iMilesHo / FineTuningViaLoRA

Fine-tuning a Pretrained Model Using LoRA

Fine-tuning a Pretrained Model Using LoRA

Fine-tuning a Pretrained Model Using LoRA

Objectives:

Leverage LoRA: Low-Rank Adaptation to fine-tune a pretrained language model for a programming-related Question-Answering (QA) system on the "flytech/python-codes-25k" dataset.

Understanding LoRA

1. Review the concept, benefits, and mechanism of Low-Rank Adaptation (LoRA) for adap- ting pretrained models.
2. Discuss the suitability of pretrained language models for code-related QA tasks and the advantages of using LoRA for fine-tuning.

Dataset Preparation

1. Provide an overview of the "flytech/python-codes-25k"dataset, focusing on its structure and relevance for a QA system.
2. Describe necessary preprocessing steps, including tokenization and encoding strategies for code snippets.

Model Fine-Tuning with LoRA

1. Select a suitable pretrained language model and justify the choice based on its architecture and expected performance on code-related QA tasks.
2. Detail the integration of LoRA, specifying the adaptation process and adjustments made to the model for the QA task.

Training and Evaluation

1. Outline the training process, including configurations related to LoRA, learning rate settings, and QA-specific adaptations.
2. Evaluate the fine-tuned model using appropriate metrics, comparing its performance with a baseline model.
3. Analyze the results, focusing on improvements or limitations introduced by LoRA in the context of programming-related QA