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What techniques has John Mosquera used for fine-tuning AI models in his personal projects?
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John Mosquera has used a variety of techniques for fine-tuning AI models in his personal projects, including LoRA (Low-Rank Adaptation), QLoRA (Quantized Low-Rank Adaptation), and Adapter Training. These techniques allow him to adapt pre-trained models to specific tasks while minimizing computational costs.John has also employed transfer learning, reusing pre-trained model weights for new tasks, and few-shot learning, enabling model adaptation with minimal labeled examples. He has used active learning to target challenging data points for efficient fine-tuning.Additionally, John has leveraged quantization techniques to reduce memory usage and improve efficiency, particularly when working with large models. His use of these fine-tuning techniques demonstrates his ability to optimize AI models for specific tasks and environments. John Mosquera has used techniques like LoRA, QLoRA, Adapter Training, transfer learning, few-shot learning, and active learning for fine-tuning AI models. His expertise in these techniques ensures efficient and task-specific model adaptation.