AI is happening. The future is updating.Hy-Memory-30B-A3B-GGUF
This repository provides GGUF quantizations of Hy-Memory-30B-A3B for efficient local inference with llama.cpp. The releases use **Tencent Hunyuan's Hy-Memory quantization recipe**: imatrix calibration plus MoE-aware mixed precision. Routed experts, which dominate parameter count, can be compressed aggressively, while shared experts, attention, and input/output layers keep higher precision. On the same fixed Mem0 LoCoMo setup, even the **~8 GB** expert-mixed build stays within about **1.5 points** of the strongest Q6_K result (**57.25%** vs **58.78%**), so the quality drop remains small while the file size falls to roughly **one quarter** of Q8_0. All six files passed llama.cpp `--check-tensors`, real no-think generation, and the complete fixed 20% Mem0 LoCoMo evaluation. Each version builds its own memory bank under identical settings. These are single-run scores: they do not claim lossless quantization, and lower-bit variants are not guaranteed to beat higher-bit ones.
This repository provides GGUF quantizations of Hy-Memory-30B-A3B for efficient local inference with llama.cpp. The releases use **Tencent Hunyuan's Hy-Memory quantization recipe**: imatrix calibration plus MoE-aware mixed precision. Routed experts, which dominate parameter count, can be compressed aggressively, while shared experts, attention, and input/output layers keep higher precision. On the same fixed Mem0 LoCoMo setup, even the **~8 GB** expert-mixed build stays within about **1.5 points** of the strongest Q6_K result (**57.25%** vs **58.78%**), so the quality drop remains small while the file size falls to roughly **one quarter** of Q8_0. All six files passed llama.cpp `--check-tensors`, real no-think generation, and the complete fixed 20% Mem0 LoCoMo evaluation. Each version builds its own memory bank under identical settings. These are single-run scores: they do not claim lossless quantization, and lower-bit variants are not guaranteed to beat higher-bit ones.