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hchang
/
reward_modeling

PEFT
TensorBoard
Safetensors
Model card Files Files and versions
xet
Metrics Training metrics Community

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

  • Libraries
  • PEFT

    How to use hchang/reward_modeling with PEFT:

    from peft import PeftModel
    from transformers import AutoModelForSequenceClassification
    
    base_model = AutoModelForSequenceClassification.from_pretrained("meta-llama/Meta-Llama-3-8B")
    model = PeftModel.from_pretrained(base_model, "hchang/reward_modeling")
  • Notebooks
  • Google Colab
  • Kaggle
reward_modeling
30.3 MB
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  • 1 contributor
History: 4 commits
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hchang
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  • runs
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  • .gitattributes
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  • README.md
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  • adapter_config.json
    679 Bytes
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  • adapter_model.safetensors
    6.84 MB
    xet
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  • merges.txt
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  • special_tokens_map.json
    335 Bytes
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  • tokenizer.json
    9.09 MB
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  • tokenizer_config.json
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  • vocab.json
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