Building Large Language Models from Scratch: In-Depth Analysis of the Under the Hood Project

Under the Hood is an open-source tutorial created by Ramchand Kumaresan. It includes 35 progressive hands-on projects that guide developers from the basics of scalar automatic differentiation to building a fully functional GPT model with their own hands, covering full-stack technologies like pre-training, fine-tuning, inference optimization, and RLHF. The project's core philosophy is Build it, Break it, Measure it, aiming to help developers open the LLM black box and deeply understand its working principles.

The core philosophy of Under the Hood can be summarized as Build it, Break it, Measure it. It is complemented by a Leanpub book (for theoretical explanations) and a GitHub repository (for runnable code). Unlike most tutorials on the market that only teach "calling APIs", this project adopts a "first principles" learning approach, requiring learners to implement every component (such as scaled dot-product attention) by themselves, deeply understanding the interaction process of query, key, and value instead of just staying at the conceptual level.

The 35 exercises of the project are divided into three key stages:

Stage 1 (1-7): Basic Construction

Implement scalar automatic differentiation, neural networks, embedding layers, BPE tokenizers, build attention mechanisms and a minimal complete GPT system from scratch, and compare the implementation details with nanoGPT.

Stage 2 (8-19): Training and Optimization

Introduce inference optimization techniques such as Flash Attention and chunked kernels, covering large-scale pre-training (FineWeb-EDU dataset, mixed-precision training, distributed strategies) and inference optimization (KV caching, speculative decoding, GQA, long context extension, production-level deployment).

Stage 3 (20-35): Post-Training and Advanced Topics

Covers preference optimization like supervised fine-tuning, LoRA, RLHF/DPO; inference strategies during testing (chain of thought, self-consistency); quantization deployment; RAG systems; and cutting-edge topics such as multimodal models and non-Transformer architectures (Mamba, RWKV).

1. Bridging the Gap Between Theory and Practice: It lies between highly theoretical academic papers and quick tutorials that only teach "calling APIs in three lines of code". Each line of code corresponds to a specific concept, helping to understand "why".
2. Covers the Full Lifecycle of LLMs: From data preparation, pre-training, fine-tuning to deployment and inference optimization, it provides a structured learning path for developers in the AI engineering field.
3. Keeps Up with Cutting-Edge Technologies: The content reflects the latest developments in the LLM field for 2024-2025, such as Flash Attention 2, YaRN long context extension, GGUF quantization format, etc., all of which are technologies currently used in the industry.

This project is most suitable for developers who have a certain foundation in Python and deep learning and want to deeply understand Transformers and LLMs. If you already know how to train simple neural networks with PyTorch and want to figure out issues like attention mechanism calculation, KV cache acceleration principles, LoRA parameter fine-tuning, etc., this project is an ideal choice.

• Beginners with no foundation: It is recommended to first supplement knowledge of linear algebra, probability theory, and basic neural networks.
• Developers proficient in using LangChain/LlamaIndex: They can understand the underlying model principles through this project to better debug and optimize applications. Project address: https://github.com/mechramc/Under-the-hood

Large language models are reshaping software development, but developers who truly understand their working principles are still scarce. Under the Hood provides an opportunity to build LLMs with your own hands, allowing learners to transform from spectators to builders and master the core infrastructure of the era. As the project's slogan says: "Think like an engineer, not a bystander."