Image-Text-to-Text
MLX
Safetensors
nemotron_parse
ocr
document-parsing
vision-encoder-decoder
multimodal
custom_code
4-bit precision
Instructions to use mlx-community/Nemotron-Parse-2.0-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Nemotron-Parse-2.0-4bit 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("mlx-community/Nemotron-Parse-2.0-4bit") config = load_config("mlx-community/Nemotron-Parse-2.0-4bit") # 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) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
- Xet hash:
- d6dd770f5073953bbad656d5d9e076f73f3ead6de47cf0fad61b91182dfbb180
- Size of remote file:
- 1.5 GB
- SHA256:
- ae15200d52f9a025cfee65615925edf9a68710b1c1e68f617a9d96752f9fe809
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