Our weights for the instruction tuning model is uploading here

TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation is available at https://arxiv.org/abs/2305.00447.

Wrongly delete the line in evaluate.py by mistake, now it has been updated

We introduce a novel framework (TALLRec) that enables the efficient and effective adaptation of LLMs to recommendation tasks.

Table 1. we shown the AUC results of the baseline models and our frameworks on movie and book scenarios.

Train TALLRec base on LLaMA7B:

bash ./shell/instruct_7B.sh  gpu_id random_seed

If you want to run it under your environment, you need to make changes to the sh file:

• output_dir: Model save path，we will automatically add the seed and the sample to the end of the path for each experiments.
• base_model: LLaMA parameter weight path in Hugginface format
• train_data: Training data path such as "./data/movie/train.json" for movie dataset.
• val_data: Validation data set path such as "./data/movie/valid.json" for movie dataset.
• instruction_model: The LoRA weights after the instruction tuning, for example lora weight from alpaca-lora.

After training, you need to evluate the test result on the best model evaluated by the validation set.

bash ./shell/evaluate.sh  gpu_id  output_dir

If you want to run it under your environment, you need to make changes to the sh file:

• base_model: LLaMA parameter weight path in Hugginface format
• test_data: Test data set path such as "./data/movie/test.json" for movie dataset.

Note that we will automatically detect all the different seed and sample files in the output_dir directory, and then integrate these results into the output_dir.json file.

Our project is developed based on the Alpaca_lora repo (https://github.com/tloen/alpaca-lora), thanks for their contributions.