metadata
base_model:
- Laxhar/noobai-XL-Vpred-1.0
tags:
- quantization
quantized_by: btaskel
pipeline_tag: text-to-image
From Laxhar/noobai-XL-Vpred-1.0 Harem: https://huggingface.co/Laxhar/noobai-XL-Vpred-1.0
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。