| --- |
| 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} |
| } |
| ``` |
