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I am rooting for Mistral with their different approach: not really competing on the largest and advanced models, instead doing custom engineering for customers and generally serving the needs of EU customers.
their ocr model is goated
Earlier quoted context omitted.
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But the OP's blog is more about ZK than about NFTs, and crypto is the only place funding work on ZK. It's kind of a devil's bargain, but I've taken crypto money to work on privacy preserving tech before and would again.
How many proprietary use cases truly need pre-training or even fine-tuning as opposed to RAG approach? And at what point does it make sense to pre-train/fine tune? Curious.
I am rooting for Mistral with their different approach: not really competing on the largest and advanced models, instead doing custom engineering for customers and generally serving the needs of EU customers.
next, it sounds like it's going to be .eu
but what about ai.eu
> Pre-training allows organizations to build domain-aware models by learning from large internal datasets. > Post-training methods allow teams to refine model behavior for specific tasks and environments. How do you suppose this works? They say "pretraining" but I'm certain that the amount of clean data available in proper dataset format is not nearly enough to make a "foundation model". Do you suppose what they are…
How many proprietary use cases truly need pre-training or even fine-tuning as opposed to RAG approach? And at what point does it make sense to pre-train/fine tune? Curious.
Not everyone is obsessed with code generation. There is a whole world out there.