Build a LLM from Scratch using MLX

You probably have a burning desire to grasp the inner workings of LLMs. By now, terms like Attention, Transformers, and Tokenizers are likely ringing in your ears, yet the actual mechanics often feel like they slip away just as quickly as you study them.

The truth is, the most effective path to comprehension is to roll up your sleeves and actually construct one.

I set out to develop a Nano LLM—a model with roughly 20.2M parameters—right on my Macbook Air. It turns out that Apple’s MLX framework makes this entirely possible.

You can find the full implementation here: https://github.com/samair/nanoLLM/blob/main/nanoLLM.ipynb

While I have spent time fine-tuning existing models, that always felt like just skimming the surface. The real insight comes from building from the ground up.

So, let’s break down the essentials for creating a Large Language Model from scratch.

Our requirements are simple:

1. A Macbook (M1 or later) to leverage the MLX framework.
2. A foundational grasp of Python.
3. Believe me, you don’t need a high-end GPU; a basic Macbook Air is more than sufficient.

Edit 1 (Added substack link) - Read more - https://samairtimer.substack.com/p/build-a-llm-from-scratch-using-mlx