Tencent AI(@TencentAI_News):1.5TB → 214GB. Seven times smaller, barely a dent. That's Hy4 preview. The trick is Sherry — our quantization method that packs weights down to 1.25 bits each. (The image shows how.) It also unlocks a new way to run it: stitch the GPUs you already have across machines, and they work as one. GGUF (quantized): https://t.co/EC2ZYpVnC4 Original model:

2026.09.01 08:39

1.5TB → 214GB. Seven times smaller, barely a dent. That's Hy4 preview. The trick is Sherry — our quantization method that packs weights down to 1.25 bits each. (The image shows how.) It also unlocks a new way to run it: stitch the GPUs you already have across machines, and they work as one. GGUF (quantized): Original model:

Tencent Hy@TencentHunyuan

2026.08.29 05:31

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 👇 #LLM# #Quantization# #llamacpp# #Hy#