💰Redefining the performance economics of agentic AI. M3.1 Flash achieves frontier-level intelligence across key tasks such as Cowork (Automation-Bench: 62%; ALE: 30%), Coding (DeepSWE: 74%) and Visual Reasoning (BabyVision: 91%), at 1/3 the cost of M3 and 3x the speed. API pricing (flat rate): $0.15 / $0.60 per 1M input/output tokens, with cache reads at $0.015 / 1M tokens. Model weights will arrive on Oct 23. API: http://platform.minimax.io M Plan: https://www.minimax.io/m-plan MiniMax Code for desktop: https://agent.minimax.io/download在 X 查看被引用的帖子
来源：Unsloth AI · x.com