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Fine-Tuning and LLM Exploration Repository

This repository is a professional showcase project for exploring and fine-tuning various Language Models (LLMs). It includes Jupyter notebooks, scripts, and a Flask application to demonstrate practical applications and advanced techniques in NLP. If you have any suggestions or improvements, feel free to contribute! Let me know if there are skills I can learn to enhance my expertise as a Machine Learning Engineer.

Project Structure

Fine-Tuning-LLM/
│
├── notebooks/               # Jupyter notebooks for various NLP tasks and tutorials
├── app.py                   # Flask application for sentiment analysis
├── data_preprocessing.py    # Script for data preprocessing
├── utils.py                 # Utility functions for the project
├── requirements.txt         # List of dependencies
└── README.md                # Project documentation

Scripts and Notebooks

  1. Text Generation
    Practice generating text using pre-trained models like GPT-2 or GPT-3.

    • Example: notebooks/text_generation.ipynb

  2. Summarization
    Experiment with summarizing long text using models like BART and PEGASUS.

    • Example: notebooks/summarization.ipynb

  3. Fine-Tuning for Classification
    Fine-tune models like DistilBERT for multi-label or binary classification tasks.

    • Example: notebooks/fine_tune_classification.ipynb

  4. Sentiment Analysis
    Analyze the sentiment of text using pre-trained or fine-tuned models.

    • Example: notebooks/sentiment_analysis.ipynb

  5. Named Entity Recognition (NER)
    Train or fine-tune models for identifying named entities in text.

    • Example: notebooks/ner_tagging.ipynb

  6. Fine-Tuning T5 with QLoRA
    Fine-tune T5 for Question and Answer tasks using QLoRA.

    • Example: notebooks/finetune_t5_qanda.ipynb

  7. Fine-Tuning DistilBERT with Prompt Engineering
    Fine-tune DistilBERT using prompt engineering techniques for specific tasks.

    • Example: notebooks/finetune_distilbert_prompt.ipynb

Flask Application

The repository includes a Flask application to make sentiment analysis accessible to users. The app utilizes a model fine-tuned using full fine-tuning of DistilBERT. Users can input text and get the sentiment prediction via the web interface.

  • Application File: app.py
  • How to run the app: 
    python app.py

Techniques and Approaches

Full Fine-Tuning

Full fine-tuning involves updating all the weights of a pre-trained model on a new dataset. This approach allows the model to learn domain-specific nuances but can be computationally expensive and requires significant labeled data. It is particularly effective when the pre-trained model and target task have minimal overlap.

Key Advantages:

  • Maximizes model adaptation to the target task.
  • Retains the flexibility to fine-tune for complex and specific use cases.

Limitations:

  • Requires substantial computational resources.
  • Higher risk of overfitting on small datasets.

Prompt Engineering

Prompt engineering involves crafting inputs to guide the model's behavior without altering its internal weights. By providing task-specific prompts, we can leverage pre-trained models effectively without additional training.

Key Concepts:

  • Task-specific prompting: Framing inputs to align with the target task.
  • Few-shot prompting: Including a few examples in the input to guide the model.

Advantages:

  • Quick to implement and does not require model retraining.
  • Cost-effective for tasks with limited data.

Challenges:

  • Prompt design can be non-trivial and may require iterative refinement.

QLoRA (Quantized LoRA)

QLoRA is an advanced technique that combines low-rank adaptation (LoRA) with quantization for efficient fine-tuning. By freezing most of the model's parameters and training smaller rank adaptation layers, QLoRA reduces the computational burden while maintaining performance.

Key Steps:

  1. Freeze pre-trained model weights.
  2. Insert trainable low-rank adapters into specific layers.
  3. Quantize model weights to reduce memory footprint.

Advantages:

  • Reduces computational requirements and memory usage.
  • Preserves the pre-trained model's generalization capabilities.

Use Cases:

  • Ideal for deploying fine-tuned models on edge devices.
  • Suitable for iterative experimentation with limited resources.

LoRA (Low-Rank Adaptation)

LoRA focuses on fine-tuning a subset of parameters within the model, typically through low-rank matrices inserted into existing layers. This method is highly parameter-efficient and reduces the cost of fine-tuning.

Key Benefits:

  • Dramatically reduces the number of trainable parameters.
  • Enables efficient fine-tuning even on smaller GPUs.

Applications:

  • Task-specific adaptation for LLMs.
  • Efficient transfer learning in resource-constrained environments.

Getting Started

Prerequisites

Ensure you have Python 3.8 or higher installed on your machine.

Installation

  1. Clone the repository:

    git clone https://github.com/yourusername/Fine-Tuning-LLM.git
cd Fine-Tuning-LLM

  2. Create a virtual environment (optional but recommended):

    conda create -n finetune-llm python=3.8
conda activate finetune-llm

  3. Install the required dependencies:

    pip install -r requirements.txt

Running

  1. Launch Jupyter Notebook:

    jupyter notebook

  2. Navigate to the notebooks directory and open the desired .ipynb file.

  3. To run the Flask application for sentiment analysis:

    python app.py