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)
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"])
How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16 with MLX LM:
# 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"
# 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"} ] }'
https://huggingface.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16/resolve/main/figures/benchmark.svg
hf download hf://chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16/figures/benchmark.svg
curl -L -o benchmark.svg https://huggingface.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16/resolve/main/figures/benchmark.svg
