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ReNoteTech
/
VisionLLM

Feature Extraction
Transformers
Safetensors
minicpmv
custom_code
4-bit precision
bitsandbytes
Model card Files Files and versions
xet
Community

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

  • Libraries
  • Transformers

    How to use ReNoteTech/VisionLLM with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="ReNoteTech/VisionLLM", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("ReNoteTech/VisionLLM", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
VisionLLM
6.17 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
ReNoteTech's picture
ReNoteTech
Upload tokenizer
621928e verified about 2 years ago
  • .gitattributes
    1.52 kB
    initial commit about 2 years ago
  • README.md
    5.17 kB
    Upload MiniCPMV about 2 years ago
  • config.json
    1.99 kB
    Upload MiniCPMV about 2 years ago
  • generation_config.json
    121 Bytes
    Upload MiniCPMV about 2 years ago
  • model-00001-of-00002.safetensors
    4.65 GB
    xet
    Upload MiniCPMV about 2 years ago
  • model-00002-of-00002.safetensors
    1.51 GB
    xet
    Upload MiniCPMV about 2 years ago
  • model.safetensors.index.json
    244 kB
    Upload MiniCPMV about 2 years ago
  • special_tokens_map.json
    459 Bytes
    Upload tokenizer about 2 years ago
  • tokenizer.json
    9.08 MB
    Upload tokenizer about 2 years ago
  • tokenizer_config.json
    51.1 kB
    Upload tokenizer about 2 years ago