Most absolutely not with the 7B llama model as described here.
…but, potentially, with a much larger fine tuned foundational model, if you have a lot of open source code on GitHub and lots of public samples.
The question is why you would bother? very large models would most likely not be meaningfully improved by fine tuning on a specific individual.
The only reason to do this would be to coax a much smaller model into having characteristics of a much larger one by fine tuning… but, realistically, right now, it seem pretty skeptical anyone would bother.
Why not just invest in a much larger more capable model?
ChatGPT’s “voice” changes dramatically in diction and prose when you ask it to generate text in the style of a popular author like Hunter S Thompson, Charles Bukowski, or Terry Pratchett. You can even ask it to generate text in the style of a specific HN user if they’re prolific enough in the training data set.
Fine tuning would allow you to achieve that for people who aren’t notable enough to be all over the training data
Most absolutely not with the 7B llama model as described here.
…but, potentially, with a much larger fine tuned foundational model, if you have a lot of open source code on GitHub and lots of public samples.
The question is why you would bother? very large models would most likely not be meaningfully improved by fine tuning on a specific individual.
The only reason to do this would be to coax a much smaller model into having characteristics of a much larger one by fine tuning… but, realistically, right now, it seem pretty skeptical anyone would bother.
Why not just invest in a much larger more capable model?