Instructions to use stefanschmidt/jina-ocr-v1-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use stefanschmidt/jina-ocr-v1-8bit 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("stefanschmidt/jina-ocr-v1-8bit") config = load_config("stefanschmidt/jina-ocr-v1-8bit") # 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 processor_config.json from stefanschmidt/jina-ocr-v1-8bit: direct link, hf CLI and curl.
- Browser
- Download file 458 Bytes
-
https://huggingface.co/stefanschmidt/jina-ocr-v1-8bit/resolve/main/processor_config.json
- Command line
-
hf download hf://stefanschmidt/jina-ocr-v1-8bit/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/stefanschmidt/jina-ocr-v1-8bit/resolve/main/processor_config.json
458 Bytes
| { | |
| "add_special_token": false, | |
| "candidate_resolutions": [ | |
| [ | |
| 1024, | |
| 1024 | |
| ] | |
| ], | |
| "downsample_ratio": 4, | |
| "ignore_id": -100, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_token": "<image>", | |
| "mask_prompt": false, | |
| "normalize": true, | |
| "pad_token": "<\uff5c\u2581pad\u2581\uff5c>", | |
| "patch_size": 16, | |
| "processor_class": "DeepseekOCRProcessor", | |
| "sft_format": "deepseek" | |
| } |