🔥 Pipeline for Fine-Tuning your Large Language Model

A comprehensive pipeline for Different Fine-Tuning Methods for Large Language Models with optimized performance and resource efficiency

Features • Installation • Usage • Pipeline • Documentation • Contributing

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🌟 Overview

The LLM Fine-Tuning Pipeline is a robust and flexible framework designed to streamline the process of fine-tuning Large Language Models (LLMs) for specific tasks and domains. This pipeline handles the entire workflow from data preparation to model evaluation, making advanced LLM customization accessible and efficient.

🎯 Key Objectives

• Simplified end-to-end LLM fine-tuning process
• Resource-efficient training with performance optimization
• Reproducible experiments with comprehensive logging
• Flexible architecture supporting multiple model types and training strategies

✨ Features

🤖 Core Capabilities

• Comprehensive Data Pipeline

  • Versatile data loading from multiple sources
  • Advanced preprocessing and augmentation techniques
  • Custom dataset creation for specific tasks

• Flexible Training Framework

  • Support for multiple fine-tuning techniques (LoRA, QLoRA, Full Fine-tuning)
  • Mixed precision training and quantization options
  • Gradient accumulation and checkpointing for memory efficiency

• Robust Evaluation Suite

  • Automatic evaluation on common benchmarks
  • Custom metric implementation and tracking
  • Interactive model output comparison

🛠️ Technology Stack

Core Technologies

• Python 3.8+
• PyTorch
• Hugging Face Transformers & PEFT
• Weights & Biases for experiment tracking
• DeepSpeed for distributed training

📋 Prerequisites

Before using the LLM Fine-Tuning Pipeline, ensure that your environment is properly set up:

System Requirements:

• RAM: Minimum 16GB (32GB+ recommended for larger models)
• GPU: NVIDIA GPU with 8GB+ VRAM (24GB+ recommended for efficient training)
• Storage: 50GB+ free space for models and datasets
• Operating System: Linux (recommended), macOS, or Windows with WSL2

📰 Published Article

Explore other Detailed Fine-Tuning Methods of Large Language Models with Mathematical Calculations:

🔗 Read the article here: Customizing AI for your Brand: A Deep Dive into LLM Fine-Tuning

🚀 Installation

Quick Start

# Clone the repository
git clone https://github.com/priyam-hub/LLM-Fine-Tuning-Pipeline.git
cd LLM-Fine-Tuning-Pipeline

# Setup Enviroment Dependencies and Finetuning Modules
bash setup.sh

# Run the Pipeline
python run.py

📁 Project Structure

LLM-Fine-Tuning-Pipeline/
├── LICENSE                                   # MIT License
├── README.md                                 # Project documentation
├── .gitignore                                # Ignoring files for Git
├── requirements.txt                          # Python dependencies
├── run.py                                    # Run the Fine-Tuning Pipeline
├── setup.sh                                  # Package installation configuration
├── config/                                   # Configuration files
│   └── config.py/                            # All Configuration Variables of Pipeline
├── docs/                                     # Documents Directory
│   ├── Instruction_Fine_Tuning_for_LLM.pdf/  # Research Paper of Instruction Fine-Tuning
|   ├── LoRA_Fine_Tuning.pdf/                 # Research Paper of LoRA Fine-Tuning
│   ├── RLHF_Fine_Tuning.pdf/                 # Research Paper of RLHF Fine-Tuning
│   └── Supervised_Fine_Tuning_for_LLM.pdf/   # Research Paper of Supervised Fine-Tuning
├── data/                                     # Data directory
│   ├── raw/                                  # Raw dataset files
|   ├── cleaned/                              # Cleaned dataset files
│   ├── prepared/                             # Prepared for Fine-tuning datasets
│   └── evaluation/                           # Evaluation datasets
├── notebooks/                                # Jupyter notebooks for experimentation
├── reports/                                  # Reports of the Project
├── src/                                      # Source code
│   ├── data_preparation/                     # Data Preparation modules
│   │   └── prepare_dataset.py/               # Preparing the Dataset for Fine-Tuning and Evaluation
│   ├── fine_tuning_methods/                  # All Fine-Tuning methods of LLM
│   │   ├── instruction_fine_tuning.py/       # Instruction Fine-Tuning
│   │   ├── lora_fine_tuning.py/              # LoRA Fine-Tuning
│   │   ├── rlhf_fine_tuning.py/              # RLHF Fine-Tuning
│   │   └── supervised_fine_tuning.py/        # Supervised Fine-Tuning
│   ├── llm_evaluation/                       # Evaluation of LLM
│   │   └── llm_evaluation.py/                # Evaluating the LLM by BLEU, Perplexity 
│   ├── llm_fine_tuning/                      # LLM Fine-tuner
│   │   └── llm_fine_tuning.py/               # Fine-tune the LLM with Specific Method
│   ├── llm_inference/                        # Inference of LLM
│   │   └── llm_inference.py/                 # Inference of LLM
│   └── utils/                                # Utility functions
│       ├── dataset_loader.py/                # Dataset Load and Save Operation
│       ├── logger.py/                        # Logging Setup
│       └── model_loader.py/                  # Model Load and Save Operation

🔄 Pipeline

The LLM Fine-Tuning Pipeline follows these key steps:

1. Data Preparation

   • Load and preprocess raw text data
   • Convert to instruction/response format if needed
   • Split into train/validation sets

2. Model Configuration

   • Select base model and tokenizer
   • Configure PEFT method (LoRA, QLoRA, etc.)
   • Set up quantization parameters

3. Training Setup

   • Configure optimizer and learning rate scheduler
   • Set up mixed precision training
   • Initialize tracking and logging

4. Fine-Tuning Process

   • Execute training loops with gradient accumulation
   • Track metrics and save checkpoints
   • Apply early stopping if configured

5. Evaluation

   • Evaluate on validation datasets
   • Generate benchmark metrics
   • Compare against baseline models

6. Deployment Preparation

   • Merge adapter weights if using PEFT
   • Quantize model for inference
   • Package model for deployment

📚 Documentation

Comprehensive documentation is available in the /docs directory:

• 📖 Instruction Fine-Tuning Research Paper
• 🔧 LoRA Fine-Tuning Research Paper
• 📝 RLHF Fine-Tuning Research Paper
• 🧪 Supervised Fine-Tuning Research Paper

🗺️ Future Roadmap

Phase 1: Enhanced Training Efficiency

• Implement Flash Attention 2
• Add DeepSpeed ZeRO-3 integration
• Support for distributed training across multiple GPUs

Phase 2: Advanced Techniques

• Add RLHF (Reinforcement Learning from Human Feedback)
• Implement DPO (Direct Preference Optimization)
• Add support for multi-modal fine-tuning

Phase 3: Deployment Options

• ONNX export for optimized inference
• Quantization-aware training
• API deployment templates

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Hugging Face team for their incredible transformers library
• PEFT library contributors for efficient fine-tuning methods
• Open-source LLM providers: Meta AI (LLaMA), Mistral AI, TII (Falcon)

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Pipeline Built by Priyam Pal - AI and Data Science Engineer

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