base-seoul

#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