Update README.md

README.md

@@ -122,4 +122,13 @@ innovators balancing all perspectives in the Small Language Model space.
 9. Prepare Datasets
 10. Fine-tune via GRPO Trainer 
 11. Evaluate PY in Sandbox
-12. Create task-specific Variants like Code Tutors 
+12. Create task-specific Variants like Code Tutors
+
+### OVERVIEW
+
+In terms of applications, small distilled models like **MICROD_v1** align with broader trends in SLMs, which prioritize efficiency, accessibility, 
+and specialization over the scale of large language models (LLMs). For example, they can be fine-tuned for targeted tasks such as customer support 
+chatbots, where quick responses on edge devices are crucial, or educational tools for teaching natural language processing concepts. In healthcare, 
+distilled models might power privacy-focused symptom checkers on mobile apps, avoiding data transmission to cloud servers. Automation and control 
+systems benefit from their low latency, as surveyed in research on tiny language models (TLMs), which use techniques like knowledge distillation and 
+quantization to enable on-device inference for robotics or IoT devices.