Update README.md

README.md

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 ### Data
 For training data details, please see the [GRAG-SFT-Dataset](https://huggingface.co/datasets/avemio/GRAG-SFT-ShareGPT-HESSIAN-AI) documentation.
-The SFT tasks represent a focused approach to enhance model capabilities through specialized RAG examples. Most of these tasks were developed using synthetically enhanced data derived from the German Wikipedia, accessed through Cohere's prepared dataset on HuggingFace (licensed CC-BY-SA 4.0). This data was structured in a training knowledge graph where Question-Answer nodes were connected to both relevant and irrelevant Context nodes from the same Wikipedia page, creating a rich and challenging network of relationships for training. The only exceptions are the function calling dataset, which was derived and extended from Salesforce's XLAM Function calling dataset by including function call results and final answer generation, and the reasoning task which synthetic generation was inspired by the Paper from Tencent “Scaling Synthetic Data Creation with 1,000,000,000 Personas”, to generate a diverse set of reasoning tasks across various domains.
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+#### Description
+The SFT tasks represent a focused approach to enhance model capabilities through specialized RAG examples. Most of these tasks were developed using synthetically enhanced data derived from the German Wikipedia, accessed through Cohere's prepared dataset on HuggingFace (licensed CC-BY-SA 4.0). This data was structured in a training knowledge graph where Question-Answer nodes were connected to both relevant and irrelevant Context nodes from the same Wikipedia page, creating a rich and challenging network of relationships for training. The only exceptions are the function calling dataset, which was derived and extended from Salesforce's XLAM Function calling dataset by including function call results and final answer generation, and the reasoning task which synthetic generation was inspired by the Paper from Tencent ([“Scaling Synthetic Data Creation with 1,000,000,000 Personas”](https://arxiv.org/abs/2406.20094)), to generate a diverse set of reasoning tasks across various domains.
 This comprehensive set of SFT tasks ensures the model develops robust capabilities across a wide range of practical applications while maintaining consistent output formats and clear communication patterns. Each task type has been carefully designed to address specific business needs while maintaining high standards of accuracy and reliability, making them valuable tools for organizations looking to enhance their information processing and knowledge management capabilities.
 
 #### Task Instruction Format