---
language:
- zh
---

# COIG-Kun PrimaryChatModel

## Model Details
- **Name:** COIG-Kun PrimaryChatModel
- **Release Date:** 2024.04.08
- **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 PrimaryChatModel is a model used in the Kun project to transform raw data into a standard response format. It can read through the raw data using a reading comprehension paradigm and answer questions generated by the Label model. This model has been specially fine-tuned to better suit the required tasks, making it one of the core processes in Kun.

## Intended Use
- **Primary Use:** The PrimaryChatModel is designed to transform raw data into a standard response format based on generated instructions.
- **Target Users:** Researchers and developers in NLP and ML, particularly those working on language model training and data augmentation.

## Training Data
The PrimaryChatModel is trained using 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:** 2
- **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 PrimaryChatModel was evaluated on its ability to transform raw data into a standard response format, 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 PrimaryChatModel 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}
}
```