📋 A README.md for code accompanying our paper RPP
Prepare the environment to evaluate on ChatGPT and LLaMa2-7b-chat
cd RPP
pip install -r requirements.txt
Prepare the environment to evaluate on Alpaca
pip install -r requirements_A.txt
Insert the API of gpt-3.5-turbo to openai_api.yaml to test the performance of ChatGPT
Download the pre-trained Bert model to transform prompts into embedding(https://huggingface.co/bert-base-chinese)
Download the pre-trained LLaMa2-7B-chat model (https://ai.meta.com/llama/)
Download the pre-trained huggingface model of LLaMA2-7B to finetune Alpaca (https://huggingface.co/meta-llama/Llama-2-7b-hf)
Put the checkpoints of RPP to the dir path saved/ and the checkpoints of finetuned Alpaca to the dir path model/
We use the NVIDIA A100 to conduct all our experiments on LLMs:
For the dataset of ML-1M and Lastfm, the initial interaction history length (few-shot) ini_len: 1
For the dataset of Games, the initial interaction history length (few-shot) ini_len: 5
To train the frozen LLM of LLaMa2-7B-chat:
selected_user_suffix: train
CUDA_VISIBLE_DEVICES=[] torchrun --nproc_per_node 1 --master_port=[] train.py
To evaluate the frozen LLM of LLaMa2-7B-chat:
selected_user_suffix: test
CUDA_VISIBLE_DEVICES=[] torchrun --nproc_per_node 1 --master_port=[] evaluate.py