How to use jwindle47/chandra-ocr-2-8bit-mlx with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("jwindle47/chandra-ocr-2-8bit-mlx") config = load_config("jwindle47/chandra-ocr-2-8bit-mlx") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output)
How to use jwindle47/chandra-ocr-2-8bit-mlx with Pi:
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "jwindle47/chandra-ocr-2-8bit-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": "jwindle47/chandra-ocr-2-8bit-mlx" } ] } } }
# Start Pi in your project directory: pi
How to use jwindle47/chandra-ocr-2-8bit-mlx with Hermes Agent:
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default jwindle47/chandra-ocr-2-8bit-mlx
hermes
