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README.md
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---
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language:
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- zh
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base_model:
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- Seikaijyu/RWKV7-2.9B-v3-UnlimitedRP-mini-novel-chat-preview
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tags:
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- quantization
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quantized_by: btaskel
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---
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From Seikaijyu/RWKV7-2.9B-v3-UnlimitedRP-mini-novel-chat-preview:
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https://huggingface.co/Seikaijyu/RWKV7-2.9B-v3-UnlimitedRP-mini-novel-chat-preview
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Based on my experience, Q4_K_S and Q4_K_M are usually the balance points between model size, quantization, and speed.
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In some benchmarks, selecting a large-parameter low-quantization LLM tends to perform better than a small-parameter high-quantization LLM.
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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
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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。
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