Update README.md

README.md

@@ -1,3 +1,75 @@
 ---
 license: mit
 ---
+
+# CLEX: Continuous Length Extrapolation for Large Language Models
+This repo stores the checkpoint of CLEX-LLaMA-2-7B-64K.
+
+
+## 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.
+
+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 | base | LLaMA-2-7B | [Redpajama-Book](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) | 64k | 256K | Pending Upload |
+| CLEX-Phi-2-7B-32K | base | Phi-2-2.7B | [LongCorpus-2.5B](https://huggingface.co/datasets/DAMO-NLP-SG/LongCorpus-2.5B) | 32k | 128K | Pending Upload |
+| CLEX-Mixtral-8x7B-32K | base | Mixtral-8x7B-v0.1 | [LongCorpus-2.5B](https://huggingface.co/datasets/DAMO-NLP-SG/LongCorpus-2.5B) | 32k | >128K | Pending Upload |
+| CLEX-Mixtral-8x7B-Chat-32k | chat | CLEX-Mixtral-8x7B-32K | [Ultrachat 200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) | 32k | >128K | Pending Upload |
+</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)
+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}
+}
+```
+