This repo contains the code and data for the YaRN context window extension method.
Paper (ICLR 2024): YaRN: Efficient Context Window Extension of Large Language Models
Old Preprint (arXiv)
We publish variants of Llama 2 fine-tuned with YaRN at 32K, 64K and 128K context window length. They are available under the Llama 2 license on ๐ค Hugging Face.
| Size | Context | Link |
|---|---|---|
| 7B | 64K | NousResearch/Yarn-Llama-2-7b-64k |
| 7B | 128K | NousResearch/Yarn-Llama-2-7b-128k |
| 13B | 64K | NousResearch/Yarn-Llama-2-13b-64k |
| 13B | 128K | NousResearch/Yarn-Llama-2-13b-128k |
| 70B | 32K | NousResearch/Yarn-Llama-2-70b-32k |
In addition, we also publish 8K context window versions of Llama 2 7B fine-tuned with NTK-aware and YaRN (Table 1 in the conference paper).
With the release of v2 of our paper we are also publishing 64K and 128K variants of Mistral 7B v0.1.
| Size | Context | Link |
|---|---|---|
| 7B | 64K | NousResearch/Yarn-Mistral-7b-64k |
| 7B | 128K | NousResearch/Yarn-Mistral-7b-128k |
The SOLAR 10.7B v1.0 model utilizes depth-up scaling to add layers to Mistral 7B v0.1, which may potentially improve long context performance on a per-parameter basis. We publish 32K and 64K variants.
| Size | Context | Link |
|---|---|---|
| 10.7B | 32K | NousResearch/Yarn-Solar-10b-32k |
| 10.7B | 64K | NousResearch/Yarn-Solar-10b-64k |
We strongly believe in open science, and thus publish all code and data to reproduce the results in our paper. To reproduce, clone the repository and perform a local installation.
git clone https://github.com/jquesnelle/yarn
cd yarn
pip install -e .To train the models, run accelerate config and enable DeepSpeed acceleration. deepspeed/zero3.json was the configuration file used for training.
# ./train.shThe tokenized training data is available on ๐คHugging Face and was derived from the pg19 dataset. For the Mistral models, a mix of the pretrain and fine-tune splits of Long-Data-Collections was used and the tokenized dataset is also available on ๐คHugging Face.
To reproduce the evaluations, install lm-evaluation-harness with pip install git+https://github.com/EleutherAI/lm-evaluation-harness and then run the two provided scripts.
# ./eval.sh
# ./eval-harness.sh@inproceedings{
peng2024yarn,
title={Ya{RN}: Efficient Context Window Extension of Large Language Models},
author={Bowen Peng and Jeffrey Quesnelle and Honglu Fan and Enrico Shippole},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=wHBfxhZu1u}
}
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