llm-scratch-pytorch and scratch-llm
These are complementary educational resources that serve different learning depths: the first prioritizes foundational PyTorch concepts through step-by-step implementation, while the second focuses on replicating a specific production-grade architecture (Llama 2), making them best used sequentially or in parallel depending on the learner's starting level.
About llm-scratch-pytorch
skyloevil/llm-scratch-pytorch
lm-scratch-pytorch - The code is designed to be beginner-friendly, with a focus on understanding the fundamentals of PyTorch and implementing LLMs from scratch,step by step.
This project helps aspiring machine learning engineers and researchers understand how large language models (LLMs) like GPT-2 are built from the ground up using PyTorch. It guides you step-by-step through implementing the core components, starting from basic PyTorch concepts, all the way to optimizing performance with techniques like Flash Attention. You'll work with actual LLM architectures and gain practical knowledge of their internal workings.
About scratch-llm
clabrugere/scratch-llm
Implements a LLM similar to Meta's Llama 2 from the ground up in PyTorch, for educational purposes.
This project offers a clear, basic implementation of a large language model like Meta's Llama, built using PyTorch. It helps developers and researchers understand how these models work internally by showing the mechanics of components like positional encoding and attention. The project takes text data, processes it, and demonstrates the core computational steps that lead to a trained language model.
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