### Install FollowBench Dependencies Source: https://github.com/yjiangcm/followbench/blob/master/README.md Sets up a conda environment and installs the required PyTorch and project dependencies. ```bash conda create -n followbench python=3.10 conda activate followbench conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia pip install -r requirements.txt ``` -------------------------------- ### Cite FollowBench via BibTeX Source: https://github.com/yjiangcm/followbench/blob/master/README.md Use this BibTeX entry to cite the FollowBench paper in academic publications. ```bibtex @inproceedings{jiang-etal-2024-followbench, title = "{F}ollow{B}ench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models", author = "Jiang, Yuxin and Wang, Yufei and Zeng, Xingshan and Zhong, Wanjun and Li, Liangyou and Mi, Fei and Shang, Lifeng and Jiang, Xin and Liu, Qun and Wang, Wei", editor = "Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek", booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = aug, year = "2024", address = "Bangkok, Thailand", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.acl-long.257", pages = "4667--4688", } ``` -------------------------------- ### Run Model Inference Source: https://github.com/yjiangcm/followbench/blob/master/README.md Executes model inference using the vLLM framework. ```bash cd FollowBench/ python code/model_inference_vllm.py --model-path ``` -------------------------------- ### Perform LLM-based Evaluation Source: https://github.com/yjiangcm/followbench/blob/master/README.md Runs the LLM-based evaluation script using a specified model path and API key. ```bash cd FollowBench/ python code/llm_eval.py --model_path --api_key ``` -------------------------------- ### Merge Evaluation Results Source: https://github.com/yjiangcm/followbench/blob/master/README.md Combines rule-based and LLM-based evaluation results and saves them to the evaluation_result folder. ```bash cd FollowBench/ python code/eval.py --model_paths ``` === COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.