---
language:
- zh
base_model:
- ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4
tags:
- quantization
quantized_by: btaskel
---
From Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4:
https://huggingface.co/ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4

Based on my experience, Q4_K_S and Q4_K_M are usually the balance points between model size, quantization, and speed.

In some benchmarks, selecting a large-parameter high-quantization LLM tends to perform better than a small-parameter low-quantization LLM.

根据我的经验，通常Q4_K_S、Q4_K_M是模型尺寸/量化/速度的平衡点

在某些基准测试中，选择大参数低量化模型往往比选择小参数高量化模型表现更好。