Welcome to the GitHub repository for the "Quick Start Guide to Large Language Models" book. This repository contains the code snippets and notebooks used in the book, demonstrating various applications of Transformer models.
notebooks: This directory contains Jupyter notebooks for each chapter in the book.data: Contains the datasets used in the notebooks.images: Contains images and graphs used in the notebooks.
Here are some of the notebooks included in the notebooks directory:
2_semantic_search.ipynb: An introduction to semantic search using OpenAI and open source models.3_prompt_engineering.ipynb: A guide to effective prompt engineering for instruction aligned LLMs.
4_fine_tuned_classification.ipynb: Learn how to perform text classification through fine-tuning OpenAI models5_adv_prompt_engineering.ipynb: Advanced techniques for prompt engineering including k-shot, semantic k-shot, chain of thought prompting, chaining, and building a retrieval augmented generating (RAG) enabled chatbot with GPT-4.5_VQA.ipynb: Introduction to prompt chaining and Visual Question Answering (VQA) with open source LLMs6_recommendation_engine.ipynb: Building a recommendation engine using custom fine-tuned LLMs
7_constructing_a_vqa_system.ipynb: Step-by-step guide to constructing a Visual Question Answering system using open-source GPT2 and the Vision Transformer.7_using_our_vqa.ipynb: A notebook to use the VQA system we built in the previous notebook.7_rl_flan_t5_summaries.ipynb: Using Reinforcement Learning (RL) to produce more neutral and grammatically correct summaries with the FLAN-T5 model.8_latex_gpt2.ipynb: Fine-tuning GPT-2 to generate LaTeX formulas8_anime_category_classification_model_freezing.ipynb: Fine-tuning a BERT model to classify anime categories with a comparison between freezing model layers and keeping the model unfrozen.8_optimizing_fine_tuning.ipynb: Best practices for optimizing fine-tuning of transformer models - dynamic padding, gradient accumulation, mixed precision, and more.8_sawyer_1_instruction_ft.ipynb: Fine-tuning the instruction model for the SAWYER bot.8_sawyer_2_train_reward_model.ipynb: Training a reward model for the SAWYER bot from human preferences.8_sawyer_3_rl.ipynb: Using Reinforcement Learning from Human Feedback (RLHF) to further align the SAWYER bot8_sawyer_4_use_sawyer.ipynb: Using our SAWYER bot9_distillation.ipynb: An exploration of knowledge distillation techniques for transformer models.
We will continue to add more notebooks exploring topics like fine-tuning, advanced prompt engineering, combining transformers, and various use-cases. Stay tuned!
To use this repository, clone it to your local machine, navigate to the notebooks directory, and open the Jupyter notebook of your choice. Note that some notebooks may require specific datasets, which can be found in the data directory.
Please ensure that you have the necessary libraries installed and that they are up to date. This can usually be done by running pip install -r requirements.txt in the terminal.
Contributions are welcome! Feel free to submit a pull request if you have any additions, corrections, or enhancements to submit.
This repository is for educational purposes and is meant to accompany the "Quick Start Guide to Large Language Models" book. Please refer to the book for in-depth explanations and discussions of the topics covered in the notebooks.