Most of the entire field of machine learning is “try shit and see what works”. So it seems like they’re par for the course.

Same as software engineering field too.

It's even worse for AI given that nobody really understands why anything works.

I wonder what we don’t understand from the SE POV?

Found my answer for why thanks to the issues in latest dolphin fine-tune. They do these types of fine tunes mainly to reduce refusal rates and increase intelligence. They did the knee-jerk rerun of the same old data this time, as I suspected, just for lols to see where open-source is at.

Spoiler alert, fine-tunes won't be better until the data quality is better than meta's instruction fine-tune. Give it some weeks.

Why does [doplin-l3-8B] perform substantially worse in some tests?

Essentially, it's trained like this:

  LLama-3-8B-base_model --> LLama-3-8B-Instruct
  LLama-3-8B-base_model --> dolphin-2.9-llama3-8B

And not like this:

  LLama-3-8B-Instruct --> dolphin-2.9-llama3-8B

https://huggingface.co/cognitivecomputations/dolphin-2.9-lla...

One additional problem with people who write breathless tutorials about doing things with AI is that they are more likely than average to have been written with ChatGPT. Which, given the knowledge cutoff for most models, is not where I'd personally turn for data on recent technical developments, but is par for the course for the kind of low-effort copy-paste bloggers doing it for attention.

This particular one seems to be from someone who is documenting their learning process, which is a valuable contribution but, obviously, not a source of great authority on the how's and why's.