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How has John Mosquera approached large language model (LLM) fine-tuning in his personal AI projects?
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John has explored various advanced techniques for LLM fine-tuning. He's used methods like Transfer Learning, Few-Shot Learning, Zero-Shot Learning, and Active Learning to adapt models to specific tasks. He's also worked with Quantization techniques like QLoRA for memory-efficient fine-tuning and employed Distillation to improve model efficiency. He's familiar with using Hugging Face Transformers for seamless integration and fine-tuning of open-source models. John Mosquera has fine-tuned LLMs using Transfer Learning, Few-Shot Learning, Zero-Shot Learning, Active Learning, QLoRA, Distillation, and Hugging Face Transformers in his personal AI projects.