AI-Hypercomputer/maxtext
A simple, performant, and scalable Jax LLM!
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
5 releases in the last 90 days, the latest 16 days ago, with 264 contributors on GitHub.
Gained 30% fewer stars in the last 90 days than in the 90 days before.
Use Cases & Benefits
- Provides a high performance, scalable JAX-based library for training and fine-tuning large language models on TPUs and GPUs.
- Delivers optimized model FLOPs utilization and token throughput with minimal manual optimization by leveraging JAX and XLA compiler technologies.
- Use for pre-training custom large language models from scratch with flexible model configurations and efficient sharding on cloud hardware.
- Use for scalable post-training workflows including supervised fine-tuning and reinforcement learning with support for multiple advanced LLM architectures.
- Use for experimenting with multi-modal models and state-of-the-art architectures like Gemma, Llama, Qwen, and DeepSeek in research or production.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
Watch-outs
No watch-outs from GStars' checks.
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From the README
MaxText is a high performance, highly scalable, open-source LLM library and reference implementation written in pure Python/JAX and targeting Google Cloud TPUs and GPUs for training.
MaxText provides a library of high performance models to choose from, including Gemma, Llama, DeepSeek, Qwen, and Mistral. For each of these models, MaxText supports pre-training (up to tens of thousands of chips) and scalable post-training, with popular techniques like Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO, a type of Reinforcement Learning).
MaxText achieves high Model FLOPs Utilization (MFU) and tokens/second from single host to very large clusters while staying simple and largely "optimization-free" thanks to the power of JAX and the XLA compiler.
MaxText is the launching point for ambitious LLM projects both in research and production. We encourage you to start by experimenting with MaxText out of the box and then fork and modify MaxText to meet your needs.
Check out our Read The Docs site or directly Get Started with your first MaxText run. If you’re interested in Diffusion models (Wan 2.1, Flux, etc), see the MaxDiffusion repository in our AI Hypercomputer GitHub organization.
See our installation guide to install MaxText with pip.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
636+58
Open issues
495+200
Compared with peers in AI
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
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| thedotmack/claude-mem | 113.6K | +8.8K | 35 | today |
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| calesthio/OpenMontage | 77.2K | +4.2K | 40 | — |
| heygen-com/hyperframes | 79.6K | +4.1K | 45 | today |
| AI-Hypercomputer/maxtext | 2.5K | — | 59 | 16 days ago |
| Category median | 98.1K | +5.2K | 45 | — |
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