Image-Text-to-Text
Transformers
TensorBoard
Safetensors
vision-encoder-decoder
Generated from Trainer
Instructions to use Mavish/donut-base-sroie with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mavish/donut-base-sroie with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Mavish/donut-base-sroie")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Mavish/donut-base-sroie") model = AutoModelForMultimodalLM.from_pretrained("Mavish/donut-base-sroie", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Mavish/donut-base-sroie with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Mavish/donut-base-sroie" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Mavish/donut-base-sroie", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Mavish/donut-base-sroie
- SGLang
How to use Mavish/donut-base-sroie 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 "Mavish/donut-base-sroie" \ --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": "Mavish/donut-base-sroie", "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 "Mavish/donut-base-sroie" \ --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": "Mavish/donut-base-sroie", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Mavish/donut-base-sroie with Docker Model Runner:
docker model run hf.co/Mavish/donut-base-sroie
Download training_args.bin from Mavish/donut-base-sroie: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/Mavish/donut-base-sroie/resolve/55929dc1684b1380a8009c7542c9edd6aa1c6dc1/training_args.bin
- Command line
-
hf download hf://Mavish/donut-base-sroie@55929dc1684b1380a8009c7542c9edd6aa1c6dc1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Mavish/donut-base-sroie/resolve/55929dc1684b1380a8009c7542c9edd6aa1c6dc1/training_args.bin
5.11 kB
- Xet hash:
- 84f9be0f824f677b84fc95e6e7296a77444dcb5433109048d38155ec79d48138
- Size of remote file:
- 5.11 kB
- SHA256:
- 7bd92e3ea03bb566409e3fa6f87b308be40b84acec7ac6d97c26886344e8904f
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