fine tuning a small LLMs or even real small language models (like bert) is what the recommended way since the introduction of LLM. the benefit is pretty much ob

fine tuning a small LLMs or even real small language models (like bert) is what the recommended way since the introduction of LLM. the benefit is pretty much obvious: faster to run, fully controlling the stack, better fit for the custom domain... but in practice not many people do the fine tuning, for pretty much a single reason: large language models API cost are still very cheap, fast enough, and get improvement all the times. what the point of spend time (and money) to fine tune a specified model, then just when you release it a newer gen generic model is released and beat it? but if someday the progress for LLM is slowed, or the price increased to the point calling api is not a viable approach any more, then surely the day of fine tuning and small models will come again.