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Maincode
/
Matilda-Decision-v1-Edge

Zero-Shot Classification
Transformers
Safetensors
qwen3_5
feature-extraction
decision-model
classification
system-one
multimodal
vision
maincode
matilda
custom_code
Model card Files Files and versions
xet
Community

Instructions to use Maincode/Matilda-Decision-v1-Edge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Maincode/Matilda-Decision-v1-Edge with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("zero-shot-classification", model="Maincode/Matilda-Decision-v1-Edge", trust_remote_code=True)
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoProcessor, AutoModel
    
    processor = AutoProcessor.from_pretrained("Maincode/Matilda-Decision-v1-Edge", trust_remote_code=True)
    model = AutoModel.from_pretrained("Maincode/Matilda-Decision-v1-Edge", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
Matilda-Decision-v1-Edge
1.73 GB
Ctrl+K
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  • 1 contributor
History: 2 commits
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arsh-maincode
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  • .gitattributes
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  • LICENSE
    11.5 kB
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  • README.md
    5.22 kB
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  • chat_template.jinja
    7.76 kB
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  • config.json
    2.78 kB
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  • decision_config.json
    5.48 kB
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  • matilda_decision_engine.py
    3.83 kB
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  • matilda_decision_runtime.py
    20.8 kB
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  • model.safetensors
    1.71 GB
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  • modeling_matilda_decision.py
    5.78 kB
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  • processor_config.json
    1.22 kB
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  • readout.safetensors
    1.04 MB
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  • requirements-runtime.txt
    285 Bytes
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  • tokenizer.json
    20 MB
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  • tokenizer_config.json
    1.17 kB
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