Home Projects minimind
minimind
Python

minimind

🧠 Train a 64M-parameter LLM from scratch in just 2h!

by jingyaogong · GitHub
Top 5% most starred in the catalogue
Stars
Forks
License
Created
Last commit
Language
artificial-intelligencelarge-language-modelApache-2.0Python
View on GitHub
In plain words

Train a small language model from scratch in just two hours, even on a personal computer.

From the README

Excerpts from the project README on GitHub. Copyright and licensing remain with the respective authors.

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 →
minimind — GitHub preview card
📈 Star history
55k53k
2026-07-122026-08-31
📈 Track minimind

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

🧠 Train a 64M-parameter LLM from scratch in just 2h!

minimind has 55.4k stars on GitHub. It has been forked 7.2k times. minimind is written mainly in Python. It has been in active development since 2024. minimind is available under the Apache-2.0 license. Its main topics are artificial-intelligence, large-language-model.

Frequently asked questions

What is minimind?

🧠 Train a 64M-parameter LLM from scratch in just 2h!

Is minimind open source?

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

Is minimind free?

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

What license does minimind use?

minimind is available under the Apache-2.0 license.

What language is minimind written in?

minimind is written mainly in Python.

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

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

[![olud.ai](https://olud.ai/badge.php?tool=jingyaogong-minimind)](https://olud.ai/project/jingyaogong-minimind.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.