> So why should anyone except AI researchers and the big 4 AI providers care about fine-tuning?

I currently have Qwen3 Coder 30B A3B on prem for developers to use and it's pretty good for that: fits within two Nvidia L4 cards (with quantization), has about 60 tok/s performance and can even use tools with something like RooCode or OpenWebUI.

However, if anyone asks it stuff in Latvian, it does mess up more often than not, like someone who has half-learned the language: more or less uses the words that represent the correct concepts, but not always the best pick and really often missing or wrongly used diacritics (ā, č, ē, ģ, ī, ķ, ļ, ņ, š, ū, ž). In a word, some basis of the language is there, but sadly in practice it's still unusable.

So far working with Latvian text I need to maintain EuroLLM running in parallel, which has great Latvian knowledge, but at the same time just knows less (not a model that's good for programming) and doesn't know how to call tools as well and isn't really meant for long contexts: https://huggingface.co/collections/utter-project/eurollm-66b...

So my ideal model (for this use case) would be something along the lines of: around 30B since can't fit anything much bigger into the VRAM for now, MoE, supports tool calling, okay programming knowledge, has a long enough context support, can converse in Latvian so I don't need to run 2 models (which means that the context sizes that can fit within VRAM are way too small for either of the models).

Without finetuning, I just have to sit and wait for someone to release that and it feels unlikely that it'll just pop into existence (even the bigger EuroLLM model is nowhere to be seen, TildeOpen works bad). With finetuning, maybe I have a chance to take the Latvian Wikipedia or whatever other samples of the language being used I can get, filter down to topics I care about, maybe use EuroLLM to generate question/answer pairs for training from that input data and then just run Unsloth over a weekend or something (who knows, maybe that'd be enough to bring Qwen3 up to speed).

Do most people care about that stuff? Probably not, but when you have to deal with a language that's less represented in the training data but will never have the budget to train something from scratch, finetuning is nice to have. RAG can't fix it not knowing how to use language.

GPT-OSS Reinforcement Learning