How to use BlairQ/OCRonos-Q4 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("BlairQ/OCRonos-Q4") 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 BlairQ/OCRonos-Q4 with MLX LM:
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "BlairQ/OCRonos-Q4"
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "BlairQ/OCRonos-Q4" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BlairQ/OCRonos-Q4", "messages": [ {"role": "user", "content": "Hello"} ] }'
https://huggingface.co/BlairQ/OCRonos-Q4/resolve/73fbbf3f71d3f53b77c0589b8c14f1994d9a424b/README.md
hf download hf://BlairQ/OCRonos-Q4@73fbbf3f71d3f53b77c0589b8c14f1994d9a424b/README.md
curl -L -o README.md https://huggingface.co/BlairQ/OCRonos-Q4/resolve/73fbbf3f71d3f53b77c0589b8c14f1994d9a424b/README.md
language: en tags: - mlx library_name: mlx pipeline_tag: text-generation
