Update README.md

README.md

@@ -22,7 +22,7 @@ Model Name: Moe-4x7b-QA-Code-Inst Publisher: nextai-team Model Type: Question An
 
 Overview
 
-Moe-4x7b-QA-Code-Inst is an advanced AI model designed by the nextai-team for the purpose of enhancing question answering and code generation capabilities. Building upon the foundation of its predecessor, Moe-2x7b-QA-Code, this iteration introduces refined mechanisms and expanded training datasets to deliver more precise and contextually relevant responses.
+ is an advanced AI model designed by the nextai-team for the purpose of enhancing question answering and code generation capabilities. Building upon the foundation of its predecessor, Moe-2x7b-QA-Code, this iteration introduces refined mechanisms and expanded training datasets to deliver more precise and contextually relevant responses.
 
 Intended Use
 
@@ -30,7 +30,7 @@ This model is intended for developers, data scientists, and researchers seeking
 
 Automated coding assistance Technical support bots Educational tools for learning programming Enhancing code review processes
 
-Model Architecture Moe-4x7b-QA-Code-Inst employs a Mixture of Experts (MoE) architecture, which allows it to efficiently manage its vast number of parameters for specialized tasks. This architecture facilitates the model's ability to discern subtle nuances in programming languages and natural language queries, leading to more accurate code generation and question answering performance.
+Model Architecture  employs a Mixture of Experts (MoE) architecture, which allows it to efficiently manage its vast number of parameters for specialized tasks. This architecture facilitates the model's ability to discern subtle nuances in programming languages and natural language queries, leading to more accurate code generation and question answering performance.
 
 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.