Gemma2's impressive performance while being significantly smaller than GPT-4 can be attributed to several key architectural and training innovations. Firstly, Gemma2 leverages a more efficient transformer architecture that optimizes the utilization of parameters, allowing for better parameter efficiency. This architecture includes enhancements such as sparse attention mechanisms, which reduce the computational burden by focusing only on relevant parts of the input sequence. Additionally, Gemma2 incorporates advanced training techniques like knowledge distillation, where a larger pre-trained model like GPT-4 is used to distill knowledge into the smaller Gemma2 model, enabling it to achieve comparable performance with fewer parameters. These innovations collectively contribute to Gemma2's ability to deliver impressive performance while maintaining a significantly smaller model size.