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---
license: mit
language:
- en
---
# CLEX: Continuous Length Extrapolation for Large Language Models
This repo stores the checkpoint of CLEX-LLaMA-2-7B-64K.
## Features and Highlights of CLEX
- **Simple and Clear**: _MINIMAL_ code and architecture changes. Only one up-and-down projection layer introduced, _NO_ recurrent memory caching or sparse attention required.
- **Train Short, Test Long**: _NO_ performance drop on the sequences _4x~8x longer_ than the training ones (see [here](https://github.com/DAMO-NLP-SG/CLEX#language-modelling)).
- **Continuous Length Extrapolation**: Explicitly modeling the continuous dynamics of context window size during length extrapolation.
If you have any questions, feel free to contact us. (Emails: guanzzh.chen@gmail.com, lixin4ever@gmail.com)
## Model Zoo
<div align="center">
| Model Name | Model Type | Starting Point | Train Data |Train Length | MAX Test Length | HF Repo |
|:-----|:-----|:-----------|:-----------|:-----------|:-----------|:------:|
| CLEX-LLaMA-2-7B-16K | base | LLaMA-2-7B | [Redpajama-Book](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) | 16K | 64K | [link](https://huggingface.co/DAMO-NLP-SG/CLEX-7B-16K) |
| CLEX-LLaMA-2-7B-Chat-16K | chat | CLEX-7B-16K | [UltraChat](https://github.com/thunlp/UltraChat) | 16K | 64K | [link](https://huggingface.co/DAMO-NLP-SG/CLEX-7B-Chat-16K) |
| **CLEX-LLaMA-2-7B-64K** (this checkpoint) | base | LLaMA-2-7B | [Redpajama-Book](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) | 64k | 256K | [link](https://huggingface.co/DAMO-NLP-SG/CLEX-LLaMA-2-7B-64K) |
| CLEX-Phi-2-32K | base | Phi-2-2.7B | [LongCorpus-2.5B](https://huggingface.co/datasets/DAMO-NLP-SG/LongCorpus-2.5B) | 32k | 128K | [link](https://huggingface.co/DAMO-NLP-SG/CLEX-Phi-2-32K) |
| CLEX-Mixtral-8x7B-32K | base | Mixtral-8x7B-v0.1 | [LongCorpus-2.5B](https://huggingface.co/datasets/DAMO-NLP-SG/LongCorpus-2.5B) | 32k | >128K | [link](https://huggingface.co/DAMO-NLP-SG/CLEX-Mixtral-8x7B-32K) |
| CLEX-Mixtral-8x7B-Chat-32k | chat | CLEX-Mixtral-8x7B-32K | [Ultrachat 200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) | 32k | >128K | [link](https://huggingface.co/DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K) |
</div>
## Usage
```bash
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-SG/CLEX-LLaMA-2-7B-64K", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-SG/CLEX-LLaMA-2-7B-64K", torch_dtype=torch.bfloat16, trust_remote_code=True)
inputs = tokenizer("What is CLEX?", return_tensors="pt")
sample = model.generate(**inputs, max_length=128)
print(tokenizer.decode(sample[0]))
```
## Evaluation
### Language Modelling
Here are the evaluation PPLs of the base models trained with CLEX. We apply training and evaluation on a subset of 2B tokens from the [RedPajama-Book](https://github.com/togethercomputer/RedPajama-Data) corpus, where the training and test sets are split by 99:1.
| | Train Length | Eval.(32k) | Eval.(64k) | Eval.(128k) | Eval.(256k) |
| --------------- | ------------ | ---------- | ---------- | ----------- | ----------- |
| CLEX-LLaMA-2-7B | 64k | 5.99 | 5.89 | 6.04 | 5.98 |
## Citation
If you find our project useful, hope you can star our repo and cite our paper as follows:
```
@article{damonlpsg2023clex,
author = {Chen, Guanzheng and Li, Xin and Meng, Zaiqiao and Liang, Shangsong and Bing, Lidong},
title = {CLEX: Continuous Length Extrapolation for Large Language Models},
year = 2023,
journal = {arXiv preprint arXiv:2310.16450},
url = {https://arxiv.org/abs/2310.16450}
}
``` |