nextai-team
/

Moe-2x7b-QA-Code

@@ -44,7 +44,17 @@ response = generate_resposne("How to learn coding .Please provide a step by step
 print(response)
 ```
-***Limitations and Bias***
 This model, like any other, has its limitations. It may exhibit biases inherent in the training data or struggle with questions outside its training scope. Users should critically assess the model's outputs, especially for sensitive or critical applications.
@@ -52,15 +62,14 @@ Training Data:
 The Moe-2x7b-QA-Code model was trained on a curated dataset comprising technical documentation, Stack Overflow posts, GitHub repositories, and other code-related content. This extensive training set ensures the model's proficiency in understanding and generating code-related content alongside general language understanding.
-***Training Procedure***
 The model was trained using a Mixture of Experts (MoE) approach, allowing it to dynamically leverage different subsets of parameters for different types of input data. This method enhances the model's capacity and efficiency, enabling it to excel in a wide range of QA and coding tasks.
-***Model Architecture**
 Moe-2x7b-QA-Code employs an advanced MoE architecture with 2x7 billion parameters, optimized for high performance in QA and coding tasks. This architecture enables the model to efficiently process and generate accurate responses to complex queries.
-***Contact**
 Https://nextai.co.in

 print(response)
 ```
+**Intended Use**
+This model is intended for developers, data scientists, and researchers seeking to integrate sophisticated natural language understanding and code generation functionalities into their applications. Ideal use cases include but are not limited to:
+Automated coding assistance Technical support bots Educational tools for learning programming Enhancing code review processes
+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.
+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 Bias**
 This model, like any other, has its limitations. It may exhibit biases inherent in the training data or struggle with questions outside its training scope. Users should critically assess the model's outputs, especially for sensitive or critical applications.
 The Moe-2x7b-QA-Code model was trained on a curated dataset comprising technical documentation, Stack Overflow posts, GitHub repositories, and other code-related content. This extensive training set ensures the model's proficiency in understanding and generating code-related content alongside general language understanding.
+**Training Procedure**
 The model was trained using a Mixture of Experts (MoE) approach, allowing it to dynamically leverage different subsets of parameters for different types of input data. This method enhances the model's capacity and efficiency, enabling it to excel in a wide range of QA and coding tasks.
+**Model Architecture**
 Moe-2x7b-QA-Code employs an advanced MoE architecture with 2x7 billion parameters, optimized for high performance in QA and coding tasks. This architecture enables the model to efficiently process and generate accurate responses to complex queries.
+**Contact**
 Https://nextai.co.in