Update README.md

README.md

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 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
-The lee12ki/llama2-finetune-7b model is a fine-tuned version of NousResearch/Llama-2-7b-chat-hf, optimized for text generation and conversational tasks. It enhances the base model's ability to follow instructions and generate coherent, context-aware responses, making it suitable for applications like chatbots and interactive AI systems. Fine-tuned using mlabonne/guanaco-llama2-1k, the model focuses on instruction tuning for dialogue-based tasks.
+The lee12ki/llama2-finetune-7b model is a fine-tuned version of NousResearch/Llama-2-7b-chat-hf, optimized for text generation and conversational tasks. 
+It enhances the base model's ability to follow instructions and generate coherent, context-aware responses, making it suitable for applications like chatbots 
+and interactive AI systems. Fine-tuned using mlabonne/guanaco-llama2-1k, the model focuses on instruction tuning for dialogue-based tasks.
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
 
 ## Model Details
 
@@ -31,9 +32,12 @@ This modelcard aims to be a base template for new models. It has been generated
 
 <!-- Provide a longer summary of what this model is. -->
 
-This model, lee12ki/llama2-finetune-7b, is a fine-tuned version of NousResearch/Llama-2-7b-chat-hf. It has been optimized for text-generation tasks, particularly for instruction-following and conversational applications. The fine-tuning was performed using the Guanaco-LLaMA2-1K dataset, a high-quality dataset designed for aligning language models to human preferences.
+This model, lee12ki/llama2-finetune-7b, is a fine-tuned version of the NousResearch/Llama-2-7b-chat-hf base model. It is optimized for text generation tasks, leveraging the QLoRA (Quantized Low-Rank Adaptation) technique for efficient fine-tuning on limited computational resources. The model has been fine-tuned using the mlabonne/guanaco-llama2-1k dataset, which includes diverse instruction-following examples to enhance its capabilities in conversational and instruction-based tasks.
+
+With its causal language modeling architecture, this model can generate coherent and contextually relevant text outputs in English. It is particularly well-suited for applications requiring high-quality conversational responses, content generation, and other natural language understanding tasks.
+
+By building on Llama 2, the model benefits from a robust foundation while introducing fine-tuned efficiency and improved instruction-following behavior.
 
-The fine-tuning process utilizes QLoRA (Quantized Low-Rank Adaptation), which enables efficient and memory-friendly training with 4-bit precision. LoRA configuration parameters include:
 
 LoRA rank (r): 64
 Alpha parameter: 16