LLMs-from-scratch open source analysis
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
Project overview
⭐ 78754 · Jupyter Notebook · Last activity on GitHub: 2025-11-13
Why it matters for engineering teams
LLMs-from-scratch addresses the need for engineering teams to deeply understand and customise large language models by providing a clear, step-by-step implementation of a ChatGPT-like model in PyTorch. This open source tool for engineering teams is particularly suited to machine learning and AI engineers who require hands-on experience with model architecture and training processes rather than relying on pre-built libraries. While it offers valuable educational insight and a foundation for experimentation, it is not a production ready solution for deployments demanding high scalability or robustness. Teams seeking a self hosted option for language model inference in production environments may find more mature frameworks better suited to their needs, as this project prioritises transparency and learning over optimised performance and reliability.
When to use this project
Choose LLMs-from-scratch when your team needs to build foundational knowledge or prototype custom language models from the ground up. For production systems requiring stability, scalability, or integration with existing ML pipelines, established libraries and frameworks should be considered instead.
Team fit and typical use cases
Machine learning and AI engineers benefit most from this repository, using it to experiment with transformer architectures and gain practical insights into large language model training. It typically appears in research prototypes, educational projects, and early-stage development of generative AI features where a self hosted option for customisation is important.
Best suited for
Topics and ecosystem
Activity and freshness
Latest commit on GitHub: 2025-11-13. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.