I finally stopped trying to make every AI model bigger and saw better results

For months, I was just adding more layers and parameters to my language models, thinking that was the only path forward. The tipping point was a small project for a local library in Boise where their old server couldn't handle my 'improved' model. I had to strip it down to basics, and the simpler version actually performed the task faster and more reliably. Has anyone else found that sometimes the innovation is in making things simpler, not just bigger?

2 comments

2 Comments

kim.charlie23d ago

That Boise library project is a perfect example. The real trick is finding the right size for the job, not just the biggest one. You can waste a lot of time and power on extra layers that don't help.

wood.noah23d ago

Watched my buddy spend weeks training a huge model for his senior project. He kept adding layers thinking it would get smarter, but the accuracy actually got worse. His professor finally told him to strip it back to the basics. He cut it down by half and the thing ran faster and nailed the task. He was so mad at all the time he wasted.