Image-Text-to-Text
MLX
Safetensors
oxr
mlx-vlm
ocr
document-parsing
apple-silicon
conversational
8-bit precision
Instructions to use npario/OXR-1.0-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use npario/OXR-1.0-mlx-8bit 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("npario/OXR-1.0-mlx-8bit") config = load_config("npario/OXR-1.0-mlx-8bit") # 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
Download tokenizer.json from npario/OXR-1.0-mlx-8bit: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/npario/OXR-1.0-mlx-8bit/resolve/main/tokenizer.json
- Command line
-
hf download hf://npario/OXR-1.0-mlx-8bit/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/npario/OXR-1.0-mlx-8bit/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- dde3c5a4d468da6fe61f11ffa8f04af2d101f8f4a0fe118c37bd31bb8c3017b6
- Size of remote file:
- 11.4 MB
- SHA256:
- be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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