COIG-Kun Label Model
Model Details
- Name: COIG-Kun Label Model
- Release Date: 2023.12.04
- Github URL: COIG-Kun
- Developers: Tianyu Zheng, Shuyue Guo, Xingwei Qu, Xinrun Du, Wenhu Chen, Jie Fu, Wenhao Huang, Ge Zhang
Model Description
The Label Model is a part of the Kun project, which aims to enhance language model training through a novel data augmentation paradigm, leveraging principles of self-alignment and instruction backtranslation. The model is specifically fine-tuned to generate high-quality instructional data, a critical component in the project's approach to data augmentation and language model training.
Intended Use
- Primary Use: The Label Model is designed for generating instructional data to fine-tune language models.
- Target Users: Researchers and developers in NLP and ML, particularly those working on language model training and data augmentation.
Training Data
The Label Model is trained using approximately ten thousand high-quality seed instructions. These instructions were meticulously curated to ensure the effectiveness of the training process and to produce high-quality outputs for use as instructional data.
Training Process
- Base Model: Yi-34B
- Epochs: 6
- Learning Rate: 1e-5
- Fine-Tuning Method: The model was fine-tuned on high-quality seed instructions, with the responses to these instructions used as outputs and the instructions themselves as inputs.
Evaluation
The Label Model was evaluated on its ability to generate high-quality instructional data, focusing on the relevancy, clarity, and usability of the instructions for language model training.
Ethical Considerations
- Users should be aware of potential biases in the training data, which could be reflected in the model's outputs.
- The model should not be used for generating harmful or misleading content.
Citing the Model
To cite the Label Model in academic work, please use the following reference:
@misc{COIG-Kun,
title={Kun: Answer Polishment Saves Your Time for Using Intruction Backtranslation on Self-Alignment},
author={Tianyu, Zheng* and Shuyue, Guo* and Xingwei, Qu and Xinrun, Du and Wenhu, Chen and Jie, Fu and Wenhao, Huang and Ge, Zhang},
year={2023},
publisher={GitHub},
journal={GitHub repository},
howpublished={https://github.com/Zheng0428/COIG-Kun}
}
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