Image-Text-to-Text
Transformers
TensorBoard
Safetensors
vision-encoder-decoder
Generated from Trainer
Instructions to use MozerA/donut-base-graph-json with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MozerA/donut-base-graph-json with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="MozerA/donut-base-graph-json")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("MozerA/donut-base-graph-json") model = AutoModelForImageTextToText.from_pretrained("MozerA/donut-base-graph-json") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MozerA/donut-base-graph-json with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MozerA/donut-base-graph-json" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MozerA/donut-base-graph-json", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MozerA/donut-base-graph-json
- SGLang
How to use MozerA/donut-base-graph-json 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 "MozerA/donut-base-graph-json" \ --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": "MozerA/donut-base-graph-json", "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 "MozerA/donut-base-graph-json" \ --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": "MozerA/donut-base-graph-json", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MozerA/donut-base-graph-json with Docker Model Runner:
docker model run hf.co/MozerA/donut-base-graph-json
- Xet hash:
- 5dd30534afd584e9ab3b4c253be5ee3a9da4455cb988f80942bd5247f9d76af9
- Size of remote file:
- 5.18 kB
- SHA256:
- 60de7430654a4610121c9c3d8933a2c846f0c38db064e3c9652c0813a1c29cfd
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