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 preprocessor_config.json from sahilchachra/TeleOCR-INT8: direct link, hf CLI and curl.
- Browser
- Download file 350 Bytes
-
https://huggingface.co/sahilchachra/TeleOCR-INT8/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://sahilchachra/TeleOCR-INT8/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/sahilchachra/TeleOCR-INT8/resolve/main/preprocessor_config.json
350 Bytes
| { | |
| "min_pixels": 3136, | |
| "max_pixels": 12845056, | |
| "patch_size": 14, | |
| "temporal_patch_size": 2, | |
| "merge_size": 2, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "image_processor_type": "Qwen2VLImageProcessor", | |
| "processor_class": "Qwen2_5_VLProcessor" | |
| } |