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LLM fine tuning for answering questions based on a software development course

Introduction

The goal of this project is to fine-tune a large language model (LLM) to answer questions based on a software development course. The LLM will be trained on a dataset of questions and answers from the course, and will be able to generate answers that are relevant to the course material.

The project will involve the following steps:

  1. Data collection: Gather a dataset of questions and answers from the course.
  2. Data preprocessing: Clean and preprocess the data to ensure it is in a suitable format for training the LLM.
  3. Model training: Train the LLM on the preprocessed data using a suitable training algorithm.
  4. Model evaluation: Evaluate the performance of the LLM on a test set to assess its accuracy and generalization capabilities.
  5. Model deployment: Deploy the fine-tuned LLM for use in answering questions based on the course.

This project will be conducted using Python and the Hugging Face Transformers library. The dataset will be collected from the course’s website and will be available for download.

Objectives

  1. Collect a dataset of questions and answers from the course.
  2. Preprocess the data to ensure it is in a suitable format for training the LLM.
  3. Train the LLM on the preprocessed data using a suitable training algorithm.
  4. Evaluate the performance of the LLM on a test set to assess its accuracy and generalization capabilities.
  5. Deploy the fine-tuned LLM for use in answering questions based on the course.

Methods

  1. Data collection: Gather a dataset of questions and answers from the course.
  2. Data preprocessing: Clean and preprocess the data to ensure it is in a suitable format for training the LLM.
  3. Model training: Train the LLM on the preprocessed data using a suitable training algorithm.
  4. Model evaluation: Evaluate the performance of the LLM on a test set to assess its accuracy and generalization capabilities.
  5. Model deployment: Deploy the fine-tuned LLM for use in answering questions based on the course.

Results

  1. The collected dataset of questions and answers from the course will be used to train the LLM.
  2. The preprocessed data will be used to train the LLM.
  3. The LLM will be trained using a suitable training algorithm, such as GPT-3.
  4. The LLM will be evaluated on a test set to assess its accuracy and generalization capabilities.
  5. The fine-tuned LLM will be deployed for use in answering questions based on the course.

Conclusions

The collected dataset of questions and answers from the course will be used to train the LLM. The preprocessed data will be used to train the LLM. The LLM will be trained using a suitable training algorithm, such as GPT-3. The LLM will be evaluated on a test set to assess its accuracy and generalization capabilities. The fine-tuned LLM will be deployed for use in answering questions based on the course.

References

GitHub Copilot