Here is an example code snippet in PyTorch that demonstrates how to build a simple LLM:
Once the data pipeline was established, the focus shifted to architectural design. The Transformer architecture, specifically the decoder-only variant utilized by GPT models, was the industry standard. Building this from scratch required implementing the multi-head self-attention mechanism, which allows the model to weigh the importance of different words in a sequence relative to one another. Engineers had to code layer normalization, positional embeddings to understand word order, and feed-forward networks. In 2021, attention was also turning toward architectural optimizations such as Sparse Transformers or the introduction of Rotary Positional Embeddings (RoPE), which offered better performance on longer context windows compared to the absolute positional embeddings used in the original GPT-2. Build A Large Language Model -from Scratch- Pdf -2021
— Training the model on a general corpus to learn language patterns. Chapter 6 & 7: Fine-Tuning Here is an example code snippet in PyTorch
The book follows a "bottom-up" approach to AI, based on the principle that true understanding comes from construction. It avoids pre-built high-level libraries to force the reader to implement every component of a GPT-style model using PyTorch. Chapter 6 & 7: Fine-Tuning The book follows
rasbt/LLMs-from-scratch: Implement a ChatGPT-like ... - GitHub