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--- |
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license: mit |
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datasets: |
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- DAMO-NLP-SG/LongCorpus-2.5B |
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--- |
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# CLEX: Continuous Length Extrapolation for Large Language Models |
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This repo stores the checkpoint of CLEX-Mixtral-8x7B-Chat-32K. |
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## Features and Highlights of CLEX |
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- **Simple and Clear**: _MINIMAL_ code and architecture changes. Only one up-and-down projection layer introduced, _NO_ recurrent memory caching or sparse attention required. |
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- **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)). |
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- **Continuous Length Extrapolation**: Explicitly modeling the continuous dynamics of context window size during length extrapolation. |
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If you have any questions, feel free to contact us. (Emails: guanzzh.chen@gmail.com, lixin4ever@gmail.com) |
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## Model Zoo |
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<div align="center"> |
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| Model Name | Model Type | Starting Point | Train Data |Train Length | MAX Test Length | HF Repo | |
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|:-----|:-----|:-----------|:-----------|:-----------|:-----------|:------:| |
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| 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) | |
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| 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) | |
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| CLEX-LLaMA-2-7B-64K | 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) | |
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| 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) | |
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| 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) | |
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| **CLEX-Mixtral-8x7B-Chat-32k** (this checkpoint) | 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) | |
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</div> |
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## Usage |
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```bash |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-SG/CLEX-Mixtral-8x7B-Chat-32K", torch_dtype=torch.bfloat16) |
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inputs = tokenizer("What is CLEX?", return_tensors="pt") |
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sample = model.generate(**inputs, max_length=128) |
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print(tokenizer.decode(sample[0])) |
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``` |
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## Evaluation |
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## InfiniteBench |
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We also evaluate CLEX-Mixtral-8x7B-Chat-32k on [InfiniteBench](https://github.com/OpenBMB/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. |
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| Task Name | GPT-4 | YaRN-Mistral-7B | Kimi-Chat | Claude 2 | CLEX-Mixtral-8x7B-Chat-32k | Mixtral-8x7B-Instruct-v0.1 | |
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| ------------------- | ------ | --------------- | --------- | -------- | -------------------------- | -------------------------- | |
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| Retrieve.PassKey | 100% | 92.71% | 98.14% | 97.80% | 99.72% | 96.78% | |
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| **Retrieve.Number** | 100% | 56.61% | 95.42% | 98.14% | 76.10% | 76.61% | |
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| **Retrieve.KV** | 89.00% | < 5% | 53.60% | 65.40% | <5% | <5% | |
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| En.Sum | 14.73% | 9.09% | 17.93% | 14.45% | 15.48% | 14.3% | |
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| En.QA | 22.22% | 9.55% | 16.52% | 11.97% | 15.52% | 16.81% | |
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| En.MC | 67.25% | 27.95% | 72.49% | 62.88% | 58.96% | 56.77% | |
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| En.Dia | 8.50% | 7.50% | 11.50% | 46.50% | 9% | <5% | |
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| Code.Debug | 39.59% | < 5% | 18.02% | < 5% | 21.32% | <5% | |
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| Code.Run | 23.25% | < 5% | < 5% | < 5% | < 5% | <5% | |
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| Math.Calc | < 5% | < 5% | < 5% | < 5% | < 5% | <5% | |
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| Math.Find | 60.00% | 17.14% | 12.57% | 32.29% | 28% | 26.57% | |
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## Citation |
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If you find our project useful, hope you can star our repo and cite our paper as follows: |
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``` |
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@article{damonlpsg2023clex, |
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author = {Chen, Guanzheng and Li, Xin and Meng, Zaiqiao and Liang, Shangsong and Bing, Lidong}, |
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title = {CLEX: Continuous Length Extrapolation for Large Language Models}, |
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year = 2023, |
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journal = {arXiv preprint arXiv:2310.16450}, |
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url = {https://arxiv.org/abs/2310.16450} |
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} |
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``` |
