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llm-jp
/
Jagle-VL-2.2B-FineVision

Feature Extraction
Transformers
Safetensors
llmjpvl
custom_code
Model card Files Files and versions
xet
Community

Instructions to use llm-jp/Jagle-VL-2.2B-FineVision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use llm-jp/Jagle-VL-2.2B-FineVision with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="llm-jp/Jagle-VL-2.2B-FineVision", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("llm-jp/Jagle-VL-2.2B-FineVision", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
Jagle-VL-2.2B-FineVision
4.34 GB
Ctrl+K
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  • 1 contributor
History: 15 commits
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speed
Update README.md
dc3a12c verified 22 days ago
  • .gitattributes
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  • README.md
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  • added_tokens.json
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  • chat_template.jinja
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  • config.json
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  • configuration_llmjpvl.py
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  • constants.py
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  • generation_config.json
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  • jagle-construction-pipeline.svg
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  • merges.txt
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  • model.safetensors
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  • modeling_llmjpvl.py
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  • preprocessor_config.json
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  • processing_llmjpvl.py
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  • processor_config.json
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  • special_tokens_map.json
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  • tokenizer.json
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  • tokenizer_config.json
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  • vocab.json
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