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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... show more

OpenLedger

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61,697 görüntüleme • 1 yıl önce •via X (Twitter)

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How to Make Sales on UserInterface Freelance Marketplace #freelance

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"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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We know it’s all about #Openledger

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

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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

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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I am excited to present you with a Christmas gift :P 😆😆 Our new fine-tunable generalist agent is what we call the Pangu-Agent! Our agent is fantastic - ok! It supports the following: 1. Structured reasoning via a new notion of intrinsic functions that allows you to change the internal structure of your agent to better suit your task - in fact, we think this notion is critical to scale reinforcement learning to generalisable setups. Those functions are easy to write, compose, fine-tune, nest, and do whatever you like to do with them. 2. With those, we have a framework that allows you to consider prompt engineering methods as a particular case nesting of intrinsic forms - in other words, those prompting techniques we have, ReACT, ZS-CoT and so forth, are just a particular case. Now, we can make new ones with simple lines of code. 3. All agents need to learn new skills and adapt. Agents without abilities to fine-tune are not agents. They are as good as your pre-trained LLM. Well, we now can fine-tune using three different techniques: 3.1) Standard supervised fine-tuning; 3.2) [New] Bootstrapped supervised fine-tuning (mix-n-match) intrinsic functions; and 3.3) Reinforcement learning fine-tuning [We did so many tricks here to get RL fine-tuning to work well] 4. We show awesome zero-shot transfer results without ingesting #GPT4 data. Just using open-source models! 5. We also show that you can train one cross-domain model across BabyAI and AlfWorld tasks, fine-tune with RL, and perform well in held-out tasks. 6. What is also nice is that we can now have rigorous benchmarking experimentation as well - it seems not all that is reported is what really works :P Paper: #AI #MachineLearning This work has been done by a fantastic team, which I am proud to co-lead and work with! This project has been managed by the amazing Kun Shao , who has been impressive, to say the least! Technically supervised by Professor Jun Wang, who had the view of where we needed to go and guided by Professor Hao from tech and application aspects!! Wow! Nice end of the year!!!

I am excited to present you with a Christmas gift :P 😆😆 Our new fine-tunable generalist agent is what we call the Pangu-Agent! Our agent is fantastic - ok! It supports the following: 1. Structured reasoning via a new notion of intrinsic functions that allows you to change the internal structure of your agent to better suit your task - in fact, we think this notion is critical to scale reinforcement learning to generalisable setups. Those functions are easy to write, compose, fine-tune, nest, and do whatever you like to do with them. 2. With those, we have a framework that allows you to consider prompt engineering methods as a particular case nesting of intrinsic forms - in other words, those prompting techniques we have, ReACT, ZS-CoT and so forth, are just a particular case. Now, we can make new ones with simple lines of code. 3. All agents need to learn new skills and adapt. Agents without abilities to fine-tune are not agents. They are as good as your pre-trained LLM. Well, we now can fine-tune using three different techniques: 3.1) Standard supervised fine-tuning; 3.2) [New] Bootstrapped supervised fine-tuning (mix-n-match) intrinsic functions; and 3.3) Reinforcement learning fine-tuning [We did so many tricks here to get RL fine-tuning to work well] 4. We show awesome zero-shot transfer results without ingesting #GPT4 data. Just using open-source models! 5. We also show that you can train one cross-domain model across BabyAI and AlfWorld tasks, fine-tune with RL, and perform well in held-out tasks. 6. What is also nice is that we can now have rigorous benchmarking experimentation as well - it seems not all that is reported is what really works :P Paper: #AI #MachineLearning This work has been done by a fantastic team, which I am proud to co-lead and work with! This project has been managed by the amazing Kun Shao , who has been impressive, to say the least! Technically supervised by Professor Jun Wang, who had the view of where we needed to go and guided by Professor Hao from tech and application aspects!! Wow! Nice end of the year!!!

Haitham Bou Ammar

15,534 görüntüleme • 2 yıl önce