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 ## Notes
 
-Unfortunately, The lack of a persistent GPU on Hugginface Zero spaces posed some challenges in using a [fine tuned model](https://huggingface.co/tykiww/llama3-8b-meetingQA) based on instruction tuned alpaca datasets and a noisy synthetic dataset of over 3000 product, technical, and academic meetings. However, the 3.1 outputs are a massive improvement over the base Llama 3 family of models.
+For the purposes of this demo, the pre-stored meetings are a "clean" set of synthetic meetings aimed at inquiring about challenges with meeting effectiveness. I know, how meta. This demo can be taken a step further by using our [fine tuned model](https://huggingface.co/tykiww/llama3-8b-meetingQA) trained both on the alpaca dataset and a *noisy* synthetic dataset of over 3000 product, technical, and academic meetings. Unfortunately, the lack of a persistent GPU on the free Huggingface Zero GPU spaces posed some challenges, so the fancy 3.1 model should do just fine! It's already a massive improvement over the base Llama 3 family of models.
 
 
 This demo is just a peek and is subject to a demand queue. More to come!