## 1 Introduction
only requires a huge initial investment but also complicates operation and maintenance, limiting the possibility of widespread application. Furthermore [5; 6; 7; 8], although models like Llama3 and ChatGLM can reduce hardware requirements through quantization, they still require relatively high configurations, which remain a significant burden for ordinary users or small businesses. In addition, the enormous scale of these models, such as ChatGPT with approximately 175 billion parameters and Wenxin Yiyan and iFLYTEK Spark with about 100 billion parameters, brings powerful language processing capabilities but also exacerbates issues related to resource consumption, model efficiency, sustainability, and environmental impact. As shown in Table 1, the above models excel in supporting multiple languages, covering a wide range of linguistic groups. However, their common shortcoming lies in the lack of direct support for ethnic minority languages such as Uyghur, as illustrated in Figure 2. This deficiency not only restricts the communication and information access of users of these languages but also reflects the current lack of inclusiveness of large language models in terms of global linguistic diversity. The absence of support for minority languages like Uyghur not only hinders the goal of inclusive technology but also ignores the value of cultural diversity. This underscores the importance of strengthening the construction of datasets for minority languages and making adjustments to improve model adaptability in research. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Supported** & **Deployment** & **Model Type** & **Parameter** & **Key Features** \\  & **Languages** & **Environment** & **Environment** & **Volume** & **Volume** & **\\ \hline \multirow{4}{*}{ChatGPT v3.5} & English, Chinese, French, German, etc. & High-performance distributed GPU integrated computing & General-purpose large language model & Approximately 175 billion & Strong multi-language processing capability, supports broad domain conversations, resource-intensive, requiring a high-performance computing environment for deployment. & Strong multi-language processing capability, supports broad domains & \\  & & & & & & \\  & & &  
In Section 1, we introduced the research object, including its background and application domains, highlighting the significance of modern information technology in knowledge dissemination and updating, and analyzed the limitations of existing large-scale language models in terms of support for specific domains and languages. In Section 2, we detailed the process of data collection and processing, encompassing the construction of a corpus through data collection, textual conversion, cleaning, traditional to simplified Chinese character conversion, and the development and accuracy assessment of a Uyghur language translation tool. In Section 3, we examined word segmentation and vocabulary construction, selecting SentencePiece as the core segmentation technology, and described the selection and implementation of segmentation methods, as well as the division and encoding of datasets. In Section 4, we proposed a model and introduced its architectural design, presenting a Transformer-based generative pre-training model and discussing the impact of model parameter quantization on hardware resource requirements. In Section 5, we studied model training, showcasing the training process, data processing and training flow, model evaluation, and generated examples, while also exploring the performance of our model compared to other models such as LSTM and TKAN. In Section 6, we investigated the application and deployment of the model, exploring the detection of context in generated text and a fuse mechanism, and proposing solutions for user interaction to address issues in model deployment.