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Comment by redox99 | Hacker News Reader

That's a pretty odd stance. I've finetuned llama/mistral models that greatly outperform GPT4 with just a prompt.

You have to know when to RAG, finetune, or RAG+finetune.

"I've finetuned llama/mistral models that greatly outperform GPT4 with just a prompt"

If you write about your experiments with that in detail I guarantee you'll get a lot of interest. The community is crying out for good, well documented, replicable examples of this kind of thing.

I'm so behind in this area. I had finetuned a model that was SOTA and worth publishing about in October, but procrastinated. I'm scared to check if somebody else already published on this topic.

    greatly outperform GPT4 *for* just a prompt

your overfitting to training data convinces no-one that you created a "better GPT4"

Do you always assume other people are incompetent? That's not very nice of you.

I mostly work on AI, so I know if I'm overfitting or not. It performs provably better in it's domain (a niche programming language). GPT4 can barely write a hello world for it.

I'm not creating a "better GPT4" general chatbot. I'm finetuning for a specific task.

You are making an extraordinary claim, and they require extraordinary evidence. Unless presented it is a good idea to assume they are bogus.

How narrow is the dataset to be outperforming greatly?

Just curious about what the usecase is for a 7b model in a business context - ie. what does it do?

Code assistant for a niche programming language that GPT4 knows very little about and barely gets a hello world right.

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