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MOSS550V
/
divination

Image Feature Extraction
Transformers
PyTorch
chatglm
custom_code
Model card Files Files and versions
xet
Community
1

Instructions to use MOSS550V/divination with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use MOSS550V/divination with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-feature-extraction", model="MOSS550V/divination", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("MOSS550V/divination", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
divination
355 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 2 commits
Vincent Huang
init
0747df2 about 3 years ago
  • .gitattributes
    1.48 kB
    initial commit about 3 years ago
  • config.json
    904 Bytes
    init about 3 years ago
  • configuration_chatglm.py
    4.38 kB
    init about 3 years ago
  • generation_config.json
    142 Bytes
    init about 3 years ago
  • ice_text.model
    2.71 MB
    xet
    init about 3 years ago
  • modeling_chatglm.py
    59.4 kB
    init about 3 years ago
  • optimizer.pt
    235 MB
    xet
    init about 3 years ago
  • pytorch_model.bin
    117 MB
    xet
    init about 3 years ago
  • quantization.py
    31 kB
    init about 3 years ago
  • rng_state.pth
    14.6 kB
    xet
    init about 3 years ago
  • scheduler.pt
    627 Bytes
    xet
    init about 3 years ago
  • special_tokens_map.json
    125 Bytes
    init about 3 years ago
  • tokenization_chatglm.py
    17 kB
    init about 3 years ago
  • tokenizer_config.json
    517 Bytes
    init about 3 years ago
  • trainer_state.json
    5.81 kB
    init about 3 years ago
  • training_args.bin
    3.71 kB
    xet
    init about 3 years ago