Update README.md

README.md

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 ### Use Cases
-1. **High-Throughput Screening in Drug Discovery:** The distilled ProtGPT2 is ideal for rapid screening of mutation effects in protein sequences within pharmaceutical research. For example, it can quickly predict the stability of protein variants in large datasets, speeding up the identification of viable drug targets.
-2. **Portable Diagnostics in Healthcare:** This model is suitable for use in handheld diagnostic devices that perform real-time protein analysis in clinical settings. For instance, it can be used in portable devices to analyze blood samples for markers of diseases, providing immediate results to healthcare providers in remote areas.
-3. **Interactive Learning Tools in Academia:** The distilled model can be integrated into educational software tools that allow biology students to simulate and study the impact of genetic mutations on protein structures. This hands-on learning helps students understand protein dynamics without the need for high-end computational facilities.
+1. **High-Throughput Screening in Drug Discovery:** The distilled ProtGPT2 facilitates rapid mutation screening in drug discovery by predicting protein variant stability efficiently. Its reduced size allows for swift fine-tuning on new datasets, enhancing the pace of target identification.
+2. **Portable Diagnostics in Healthcare:** Suitable for handheld devices, this model enables real-time protein analysis in remote clinical settings, providing immediate diagnostic results.
+3. **Interactive Learning Tools in Academia:** Integrated into educational software, the distilled model helps biology students simulate and understand protein dynamics without advanced computational resources.
 
 ### References
 - Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the Knowledge in a Neural Network. arXiv:1503.02531.