# Ulysses Sequence Parallelism: Training with Million-Token Contexts

DevFeed: [Ulysses Sequence Parallelism: Training with Million-Token Contexts](<https://devfeed.tech/articles/ulysses-sequence-parallelism-training-with-million-token-contexts-7544.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/ulysses-sp>)

Author: Kashif Rasul; Stas Bekman

Published: 2026-03-09T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [trl](<https://devfeed.tech/topics/trl.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [ai](<https://devfeed.tech/tags/ai.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [distributed-training](<https://devfeed.tech/tags/distributed-training.md>), [ecosystem](<https://devfeed.tech/tags/ecosystem.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [guide](<https://devfeed.tech/tags/guide.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [transformers](<https://devfeed.tech/tags/transformers.md>), [trl](<https://devfeed.tech/tags/trl.md>)

## AI overview

This article explains Ulysses Sequence Parallelism, a method for training transformer models with very long or million-token contexts by sharding sequences and partitioning attention heads across multiple GPUs. It describes the all-to-all communication steps and integration across the Hugging Face ecosystem, including Accelerate, Transformers Trainer, and TRL's SFTTrainer.

## Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.