LLM continuous self-instruct fine-tuning framework powered by a compound AI system on Amazon SageMaker
SMRTR summary
The continuous self-instruct fine-tuning framework improves large language model performance through synthetic data generation, supervised fine-tuning, and preference alignment using human/AI feedback. Implemented as a compound AI system with DSPy, it demonstrates significant accuracy gains in question-answering tasks through RAG optimization and various fine-tuning methods.
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