AI 导读
腾讯混元将 Hy4-preview 从 1.5TB 压缩至约 200GiB GGUF，提出 MIX-STQ1_0 混合量化方案，按校准数据为每层选择位宽，部分低至 1.31-bit STQ1_0、部分高至 2.06-bit IQ2_XXS。
正文
We compressed Hy4-preview from 1.5TB to ～200GiB GGUF and it still works well !
Meet MIX-STQ1_0.The trick isn’t just going low, it’s deciding where: calibration data picks each layer’s bit-width, some down to 1.31-bit STQ1_0, some up to 2.06-bit IQ2_XXS. Same budget, lower error.
Accuracy barely moves vs BF16
📊 MCP Atlas 83.7→83.2
📊 SWE-Bench multi 82.9→81.3
📊 MRCR 81.3→81.1
📊 IFBench 73.5→72.5
See the details on HF : AngelSlim/Hy4-preview-GGUF
Weights & low-bit GGUFs 👇
https://t.co/9uM9NT9Wem
#LLM #Quantization #llamacpp #Hy
🚀 Hy4 preview is here. 770B, 49B active, 1M context. Built for productivity. Open source frontier. Consistent affordable price. Use it. Tell us what breaks. More on Hy blog：https://t.co/rbl1IWRk3C HuggingFace：https://t.co/mE9wevH5XR Github：https://t.co/pyl9zckpoL https://t.co/4iW6gSuZKr在 X 查看被引用的帖子
来源：@TencentHunyuan · x.com