Libraries jev-style How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF with jev-style:
pip install jev-style
# GGUF builds score through llama.cpp: build the jev-score binary once
hf download chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF build_jev_score.sh jev_score.cpp --local-dir jev-score
export JEV_SCORE_BIN=$(sh jev-score/build_jev_score.sh /path/to/llama.cpp | tail -n 1) from jev_style import JevStyle, noul, choice
js = JevStyle.from_pretrained("chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF")
out = js.decide("I was charged twice for one order.", {
"billing": noul("This message is about billing."),
"team": choice("Which team should handle it?", ["billing", "shipping", "tech"]),
})
print(out["answers"]["team"]["choice"]) Notebooks Google Colab Kaggle Local Apps Settings llama.cpp How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF with 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 chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-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 chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-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 chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-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 chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M Use Docker docker model run hf.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M LM Studio Jan vLLM How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF with vLLM:
Install from pip and serve model # Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' Use Docker docker model run hf.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M Ollama How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF with Ollama:
ollama run hf.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M Unsloth Desktop Docker Model Runner How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF with Docker Model Runner:
docker model run hf.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M Lemonade How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF with Lemonade:
Pull the model # Download Lemonade from https://lemonade-server.ai/
lemonade pull chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M Run and chat with the model lemonade run user.Jev-Style-Qwen3.5-2B-Decision-v2-GGUF-Q4_K_M List all available models lemonade list Atomic Chat 
