
# COIG-Kun Label Model 

## Model Details
- **Name:** COIG-Kun Label Model
- **Release Date:** 2023.12.04
- **Github URL:** [COIG-Kun](https://github.com/Zheng0428/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:

```bibtex
@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}
}
```
{"language": ["zh"]}
transformers safetensors llama text-generation zh autotrain_compatible endpoints_compatible has_space text-generation-inference region:us