Instructions to use aymanechilah/donut-docvqa-model60 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aymanechilah/donut-docvqa-model60 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="aymanechilah/donut-docvqa-model60")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("aymanechilah/donut-docvqa-model60") model = AutoModelForMultimodalLM.from_pretrained("aymanechilah/donut-docvqa-model60", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use aymanechilah/donut-docvqa-model60 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aymanechilah/donut-docvqa-model60" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aymanechilah/donut-docvqa-model60", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aymanechilah/donut-docvqa-model60
- SGLang
How to use aymanechilah/donut-docvqa-model60 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "aymanechilah/donut-docvqa-model60" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aymanechilah/donut-docvqa-model60", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "aymanechilah/donut-docvqa-model60" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aymanechilah/donut-docvqa-model60", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aymanechilah/donut-docvqa-model60 with Docker Model Runner:
docker model run hf.co/aymanechilah/donut-docvqa-model60
Download preprocessor_config.json from aymanechilah/donut-docvqa-model60: direct link, hf CLI and curl.
- Browser
- Download file 440 Bytes
-
https://huggingface.co/aymanechilah/donut-docvqa-model60/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://aymanechilah/donut-docvqa-model60/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/aymanechilah/donut-docvqa-model60/resolve/main/preprocessor_config.json
440 Bytes
| { | |
| "do_align_long_axis": false, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "do_thumbnail": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "DonutImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "processor_class": "DonutProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 1280, | |
| "width": 960 | |
| } | |
| } | |