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--- |
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license: mit |
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--- |
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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. |
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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`. |
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``` |
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CUDA_LAUNCH_BLOCKING=1 |
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lm_eval --model hf \ |
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--model_args pretrained=Gausson/pythia-160m-deduped-n128-SepLLM \ |
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--tasks arc_challenge,arc_easy,lambada_openai,logiqa,piqa,sciq,winogrande,wsc,wikitext \ |
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--num_fewshot 5 \ |
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--device cuda:0\ |
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--batch_size 32 |
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``` |
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``` |
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hf (pretrained=Gausson/pythia-160m-deduped-n128-SepLLM), gen_kwargs: (), limit: None, num_fewshot: 5, batch_size: 32 |
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| Tasks |Version|Filter|n-shot| Metric | Value | |Stderr| |
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|--------------|-------|------|-----:|---------------|------:|---|-----:| |
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|arc_challenge |Yaml |none | 5|acc | 0.2014|± |0.0117| |
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| | |none | 5|acc_norm | 0.2346|± |0.0124| |
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|arc_easy |Yaml |none | 5|acc | 0.4731|± |0.0102| |
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| | |none | 5|acc_norm | 0.4520|± |0.0102| |
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|lambada_openai|Yaml |none | 5|perplexity |30.1605|± |1.0128| |
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| | |none | 5|acc | 0.3315|± |0.0066| |
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|logiqa |Yaml |none | 5|acc | 0.2273|± |0.0164| |
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| | |none | 5|acc_norm | 0.2857|± |0.0177| |
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|piqa |Yaml |none | 5|acc | 0.6464|± |0.0112| |
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| | |none | 5|acc_norm | 0.6447|± |0.0112| |
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|sciq |Yaml |none | 5|acc | 0.8260|± |0.0120| |
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| | |none | 5|acc_norm | 0.8150|± |0.0123| |
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|wikitext |Yaml |none | 5|word_perplexity|30.3488| | | |
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| | |none | 5|byte_perplexity| 1.8931| | | |
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| | |none | 5|bits_per_byte | 0.9207| | | |
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|winogrande |Yaml |none | 5|acc | 0.5178|± |0.0140| |
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|wsc |Yaml |none | 5|acc | 0.3750|± |0.0477| |
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``` |
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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 😄 |
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``` |
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@inproceedings{chen2025sepllm, |
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title={{SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator}}, |
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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}, |
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booktitle={International Conference on Machine Learning}, |
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year={2025}, |
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note={Also available at arXiv:2412.12094} |
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} |
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``` |
