Instructions to use subhavarshith/donut_table_data_u with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subhavarshith/donut_table_data_u with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="subhavarshith/donut_table_data_u")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("subhavarshith/donut_table_data_u") model = AutoModelForImageTextToText.from_pretrained("subhavarshith/donut_table_data_u") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use subhavarshith/donut_table_data_u with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "subhavarshith/donut_table_data_u" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "subhavarshith/donut_table_data_u", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/subhavarshith/donut_table_data_u
- SGLang
How to use subhavarshith/donut_table_data_u 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 "subhavarshith/donut_table_data_u" \ --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": "subhavarshith/donut_table_data_u", "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 "subhavarshith/donut_table_data_u" \ --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": "subhavarshith/donut_table_data_u", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use subhavarshith/donut_table_data_u with Docker Model Runner:
docker model run hf.co/subhavarshith/donut_table_data_u
| { | |
| "_valid_processor_keys": [ | |
| "images", | |
| "do_resize", | |
| "size", | |
| "resample", | |
| "do_thumbnail", | |
| "do_align_long_axis", | |
| "do_pad", | |
| "random_padding", | |
| "do_rescale", | |
| "rescale_factor", | |
| "do_normalize", | |
| "image_mean", | |
| "image_std", | |
| "return_tensors", | |
| "data_format", | |
| "input_data_format" | |
| ], | |
| "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": [ | |
| 960, | |
| 1280 | |
| ] | |
| } | |