Summary
This project explores a novel method for fine-tuning domain-specific LLMs on consumer hardware, aiming to overcome their limitations with structured, specialized data. The approach involves using an existing RAG pipeline to automatically generate training examples, like question-SQL-data-answer pairs, for LoRA fine-tuning. This technique promises to enhance LLM accuracy and consistency for niche applications by making advanced customization more accessible.
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