Update README.md

README.md

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 Model Details
 
-Model Name: Moe-4x7b-QA-Code-Inst Publisher: nextai-team Model Type: Question Answering & Code Generation Architecture: Mixture of Experts (MoE) Model Size: 4x7 billion parameters
+Model Name: Moe-4x7b-reason-code-qa Publisher: nextai-team Model Type: Question Answering & Code Generation Architecture: Mixture of Experts (MoE) Model Size: 4x7 billion parameters
 
 Overview
 
@@ -34,20 +34,20 @@ Model Architecture  employs a Mixture of Experts (MoE) architecture, which allow
 
 Training Data The model has been trained on a diverse and extensive corpus comprising technical documentation, open-source code repositories, Stack Overflow questions and answers, and other programming-related texts. Special attention has been given to ensure a wide range of programming languages and frameworks are represented in the training data to enhance the model's versatility.
 
-Performance Moe-4x7b-QA-Code-Inst demonstrates significant improvements in accuracy and relevance over its predecessor, particularly in complex coding scenarios and detailed technical queries. Benchmarks and performance metrics can be provided upon request.
+Performance  demonstrates significant improvements in accuracy and relevance over its predecessor, particularly in complex coding scenarios and detailed technical queries. Benchmarks and performance metrics can be provided upon request.
 
 Limitations and Biases
 
-While Moe-4x7b-QA-Code-Inst represents a leap forward in AI-assisted coding and technical Q&A, it is not without limitations. The model may exhibit biases present in its training data, and its performance can vary based on the specificity and context of the input queries. Users are encouraged to critically assess the model's output and consider it as one of several tools in the decision-making process.
+While  represents a leap forward in AI-assisted coding and technical Q&A, it is not without limitations. The model may exhibit biases present in its training data, and its performance can vary based on the specificity and context of the input queries. Users are encouraged to critically assess the model's output and consider it as one of several tools in the decision-making process.
 
 Ethical Considerations
 
-We are committed to ethical AI development and urge users to employ Moe-4x7b-QA-Code-Inst responsibly. This includes but is not limited to avoiding the generation of harmful or unsafe code, respecting copyright and intellectual property rights, and being mindful of privacy concerns when inputting sensitive information into the model.
+We are committed to ethical AI development and urge users to employ Moe-4x7b-reason-code-qa responsibly. This includes but is not limited to avoiding the generation of harmful or unsafe code, respecting copyright and intellectual property rights, and being mindful of privacy concerns when inputting sensitive information into the model.
 
 Usage Instructions
 
-For detailed instructions on how to integrate and utilize Moe-4x7b-QA-Code-Inst in your projects, please refer to our GitHub repository and Hugging Face documentation.
+For detailed instructions on how to integrate and utilize Moe-4x7b-reason-code-qa in your projects, please refer to our GitHub repository and Hugging Face documentation.
 
-Citation If you use Moe-4x7b-QA-Code-Inst in your research or application, please cite it as follows:
+Citation If you use Moe-4x7b-reason-code-qa in your research or application, please cite it as follows:
 
-@misc{nextai2024moe4x7b, title={Moe-4x7b-QA-Code-Inst: Enhancing Question Answering and Code Generation with Mixture of Experts}, author={NextAI Team}, year={2024}, publisher={Hugging Face} }
+@misc{nextai2024moe4x7b, title={Moe-4x7b-reason-code-qa: Enhancing Question Answering and Code Generation with Mixture of Experts}, author={NextAI Team}, year={2024}, publisher={Hugging Face} }