CLEX: Continuous Length Extrapolation for Large Language Models
This repo stores the checkpoint of CLEX-Mixtral-8x7B-Chat-32K.
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 | 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 (this checkpoint) | 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-Mixtral-8x7B-Chat-32K", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K", 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
InfiniteBench
We also evaluate CLEX-Mixtral-8x7B-Chat-32k on InfiniteBench, which is a 128k-length benchmark covering various tasks. We compare our CLEX-Mixtral-8x7B-Chat-32k with GPT-4, Claude, KimiChat, and vanilla Mixtral-8x7B.
| Task Name | GPT-4 | YaRN-Mistral-7B | Kimi-Chat | Claude 2 | CLEX-Mixtral-8x7B-Chat-32k | Mixtral-8x7B-Instruct-v0.1 |
|---|---|---|---|---|---|---|
| Retrieve.PassKey | 100% | 92.71% | 98.14% | 97.80% | 99.72% | 96.78% |
| Retrieve.Number | 100% | 56.61% | 95.42% | 98.14% | 76.10% | 76.61% |
| Retrieve.KV | 89.00% | < 5% | 53.60% | 65.40% | <5% | <5% |
| En.Sum | 14.73% | 9.09% | 17.93% | 14.45% | 15.48% | 14.3% |
| En.QA | 22.22% | 9.55% | 16.52% | 11.97% | 15.52% | 16.81% |
| En.MC | 67.25% | 27.95% | 72.49% | 62.88% | 58.96% | 56.77% |
| En.Dia | 8.50% | 7.50% | 11.50% | 46.50% | 9% | <5% |
| Code.Debug | 39.59% | < 5% | 18.02% | < 5% | 21.32% | <5% |
| Code.Run | 23.25% | < 5% | < 5% | < 5% | < 5% | <5% |
| Math.Calc | < 5% | < 5% | < 5% | < 5% | < 5% | <5% |
| Math.Find | 60.00% | 17.14% | 12.57% | 32.29% | 28% | 26.57% |
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}
}
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