🚀 GongBU is a comprehensive, all-in-one, no-code, local finetuning platform for large language models. Once deployed, there is no need to write any code to finetune a model, and the platform can be used by anyone with a web browser. The platform is built on top of the Transformers, Peft, etc. and is designed to be easy to use for non-technical users.

📈 You can easily finetune, evaluate, and deploy models with the platform.

📦 Currently, by default, the platform should be started with docker-compose in a linux system with nvidia-docker. However, by installing the required dependencies, with correct configurations, the platform can be run natively.

The recommended installation is to use the docker-compose file provided in the repository on a linux system with nvidia-docker. This is the easiest way to install the platform.

It's simple, clone the repository, then run the download.py (inquirer and rich libraries are required) to download necessary files that are not included in the repository, micromamba and a bert model to be exact.

You need to install docker and nvidia-docker.

Then run the following command:

docker compose -f docker-compose.prod.yaml up

The platform should be running on localhost:5173 behind an nginx reverse proxy. Simply use localhost:5173/home to access the platform.

After accessing the platform, you will be welcomed by the log in page. By default, the platform does not provide any user, so you need to create a user by clicking the sign up button.

When signing up, by default, you will need a token to sign up. It will be in the sign_up_token.txt file in the root directory of the project. You can change the token in the sign_up_token.txt however you want. Nevertheless, you can use a permissive registering strategy by setting the environment variable NO_SIGNUP_TOKEN in the docker compose file, which will allow anyone to sign up.

After logging in, you will find everything to be empty, here's how you can import assets into the platform, only two kinds of assets are needed, the dataset and the model.

• To upload a dataset, you need to go to the data page. The datasets are arranged in pools, with each pools containing several datasets. You can create a new pool by clicking the create button, and follow the instructions to upload a dataset. You can also click the details button of an existing pool to upload a dataset to that pool.

• To download models, you need to go to the model download page.

  Model_DownloadPage

  Type in the relavent information in the form, and click the add button, there will be a list down below showing the models that you have added.

After you have uploaded the datasets and downloaded the models, you can fintune, evaluate and deploy the models on respective pages.

The platform is now fully functional, albeit under constant development and polishing, especially the frontend, and breaking changes may take place.

• We used bun instead of node, which is a blazingly-fast, all-in-one js bundler, runtime, test runner, package manager, etc. It is a relatively new project, but its performance attracted us to use it. Currently, it is stable for our use. However, if you want to switch to node for better stability, simply change all the bun-related command in the dockerfile to node or npm.

FOR ANY LOSS OR DAMAGE, THE DEVELOPER IS NOT RESPONSIBLE, AS IS STATED IN THE MIT LICENSE.

This project uses plain text to transfer data between the client and the server, which includes the username, password, and dataset. The password is hashed before being stored in the database, but the dataset is stored in plain text.

If you need to use the platform in open networks, please secure the database and secure the connections with SSL manually, this is not what we can provide (by changing the nginx config under the proxy folder, all requests in production goes through nginx). However, so long as SSL is enabled and the database is secured, equipped with other measures that are implemented by us, the platform should be secure.

@inproceedings{zhang2024gongbu,
 title={GongBu: Easily Fine-tuning LLMs for Domain-specific Adaptation},
 author={Zhang, Bolin and Tian, Yimin and Wang, Shengwei and Tu, Zhiying and Chu, Dianhui and Shen, Zhiqi},
 booktitle={Proceedings of the 33rd ACM International Conference on Information and Knowledge Management},
 pages={5309--5313},
 year={2024}
}