Update README.md

README.md

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 # SearchUnify-ML/xgen-7b-8k-open-instruct-gptq
 
-These are GPTQ 4bit model files for [VMWare's XGEN 7B 8K Open Instruct](https://huggingface.co/VMware/xgen-7b-8k-open-instruct).
+With its industry-first robust LLM Integrations across its suite of products ([Cognitive Search](https://www.searchunify.com/products/cognitive-search/?utm_source=link&utm_medium=ml-model&utm_campaign=hugging-face), [SUVA](https://www.searchunify.com/products/suva/), [Knowbler](https://www.searchunify.com/products/knowbler/?utm_source=link&utm_medium=ml-model&utm_campaign=hugging-face), [Escalation Predictor](https://applications.searchunify.com/escalation-predictor?utm_source=link&utm_medium=ml-model&utm_campaign=hugging-face), [Agent Helper](https://applications.searchunify.com/agent-helper?utm_source=link&utm_medium=ml-model&utm_campaign=hugging-face) and [Community Helper](https://applications.searchunify.com/community-helper?utm_source=link&utm_medium=ml-model&utm_campaign=hugging-face)) coupled with the federated retrieval augmented generation (FRAG) architecture, [SearchUnify's unified cognitive platform](https://www.searchunify.com/?utm_source=link&utm_medium=ml-model&utm_campaign=hugging-face) fetches relevant information or responses to deliver more accurate and contextually appropriate support and self-service experiences. 
 
-It is the result of quantizing to 4bit using GPTQ-for-LLaMa.
+Leveraging the state-of-the-art GPTQ quantization method, SearchUnify optimized the XGen-7B Model for low memory footprint and rapid response generation.
 
+These are GPTQ 4bit model files for [VMWare's XGEN 7B 8K Open Instruct](https://huggingface.co/VMware/xgen-7b-8k-open-instruct). It is the result of quantizing to 4bit using GPTQ-for-LLaMa.
 
 
 # How to use this GPTQ model from Python code