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Generating from a finetuned GPT 3.5 Turbo is 8x the cost of generating from the base model, so you really have to be in the “reduce prompt size by 90%” bucket they mention to get cost effectiveness out of it.
ELI5 what this means and impact?
It's an announcement about the availability of a feature to do that. The article doesn't mention the biggest issue with fine-tuned models though - cost.
gotta wait til fall for general availability of fine tuning? looks like there's a legacy version too? openai is streets ahead
Support for fine-tuning with function calling and gpt-3.5-turbo-16k will be coming later this fall.
What about data privacy? Anyone know if OpenAI can use your fine tuning data?
"As with all our APIs, data sent in and out of the fine-tuning API is owned by the customer and is not used by OpenAI, or any other organization, to train other models."
Generating from a finetuned GPT 3.5 Turbo is 8x the cost of generating from the base model, so you really have to be in the “reduce prompt size by 90%” bucket they mention to get cost effectiveness out of it.
I wonder why is much more expensive.
gotta wait til fall for general availability of fine tuning? looks like there's a legacy version too? openai is streets ahead
my bad - Fine-tuning for GPT-3.5 Turbo is now available, with fine-tuning for GPT-4 coming this fall. Support for fine-tuning with function calling and gpt-3.5-turbo-16k will be coming later this fall.
Fine-tuning GPT models can make them better for specific applications, but it requires a careful investment of time and effort. We recommend first attempting to get good results with prompt engineering, prompt chaining (breaking complex tasks into multiple prompts), and function calling, with the key reasons being: * There are many tasks for which our models may initially appear to not perform well at, but with better prompting we can achieve much better results and potentially not need to be fine-tune * Iterating over prompts and other tactics has a much faster feedback loop than iterating with fine-tuning, which requires creating datasets and running training jobs * In cases where fine-tuning is still necessary, initial prompt engineering work is not wasted - we typically see best results when using a good prompt in the fine-tuning data (or combining prompt chaining / tool use with fine-tuning) ```
Even if it's quite specialized like in Medical/Legal, it would be great to see the expected value one can derive from fine-tuning.
I wish there was some documentation on what kinds of things are determined unsafe. There are plenty of things I think we would all agree are unsafe. I'm sure we don't want fine tuned models on how to cause physical harm on other people.
I don't envy the challenge of making the call for more gray area, sometimes even cultural differences, in what is safe or not. Seems like a very hard problem we've seen social media struggle with. I'm reminded of some of the Covid "misinformation" being deemed as unsafe
Are there any benchmarks/results showcasing the performance increase with fine tuning? Like showcasing a list of prompt and answers between a fine-tuned LLM and a non fine-tuned one. Could be with LLAMA 2 as well. Even if it's quite specialized like in Medical/Legal, it would be great to see the expected value one can derive from fine-tuning.