Home› Projects› MARS
MARS
Python

MARS

The official implementation of MARS: Unleashing the Power of Variance Reduction for Training Large Models

by AGI-Arena · GitHub
Stars
Forks
License
Created
Last commit
Category
Language
fine-tuninglarge-language-modelsoptimization-algorithmsApache-2.0Python
View on GitHub
In plain words

Train large AI models more efficiently using advanced techniques for better performance.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
MARS — GitHub preview card
📈 Star history
724722
2026-07-202026-08-31
📈 Track MARS

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

The official implementation of MARS: Unleashing the Power of Variance Reduction for Training Large Models

MARS has 722 stars on GitHub. It has been forked 49 times. MARS is written mainly in Python. It has been in active development since 2024. MARS is available under the Apache-2.0 license. Its main topics are fine-tuning, large-language-models, optimization-algorithms, optimizer.

❓ Frequently asked questions

What is MARS?

The official implementation of MARS: Unleashing the Power of Variance Reduction for Training Large Models

Is MARS open source?

MARS is an open-source project. It is released under the Apache-2.0 license.

Is MARS free?

Yes. MARS is free and open source — you can use, modify and self-host it.

What license does MARS use?

MARS is available under the Apache-2.0 license.

What language is MARS written in?

MARS is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — MARS

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=agi-arena-mars)](https://olud.ai/project/agi-arena-mars.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.