"Codebases, docs, large corpses of internal datasets"
I still haven't seen a convincing demo of using fine-tuning to "teach" a model new information from additional documents. I'd love to see one.
(Closest I've come to that is I heard a rumor that Jane Street have fine-tuned an LLM for OCaml)
Here is a small LLM I trained to output dollars and cents from a verbal numeric amount:
https://huggingface.co/TrevorJS/check-amount-deverbalizer-sm...
1. Codebases, docs, large corpses of internal datasets - fill in the middle, auto completion etc.
2. I know a tonne of financial institutions use fine-tuning for trading, real time data parsing headline analysis, signal creation etc
3. Distillation is also relatively common - taking outputs of a large model and distilling it to a small model
4. Accuracy increasing is the most important - not cost or latency - we find if you solve the finetuning life cycle ie continuous auto fine-tuning, data filtering, reinforcement learning via DPO, that works well!
5. Lots of organizations use DPO and preference fine-tuning to align models since they have tonnes of feedback data!
6. Yep vision fine-tuning! For eg medical diagnosis, docs, qa on pics etc
7. And obviously large model labs finetune all base models ie chatgpt4.5 is a finetune of a base model
8. Finally reasoning finetuning via GRPO is very cool! If you have inputs and outputs but no labelled cot in between, GRPO is the way to go! Custom reward functions by companies!