How to use jedisct1/NextCoder-32B-q4-mlx 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("jedisct1/NextCoder-32B-q4-mlx") 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 jedisct1/NextCoder-32B-q4-mlx with Pi:
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "jedisct1/NextCoder-32B-q4-mlx"
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "jedisct1/NextCoder-32B-q4-mlx" } ] } } }
# Start Pi in your project directory: pi
How to use jedisct1/NextCoder-32B-q4-mlx with MLX LM:
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "jedisct1/NextCoder-32B-q4-mlx"
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "jedisct1/NextCoder-32B-q4-mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jedisct1/NextCoder-32B-q4-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }'
