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hltcoe
/
MemTok_qwen2.5-omni_multivent

Feature Extraction
Transformers
Safetensors
English
qwen2_5_omni_thinker
video-retrieval
multi-vector
late-interaction
colbert
index-compression
memory-tokens
text-to-video
audiovisual
Model card Files Files and versions
xet
Community

Instructions to use hltcoe/MemTok_qwen2.5-omni_multivent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use hltcoe/MemTok_qwen2.5-omni_multivent with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="hltcoe/MemTok_qwen2.5-omni_multivent")
    # Load model directly
    from transformers import AutoTokenizer, Qwen2_5OmniForEmbedding
    
    tokenizer = AutoTokenizer.from_pretrained("hltcoe/MemTok_qwen2.5-omni_multivent")
    model = Qwen2_5OmniForEmbedding.from_pretrained("hltcoe/MemTok_qwen2.5-omni_multivent")
  • Notebooks
  • Google Colab
  • Kaggle
MemTok_qwen2.5-omni_multivent
9.42 GB
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  • 1 contributor
History: 5 commits
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alexmartin1722
Update 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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  • generation_config.json
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  • merges.txt
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  • model-00001-of-00002.safetensors
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  • model-00002-of-00002.safetensors
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  • model.safetensors.index.json
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  • preprocessor_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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  • video_preprocessor_config.json
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  • vocab.json
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