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https://github.com/muneeb1030/finetune-tiny-llama

Fine-tuning the Tiny Llama model to mimic my professor's writing style using the Llama Factory. The project involves data collection, preprocessing, preparation, fine-tuning, and evaluation.
https://github.com/muneeb1030/finetune-tiny-llama

data data-preparation data-preprocessing finetuning llama-factory llm pymupdf selenium-python spacy tinyllama webscraping

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Fine-tuning the Tiny Llama model to mimic my professor's writing style using the Llama Factory. The project involves data collection, preprocessing, preparation, fine-tuning, and evaluation.

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# Fine-Tuning Tiny Llama using Llama Factory To Mimic Professor's Writing Style

## Overview

This project aims to fine-tune the Tiny Llama model using the Llama Factory to mimic my professor's writing style. The process involves several phases, including data collection, preprocessing, preparation, model fine-tuning, and evaluation. The final goal is to create a model that can generate text in the style of my professor's academic writings.



## Phases of the Project

### Phase 1: Data Collection

The first step in this project was to collect data by scraping my professor's Google Scholar page. The objective was to gather a comprehensive set of research articles published by the professor.



- **Tool Used:** Selenium

- **Details:** Selenium was used to automate the process of accessing the Google Scholar page and downloading the available PDFs of the research articles.

### Phase 2: Data Preprocessing

After collecting the PDFs, the next step was to preprocess these documents to ensure they were in a usable format for training the model.



- **Purpose:** Normalize the content while preserving the writing style.

- **Tools Used:** pyMuPDF

### Steps:

- Remove page headers, footers, images, and tables along with their captions.

- Convert the remaining content into paragraph format, as individual words and phrases are insufficient for capturing writing style.

### Phase 3: Data Preparation

The preprocessed data needed to be formatted according to the requirements of the Llama Factory model training process.



- **Initial Tools Tried:** spaCy, TF-IDF, BERT

- **Tool That Worked:** OpenAI API

### Process:

- Use the OpenAI API to generate the required data format.

- Ensure that the data is structured correctly for input into the Llama Factory model.

### Phase 4: Model Fine-Tuning

With the data prepared, the next phase involved fine-tuning the Tiny Llama model.



- **Environment:** Google Colab

- **Tools Used:** Llama Factory

### Steps:

- Set up the Google Colab notebook and import necessary libraries.

- Load the Llama Factory UI and integrate the dataset.

- Define the prompt format and other configurations required by Llama Factory.

- Run the fine-tuning process to train the Tiny Llama model on the professor's writing style.

### Phase 5: Model Evaluation

The final phase focused on evaluating the performance of the fine-tuned model to ensure it accurately mimics the professor's writing style.



### Process:

- Generate sample texts using the fine-tuned model.

- Compare the generated texts with the original writings to assess similarity in style and content.

- Make any necessary adjustments and re-train if needed.

## Getting Started

### Prerequisites

- Python 3.x

- Selenium

- pyMuPDF

- OpenAI API

### Installation

- Clone the repository

```bash

git clone https://github.com/yourusername/finetuning-tiny-llama.git

```

- Install the necessary Python packages

```bash

pip install selenium pymupdf openai

```

## Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.



## Contact

For any questions or suggestions, please contact me at muhammadmuneeburrehman.vercel.app