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weepcat
/
hh_sft_RM-Gemma-2B_partial_rm

Text Classification
Transformers
Safetensors
gemma
Model card Files Files and versions
xet
Community

Instructions to use weepcat/hh_sft_RM-Gemma-2B_partial_rm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use weepcat/hh_sft_RM-Gemma-2B_partial_rm with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="weepcat/hh_sft_RM-Gemma-2B_partial_rm")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("weepcat/hh_sft_RM-Gemma-2B_partial_rm")
    model = AutoModelForSequenceClassification.from_pretrained("weepcat/hh_sft_RM-Gemma-2B_partial_rm", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
hh_sft_RM-Gemma-2B_partial_rm
10.1 GB
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  • 1 contributor
History: 3 commits
weepcat's picture
weepcat
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90356eb verified over 1 year ago
  • .gitattributes
    1.57 kB
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  • README.md
    5.17 kB
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  • config.json
    826 Bytes
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  • model-00001-of-00003.safetensors
    4.91 GB
    xet
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  • model-00002-of-00003.safetensors
    4.98 GB
    xet
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  • model-00003-of-00003.safetensors
    134 MB
    xet
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  • model.safetensors.index.json
    13.5 kB
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  • special_tokens_map.json
    636 Bytes
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  • tokenizer.json
    34.3 MB
    xet
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  • tokenizer.model
    4.24 MB
    xet
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  • tokenizer_config.json
    2.33 kB
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