Image-Text-to-Text
MLX
Safetensors
Chinese
English
qwen2_5_vl
ocr
document-parsing
multimodal
int8
apple-silicon
conversational
custom_code
8-bit precision
Instructions to use sahilchachra/TeleOCR-INT8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use sahilchachra/TeleOCR-INT8 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("sahilchachra/TeleOCR-INT8") config = load_config("sahilchachra/TeleOCR-INT8") # 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 sahilchachra/TeleOCR-INT8: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/sahilchachra/TeleOCR-INT8/resolve/main/tokenizer.json
- Command line
-
hf download hf://sahilchachra/TeleOCR-INT8/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/sahilchachra/TeleOCR-INT8/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 4bd2f092eeec244c0448f13e31edd4b25e625568713e4155993a3dea90c7437a
- Size of remote file:
- 11.4 MB
- SHA256:
- 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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