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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Legible, Scalable, Reproducible Foundation Models with Named Tensors and Jax
| Date | Stars |
|---|---|
| 2026-07-31 | 708 |
| 2026-08-05 | 708 |
| 2026-08-06 | 708 |
Today
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growth rate 0.00%/day
# Levanter
> [!IMPORTANT]
> **Levanter has been merged into [Marin](https://github.com/marin-community/marin)** as of November 2025.
>
> All active development now happens in the [Marin monorepo](https://github.com/marin-community/marin) at [`lib/levanter/`](https://github.com/marin-community/marin/tree/main/lib/levanter).
>
> - **Issues**: Please open new issues at [marin-community/marin](https://github.com/marin-community/marin/issues)
> - **Pull Requests**: Submit new PRs to [marin-community/marin](https://github.com/marin-community/marin)
> - **Installation**: `pip install levanter` still works
>
> See [marin#1773](https://github.com/marin-community/marin/issues/1773) and [marin#1723](https://github.com/marin-community/marin/pull/1723) for details on the merger.
---
<a href="https://github.com/stanford-crfm/levanter/actions?query=branch%3Amain++">
<img alt="Build Status" src="https://img.shields.io/github/actions/workflow/status/stanford-crfm/levanter/run_tests.yaml?branch=main">
</a>
<a href="https://levanter.readthedocs.io/en/latest/?badge=latest">
<img alt="Documentation Status" src="https://readthedocs.org/projects/levanter/badge/?version=latest">
</a>
<a href="">
<img alt="License" src="https://img.shields.io/github/license/stanford-crfm/levanter?color=blue" />
</a>
<a href="https://https://pypi.org/project/levanter/">
<img alt="PyPI" src="https://img.shields.io/pypi/v/levanter?color=blue" />
</a>
<!--levanter-intro-start-->
> *You could not prevent a thunderstorm, but you could use the electricity; you could not direct the wind, but you could trim your sail so as to propel your vessel as you pleased, no matter which way the wind blew.* <br/>
> — Cora L. V. Hatch
Levanter is a framework for training large language models (LLMs) and other foundation models that strives for legibility, scalability, and reproducibility:
1. **Legible**: Levanter uses our named tensor library [Haliax](https://github.com/stanford-crfm/haliax) to write easy-to-follow, composable deep learning code, while still being high performance.
2. **Scalable**: Levanter scales to large models, and to be able to train on a variety of hardware, including GPUs and TPUs.
3. **Reproducible**: Levanter is bitwise deterministic, meaning that the same configuration will always produce the same results, even in the face of preemption and resumption.
We built Levanter with [JAX](https://github.com/jax-ml/jax), [Equinox](https://github.com/patrick-kidger/equinox), and [Haliax](https://github.com/stanford-crfm/haliax).
## Documentation
Levanter's documentation is available at [levanter.readthedocs.io](https://levanter.readthedocs.io/en/latest/).
Haliax's documentation is available at [haliax.readthedocs.io](https://haliax.readthedocs.io/en/latest/).
## Features
* **Distributed Training**: We support distributed training on TPUs and GPUs, including FSDP and tensor parallelism.
* **Compatibility**: Levanter supports importing and exporting models to/from the Hugging Face ecosystem, including tokenizers, datasets, and models via [SafeTensors](https://github.com/huggingface/safetensors).
* **Performance**: Levanter's performance rivals commercially-backed frameworks like MosaicML's Composer or Google's MaxText.
* **Resilience**: Levanter supports fast, distributed checkpointing and fast resume from checkpoints with no data seek, making Levanter robust to preemption and hardware failure.
* **Cached On-Demand Data Preprocessing**: We preprocess corpora online, but we cache the results of preprocessing so
that resumes are much faster and so that subsequent runs are even faster. As soon as the first part of the cache is complete, Levanter will start training.
* **Logging**: Levanter logs a rich and detailed set of metrics covering loss and performance. Levanter also supports a few different logging backends, including [WandB](https://wandb.ai/site) and [TensorBoard](https://www.tensorflow.org/tensorboard). (Adding a new logging backend is easy!) LeExcerpt of 11,860 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f15d645cf6cedd28, desc:reproducible