| library_name: peft | |
| ## Training procedure | |
| ### Framework versions | |
| - PEFT 0.4.0 | |
| - PEFT 0.4.0 | |
| - use | |
| in https://github.com/hiyouga/ChatGLM-Efficient-Tuning/tree/main | |
| ``` | |
| CUDA_VISIBLE_DEVICES=3 nohup python src/web_demo.py \ | |
| --model_name_or_path /HOME/jack/model/chatglm-6b \ | |
| --checkpoint_dir paper_meta\ \ | |
| > log_web_demo.txt 2>&1 & tail -f log_web_demo.txt | |
| ``` | |
| ### 🚩Citation | |
| Please cite the following paper if you use jackkuo/ChatPaperGPT_32k in your work. | |
| ```bibtex | |
| @INPROCEEDINGS{10412837, | |
| author={Guo, Menghao and Wu, Fan and Jiang, Jinling and Yan, Xiaoran and Chen, Guangyong and Li, Wenhui and Zhao, Yunhong and Sun, Zeyi}, | |
| booktitle={2023 IEEE International Conference on Knowledge Graph (ICKG)}, | |
| title={Investigations on Scientific Literature Meta Information Extraction Using Large Language Models}, | |
| year={2023}, | |
| volume={}, | |
| number={}, | |
| pages={249-254}, | |
| keywords={Measurement;Knowledge graphs;Information retrieval;Data mining;Task analysis;information extraction;large language model;scientific literature}, | |
| doi={10.1109/ICKG59574.2023.00036}} | |
| ``` |