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[Readme] Fix title #58

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6 changes: 3 additions & 3 deletions README.md
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# 🚧 pipegoose: Large-scale 4D parallelism multi-modal pre-training for 🤗 `transformers` in Mixture of Experts
# 🚧 pipegoose: Decentralized large-scale 4D parallelism multi-modal pre-training for 🤗 `transformers` in Mixture of Experts

[<img src="https://img.shields.io/badge/license-MIT-blue">](https://github.com/xrsrke/pipegoose) [![tests](https://github.com/xrsrke/pipegoose/actions/workflows/tests.yaml/badge.svg)](https://github.com/xrsrke/pipegoose/actions/workflows/tests.yaml) [<img src="https://img.shields.io/discord/767863440248143916?label=discord">](https://discord.gg/s9ZS9VXZ3p) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [<img alt="Codecov" src="https://img.shields.io/codecov/c/github/xrsrke/pipegoose">](https://app.codecov.io/gh/xrsrke/pipegoose) [![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)

![pipeline](3d-parallelism.png)

<!-- [![docs](https://img.shields.io/github/deployments/Production?label=docs&logo=vercel)](https://docs.dev/) -->

We're building a library for an end-to-end framework for training multi-modal MoE in a decentralized way, as proposed by the paper [DiLoCo](https://arxiv.org/abs/2311.08105). The core papers that we are replicating are:
We're building a library for an end-to-end framework for **training multi-modal MoE in a decentralized way, as proposed by the paper [DiLoCo](https://arxiv.org/abs/2311.08105)**. The core papers that we are replicating are:
- DiLoCo: Distributed Low-Communication Training of Language Models [[link]](https://arxiv.org/abs/2311.08105)
- Pipeline MoE: A Flexible MoE Implementation with Pipeline Parallelism [[link]](https://arxiv.org/abs/2304.11414)
- Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity [[link]](https://arxiv.org/abs/2101.03961)
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**Appreciation**

- Big thanks to 🤗 [Hugging Face](https://huggingface.co/) for sponsoring this project with GPUs for testing! And [Zach Schrier](https://twitter.com/zach_schrier) for monthly twitch donations
- Big thanks to 🤗 [Hugging Face](https://huggingface.co/) for sponsoring this project with GPUs for testing!

- The library's APIs are inspired by [OSLO](https://github.com/EleutherAI/oslo)'s and [ColossalAI](https://github.com/hpcaitech/ColossalAI)'s APIs.

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