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
File size: 718 Bytes
23ffc1d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | {
"format": "safetensors",
"description": "Auxiliary decoder prediction-head tensors retained from training. These are not loaded by the default HF/vLLM inference path.",
"tensor_file": "auxiliary_prediction_heads.safetensors.extra",
"tensors": {
"decoder.extra_heads.0.bias": {
"shape": [
1024
],
"dtype": "float32"
},
"decoder.extra_heads.0.weight": {
"shape": [
1024,
1024
],
"dtype": "float32"
},
"decoder.extra_proj.0.bias": {
"shape": [
1024
],
"dtype": "float32"
},
"decoder.extra_proj.0.weight": {
"shape": [
1024,
1024
],
"dtype": "float32"
}
}
}
|