Update README.md

README.md

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 ## Model Details
 
-### Model Description
+### Introduction
+
+According to the August 2025 jobs report, the US economy gained 22,000 jobs in August, which is significantly lower than projected by economists. 
+Overall unemployment has risen, with the unemployment rate for workers aged 16-24 rising to 10.5% (Bureau of Labor Statistics, 2025). The primary demographic 
+of this age range is recent college graduates, many of whom carry student loan debt and are unable to find stable, long-term employment. While this could be 
+attributed to any of the various economic challenges facing the US today, which ironically include AI development, there is speculation that it may 
+also be due to insufficient skills regarding job-hunting and interviews. There are many resources that seek to fill this gap, from job-posting sites 
+including LinkedIn (LinkedIn, 2025), to interview-prep LLMs such as InterviewsPilot (InterviewsPilot, 2025). However, there is not an LLM that 
+combines multiple features into an end-to-end, user-friendly application, specifically designed to improve an applicant's chances of successfully 
+completing the job-application cycle. Current LLMs struggle to provide accurate interview preparation based on specific jobs and do not finetune based on
+the user's profile. My interview prep career assistant LLM seeks to provide a full user experience by specifically developing practice job interview questions
+based on the description of the job they are applying for. Additionally, it provides users with an 'optimal' answer to the interview questions based on their
+profile and resume. The interview prep LLM is finetuned from model Qwen2.5-7B-Instruct using LoRA with hyperparameters rank: 64, alpha: 128, and dropout: 0.15. 
+That hyperparameter combination resulted in the lowest validation loss, 2.055938. After finetuning, the LLM performed with a 21.578 in the SqUAdv2 benchmark, a 
+0.597 in the humaneval benchmark, a 5.040 in the E2E NLG Challenge benchmark, and a bert score mean precision of 0.813, mean recall of 0.848, and mean f1 of 0.830
+on a train/test split. 
 
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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