- Libraries
- MLX
How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16 with MLX:
# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# Generate text with mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16")
prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
text = generate(model, tokenizer, prompt=prompt, verbose=True) - jev-style
How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16 with jev-style:
# Apple silicon
pip install "jev-style[mlx]"
from jev_style import JevStyle, noul, choice
js = JevStyle.from_pretrained("chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16")
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
- LM Studio
- MLX LM
How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16 with MLX LM:
Generate or start a chat session
# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16"
Run an OpenAI-compatible server
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16",
"messages": [
{"role": "user", "content": "Hello"}
]
}' - Atomic Chat
