There are some interesting challenges in fine tuning LLMs but this doesn't seems to address them.
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.
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.
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.
Overall this post looks 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)
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.
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.
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.
Overall this post looks 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)