How to use glacio-dev/Qwen1.5-1.8B-Chat-Q 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("glacio-dev/Qwen1.5-1.8B-Chat-Q") 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 glacio-dev/Qwen1.5-1.8B-Chat-Q with MLX LM:
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "glacio-dev/Qwen1.5-1.8B-Chat-Q"
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "glacio-dev/Qwen1.5-1.8B-Chat-Q" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "glacio-dev/Qwen1.5-1.8B-Chat-Q", "messages": [ {"role": "user", "content": "Hello"} ] }'
https://huggingface.co/glacio-dev/Qwen1.5-1.8B-Chat-Q/resolve/main/tokenizer.json
hf download hf://glacio-dev/Qwen1.5-1.8B-Chat-Q/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/glacio-dev/Qwen1.5-1.8B-Chat-Q/resolve/main/tokenizer.json
