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leafyseay
/
RIME-2B

Feature Extraction
Transformers
Safetensors
qwen2_vl
image-text-to-text
Model card Files Files and versions
xet
Community
1

Instructions to use leafyseay/RIME-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use leafyseay/RIME-2B with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="leafyseay/RIME-2B")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForMultimodalLM
    
    processor = AutoProcessor.from_pretrained("leafyseay/RIME-2B")
    model = AutoModelForMultimodalLM.from_pretrained("leafyseay/RIME-2B", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
RIME-2B
4.42 GB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 7 commits
leafyseay's picture
leafyseay
nielsr's picture
nielsr HF Staff
Add pipeline tag, library name, and link to paper (#1)
4d8ad8b 18 days ago
  • .gitattributes
    1.52 kB
    initial commit 3 months ago
  • README.md
    1.12 kB
    Add pipeline tag, library name, and link to paper (#1) 18 days ago
  • added_tokens.json
    439 Bytes
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  • chat_template.jinja
    1.02 kB
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  • chat_template.json
    1.05 kB
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  • config.json
    2.92 kB
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  • generation_config.json
    189 Bytes
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  • merges.txt
    1.67 MB
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  • model.safetensors
    4.42 GB
    xet
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  • preprocessor_config.json
    570 Bytes
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  • special_tokens_map.json
    613 Bytes
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  • tokenizer_config.json
    4.64 kB
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  • vocab.json
    3.38 MB
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