---
license: llama3
datasets:
- princeton-nlp/prolong-data-64K
- princeton-nlp/prolong-data-512K
base_model:
- princeton-nlp/Llama-3-8B-ProLong-64k-Base
---

# princeton_nlp/Llama-3-8B-ProLong-512k-Base

[[Paper](https://arxiv.org/pdf/2410.02660)] [[HF Collection](https://huggingface.co/collections/princeton-nlp/prolong-66c72d55d2051a86ac7bd7e4)] [[Code](https://github.com/princeton-nlp/ProLong)]


**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](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-512k-Instruct) is one of the best-performing long-context models at the 10B scale (evaluated by [HELMET](https://github.com/princeton-nlp/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)](https://arxiv.org/pdf/2410.02660).


Authors: [Tianyu Gao](https://gaotianyu.xyz/about)\*, [Alexander Wettig](https://www.cs.princeton.edu/~awettig/)\*, [Howard Yen](https://howard-yen.github.io/), [Danqi Chen](https://www.cs.princeton.edu/~danqic/) (* equal contribution)

Contact: `{tianyug, awettig}@princeton.edu`

## The ProLong Models

- [princeton_nlp/Llama-3-8B-ProLong-64k-Base](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-64k-Base)
- [princeton_nlp/Llama-3-8B-ProLong-64k-Instruct](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-64k-Instruct)
- [princeton_nlp/Llama-3-8B-ProLong-512k-Base](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-512k-Base) ← you are here!
- ⭐ [princeton_nlp/Llama-3-8B-ProLong-512k-Instruct](https://huggingface.co/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](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-512k-Instruct).
* Base model: [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
* Long-context continued training: 20B tokens on 64K training data ([princeton-nlp/prolong-data-64K](https://huggingface.co/datasets/princeton-nlp/prolong-data-64K)), and 20B tokens on 512K training data ([princeton-nlp/prolong-data-512K](https://huggingface.co/datasets/princeton-nlp/prolong-data-512K))
* Supervised fine-tuning (SFT): [UltraChat](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
* 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

```bibtex
@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}
}
```