Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
The Official Repo for "Quick Start Guide to Large Language Models"
| Date | Stars |
|---|---|
| 2026-07-31 | 393 |
| 2026-08-06 | 394 |
Today
+1 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Quick Start Guide to Large Language Models - Second Edition <p align="center"> <a href="https://www.amazon.com/Quick-Start-Guide-Language-Models-dp-0135346568/dp/0135346568">Get your copy today</a> and please leave a rating/review to tell me what you thought! ⭐⭐⭐⭐⭐ </p> <p align="center" href="https://www.amazon.com/Quick-Start-Guide-Language-Models-dp-0135346568/dp/0135346568"> <img src="images/qsllm2e.jpg" href="https://www.amazon.com/Quick-Start-Guide-Language-Models-dp-0135346568/dp/0135346568" alt="Quick Start Guide to Large Language Models" width="300"> </p> Welcome to the GitHub repository for the **"Quick Start Guide to Large Language Models - Second Edition"**. This repository contains the code snippets and notebooks used in the book, demonstrating various applications and advanced techniques in working with Transformer models and large language models (LLMs). [View the code for the First Edition here](https://github.com/sinanuozdemir/quick-start-guide-to-llms/tree/1e) ## Repository Structure ### Directories - `notebooks`: 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. ### Notebooks Below is a list of the notebooks included in the `notebooks` directory, organized by the chapters in the book. #### Part I - Introduction to Large Language Models - **Chapter 2: Semantic Search with LLMs** - [`02_semantic_search.ipynb`](notebooks/02_semantic_search.ipynb): An introduction to semantic search using OpenAI and open-source models. - **Chapter 3: First Steps with Prompt Engineering** - [`03_prompt_engineering.ipynb`](notebooks/03_prompt_engineering.ipynb): A guide to effective prompt engineering for instruction-aligned LLMs. - **Chapter 4: The AI Ecosystem: Putting the Pieces Together** - [`04_rag_retrieval.ipynb`](notebooks/04_rag_retrieval.ipynb): Building a Retrieval-Augmented Generation (RAG) pipeline. - [`04_agent.ipynb`](notebooks/04_agent.ipynb): Constructing an AI agent using LLMs and other tools. #### Part II - Getting the Most Out of LLMs - **Chapter 5: Optimizing LLMs with Customized Fine-Tuning** - [`05_bert_app_review.ipynb`](notebooks/05_bert_app_review.ipynb): Fine-tuning a BERT model for app review classification. - [`05_openai_app_review_fine_tuning.ipynb`](notebooks/05_openai_app_review_fine_tuning.ipynb): Fine-tuning OpenAI models for app review classification. - **Chapter 6: Advanced Prompt Engineering** - [`06_adv_prompt_engineering.ipynb`](notebooks/06_adv_prompt_engineering.ipynb): Advanced techniques in prompt engineering, including output validation and semantic few-shot learning. - [`06_adv_prompt_engineering - DEEPSEEK.ipynb`](notebooks/06_adv_prompt_engineering%20-%20DEEPSEEK.ipynb): Extending the MathQA case study to Deepseek V3 and R1. Prompting still seems to matter :) - [`06_adv_prompt_engineering - Llama 4.ipynb`](notebooks/06_adv_prompt_engineering%20-%20Llama4.ipynb): Extending the MathQA case study to Llama 4 Scout. Interesting the prompt responds well to CoT but not as much to Few-shot prompting on this benchmark. - **Chapter 7: Customizing Embeddings and Model Architectures** - [`07_recommendation_engine.ipynb`](notebooks/07_recommendation_engine.ipynb): Building a recommendation engine using custom fine-tuned LLMs and embeddings. #### Part III - Advanced LLM Usage - **Chapter 9: Moving Beyond Foundation Models** - [`09_constructing_a_vqa_system.ipynb`](notebooks/09_constructing_a_vqa_system.ipynb): Step-by-step guide to constructing a Visual Question Answering (VQA) system using GPT-2 and Vision Transformer. - [`09_using_our_vqa.ipynb`](notebooks/09_using_our_vqa.ipynb): Using the VQA system built in the previous notebook. - [`09_flan_t5_rl.ipynb`](notebooks/09_flan_t5_rl.ipynb): Using Reinforcement Learning (RL) to improve FLAN-T5 model outputs. - **Chapter 10: Advanced Open-Source LLM Fine-Tuning** - [`10_
Excerpt of 7,858 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:92b6836ce7332855, topic:llm, topic:gpt
matched fp:92b6836ce7332855, topic:quantization
matched fp:92b6836ce7332855, topic:rlhf
matched fp:92b6836ce7332855, topic:multimodal