How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf btaskel/RWKV6-3B-Chn-UnlimitedRP-mini-chat-GGUF:Q4_K_S# Run inference directly in the terminal:
llama cli -hf btaskel/RWKV6-3B-Chn-UnlimitedRP-mini-chat-GGUF:Q4_K_SUse pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf btaskel/RWKV6-3B-Chn-UnlimitedRP-mini-chat-GGUF:Q4_K_S# Run inference directly in the terminal:
./llama-cli -hf btaskel/RWKV6-3B-Chn-UnlimitedRP-mini-chat-GGUF:Q4_K_SBuild from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf btaskel/RWKV6-3B-Chn-UnlimitedRP-mini-chat-GGUF:Q4_K_S# Run inference directly in the terminal:
./build/bin/llama-cli -hf btaskel/RWKV6-3B-Chn-UnlimitedRP-mini-chat-GGUF:Q4_K_SUse Docker
docker model run hf.co/btaskel/RWKV6-3B-Chn-UnlimitedRP-mini-chat-GGUF:Q4_K_SQuick Links
From Seikaijyu/RWKV6-3B-Chn-UnlimitedRP-mini-chat: https://huggingface.co/Seikaijyu/RWKV6-3B-Chn-UnlimitedRP-mini-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。
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Base model
Seikaijyu/RWKV6-3B-Chn-UnlimitedRP-mini-chat
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf btaskel/RWKV6-3B-Chn-UnlimitedRP-mini-chat-GGUF:Q4_K_S# Run inference directly in the terminal: llama cli -hf btaskel/RWKV6-3B-Chn-UnlimitedRP-mini-chat-GGUF:Q4_K_S