Training a GPT-like Model in Rust: A Deep Dive
In this article, I’ll take you through my journey of training a GPT-like model from scratch using Rust. Forget about GPUs; I focused solely on CPUs and achieved a performance boost that was 30 times better than the native C code.
Understanding Matrix Multiplication
In my previous piece, I tackled the intricacies of matrix multiplication and its vital role in the attention algorithms that power models like GPT. I shared my experience in implementing an efficient matrix multiplication function in Rust utilizing BLAS (Basic Linear Algebra Subprograms). Let’s build upon that foundational knowledge as we delve into creating our own model.
The Road to Implementing GPT in Rust
The main goal of this article is to showcase the first crucial building block of a project I call llm.c, developed in Rust. As I embarked on this venture, my aim was specifically to train a GPT-like model entirely with Rust, starting from existing GPT weights. But there’s a catch—I’m doing all of this without the help of GPUs or TPUs. Why? I wanted to see just how far we could push these models using basic laptops and explore what the Rust ecosystem can offer for machine learning.
Imagine being able to fine-tune GPT models with just your laptop. That’s the dream, isn’t it? With the code I’ll share, anyone curious about AI or looking to experiment with language models can dive right in.
Share the Code Base
I’ve made all the relevant pieces of code available for you to explore. Whether you’re an avid programmer or just curious about the magic of AI, you’ll find the resources you need to play around and learn for yourself.
Engaging with the AI Community
The beauty of this project lies not just in the final model but in the lessons learned along the way. With every line of code, I’ve gained deeper insights into the Rust programming language, and I’m excited to share those insights with you. It’s like exploring a new city; you find hidden gems and learning opportunities everywhere.
Why Rust?
You might wonder why I chose Rust over more traditionally used languages like Python or C. Rust offers performance comparable to C while ensuring memory safety, which makes it an exciting choice for machine learning projects. The challenges of working without GPUs force me to innovate solutions that can expand what we consider possible in everyday AI development.
Conclusion
To wrap it all up, training a GPT-like model in Rust without the backing of powerful hardware is not just about the destination. It’s about the journey—each step pushing the boundaries of what we can achieve with accessible resources. If you’re as intrigued by these advancements as I am, I highly encourage you to explore the code and see what you can do.
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