From Seikaijyu/RWKV7-2.9B-v3-UnlimitedRP-mini-novel-chat-preview: https://huggingface.co/Seikaijyu/RWKV7-2.9B-v3-UnlimitedRP-mini-novel-chat-preview
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。
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API:
The model has no library tag.
Model tree for btaskel/RWKV7-2.9B-v3-UnlimitedRP-mini-novel-chat-preview-GGML
Base model
BlinkDL/rwkv-7-world