Building a Code-Focused Generative AI from Scratch: A Deep Dive!
research#llm📝 Blog|Analyzed: Mar 22, 2026 14:48•
Published: Mar 22, 2026 14:34
•1 min read
•r/deeplearningAnalysis
This project showcases an incredible feat of building a code-focused Large Language Model (LLM) from the ground up, using JAX on TPUs. The detailed pipeline, including pretraining, Fine-tuning, and Reinforcement Learning fine-tuning, highlights the core principles of LLM development in a practical way. The innovative 'Agentic Code Solver' demonstrates the potential for self-improving code generation.
Key Takeaways
- •The project meticulously outlines the entire LLM pipeline, from pretraining to advanced Reinforcement Learning fine-tuning.
- •The 'Agentic Code Solver' loop demonstrates a self-improving code generation process.
- •It emphasizes the value of understanding the fundamentals of tokenization, attention, and embeddings even in smaller LLMs.
Reference / Citation
View Original"I recently built a full-stack code-focused LLM entirely from scratch — end-to-end — using JAX on TPUs. No shortcuts, no pretrained weights. Just raw math, JAX, and a lot of debugging."