Image-Text-to-Text
MLX
Safetensors
multilingual
internvl_chat
vision-language
ocr
document-intelligence
qianfan
apple-silicon
custom_code
Eval Results
4-bit precision
Instructions to use jason1966/Qianfan-OCR-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use jason1966/Qianfan-OCR-MLX-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("jason1966/Qianfan-OCR-MLX-4bit") config = load_config("jason1966/Qianfan-OCR-MLX-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
- Xet hash:
- f4a00a93127edcd391ee7e07dde9e4ae567f15f69a651f9c04d195dca378490a
- Size of remote file:
- 3.1 GB
- SHA256:
- e308f4dd21cc358cf2eb72e399d5b69345d8ee6f36e25c3851deaa28476a4e35
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