Hemanthkumar2112/Reward-Modeling-RLHF-Finetune-and-RAG
Gemma2(9B), Llama3-8B-Finetune-and-RAG, code base for sample, implemented in Kaggle platform
This project helps AI practitioners and researchers improve the quality and relevance of large language model outputs. By collecting human preferences on different model responses, you can train a 'reward model' that guides the language model to generate text that better aligns with human expectations. This allows for fine-tuning models like Llama3 8B or Gemma2 9B to produce more desirable and contextually accurate results for various applications.
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Use this if you need to fine-tune existing large language models to produce highly relevant, human-aligned, and contextually rich text outputs for specific tasks or domains.
Not ideal if you are looking for a plug-and-play solution without any technical knowledge of machine learning or data collection for model training.
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Feb 08, 2025
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