File size: 3,553 Bytes
e86daa5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
---
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={Enabling Large Language Models to Generate Text with Citations},
author={Gao, Tianyu and Wettig, Alexander and Yen, Howard and Chen, Danqi},
year={2024},
}
``` |