| language: | |
| - zh | |
| base_model: | |
| - Seikaijyu/temp | |
| tags: | |
| - quantization | |
| quantized_by: btaskel | |
| From Seikaijyu/temp: | |
| https://huggingface.co/Seikaijyu/temp | |
| 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 low-quantization LLM tends to perform better than a small-parameter high-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。 |