Yi Cui

Cool! I will check it out.

What I meant by switching is this. Sometimes I'm not satisfied with ChatGPT answer, and realized it needs to think harder. So I switched to o1 and asked again, and most of the times the answer gets better. Then I asked a simple follow-up question which o1 overanalyzed. Then I had to switch back to gpt-4o. I don't actually have the foresight which model fits my question the best. I only know it after I read the answer which is too late.

Now imagine a conversation with a human expert. A human can do such switching remarkably well, hence a cool conversation. This can be actually a metric to read the mileage of an applicant.

hi

Done. So I understand this: you do not change model weights, but rather tweak the inference logic? Somehow remind me of speculative decoding.

Sure, this is what I intend to do.

But a HF 🤗 collection cannot include anything outside HF 🤗. It has to be a dataset, model, space, or paper. Do you have anything like those?

In my case I asked both models to write code. The model is good if the code passes tests. What are your prompts?

https://huggingface.co/datasets/onekq-ai/WebApp1K-Duo-React

I know though Anthropic weighs in on safety.

And their python package too 😜

Having AI to do the refactor is a great idea though. It will be breaking change if you switch your model from non-reasoning to reasoning.