LLM Project to Build and Fine Tune a Large Language Model

Overview

In today's data-driven world, the ability to process and generate natural language text at scale has become a transformative force across industries. Large Language Models (LLMs) represent a cutting-edge advancement in natural language processing, enabling businesses to extract valuable insights, automate tasks, and enhance user experiences. By harnessing the power of LLMs, organizations can improve customer service, automate content creation, and gain a competitive edge in the digital landscape.

This project builds the foundation for Large Language Models by diving deep into the details of their inner workings. Moreover, It shows how to optimize their use through prompt engineering and fine-tuning techniques such as LoRA. 

Prompt engineering techniques involve crafting specific instructions or queries given to the language model to influence its output will be introduced to guide LLMs in generating desired responses through zero-shot, one-shot, and few-shot inferences.

Fine-tuning entails training a pre-trained language model on a specific task or dataset to adapt it for a particular application. It explores full fine-tuning and Parameter Efficient Fine Tuning (PEFT), a technique that optimizes the fine-tuning process by focusing on a subset of the model's parameters, making it more resource-efficient.

The project also involves the application of Retrieval Augmented Generation (RAG) using OpenAI's GPT-3.5 Turbo, resulting in the development of a chatbot for online shopping for knowledge grounding. Knowledge grounding with Retrieval Augmented Generation (RAG) is implemented to mitigate hallucinations and provide trustworthy and reliable responses. This is achieved by incorporating information from external sources to validate and support the generated text.

For example, in the context of an e-commerce chatbot using RAG, knowledge grounding ensures that product information, availability, and prices are sourced from a trusted database or e-commerce platform. This prevents the chatbot from generating inaccurate or fictional details and instead provides responses based on real-world data. 

Note: Please note that the use of the OpenAI API may require the utilization of allocated free credits or additional purchases (depending on the account status) for implementing the project. Kindly take note of the free credits limit provided for your usage.

Aim

The aim of this project is to provide a comprehensive understanding and practical experience in working with Large Language Models (LLMs). It covers fundamental concepts, advanced techniques, and practical applications of LLMs, to effectively leverage them for tasks such as text generation, fine-tuning, and building knowledge-grounded applications like a chatbot for online shopping.

Data Description 

• The dialogue summarization exercises in this project utilize the knkarthick/dialogsum dataset from the Hugging Face library. 

• For the purpose of demonstrating the development of a chatbot, data about apparel and paper products is provided. This dataset includes textual information about various apparel items and paper products. 

Tech Stack

• Language: Python 3.8

• Libraries:  transformers, datasets, torchdata, torch, streamlit, openai, langchain, unstructured, sentence-transformers, chromadb, evaluate, rouge_score, loralib, peft

Approach

• Introduction to LLMs and Basics:

  • Explanation of RNNs, transformers, attention mechanism, and Large Language Models (LLMs).

  • Illustration of use cases, including text generation, to establish foundational knowledge.

• Prompt Engineering:

  • Introduction to prompt engineering techniques, including zero, one, and few-shot inferences.

• Dialogue Summarization Task:

  • Implementation of prompt engineering on a dialogue summarization task, showcasing practical application.

• Fine Tuning and PEFT:

  • Implementing full fine-tuning and parameter-efficient fine-tuning (PEFT) on dialogue summarization.

  • Model evaluation using ROUGE metrics for performance assessment.

• RLHF (Reinforcement Learning from Human Feedback):

  • Explanation and exploration of the RLHF concept

• Retrieval Augmented Generation (RAG):

  • Practical implementation of RAG using OpenAI's GPT3.5 for creating a chatbot.

• Development of Chatbot Application:

  • Step-by-step guide on building a functional chatbot application for online shopping using RAG.