There's a Manning book on creating your own LLM from scratch which answers your question exactly. There's another book from the same publisher specifically about small language models for specialty purposes.
replyTechnically, you could do that, but we trained this one from the ground up!
replyThat sounds like an enormously expensive exercise.
replyAs someone who's done something similar (https://blog.lukesalamone.com/posts/creating-tiny-semantic-s...) the expensive part wasn't the training itself but the data curation and evaluation post-training. For this, getting a reasonable distribution of tool calls when the tool call can be anything isn't easy.
replyOnce you have that, the model is small enough batch sizes are probably enormous and training can probably be done on a consumer-grade GPU in a week or less. Or even faster on a bigger GPU.
At <50M parameters, training costs are completely trivial. You'll spend a lot more on your rent this month.
replyHaha, my rent is cheap lol
replyTraining scales pretty badly, so smaller models like this are really not that bad in terms of cost.
replyYou can train a model of this size on your laptop in a day.
replyAnother option for something this small and narrowly specialized could be to get traditional LLMs to synthesize the training data. Model collapse is probably less of an issue at this size relative to terabyte sized models.
replyYour thinking is correct haha
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