metadata
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).
- 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
| 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 | 16K | 64K | link |
| CLEX-LLaMA-2-7B-Chat-16K | chat | CLEX-7B-16K | UltraChat | 16K | 64K | link |
| CLEX-LLaMA-2-7B-64K (this checkpoint) | base | LLaMA-2-7B | Redpajama-Book | 64k | 256K | link |
| CLEX-Phi-2-32K | base | Phi-2-2.7B | LongCorpus-2.5B | 32k | 128K | link |
| CLEX-Mixtral-8x7B-32K | base | Mixtral-8x7B-v0.1 | LongCorpus-2.5B | 32k | >128K | link |
| CLEX-Mixtral-8x7B-Chat-32k | chat | CLEX-Mixtral-8x7B-32K | Ultrachat 200k | 32k | >128K | link |
Usage
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 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}
}