Instructions to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

Jev-Style-Qwen3.5-2B-Decision-v2-GGUF

File size: 1,840 Bytes

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