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The article explores techniques to improve Large Language Model (LLM) accuracy, focusing on Lamini Memory Tuning. It discusses fine-tuning methods like Low-Rank Adaptation (LoRA), the advantages and disadvantages of fine-tuning, and practical steps using Lamini to achieve higher precision in SQL query generation. The author demonstrates a step-by-step approach to creating a high-quality dataset, fine-tuning, and evaluating model accuracy.
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