Train a Custom AI Coding Partner with JAX and TPUs

What you’ll get

You will learn how to train your own AI coding assistant from scratch, inspired by Anthropic’s Claude models. This workflow covers the end-to-end process including pretraining, supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF) using Direct Preference Optimization (DPO). This approach is useful for creating specialized coding partners when you need fine-grained control over the model’s behavior and capabilities.

Tools you need

• Claude Code (paid): A powerful AI model for code generation and understanding, used here as a reference and potentially for generating synthetic data.
• JAX (free): A Python library for high-performance numerical computation, particularly suited for machine learning research and development on accelerators like TPUs.
• TPUs (paid): Tensor Processing Units are specialized hardware developed by Google for accelerating machine learning workloads. Free access may be available through programs like Google TRC.
• nanochat (free): An open-source project providing a reference implementation for training chat models, adapted here for coding tasks.
• The Stack-V2 (free): A large dataset of permissively licensed code, useful for pre-training coding models.
• Fineweb-edu (free): A dataset containing educational web content, also used for pre-training.

Steps

1. Set up your environment variables: Before running any commands, you need to set up directories and model tags. The author uses a hidden directory in the home folder for caching.

   export NANOCODE_BASE_DIR="$HOME/.cache/nanocode"
   export MODEL_TAG=d24

   You should see these variables set in your current terminal session. If you are on Windows, you can use PowerShell: $env:NANOCODE_BASE_DIR = "$HOME\.cache\nanocode" and $env:MODEL_TAG = "d24".
2. Download and prepare pre-training data: This step involves downloading and sharding datasets. The author uses fineweb-edu and the-stack-v2-dedup. The command below starts the process for fineweb-edu.

   python -m data.pretrain -d fineweb-edu -n 300

   This command will run a Python script to process the fineweb-edu dataset, creating 300 shards. You should observe output indicating data download and processing progress.
3. Download and prepare The Stack-V2 data: Similar to the previous step, this command prepares the the-stack-v2-dedup dataset.

   python -m data.pretrain -d the-stack-v2-dedup -n 60

   This will process the-stack-v2-dedup, creating 60 shards. The output will show the progress of this data preparation.

Original source

This workflow is based on a discussion post by ‘desideratum’ on Hacker News, introducing ‘nanocode’. The author shares their library for training a Claude Code-like model end-to-end using JAX and TPUs, adapting techniques from Karpathy’s nanochat project.

Notes & variations

• Free Tier Tip: Google’s TRC (Test for Research Cloud) program or new Google Cloud accounts can provide free TPU access and credits, making the expensive hardware training more accessible.
• Common Pitfall: Ensure your JAX installation is compatible with your hardware (TPUs or GPUs). Incorrect configurations can lead to errors during training.
• Better Results: Incorporating additional coding data, such as from The Stack-V2 at a specific ratio (1:5 mentioned by the author), during pre-training can significantly improve the model’s coding capabilities and tokenization efficiency.