How to use from
llama.cpp
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RuimengLiu/raymond-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RuimengLiu/raymond-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RuimengLiu/raymond-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RuimengLiu/raymond-gguf:Q4_K_M
Use 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 RuimengLiu/raymond-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RuimengLiu/raymond-gguf:Q4_K_M
Build 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 RuimengLiu/raymond-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RuimengLiu/raymond-gguf:Q4_K_M
Use Docker
docker model run hf.co/RuimengLiu/raymond-gguf:Q4_K_M
Quick Links

Raymond GGUF

Qwen3-4B-Instruct-2507 fine-tuned with LoRA on synthetic chat data distilled via Claude Sonnet. Quantized to Q4_K_M for local inference via Ollama.

Usage

# Download
huggingface-cli download RuimengLiu/raymond-gguf raymond-q4_k_m.gguf --local-dir .

# Create Ollama model (requires Modelfile from the main repo)
ollama create raymond -f Modelfile
ollama run raymond

Model Details

Item Value
Base Model Qwen3-4B-Instruct-2507
Fine-tuning LoRA (rank=64, alpha=128)
Training Data 1495 samples (Claude Sonnet distilled)
Quantization Q4_K_M
Size 2.33 GB
Language Chinese (primary), English

See the main project for full pipeline details.

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Architecture
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