Home/Inference & Serving/long-context-attention

long-context-attention

feifeibear/long-context-attention

Unified Sequence Parallel Attention for Long Context Transformers

GitHub

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

37views this month

693 stars85 forksLast push Sep 16, 2026 Python Apache-2.0

Decision brief

long-context-attention uses Unified Sequence Parallel Attention techniques to improve performance of long context transformers for both training and inference.

Good fit when

When developing models that require handling longer input sequences where traditional attention mechanisms face scalability issues.
During scenarios where enhancing the processing efficiency during both training phases and at inference time is critical.

Avoid when

If your use case involves short context lengths where standard attention mechanisms suffice and adding long-context-attention doesn't provide significant benefits.
When working in environments that do not support Python, as this tool is specifically developed for the Python ecosystem.

Observed Jul 17, 2026 · Source: enrich:decision_facts

long-context-attention alternatives Trust report

Verify the decision

Maintenance and security

Full trust report

Maintenance: Very active (3d since push); As of Sep 20, 2026
Provenance: Not a fork · Personal account; As of Sep 20, 2026
Security (OSV): No lockfile; As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install long-context-attention

PyPI

Similar tools

Same-category neighbours. No typed graph edges are catalogued for this tool yet.

peft

State-of-the-art Parameter-Efficient Fine-Tuning

22k★

bitsandbytes

Large language model quantization toolkit for PyTorch.

8.5k★

Chinese-LLaMA-Alpaca-2

Chinese LLaMA-2 & Alpaca-2 LLMs with 64K long context models

7.1k★

Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Provides support for long context transformers using Hybrid and 2D sequence parallel attention techniques designed to enhance both training and inference processes.

Capability facts

Languages: python
Source: github.language+pyproject.toml · Sep 20, 2026

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Sep 20, 2026)

Option 1: pip install

Source link

README

1. Installation FlashAttention is the most important external dependency and is often the cause of errors when installing and using yunchang. Yunchang supports flash attn 2.6.x and 2.7.x, both v3 and v2 versions. Additionally, yunchang supports runs without flash attn, which is s...

For agents

This page has a .md twin and JSON over the API.

/tools/feifeibear-long-context-attention.md GET /api/graphcanon/tools/feifeibear-long-context-attention GET /api/graphcanon/graph?tool=feifeibear-long-context-attention