Ask HN: When is fine-tuning a small LLM worth it?

I've trained two small models, one I attempted to train it to be an expert (decent?) hearts (card game) player, and the other I trained on dream interpretation texts, I wanted to see if a model could interpret dreams reasonably well if it were trained on all the literature in existence (that I could find anyway). The hearts experiment was a general failure because the game of hearts as played by humans just has too many strategies to consider. I trained it purely on games (~20,000 hands of hearts in the RL dataset I generated) and the trained model did about as good as any decently written procedural game logic would have. the dream analyst training was a bit more successful. I can hand the model a dream log and it will offer some interesting insights. are they accurate? hard to say, it's a very subjective type of training, but definitely a better experiment than the hearts expert training run. in both cases the biggest and most time intensive part is generating the training dataset. building the LoRa is the easy part.

I was using Qwen3.5:2b models for both, running on Dell Pro Max GB10 Cuda,128GB.

I personally would only do it if I need it to solve a highly specialized problem that requires every inch of privacy I can get. Otherwise there are better options out there

Maybe when you want to learn the process for when/if you ever need to fine-tune a larger model?

It's faster to make iterate when you're toying around with a 1B model than a 27B one.

It's worth it if a) you have a decent sample size of data for your problem and b) you have a cost-effective infra to host it.

Notably the latter is more of the bottleneck, particularly with the price race-to-zero with models such as GPT-6 Luna.