Update README.md

README.md

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 <!-- Provide a longer summary of what this model is. -->
 
-Model Name: Mistral_7b_question_generation_v1
-Base Model: mistral_7b_v0.1
+**Model Nam**e: Mistral_7b_question_generation_v1
+
+**Base Model**: mistral_7b_v0.1
+
 Version: 1
-Description: Mistral_7b_question_generation_v1 is a fine-tuned variant of the mistral_7b_v0.1 model specifically tailored for question generation tasks. The base model, mistral_7b_v0.1, is a large-scale language model trained by Mistral AI, featuring 7 billion parameters. This finetuned version has undergone additional training on a dataset specifically curated for question-generation tasks. The model is adept at understanding context and generating meaningful questions based on given text inputs.
 
-Key Features:
+**Description**: Mistral_7b_question_generation_v1 is a fine-tuned variant of the mistral_7b_v0.1 model specifically tailored for question generation tasks. The base model, mistral_7b_v0.1, is a large-scale language model trained by Mistral AI, featuring 7 billion parameters. This finetuned version has undergone additional training on a dataset specifically curated for question-generation tasks. The model is adept at understanding context and generating meaningful questions based on given text inputs.
+
+**Key Features**:
+
+**Question Generation**: The primary function of this model is to generate questions based on input text passages. It can analyze the content of the text and formulate questions that probe various aspects of the information presented.
+
+**Natural Language Understanding**: Leveraging its pre-trained knowledge and fine-tuning question generation data, the model demonstrates a strong ability to understand nuances in language and context, enabling it to generate relevant and coherent questions.
 
-Question Generation: The primary function of this model is to generate questions based on input text passages. It can analyze the content of the text and formulate questions that probe various aspects of the information presented.
-Natural Language Understanding: Leveraging its pre-trained knowledge and fine-tuning question generation data, the model demonstrates a strong ability to understand nuances in language and context, enabling it to generate relevant and coherent questions.
+**Large Scale**: With 7 billion parameters, Mistral_7b_question_generation_v1 encompasses a vast amount of linguistic knowledge, allowing it to generate questions across a wide range of topics and domains.
 
-Large Scale: With 7 billion parameters, Mistral_7b_question_generation_v1 encompasses a vast amount of linguistic knowledge, allowing it to generate questions across a wide range of topics and domains.
+**Contextual Awareness**: The model takes into account the context provided in the input text to generate questions that are contextually relevant and meaningful, ensuring that the generated questions accurately reflect the content of the passage.
 
-Contextual Awareness: The model takes into account the context provided in the input text to generate questions that are contextually relevant and meaningful, ensuring that the generated questions accurately reflect the content of the passage.
+**Adaptability**: Mistral_7b_question_generation_v1 can be fine-tuned further on specific question generation tasks or domains to enhance its performance and tailor it to specific application scenarios.
 
-Adaptability: Mistral_7b_question_generation_v1 can be fine-tuned further on specific question generation tasks or domains to enhance its performance and tailor it to specific application scenarios.
-Potential Use Cases:
+**Potential Use Cases**:
 
-Educational Content Creation: The model can be used to automatically generate questions for educational materials, textbooks, or study guides based on given passages.
+**Educational Content Creation**: The model can be used to automatically generate questions for educational materials, textbooks, or study guides based on given passages.
 Content Summarization and Assessment: Mistral_7b_question_generation_v1 can aid in summarizing content by generating questions that cover key points, and it can also be used for assessing comprehension and understanding through question-based evaluations.
 QA Systems Enhancement: Integrating the model into question-answering systems can improve their capabilities by enabling them to generate relevant questions to further explore topics or clarify information.