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--- |
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language: |
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- zh |
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--- |
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# COIG-Kun PrimaryChatModel |
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## Model Details |
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- **Name:** COIG-Kun PrimaryChatModel |
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- **Release Date:** 2024.04.08 |
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- **Github URL:** [COIG-Kun](https://github.com/Zheng0428/COIG-Kun) |
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- **Developers:** Tianyu Zheng*, Shuyue Guo*, Xingwei Qu, Xinrun Du, Wenhu Chen, Jie Fu, Wenhao Huang, Ge Zhang |
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## Model Description |
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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. |
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## Intended Use |
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- **Primary Use:** The PrimaryChatModel is designed to transform raw data into a standard response format based on generated instructions. |
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- **Target Users:** Researchers and developers in NLP and ML, particularly those working on language model training and data augmentation. |
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## Training Data |
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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. |
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## Training Process |
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- **Base Model:** Yi-34B |
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- **Epochs:** 2 |
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- **Learning Rate:** 1e-5 |
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- **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. |
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## Evaluation |
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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. |
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## Ethical Considerations |
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- Users should be aware of potential biases in the training data, which could be reflected in the model's outputs. |
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- The model should not be used for generating harmful or misleading content. |
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## Citing the Model |
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To cite the PrimaryChatModel in academic work, please use the following reference: |
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```bibtex |
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@misc{COIG-Kun, |
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title={Kun: Answer Polishment Saves Your Time for Using Intruction Backtranslation on Self-Alignment}, |
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author={Tianyu, Zheng* and Shuyue, Guo* and Xingwei, Qu and Xinrun, Du and Wenhu, Chen and Jie, Fu and Wenhao, Huang and Ge, Zhang}, |
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year={2023}, |
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publisher={GitHub}, |
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journal={GitHub repository}, |
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howpublished={https://github.com/Zheng0428/COIG-Kun} |
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} |
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``` |
