Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

Mijavier
/
finetuned_donut_test

Image-Text-to-Text
Transformers
PyTorch
vision-encoder-decoder
Model card Files Files and versions
xet
Community

Instructions to use Mijavier/finetuned_donut_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Mijavier/finetuned_donut_test with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="Mijavier/finetuned_donut_test")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForMultimodalLM
    
    tokenizer = AutoTokenizer.from_pretrained("Mijavier/finetuned_donut_test")
    model = AutoModelForMultimodalLM.from_pretrained("Mijavier/finetuned_donut_test", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use Mijavier/finetuned_donut_test with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Mijavier/finetuned_donut_test"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Mijavier/finetuned_donut_test",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Mijavier/finetuned_donut_test
  • SGLang

    How to use Mijavier/finetuned_donut_test 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 "Mijavier/finetuned_donut_test" \
        --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": "Mijavier/finetuned_donut_test",
    		"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 "Mijavier/finetuned_donut_test" \
            --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": "Mijavier/finetuned_donut_test",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Mijavier/finetuned_donut_test with Docker Model Runner:

    docker model run hf.co/Mijavier/finetuned_donut_test

You need to agree to share your Hugging Face information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Log in or Sign Up to review the conditions and access this model content.

Gated model
You can list files but not access them

Preview of files found in this repository
  • .gitattributes
    1.38 kB
    initial commit about 4 years ago
  • added_tokens.json
    185 Bytes
    Upload processor about 4 years ago
  • config.json
    4.9 kB
    Upload model about 4 years ago
  • preprocessor_config.json
    362 Bytes
    Upload processor about 4 years ago
  • pytorch_model.bin
    809 MB
    xet
    Upload model about 4 years ago
  • sentencepiece.bpe.model
    1.3 MB
    xet
    Upload processor about 4 years ago
  • special_tokens_map.json
    355 Bytes
    Upload processor about 4 years ago
  • tokenizer.json
    4.01 MB
    Upload processor about 4 years ago
  • tokenizer_config.json
    489 Bytes
    Upload processor about 4 years ago