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Vithika
/
TestFineTuning

Image-Text-to-Text
Transformers
PyTorch
vision-encoder-decoder
Model card Files Files and versions
xet
Community
1

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

  • Libraries
  • Transformers

    How to use Vithika/TestFineTuning with Transformers:

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

    How to use Vithika/TestFineTuning with vLLM:

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

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

    How to use Vithika/TestFineTuning with Docker Model Runner:

    docker model run hf.co/Vithika/TestFineTuning
TestFineTuning
815 MB
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  • 1 contributor
History: 5 commits
Vithika's picture
Vithika
Training done
a3ba7f1 over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • added_tokens.json
    459 Bytes
    Training done over 2 years ago
  • config.json
    4.89 kB
    Training in progress, epoch 0 over 2 years ago
  • generation_config.json
    216 Bytes
    Training in progress, epoch 0 over 2 years ago
  • preprocessor_config.json
    421 Bytes
    Training done over 2 years ago
  • pytorch_model.bin

    Detected Pickle imports (4)

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

    What is a pickle import?

    809 MB
    xet
    Training in progress, epoch 0 over 2 years ago
  • sentencepiece.bpe.model
    1.3 MB
    xet
    Training done over 2 years ago
  • special_tokens_map.json
    355 Bytes
    Training done over 2 years ago
  • tokenizer.json
    4.01 MB
    Training done over 2 years ago
  • tokenizer_config.json
    510 Bytes
    Training done over 2 years ago