The case for the return of fine-tuning • Hacker News Board

Re: The case for the return of fine-tuning

#81

Post by hommes-r » Mon, Oct 20, 2025, 6:59 AM UTC

deepsquirrelnet wrote: post #48

I go back and forth on this. A year ago, I was optimistic and I have had 1 case where RL fine tuning a model made sense. But while there are pockets of that, there is a clash with existing industry skills. I work with a lot of machine learning engineers and data scientists and here’s what I observe. - many, if not most MLEs that got started after LLMs do not generally know anything about machine learning. For lack of…

My personal opinion is that true engineering, which revolves around turning complex theory into working practice, has seen a decline in grace. Why spend a lot of time trying to master the art of engineering if you can ride the wave of engineering services and get away with it?

In true hacker spirit, I don't think trying to train a model on a wonky GPU is something that needs an ROI for the individual engineer. It's something they do because they yearn to acquire knowledge.

Re: The case for the return of fine-tuning

#82

Post by CaptainOfCoit » Mon, Oct 20, 2025, 9:54 AM UTC

gdiamos wrote: post #60

CaptainOfCoit wrote: post #52

Earlier quoted context omitted.

If it works it works? :shrug:

The problem is that it doesn’t always work and when it does fail it fails silently. Debugging requires knowing some small detail about your data distribution or how you did gradient clipping which take time and painstakingly detailed experiments to uncover.

> The problem is that it doesn’t always work and when it does fail it fails silently.

Right, but why does that mean you need more employees? You need to figure out how to surface failures, rather than just adding more meat to the problem.

Re: The case for the return of fine-tuning

#83

Post by CaptainOfCoit » Mon, Oct 20, 2025, 10:44 AM UTC

coredog64 wrote: post #50

CaptainOfCoit wrote: post #42

Earlier quoted context omitted.

If people have ideas for use cases where fine-tuning can make a big difference, but don't have the time/resources to try it out yourself yet want to see if it'll work, feel free to share your ideas as I'm currently creating a bunch of examples of this and could use some inspiration, I only have 3 real/confirmed use cases as of right now.

Something that's in my personal backlog is fine-tuning of TrOCR for purse seine observer workbooks. The default TrOCR is expecting English words, and so the FAO species codes used in the workbook result in terrible accuracy. LLMs do poorly in this space because you'll commonly see repeats (e.g. 100 out of 120 samples all have the same species code) which then leads to hallucination.

You might enjoy this :) https://news.ycombinator.com/item?id=45640594 (DeepSeek OCR)

Re: The case for the return of fine-tuning

#84

Post by kgwgk » Mon, Oct 20, 2025, 11:26 AM UTC

fragmede wrote: post #80

kgwgk wrote: post #62

Earlier quoted context omitted.

There is also a reason why you don’t have general purpose applications. Most users understand that Excel is for data tables and Paint is for images even though some people have fun playing with the boundary and creating Excel paintings.

Interesting you bring up Excel. ChatGPT's chat interface is going to be Excel for the AI era. Everyone knows there's a better interface to be had, but it just works.

In the pre-AI era Excel was not « the » interface. Most people didn’t use Excel at all!

Re: The case for the return of fine-tuning

#85

Post by Der_Einzige » Mon, Oct 20, 2025, 5:22 PM UTC

gdiamos wrote: post #25

bjornsing wrote: post #24

Earlier quoted context omitted.

Will Anthropic/OpenAI really hire anyone who can fine-tune an LLM?

They will hire anyone who can produce a model better than GPT5, which is the bar for fine tuning Otherwise, you should just use gpt5 Preparing a few thousands training examples and pressing fine tune can improve the base LLM in a few situations, but it also can make the LLM worse at other tasks in hard to understand ways that only show up in production because you didn’t build evals that are good enough to catch them…

Yup! That's why civit.ai doesn't exist right?

They'll pay for anyone that can personalize models to be meaningfully diverse.