Fine tuning is a story that is nice to tell but that with modern LLMs makes less and less sense. Modern LLMs are so powerful that they are able to few shot learn complicated things, so a strong prompt and augmenting the generation (given the massive context window of Qwen3.5, too) is usually the best option available. There are models for which fine tuning is great, like image models: there with LoRa you can get good results in many ways. And LLMs of the past, too: it made sense for certain use cases. But now, why? LLMs are already released after seeing (after pre-training) massive amount of datasets for SFT and then RL. Removing the censorship is much more efficiently done with other techniques. So I have a strong feeling that fine tuning will be every day less relevant, and already is quite irrelevant. This, again, in the specific case of LLMs. For other foundational models fine tuning still makes sense and is useful (images, text to speech, ...).