MODEL PROFILE

Hy-Memory-30B-A3B-GGUF

腾讯 · 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.

Text generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/11/2 19:16:57
OVERVIEW

Model overview

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.

Key features

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Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。