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Generative One-For-All (GOFA)

The source code for paper GOFA: A generative one-for-all model for joint graph language modeling. The code is still under clean. Feel free to open an issue in GitHub if you encounter any problem.

Installation Guide.

First, clone the code repository and move to the code file. Then, create the python environment. We provide environment configuration:

conda env create -f environment.yml

Next, please download the pretrained checkpoint of ICAE from here. Specifically, download and put the following files llama-2-7b-chat-finetuned-icae_zeroweight_llama2.pt and mistral_7b_pretrained_icae.safetensors into directory ./cache_data/model/

Finally, clone the code of datasets we used from TAGLAS by running:

git clone https://github.com/JiaruiFeng/TAGLAS.git

Pre-training

Pre-training require large computation resource and time. If you want to explore GOFA, we recommend you to download our pre-trained checkpoints and directly run downstream fine-tuning. You can download checkpoints from here. We provide checkpoints for both Llama2 (qamag03_best_ckpt.pth) and Mistral (mistral_qamag03_best_ckpt.pth).

To run the pretraining by yourself, please first generate pretraining data using the following script.

python pretrain_data_generation.py

The above code will generate three pretrain data subset. Note that the generation process require huge memory and will last for long time. Please allocate enough resource for generation.

After data generation, run the following line to start the pretraining:

python run_gofa.py --override ./configs/pretrain_config.yaml

This code will run pretraining of the GOFA llama2 version on the first pretrain data subset. if you want to train the mistral version, run:

python run_gofa.py --override ./configs/pretrain_config.yaml base_llm mistral7b

To continue the training on next subset, check the last_epochs in the ./configs/default_config.yaml to the next batch and ckpt_path to the saved checkpoint on the last pretraining.

Instruction fine-tuning for zero-shot experiment.

To repeat the experiments of GOFA on zero-shot learning with arxiv instruction tuning, run:

python run_gofa.py --override ./configs/instruct_arxiv_config.yaml load_dir llama_pretrained_model_pth base_llm llama7b
python run_gofa.py --override ./configs/instruct_arxiv_config.yaml load_dir mistral_pretrained_model_pth base_llm mistral7b

Please change the load_dir to either the corresponding downloaded checkpoints or your own pretrained checkpoints.

Similarly, to repeat the experiments of GOFA on zero-shot learning with pubmed link instruction tuning, run:

python run_gofa.py --override ./configs/instruct_pubmed_config.yaml load_dir mistral_pretrained_model_pth base_llm mistral7b

Supervised fine-tuning.

To repeat the experiments of GOFA on supervised learning, run:

python run_gofa.py --override ./configs/supervised_config.yaml load_dir pretrained_model_pth base_llm model_type

Similar as the above, modify the load_dir and base_llm for validating corresponding model.

Evaluation and inference

To explore the generation result of GOFA, you can also directly run the inference mode with:

python run_gofa.py --override ./configs/inference_config.yaml load_dir finetuned_model_pth base_llm llama7b

Please modify the config file for selecting corresponding dataset. Note that for both zero-shot and supervised experiment, the trained model should be evaluated under inference model to obtain the correct evaluation result.

We also provide checkpoint of mistral version of GOFA on arxiv instruction-tuning in here with file name nb_instruct.pth. You can use this checkpoint to directly reproduce the results in the paper.

Citation

@article{kong2024gofa,
  title={GOFA: A Generative One-For-All Model for Joint Graph Language Modeling},
  author={Kong, Lecheng and Feng, Jiarui and Liu, Hao and Huang, Chengsong and Huang, Jiaxin and Chen, Yixin and Zhang, Muhan},
  journal={arXiv preprint arXiv:2407.09709},
  year={2024}
}