Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing

  • Log In
  • Sign Up

to-be
/
Pix2StructGhega

Image-Text-to-Text
Transformers
PyTorch
pix2struct
Model card Files Files and versions
xet
Community
1

Instructions to use to-be/Pix2StructGhega with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use to-be/Pix2StructGhega with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="to-be/Pix2StructGhega")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForImageTextToText
    
    processor = AutoProcessor.from_pretrained("to-be/Pix2StructGhega")
    model = AutoModelForImageTextToText.from_pretrained("to-be/Pix2StructGhega")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use to-be/Pix2StructGhega with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "to-be/Pix2StructGhega"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "to-be/Pix2StructGhega",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/to-be/Pix2StructGhega
  • SGLang

    How to use to-be/Pix2StructGhega 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 "to-be/Pix2StructGhega" \
        --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": "to-be/Pix2StructGhega",
    		"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 "to-be/Pix2StructGhega" \
            --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": "to-be/Pix2StructGhega",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use to-be/Pix2StructGhega with Docker Model Runner:

    docker model run hf.co/to-be/Pix2StructGhega
Pix2StructGhega
1.13 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 8 commits
to-be's picture
to-be
Training done
e113059 almost 3 years ago
  • .gitattributes
    1.48 kB
    initial commit almost 3 years ago
  • config.json
    4.96 kB
    Training done almost 3 years ago
  • generation_config.json
    169 Bytes
    Training done almost 3 years ago
  • preprocessor_config.json
    250 Bytes
    Training started almost 3 years ago
  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch._utils._rebuild_tensor_v2",
    • "torch.FloatStorage",
    • "collections.OrderedDict"

    What is a pickle import?

    1.13 GB
    xet
    Training done almost 3 years ago
  • special_tokens_map.json
    2.2 kB
    Training started almost 3 years ago
  • tokenizer.json
    3.27 MB
    Training done almost 3 years ago
  • tokenizer_config.json
    2.45 kB
    Training done almost 3 years ago