Update README.md

README.md

@@ -25,19 +25,15 @@ The lee12ki/llama2-finetune-7b model is a fine-tuned version of NousResearch/Lla
 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.
 
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-## Model Details
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 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 
-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.
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-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.
+The lee12ki/llama2-finetune-7b model represents a fine-tuned adaptation of the NousResearch/Llama-2-7b-chat-hf architecture, specifically tailored for instruction-following and conversational AI tasks. Fine-tuned using the mlabonne/guanaco-llama2-1k dataset, it benefits from high-quality examples designed to enhance its ability to understand and generate human-like responses.
 
-By building on Llama 2, the model benefits from a robust foundation while introducing fine-tuned efficiency and improved instruction-following behavior.
+This model uses QLoRA (Quantized Low-Rank Adaptation) to enable efficient fine-tuning, reducing computational demands while maintaining high performance. It is trained to handle a variety of text generation tasks, making it suitable for applications like interactive chatbots, content generation, and knowledge-based question answering.
 
+By incorporating these advancements, the model achieves a balance between performance and efficiency, making it accessible to users with limited computational resources while retaining the robust capabilities of the original Llama 2 model.
 
 LoRA rank (r): 64
 Alpha parameter: 16