This code was built based on our previous project The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMA.

To install requirements:

pip install -r requirements_cuda118.txt

📋 The experiments were done under CUDA 11.8

1. ./dataset_ft/Abraham***_cleared.csv already preprocessed.

To train the model(s) in the paper, move to AbraLLaMA (the main directory) and run:

python run_auto_llama.py

To check the model's metrics, loss, and etc., move to AbraLLaMA/evaluations (the main directory):

metric_1(RMSE), metric_2/loss(MAE)

We have used one of the pretrained ChemLLaMA-MTR model from our previous project

./model_mtr/ChemLlama_Medium_30m_vloss_val_loss=0.029_ep_epoch=04.ckpt

You can also train AbraLLaMA demo version wiht Jupyter

1. Open run_demo.ipynb

📋 MIT

Please use this code only for social goods and positive impact.