https://github.com/alinababer/implementation-of-the-longnet-scaling-transformers-to-1-000-000-000-tokens
implementation of the paper LongNet: Scaling Transformers to 1,000,000,000 Tokens by Jiayu Ding, Shuming Ma, Li Dong, Xingxing Zhang, Shaohan Huang, Wenhui Wang, and Furu Wei.
https://github.com/alinababer/implementation-of-the-longnet-scaling-transformers-to-1-000-000-000-tokens
ai deepspeed generative-ai llms longnet ml transformers
Last synced: 6 months ago
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implementation of the paper LongNet: Scaling Transformers to 1,000,000,000 Tokens by Jiayu Ding, Shuming Ma, Li Dong, Xingxing Zhang, Shaohan Huang, Wenhui Wang, and Furu Wei.
- Host: GitHub
- URL: https://github.com/alinababer/implementation-of-the-longnet-scaling-transformers-to-1-000-000-000-tokens
- Owner: AlinaBaber
- Created: 2024-08-12T15:14:49.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-12T15:17:23.000Z (about 1 year ago)
- Last Synced: 2025-02-09T00:47:15.570Z (8 months ago)
- Topics: ai, deepspeed, generative-ai, llms, longnet, ml, transformers
- Language: Jupyter Notebook
- Homepage:
- Size: 17.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Implementation of the paper LongNet: Scaling Transformers to 1,000,000,000 Tokens
## Overview
This repository provides an open-source implementation of the paper *LongNet: Scaling Transformers to 1,000,000,000 Tokens* by Jiayu Ding, Shuming Ma, Li Dong, Xingxing Zhang, Shaohan Huang, Wenhui Wang, and Furu Wei. LongNet introduces a Transformer variant designed to scale sequence length to over 1 billion tokens while maintaining performance on shorter sequences. This innovation addresses the limitations of traditional Transformers when dealing with extremely long sequences, making it suitable for a wide range of applications in natural language processing and beyond.
## Key Features
- **Scalability**: LongNet is engineered to handle sequences with over 1 billion tokens, overcoming the typical constraints of standard Transformers.
- **Performance**: Despite the capability to process extremely long sequences, LongNet maintains high performance on shorter sequences, ensuring its versatility across different tasks.
- **Transformer Variant**: The model builds upon the Transformer architecture, introducing novel mechanisms that allow it to efficiently scale to longer sequences without the quadratic increase in computational cost typically associated with Transformers.
## Applications
- **Natural Language Processing**: LongNet can be applied to tasks that require processing large documents, entire books, or extensive datasets in a single pass.
- **Genomics and Bioinformatics**: Suitable for analyzing long genomic sequences or other biological data that involve extremely lengthy sequences.
- **Scientific Research**: Ideal for projects that involve modeling or analyzing large-scale sequences in fields such as astrophysics, climate science, and more.
## Conclusion
LongNet represents a significant advancement in the field of sequence modeling, pushing the boundaries of what is possible with Transformer models. By scaling sequence length to over 1 billion tokens without sacrificing performance, LongNet opens up new possibilities for handling extensive data in various scientific and industrial applications.
## Reference
For more detailed information, please refer to the original paper:
Ding, Jiayu, Ma, Shuming, Dong, Li, Zhang, Xingxing, Huang, Shaohan, Wang, Wenhui, Wei, Furu. *LongNet: Scaling Transformers to 1,000,000,000 Tokens*. 2023. [Link to the paper](https://arxiv.org/abs/2307.02486)