I think at the hedge funds, AI will play out similar to how quant has in the past, i.e. as a technology that will still have alphas unlockable by good usage.

High-performing funds may "beat the market" at models, data, fine-tuning, RAG or even figuring out which AI tool should be deployed for which tasks.

Interesting parallel, but as it is packaged today - "AI" looks much lighter on technical customization than quant.

What can you really meaningfully change on the large models today? a bit of RAG, some prompting - sure. But that sounds like a much lighter lift compared to the tasks done by large teams of top-tier CS grads in older quants firms.

This looks exciting! There is a serious dearth of high-quality open-source models with multimodal capabilities. So, really looking forward to playing with this one.

Has anyone here experimented with fine-tuning this for domain-specific applications?