ShishuLM : Achieving Optimal and Efficient Parameterization with Low Attention Transformer Models

1. 1Identify architectural redundancies in transformer models
2. 2Optimize attention sub-layers in top layers
3. 3Implement low attention transformer models
4. 4Evaluate performance and adjust parameterization as needed

ML researchers and engineers on a team can benefit from ShishuLM as it provides opportunities for optimization without compromising performance, allowing for more efficient use of resources

💡 Low attention transformer models can achieve state-of-the-art performance while reducing memory and computational overhead

🚀 ShishuLM optimizes transformer models with low attention! 💡