|
|
--- |
|
|
license: mit |
|
|
--- |
|
|
|
|
|
Please refer to the [SepLLM paper - ICML 2025](https://arxiv.org/abs/2412.12094) and our [`GitHub repository`](https://github.com/HKUDS/SepLLM) for using this model. |
|
|
|
|
|
To use the checkpoint of this model, you must install the `transformers-4.38.0.post1+sepllm-py3-none-any.whl` released from our [`GitHub repository`](https://github.com/HKUDS/SepLLM). Below are the reference script for testing and a sample of test results. We conducted testing using `lm_eval==0.4.0`. |
|
|
|
|
|
``` |
|
|
CUDA_LAUNCH_BLOCKING=1 |
|
|
lm_eval --model hf \ |
|
|
--model_args pretrained=Gausson/pythia-160m-deduped-n64-SepLLM \ |
|
|
--tasks arc_challenge,arc_easy,lambada_openai,logiqa,piqa,sciq,winogrande,wsc,wikitext \ |
|
|
--num_fewshot 5 \ |
|
|
--device cuda:0\ |
|
|
--batch_size 32 |
|
|
``` |
|
|
|
|
|
``` |
|
|
hf (pretrained=Gausson/pythia-160m-deduped-n64-SepLLM), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: 32 |
|
|
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr| |
|
|
|--------------|------:|------|-----:|---------------|---|------:|---|------| |
|
|
|arc_challenge | 1|none | 5|acc |↑ | 0.1962|± |0.0116| |
|
|
| | |none | 5|acc_norm |↑ | 0.2406|± |0.0125| |
|
|
|arc_easy | 1|none | 5|acc |↑ | 0.4655|± |0.0102| |
|
|
| | |none | 5|acc_norm |↑ | 0.4377|± |0.0102| |
|
|
|lambada_openai| 1|none | 5|acc |↑ | 0.2909|± |0.0063| |
|
|
| | |none | 5|perplexity |↓ |40.0674|± |1.3492| |
|
|
|logiqa | 1|none | 5|acc |↑ | 0.2642|± |0.0173| |
|
|
| | |none | 5|acc_norm |↑ | 0.2750|± |0.0175| |
|
|
|piqa | 1|none | 5|acc |↑ | 0.6360|± |0.0112| |
|
|
| | |none | 5|acc_norm |↑ | 0.6349|± |0.0112| |
|
|
|sciq | 1|none | 5|acc |↑ | 0.8000|± |0.0127| |
|
|
| | |none | 5|acc_norm |↑ | 0.7830|± |0.0130| |
|
|
|wikitext | 2|none | 5|bits_per_byte |↓ | 0.9251|± | N/A| |
|
|
| | |none | 5|byte_perplexity|↓ | 1.8988|± | N/A| |
|
|
| | |none | 5|word_perplexity|↓ |30.8396|± | N/A| |
|
|
|winogrande | 1|none | 5|acc |↑ | 0.5178|± |0.0140| |
|
|
|wsc | 1|none | 5|acc |↑ | 0.3846|± |0.0479| |
|
|
``` |
|
|
|
|
|
If you find our work helpful, please consider giving us a star ⭐ @ our [`GitHub repository`](https://github.com/HKUDS/SepLLM) and citing our paper. We greatly appreciate your support 😄 |
|
|
``` |
|
|
@inproceedings{chen2025sepllm, |
|
|
title={{SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator}}, |
|
|
author={Chen, Guoxuan and Shi, Han and Li, Jiawei and Gao, Yihang and Ren, Xiaozhe and Chen, Yimeng and Jiang, Xin and Li, Zhenguo and Liu, Weiyang and Huang, Chao}, |
|
|
booktitle={International Conference on Machine Learning}, |
|
|
year={2025}, |
|
|
note={Also available at arXiv:2412.12094} |
|
|
} |
|
|
``` |
|
|
|
