Large Language Models (LLMs) with billions of parameters have revolutionized AI but are computationally intensive. This study supports the use of ReLU activation in LLMs as it minimally affects performance but reduces computation and weight transfer. Alternative activation functions like GELU or SiLU are popular but more computationally demanding.
ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models
Large Language Models (LLMs) with billions of parameters have revolutionized AI applications. However, their demanding computation during inference poses challenges for deployment on resource-constrained devices. While alternative activation functions like GELU or SiLU have gained popularity, this study strongly recommends using the ReLU activation in LLMs. It shows that ReLU has a minimal impact on convergence and performance while significantly reducing computation and weight transfer.
How AI Can Redefine Your Way of Work
If you want to evolve your company with AI and stay competitive, here are some practical steps:
1. Identify Automation Opportunities
Locate key customer interaction points that can benefit from AI. This will help streamline processes and improve efficiency.
2. Define KPIs
Ensure that your AI initiatives have measurable impacts on business outcomes. Set clear Key Performance Indicators (KPIs) to track progress and success.
3. Select an AI Solution
Choose AI tools that align with your specific needs and offer customization options. This will ensure that the solution fits your business requirements.
4. Implement Gradually
Start with a pilot project to test the effectiveness of AI. Gather data and insights from the pilot, and then expand the usage of AI judiciously across your organization.
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