From Seikaijyu/RWKV6-3B-v2.1-Aphrodite-yandere-chat: https://huggingface.co/Seikaijyu/RWKV6-3B-v2.1-Aphrodite-yandere-chat
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。
- Downloads last month
- 25
Hardware compatibility
Log In
to view the estimation
4-bit
8-bit
Inference Providers
NEW
This model isn't deployed by any Inference Provider.
🙋
Ask for provider support