LLM Zero to Mastery

About:
This repository is a comprehensive guide to mastering Large Language Models (LLMs) through hands-on projects. It provides step-by-step implementations of advanced techniques like Retrieval-Augmented Generation (RAG), fine-tuning GPT models, and instruction-tuning. Each module is designed to help users effectively learn and apply LLM concepts with practical examples.

Structure

1. Local-RAG

Directory: Local-RAG/
This module demonstrates the implementation of a simple local Retrieval-Augmented Generation (RAG) pipeline. Key steps include:

• PDF Processing: Extracts text from PDF files in the ./data directory.
• Vector Database Creation: Creates and stores vectorized representations of documents.
• Query Handling: Retrieves relevant documents based on user queries using a vector database.
• Response Generation: Utilizes OpenAI’s Ollama model to generate contextually accurate responses.

Outcome: Enables local document-based Q&A systems, making it a practical tool for managing and querying personal data collections.

2. Fine-Tuning GPT-2

Directory: Fine-Tuning-GPT2/
This module focuses on fine-tuning GPT-2 for custom tasks. The workflow includes:

• Dataset Preparation: Loads, tokenizes, and processes text datasets.
• Fine-Tuning: Adjusts GPT-2 on task-specific data using Hugging Face’s Trainer API.
• Model Testing and Evaluation: Assesses performance with custom prompts and task examples.
• Model Saving: Saves the fine-tuned model locally for reuse.

Outcome: Customizes GPT-2 to generate domain-specific text, making it suitable for tailored applications.

3. Instruction-Tuning

Directory: instruction_tuning/
This module introduces instruction-tuning, a method to align LLM outputs with specific tasks using task-specific instructions and examples. Key features include:

• Data Preparation: Converts datasets into instruction-response pairs.
• Fine-Tuning: Leverages pre-trained transformer models to train on task-specific instructions.
• Evaluation: Tests the model’s alignment and performance on user-defined tasks.

Outcome: Produces LLMs refined for instruction-based tasks, significantly improving their alignment to specific needs.

4. Future Modules

The repository is continuously evolving, with plans to introduce more advanced LLM techniques and projects soon.

Technologies Used

• Python, Hugging Face Transformers, PyTorch.

Key Takeaways

• Provides hands-on experience with cutting-edge LLM techniques.
• Helps users build practical applications with LLMs, from RAG systems to fine-tuned models.
• Guides users through complex processes with clear, structured implementations.

Explore the repository here: LLM Zero to Mastery