Welcome to Day 20 of 21 Days of Building a Small Language Model. The topic for today is activation functions, the components that give neural networks their ability to learn complex, non-linear patterns. Today, we’ll discover how activation functions work, why they’re essential, and how modern choices like SwiGLU have become the standard in state-of-the-art language models.
Early models relied on ReLU, but modern language models have moved forward. SwiGLU introduces a gated mechanism that controls information flow, improves gradient behavior, and delivers better performance and stability during training. That’s why today’s state-of-the-art LLMs consistently prefer SwiGLU over traditional activations.
🔗 Blog link: [https://devopslearning.medium.com/day-20-21-days-of-building-a-sma…
Welcome to Day 20 of 21 Days of Building a Small Language Model. The topic for today is activation functions, the components that give neural networks their ability to learn complex, non-linear patterns. Today, we’ll discover how activation functions work, why they’re essential, and how modern choices like SwiGLU have become the standard in state-of-the-art language models.
Early models relied on ReLU, but modern language models have moved forward. SwiGLU introduces a gated mechanism that controls information flow, improves gradient behavior, and delivers better performance and stability during training. That’s why today’s state-of-the-art LLMs consistently prefer SwiGLU over traditional activations.
🔗 Blog link: https://devopslearning.medium.com/day-20-21-days-of-building-a-small-language-model-activation-functions-703049a7c283
I’ve covered all the concepts here at a high level to keep things simple. For a deeper exploration of these topics, feel free to check out my book "Building A Small Language Model from Scratch: A Practical Guide."
✅ Gumroad: https://plakhera.gumroad.com/l/BuildingASmallLanguageModelfromScratch
✅ Amazon: https://www.amazon.com/dp/B0G64SQ4F8/
✅ Leanpub: https://leanpub.com/buildingasmalllanguagemodelfromscratch/