There are some interesting challenges in fine tuning LLMs but this doesn't seems to address them.<p>I'm not sure if the code samples actually work but they look super generic, and eg it talks about using "accuracy" to evaluate and a test split of 10% in a way that doesn't make sense to me.<p>An LLM is never going to perfectly generate the same answer as your gold standard answer, so evaluating your model is a challenge on its own that would have been great to address here, but was skipped over in favour of an ad.<p>Also a lot of the stuff under "why fine tune" seems off. You can do most of that stuff with an LLM directly without fine tuning.<p>Overall this post <i>looks</i> a lot like the in depth, long form content I usually love seeing on HN, but I am suspicious that it is actually vapourware that follows the form of a technical guide without actually being one (eg written by someone nontechnical or partially auto generated)