Llama-3-8B-ProLong-64k-Instruct

princeton_nlp/Llama-3-8B-ProLong-64k-Instruct

[Paper] [HF Collection] [Code]

ProLong (<u>Pr</u>incet<u>o</u>n <u>long</u>-context language models) is a family of long-context models that are continued trained and supervised fine-tuned from Llama-3-8B, with a maximum context window of 512K tokens. Our main ProLong model is one of the best-performing long-context models at the 10B scale (evaluated by HELMET).

To train this strong long-context model, we conduct thorough ablations on the long-context pre-training data, SFT data, and numerous other design choices. We demonstrate our findings in our paper, How to Train Long-Context Language Models (Effectively).

Authors: Tianyu Gao*, Alexander Wettig*, Howard Yen, Danqi Chen (* equal contribution)

Contact: {tianyug, awettig}@princeton.edu

The ProLong Models

• princeton_nlp/Llama-3-8B-ProLong-64k-Base
• princeton_nlp/Llama-3-8B-ProLong-64k-Instruct ← you are here!
• princeton_nlp/Llama-3-8B-ProLong-512k-Base
• ⭐ princeton_nlp/Llama-3-8B-ProLong-512k-Instruct

Model card

Here are some quick facts about our main ProLong model: princeton-nlp/Llama-3-8B-ProLong-512k-Instruct.

• Base model: meta-llama/Meta-Llama-3-8B-Instruct
• Long-context continued training: 20B tokens on 64K training data (princeton-nlp/prolong-data-64K), and 20B tokens on 512K training data (princeton-nlp/prolong-data-512K)
• Supervised fine-tuning (SFT): UltraChat
• Maximum context window: 512K tokens

<p align="center" style="margin-bottom: 0;"> <img width="80%" alt="image" src="https://github.com/user-attachments/assets/c31c9671-49fe-4776-91d2-de70ffd9f9a1"> </p> <p align="center" style="margin-top: 0; padding-top: 0;"> <em>ProLong performance on <a href="https://github.com/princeton-nlp/helmet">HELMET</a> averaged over 32K, 64K, and 128K lengths. All models are instruct models.</em> </p> <p align="center"> <img width="80%" alt="image" src="https://github.com/user-attachments/assets/a36a7d0f-4480-4a29-80f3-208477707fb7"> </p> <p align="center" style="margin-top: 0;"> <em>ProLong training recipe.</em> </p>

Citation

@article{gao2024prolong,
  title={How to Train Long-Context Language Models (Effectively)},
  author={Gao, Tianyu and Wettig, Alexander and Yen, Howard and Chen, Danqi},
  journal={arXiv preprint arXiv:2410.02660},
  year={2024}
}