Jax and Flax LLMs – Transformer Implementations Optimized for TPUs

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I've open-sourced awesome-jax-flax-llms, a curated collection of large language model (LLM) implementations built from scratch using JAX and Flax. The repo is designed for high-performance training on TPUs/GPUs, making it ideal for researchers, ML engineers, and curious tinkerers looking to explore or extend modern transformer models. Key Features: Modular, readable, and extensible codebase Implementations of GPT-2 and LLaMA 3 in pure JAX/Flax Accelerated training with XLA + Optax Google Colab support (TPU-ready) Hugging Face dataset integration Upcoming support for fine-tuning, Mistral, and DeepSeek-R This is primarily an educational resource, but it's written with performance in mind and can be adapted for more serious use. Contributions are welcome — whether you’re improving performance, adding new models, or experimenting with different attention mechanisms.

Visit product View on Hacker NewsMar 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, new · Missing: mac, agents, macos

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google, education · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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