Faster, cheaper fine-tuning and training
If I could train a useful model, on my own data, in a reasonable time
I would want to have a CI-training pipeline to always have my models up to date
If I could train a useful model, on my own data, in a reasonable time
I would want to have a CI-training pipeline to always have my models up to date
If your main concern is question answering or summarization or code completion there are plenty of ways to do that now. If you really require the advanced emergent properties of LLMs, you'll have to work with a company that can afford to train a transformer on the Entire Internet.
Apparently an RTX 4090 running overnight is sufficient to produce a fine-tuned model that can spit out new Harry Potter stories, or whatever...
What would the quality of the model be, compared to what Karpathy uses on his video “let’s build gpt from scratch”?
In that video, he builds a decoder-only transformer model, that learns Shakespeare from 1MB of data and trains in 15min
I'm not sure what the analogous term is for a similar process on LLMs, but that will be huge when there is a service for it.
If you want for example to train the model to learn to use a very large API, or access the knowledge in a whole book, it might need fine-tuning.
Then do some chat fine tuning (like what HF did with StarCoder to get ChatCoder)
And get a lightweight LLM that knows the docs and code for the thing I need it for
After that, maybe incrementally fine tune the model as part of your CI/CD process
E.g., were you trying to distinguish an object vs nothing, a bicycle vs a fish, a bird vs a squirrel, or two different species of songbird at a feeder?
How much would the training requirements increase or decrease moving up or down that scale?