This repository is the code for generating patent claims based on the patent description. It supports fine-tuning and multi-task fine-tuning of the Llama-3 model. It also includes the inference code for Llama-3 and GPT-based models from OpenAI.
sh init_env.shcd src
accelerate launch --config_file accelerate_ds_config.yaml pefts/mft_accelerate.py --train_config configs/lora_train_config.json --distributed_type "DeepSpeed" For Llama-3 inference:
python hf_inference.py --base_model [path to the base model] --peft_path [path to the trained adapter] --directory [test data directory]For OpenAI inference:
python openai_inference.py --openai_key [your key] --model_name [model name] --data_dir [test data directory]python eval.py --model_name [model outputs for evaluation] --data_dir [test data directory]The code is built based on MFTCoder, a multi-task fine-tuning framework.
If you find our work useful for your research, please feel free to cite our paper.
@article{jiang2024can,
title={Can Large Language Models Generate High-quality Patent Claims?},
author={Lekang Jiang and Caiqi Zhang and Pascal A Scherz and Stephan Goetz},
journal={arXiv preprint arXiv:2406.19465},
year={2024}
}
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