Instructions to use mjawadazad2321/donut-base-Medical_Handwritten_Blocks_Data_Extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mjawadazad2321/donut-base-Medical_Handwritten_Blocks_Data_Extraction with Transformers:

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

pipe = pipeline("image-text-to-text", model="mjawadazad2321/donut-base-Medical_Handwritten_Blocks_Data_Extraction")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("mjawadazad2321/donut-base-Medical_Handwritten_Blocks_Data_Extraction")
model = AutoModelForImageTextToText.from_pretrained("mjawadazad2321/donut-base-Medical_Handwritten_Blocks_Data_Extraction")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mjawadazad2321/donut-base-Medical_Handwritten_Blocks_Data_Extraction with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mjawadazad2321/donut-base-Medical_Handwritten_Blocks_Data_Extraction"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mjawadazad2321/donut-base-Medical_Handwritten_Blocks_Data_Extraction",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mjawadazad2321/donut-base-Medical_Handwritten_Blocks_Data_Extraction

SGLang

How to use mjawadazad2321/donut-base-Medical_Handwritten_Blocks_Data_Extraction 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 "mjawadazad2321/donut-base-Medical_Handwritten_Blocks_Data_Extraction" \
    --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": "mjawadazad2321/donut-base-Medical_Handwritten_Blocks_Data_Extraction",
		"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 "mjawadazad2321/donut-base-Medical_Handwritten_Blocks_Data_Extraction" \
        --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": "mjawadazad2321/donut-base-Medical_Handwritten_Blocks_Data_Extraction",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mjawadazad2321/donut-base-Medical_Handwritten_Blocks_Data_Extraction with Docker Model Runner:
```
docker model run hf.co/mjawadazad2321/donut-base-Medical_Handwritten_Blocks_Data_Extraction
```

donut-base-Medical_Handwritten_Blocks_Data_Extraction

This model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 2
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 6
mixed_precision_training: Native AMP

Training results

Framework versions

Transformers 4.24.0.dev0
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.1

Downloads last month: 7

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Image-Text-to-Text

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