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tadkt
/
GOT_Vietnamese

Image-Text-to-Text
Transformers
Safetensors
Vietnamese
English
GOT
feature-extraction
got
vision-language
ocr2.0
got_vietnamese
custom_code
Model card Files Files and versions
xet
Community
1

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

  • Libraries
  • Transformers

    How to use tadkt/GOT_Vietnamese with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="tadkt/GOT_Vietnamese", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("tadkt/GOT_Vietnamese", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use tadkt/GOT_Vietnamese with vLLM:

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

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

    How to use tadkt/GOT_Vietnamese with Docker Model Runner:

    docker model run hf.co/tadkt/GOT_Vietnamese
GOT_Vietnamese
1.12 GB
Ctrl+K
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  • 2 contributors
History: 6 commits
tadkt's picture
tadkt
htrnguyen's picture
htrnguyen
Update modeling_GOT.py (#1)
586546a verified about 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    877 Bytes
    Update README.md over 1 year ago
  • config.json
    1.08 kB
    New weights set! >90% accuracy over 1 year ago
  • generation_config.json
    148 Bytes
    Upload 10 files over 1 year ago
  • got_vision_b.py
    16.1 kB
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  • model.safetensors
    1.12 GB
    xet
    New weights set! >90% accuracy over 1 year ago
  • modeling_GOT.py
    33.7 kB
    Update modeling_GOT.py (#1) about 1 year ago
  • qwen.tiktoken
    2.56 MB
    Upload 10 files over 1 year ago
  • render_tools.py
    1.99 kB
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  • special_tokens_map.json
    64 Bytes
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  • tokenization_qwen.py
    9.47 kB
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  • tokenizer_config.json
    359 Bytes
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