This repository breaks down the LLM pipeline step by step, helping you understand how AI models process, generate, and optimize text responses. Instead of seeing LLMs as a black box, this framework reverse-engineers each component, giving both high-level intuition and technical deep dives.
🔹 8-Step LLM Pipeline Breakdown
🔹 Diagrams & Visuals for Each Step
🔹 Code Snippets for Key Concepts
🔹 References to Research Papers & Further Reading
🔹 Discussion Threads for Community Q&A
Each step is structured as an independent module that explains how a specific process works inside an LLM.
| Step | What Happens Here? | Docs |
|---|---|---|
| 1️⃣ Input Processing | Tokenization, chat templates, and how the model interprets text. | 🔗 Read More |
| 2️⃣ Neural Network (Core Processing) | Embeddings, attention mechanisms, transformer layers. | 🔗 Read More |
| 3️⃣ Output Processing | Decoding strategies (greedy, top-k, beam search), temperature scaling. | 🔗 Read More |
| 4️⃣ Training & Optimization | Pretraining, fine-tuning, RLHF, loss functions, optimizers. | 🔗 Read More |
| 5️⃣ Memory & Context Handling | Context windows, long-term memory, RAG (Retrieval-Augmented Generation). | 🔗 Read More |
| 6️⃣ Customization & Inference | Fine-tuning, LoRA, quantization, API deployment. | 🔗 Read More |
| 7️⃣ Evaluation & Safety | Bias audits, hallucination prevention, adversarial testing. | 🔗 Read More |
| 8️⃣ Scaling & Future Trends | Multimodal models, continual learning, efficiency improvements. | 🔗 Read More |
1️⃣ Start with the high-level breakdown in README.md
2️⃣ Deep dive into each step by exploring the docs/ folder
3️⃣ Use the diagrams and code snippets for better understanding
4️⃣ Join discussions & contribute to improving the framework
✅ AI enthusiasts & learners who want to understand LLMs deeply
✅ Engineers & researchers working on LLM-based applications
✅ Students studying transformers, NLP, and deep learning
✅ Anyone curious about how AI models generate text
📚 Research Papers: Awesome LLM Papers
📚 Hugging Face Tutorials: Hugging Face Course
📚 Transformers Library: GitHub - Transformers
📚 Stanford AI Course: CS324 - Understanding LLMs
📌 This repo is an evolving knowledge base!
💬 Have questions? Start a discussion
🔧 Want to contribute? Check the contribution guidelines
📩 Spotted an error? Submit an issue
- 📜 License: MIT
- 🔗 Authored by: Ecem Karaman
- 🌍 Join the conversation on AI & LLMs!