Instructions to use hf-internal-testing/tiny-random-VisionEncoderDecoderModel-donutswin-mbart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hf-internal-testing/tiny-random-VisionEncoderDecoderModel-donutswin-mbart with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="hf-internal-testing/tiny-random-VisionEncoderDecoderModel-donutswin-mbart")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-VisionEncoderDecoderModel-donutswin-mbart")
model = AutoModelForImageTextToText.from_pretrained("hf-internal-testing/tiny-random-VisionEncoderDecoderModel-donutswin-mbart")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hf-internal-testing/tiny-random-VisionEncoderDecoderModel-donutswin-mbart with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hf-internal-testing/tiny-random-VisionEncoderDecoderModel-donutswin-mbart"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hf-internal-testing/tiny-random-VisionEncoderDecoderModel-donutswin-mbart",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hf-internal-testing/tiny-random-VisionEncoderDecoderModel-donutswin-mbart

SGLang

How to use hf-internal-testing/tiny-random-VisionEncoderDecoderModel-donutswin-mbart 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 "hf-internal-testing/tiny-random-VisionEncoderDecoderModel-donutswin-mbart" \
    --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": "hf-internal-testing/tiny-random-VisionEncoderDecoderModel-donutswin-mbart",
		"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 "hf-internal-testing/tiny-random-VisionEncoderDecoderModel-donutswin-mbart" \
        --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": "hf-internal-testing/tiny-random-VisionEncoderDecoderModel-donutswin-mbart",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hf-internal-testing/tiny-random-VisionEncoderDecoderModel-donutswin-mbart with Docker Model Runner:
```
docker model run hf.co/hf-internal-testing/tiny-random-VisionEncoderDecoderModel-donutswin-mbart
```

Xenova HF Staff commited on Oct 24, 2024

Commit

e058ee7

verified ·

1 Parent(s): 42aafa1

Upload ONNX weights (#3)

Browse files

- Upload ONNX weights (05639d672c085967db03358d4179349c23a04b8a)

Files changed (4) hide show

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onnx/decoder_model_merged.onnx +3 -0
onnx/decoder_with_past_model.onnx +3 -0
onnx/encoder_model.onnx +3 -0

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