8 Comments
User's avatar
Nathan Lambert's avatar

Not enough people talking about this obvious fact that most people shouldn't bother training models (I very cursoryily skimmed mostly the title). It was the TLDR of my talk on "post training for applications" https://www.youtube.com/watch?v=grpc-Wyy-Zg

Most people should just not do it!

Expand full comment
Binh Pham's avatar

lol that comment on british and french was cold

Expand full comment
Devansh's avatar

Thank you

Expand full comment
Esborogardius Antoniopolus's avatar

Isn't everyone doing LoRA nowadays when they say they are fine tunning?

Expand full comment
Andrew Duncan's avatar

But isn't anyone who is doing fine-tuning really using lora adapters? Who's got the compute to be doing full fine-tuning of big models? So the capacity argument is less clear.

Expand full comment
Vincent's avatar

This post is hilarious. People like this author are the ones vetting start-ups? Please. The idea that alignment leads to a degradation in model utility is hardly news.

But let’s be clear: fine-tuning an LLM to specialize in a task isn’t just about minimizing utility loss. It’s about trade-offs. You have to weigh what you gain against what you lose.

Expand full comment
Sten Rüdiger's avatar

Great post! Adapters are still underappreciated. You may be interested in this novel adapter method:

https://stenruediger.substack.com/p/supercharge-your-llms-introducing

Expand full comment
Harrison's avatar

Fascinating read! I’m Harrison, an ex fine dining line cook. My stack "The Secret Ingredient" adapts hit restaurant recipes for easy home cooking.

check us out:

https://thesecretingredient.substack.com

Expand full comment