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This sounds quite cool, and I am very picky but this seems quite clean. It wasn't clear if this was for fine-tuning or not, the general explanation could perhap

This sounds quite cool, and I am very picky but this seems quite clean. It wasn't clear if this was for fine-tuning or not, the general explanation could perhaps be clearer, the first few sentences of the README still don't make it very clear (I can sort of guess, having seen DSPy, AutoPrompt etc). It would be awesome if this DID also explore if it needed to do: prompt tuning, fine-tuning, or soft-prompt tuning etc, I am on the lookout for a tool that does this. Obviously a general open source Q* like solution would be amazing but I get that might be a bit of a different beast! Part of my issue is that there are so many things that can be tweaked and I often don't know the most time and cost efficient thing to optimise. I get that prompt tuning is often going to be the best thing to do, especially first. But for efficient inference, shorter prompts may well be needed. Though maybe clever model key-value caching is starting to make this less of an issue, but it's still faster to have as short as prompts as possible, still, fine tuning or even resuming pretraining may be the best thing to do sometimes. BTW I would strip `gpt-3.5-turbo` from all examples, as it's more expensive than the better 4o-mini. I hope to check this out more later. Nice work!

meame2010 2y ago
Thanks for the insightful response. Good point on using 4o-mini to save cost. I'll try it out. I will check more into the soft-prompt tuning. For the current scope, we are focused on in-context learning, ways to improve model reasoning at the inference time. We use auto-differentiative framework (backpropagation) to do zero-shot instruction optimization and few-shot demonstration. currently even just zero-shot can often surpass Dspy's few-shots (as many as 40 shots). And I have come up a training paradigm that will (1) start zero-shot (2) review performance from advanced teacher model to see if we can have a gap to gain from the teacher. (3) if there is a gap to teacher, we start to do low-shot demonstrations, and gradually increase the number of shots.

meame2010 2y ago

Thanks for the insightful response. Good point on using 4o-mini to save cost. I'll try it out. I will check more into the soft-prompt tuning. For the current scope, we are focused on in-context learning, ways to improve model reasoning at the inference time. We use auto-differentiative framework (backpropagation) to do zero-shot instruction optimization and few-shot demonstration. currently even just zero-shot can often surpass Dspy's few-shots (as many as 40 shots). And I have come up a training paradigm that will (1) start zero-shot (2) review performance from advanced teacher model to see if we can have a gap to gain from the teacher. (3) if there is a gap to teacher, we start to do low-shot demonstrations, and gradually increase the number of shots.