Building Mistral 7B from Scratch in PyTorch: Code and Explanation

by dev

The Mistral 7B model is one of the most efficient and high-performing open-source large language models (LLMs) available today.

In this blog post, we'll walk through a clean PyTorch implementation of a Mistral 7B-style transformer, explaining each component and how they fit together.

This is a great way to deepen your understanding of modern LLM architectures.

Introduction to Mistral 7B Architecture

Mistral 7B is a transformer-based language model with several modern improvements:

• RMSNorm instead of LayerNorm for normalisation.
• SwiGLU activation in the feed-forward network.
• Rotary positional embeddings (RoPE) for encoding position.
• Grouped Query Attention (GQA) for efficient multi-head attention.
• Tied input/output embeddings for efficiency.