Build and Train an LLM with JAX
by DeepLearning.AI
Build and train a 20M-parameter MiniGPT end-to-end in JAX, in under an hour.
Overview
Built in partnership with Google and taught by Chris Achard, a Developer Relations Engineer at Google, this 49-minute short course walks through building and training a 20-million-parameter, GPT-2-style MiniGPT entirely in JAX. It runs across seven lessons — Introduction (2m), Overview of JAX (6m), Building the Architecture (10m), Data Loading (6m), Training and Saving (8m), Final MiniGPT (3m) and Conclusion (1m) — with four runnable code examples and an optional graded quiz. The second lesson covers JAX's three core transformations: automatic differentiation, just-in-time compilation and vectorised mapping, and why a functional, side-effect-free style is what lets them compose. The remaining lessons assemble the full JAX AI Stack around them: Flax NNX for token and positional embeddings and the transformer blocks, Grain for tokenisation and batching, Optax for the optimiser and gradient application, and Orbax for checkpointing. The final lesson loads a pretrained MiniGPT checkpoint and generates text through a chat interface, closing the loop from raw text to sampled output. Google's open JAX AI Stack documentation carries a closely matching miniGPT-on-TinyStories tutorial built from the same four libraries, so the code is verifiable and re-runnable outside the course. The video is free to watch; graded assignments and the certificate require a DeepLearning.AI Pro membership.
At a Glance
- Topic
- ML
- Level
- Intermediate
- Format
- Course
- Cost
- Freemium
- Duration
- ~49 minutes of video across 7 lessons, self-paced
- Provider
- DeepLearning.AI
- Hands-on
- Yes — code/exercises
- Certificate
- Available
What You’ll Learn
- ✓Compose JAX's automatic differentiation, JIT compilation and vectorised mapping in one training step
- ✓Implement token and positional embeddings plus transformer blocks using Flax NNX
- ✓Tokenise and batch a mini-stories dataset for JAX's functional execution model
- ✓Write a full training loop that computes loss and applies Optax gradients
- ✓Save and restore model checkpoints reliably using Orbax checkpointing
- ✓Load a pretrained MiniGPT and generate text through a chat interface
- ✓Explain why JAX's pure-function style is what makes JIT compilation possible
Highlights
- •Under an hour end-to-end, yet covers architecture, data loading, training, checkpointing and inference
- •Built with Google and taught by a Google Developer Relations Engineer, not a third-party instructor
- •Uses the real JAX AI Stack — Flax NNX, Grain, Optax, Orbax — rather than a raw NumPy toy build
- •The model is small enough at 20M parameters to train without access to a TPU pod
- •Google's public JAX AI Stack docs mirror the same miniGPT build, so you can re-run and extend it for free
Who It’s For
Best For
- ✓PyTorch engineers who need to read or maintain JAX and Flax codebases
- ✓ML practitioners evaluating JAX for TPU training workloads
- ✓Developers who want a concrete mental model of a transformer training loop
- ✓Anyone preparing to work on Google's Gemini-era model infrastructure
Prerequisites
- •Comfortable writing Python, including NumPy-style array code
- •Basic machine learning concepts: gradients, loss functions, mini-batch training
- •Familiarity with what a transformer is — the course does not derive attention from first principles
FAQ
What is Build and Train an LLM with JAX?
A short course built with Google that takes you from an empty file to a trained, chattable 20-million-parameter GPT-2-style model using JAX and the JAX AI Stack. It is aimed at Python developers and ML practitioners who have used a training loop before but have never written one in JAX, and who need to read or maintain Flax code.
Is Build and Train an LLM with JAX free?
Build and Train an LLM with JAX offers free content, with paid options for certificates or premium features.
What level is Build and Train an LLM with JAX for?
Build and Train an LLM with JAX is aimed at a intermediate audience. Recommended background: Comfortable writing Python, including NumPy-style array code, Basic machine learning concepts: gradients, loss functions, mini-batch training, Familiarity with what a transformer is — the course does not derive attention from first principles.
How long does Build and Train an LLM with JAX take?
Expect roughly ~49 minutes of video across 7 lessons, self-paced. Most learners work through it at their own pace.
What will I learn from Build and Train an LLM with JAX?
You'll learn: Compose JAX's automatic differentiation, JIT compilation and vectorised mapping in one training step; Implement token and positional embeddings plus transformer blocks using Flax NNX; Tokenise and batch a mini-stories dataset for JAX's functional execution model; Write a full training loop that computes loss and applies Optax gradients; Save and restore model checkpoints reliably using Orbax checkpointing; Load a pretrained MiniGPT and generate text through a chat interface; Explain why JAX's pure-function style is what makes JIT compilation possible.
Topics
Sources
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