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Jwalit
/
gemma4-e4b-kyc-document-extractor

Image-Text-to-Text
Transformers
PEFT
sft
trl
qlora
kyc
document-extraction
document-classification
aadhaar
pan-card
passport
visa
election-card
gemma4
vision-language-model
vllm
Model card Files Files and versions
xet
Community

Instructions to use Jwalit/gemma4-e4b-kyc-document-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Jwalit/gemma4-e4b-kyc-document-extractor with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="Jwalit/gemma4-e4b-kyc-document-extractor")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Jwalit/gemma4-e4b-kyc-document-extractor", dtype="auto")
  • PEFT

    How to use Jwalit/gemma4-e4b-kyc-document-extractor with PEFT:

    Task type is invalid.
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use Jwalit/gemma4-e4b-kyc-document-extractor with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Jwalit/gemma4-e4b-kyc-document-extractor"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Jwalit/gemma4-e4b-kyc-document-extractor",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Jwalit/gemma4-e4b-kyc-document-extractor
  • SGLang

    How to use Jwalit/gemma4-e4b-kyc-document-extractor 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 "Jwalit/gemma4-e4b-kyc-document-extractor" \
        --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": "Jwalit/gemma4-e4b-kyc-document-extractor",
    		"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 "Jwalit/gemma4-e4b-kyc-document-extractor" \
            --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": "Jwalit/gemma4-e4b-kyc-document-extractor",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Jwalit/gemma4-e4b-kyc-document-extractor with Docker Model Runner:

    docker model run hf.co/Jwalit/gemma4-e4b-kyc-document-extractor
gemma4-e4b-kyc-document-extractor
37.3 kB
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  • 1 contributor
History: 6 commits
Jwalit's picture
Jwalit
Add Colab training notebook for free GPU training
7357eb4 verified 15 days ago
  • .gitattributes
    1.52 kB
    initial commit 15 days ago
  • README.md
    8.56 kB
    Add model README with full documentation 15 days ago
  • generate_kyc_dataset.py
    809 Bytes
    Add dataset generation script reference 15 days ago
  • inference_vllm.py
    7.73 kB
    Add inference script with vLLM, Transformers, and OpenAI API backends 15 days ago
  • train_kyc_colab.ipynb
    11.2 kB
    Add Colab training notebook for free GPU training 15 days ago
  • train_kyc_vlm.py
    7.43 kB
    Add training script 15 days ago