README.md

---
license: mit
language:
- en
metrics:
- perplexity
---
# CLEX: Continuous Length Extrapolation for Large Language Models
This repo stores the checkpoint of CLEX-7B-Chat-16K

## Features and Highlights of CLEX
![CLEX_diagram](https://github.com/DAMO-NLP-SG/CLEX/assets/18526640/063ffe34-0116-4759-92bf-e22fc7264cdf)

- **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.

More details about long-text modeling with our CLEX can be found at the git [repo](https://github.com/DAMO-NLP-SG/CLEX).

## Model Zoo
| Model Name | Model Type | Starting Point | Train Data |Train Length | MAX Test Length |
|:-----|:-----|:-----------|:-----------|:-----------|:-----------|
| CLEX-7B-4K | base | LLaMA-2-7B | [Redpajama-Book](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) | 4K | 16K |
| CLEX-7B-Chat-4K | chat | CLEX-7B-4K | [UltraChat](https://github.com/thunlp/UltraChat) | 4K | 16K |
| CLEX-7B-16K | base | LLaMA-2-7B | [Redpajama-Book](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) | 16K | 64K |
| **CLEX-7B-Chat-16K** (this checkpoint) | chat | CLEX-7B-16K | [UltraChat](https://github.com/thunlp/UltraChat) | 16K | 64K |


## How to Use
```bash
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-SG/CLEX-7B-Chat-16K", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-SG/CLEX-7B-Chat-16K", 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]))
```

## 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}
}