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amitha
/
mllava-llama2-en

Visual Question Answering
Transformers
Safetensors
English
llava_llama
llava
vlm
custom_code
Model card Files Files and versions
xet
Community

Instructions to use amitha/mllava-llama2-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use amitha/mllava-llama2-en with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("visual-question-answering", model="amitha/mllava-llama2-en", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModelForVisualQuestionAnswering
    model = AutoModelForVisualQuestionAnswering.from_pretrained("amitha/mllava-llama2-en", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
mllava-llama2-en
14.7 GB
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  • 1 contributor
History: 5 commits
amitha's picture
amitha
Update README.md
2aba56d verified almost 2 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 2 years ago
  • README.md
    149 Bytes
    Update README.md almost 2 years ago
  • clip_encoder.py
    3.67 kB
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  • config.json
    1.36 kB
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  • constants.py
    941 Bytes
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  • generation_config.json
    183 Bytes
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  • llava_arch.py
    18.1 kB
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  • llava_llama.py
    5.48 kB
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  • model-00001-of-00003.safetensors
    4.94 GB
    xet
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  • model-00002-of-00003.safetensors
    4.95 GB
    xet
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  • model-00003-of-00003.safetensors
    4.85 GB
    xet
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  • model.safetensors.index.json
    73.2 kB
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  • multimodal_encoder.py
    1.16 kB
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  • multimodal_projector.py
    2.03 kB
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  • utils.py
    8.28 kB
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