Build and Train an LLM with JAX
by DeepLearning.AI
Build and train a 20M-parameter GPT-style LLM from scratch in JAX — the library behind Gemini.
Overview
Build and Train an LLM with JAX is a DeepLearning.AI short course produced with Google and taught by Chris Achard, a Developer Relations Engineer on Google's TPU software team. JAX is the open-source library behind Google's Gemini, Veo, and other frontier models, and this course uses it to build a roughly 20-million-parameter GPT-2-style language model entirely from scratch. Across seven video lessons and four code examples you learn JAX's core primitives — automatic differentiation, JIT compilation, and vectorized mapping (vmap) — then combine them with the modern JAX ecosystem to build a real training pipeline: Flax/NNX for neural-network layers, Grain for data loading, Optax for optimization, and Orbax for checkpointing. You define the model architecture, preprocess and tokenize data, run the training loop, save checkpoints, and finally chat with your trained model through a graphical interface.
At a Glance
- Topic
- ML
- Level
- Intermediate
- Format
- Course
- Cost
- Freemium
- Duration
- ~49 minutes, self-paced (7 video lessons, 4 code examples)
- Provider
- DeepLearning.AI
- Hands-on
- Yes — code/exercises
- Certificate
- Available
What You’ll Learn
- ✓JAX core primitives: automatic differentiation, JIT compilation, and vectorized mapping (vmap)
- ✓Define a GPT-2-style transformer architecture with Flax/NNX
- ✓Load and preprocess training data with Grain, and optimize with Optax
- ✓Run a full training loop and save/restore checkpoints with Orbax
- ✓Assemble and run inference on a ~20M-parameter model you trained yourself
Highlights
- •Taught by a Google TPU engineer using the library behind Gemini and Veo
- •End-to-end: architecture, data, training loop, checkpointing, and chat inference
- •Hands-on across the modern JAX ecosystem (Flax/NNX, Grain, Optax, Orbax)
Who It’s For
Best For
- ✓ML engineers who want to understand LLM training from first principles
- ✓PyTorch users curious about JAX and TPU-oriented training
- ✓Developers building a foundation for larger-scale model training
Prerequisites
- •Familiarity with Python
- •Basic machine learning concepts (neural networks, gradients)
FAQ
What is Build and Train an LLM with JAX?
A DeepLearning.AI short course, built with Google and taught by Chris Achard, that teaches developers to build and train a small GPT-style language model from scratch using JAX. It's for ML practitioners who want to understand how LLMs are actually defined, trained, and checkpointed at a low level.
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: Familiarity with Python, Basic machine learning concepts (neural networks, gradients).
How long does Build and Train an LLM with JAX take?
Expect roughly ~49 minutes, self-paced (7 video lessons, 4 code examples). Most learners work through it at their own pace.
What will I learn from Build and Train an LLM with JAX?
You'll learn: JAX core primitives: automatic differentiation, JIT compilation, and vectorized mapping (vmap); Define a GPT-2-style transformer architecture with Flax/NNX; Load and preprocess training data with Grain, and optimize with Optax; Run a full training loop and save/restore checkpoints with Orbax; Assemble and run inference on a ~20M-parameter model you trained yourself.