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Revolutionizing Move Programming with OpenLedger In this demo, we showcase how Move datasets contributed by data providers to OpenLedger’s datanets are used to fine-tune specialized models with LoRA fine-tuning. As seen in the video, we showcase an example on how builders can deploy a Move-specialized model that powers Co-pilot...

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OpenLedger

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rushivor 1 Jahr

AI MOVE

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UserInterfacevor 2 Jahren

How to Make Sales on UserInterface Freelance Marketplace #freelance

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Gorilla Furkan | Integrated 🦍⚡️vor 1 Jahr

@rushimanche GMOVE

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TOP SIGNAL🔝vor 1 Jahr

"OpenLedger's LoRA fine-tuning demo is a glimpse into how blockchain can decentralize AI training. Data integrity meets scalability—AI's future is trustless."

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dekompoza_Ng🪀⛽️vor 1 Jahr

We know it’s all about #Openledger

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Chopin Frédéricvor 1 Jahr

This whole video doesn't show a single line of generated move code. Just a scam ...

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Novastro | RWA L2vor 1 Jahr

AI on MOVE

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KRMC | SUPRAvor 1 Jahr

GN #openledger #opnup.

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AI MOVE

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Many people are flat wrong about DeepSeek. You might think everyone freaked out about DeepSeek because the model is really good—which it is—or because it was from China, which is also true. But there's a more subtle reason: DeepSeek showed they could achieve those results using reinforcement learning instead of supervised fine-tuning. In other words, they didn't need an expensive phase where a bunch of people tuned the model by answering questions. They were able to do all of this faster and cheaper using reinforcement fine-tuning. This is a huge deal! I recorded the attached video with the following goals in mind: 1. Explain how reinforcement fine-tuning works 2. Show you how you can fine-tune your own models 3. Walk you through a complete code example

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