@luuu

#dailychallenge LLM Fine-tuning Methodology - LoRA Tuning, P-Tuning, Instruction Tuning - Pre-trained model gets fine-tuned with task-based data - LoRA is best for parameter-efficient fine-tuning - P-Tuning optimizes prompt engineering - Instruction Tuning focuses on making models better at understanding and executing human instructions - LoRA Tuning& P-Tuning are both PEFT(Parameter-Efficient Fine-Tuning). - This means it doesn’t change the whole parameter but trains additional small parameters. - Instruction Tuning is used to enhance the model itself. - It uses a dataset of ‘instructions and answers’ - While PEFT is a methodology to adjust the model to fit specific tasks, instruction tuning is used for performance enhancement. - PEFT is Tuning. Instruction Tuning is Training

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