How to use vqstudio/Bielik-4.5B-v3.0-Instruct-RAG-0.8-MLX-4bit 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("vqstudio/Bielik-4.5B-v3.0-Instruct-RAG-0.8-MLX-4bit") 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 vqstudio/Bielik-4.5B-v3.0-Instruct-RAG-0.8-MLX-4bit with MLX LM:
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "vqstudio/Bielik-4.5B-v3.0-Instruct-RAG-0.8-MLX-4bit"
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "vqstudio/Bielik-4.5B-v3.0-Instruct-RAG-0.8-MLX-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vqstudio/Bielik-4.5B-v3.0-Instruct-RAG-0.8-MLX-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
language: en library_name: mlx tags: - mlx pipeline_tag: text-generation
