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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

399,779 subscribers

61,662 次观看 • 1 年前 •via X (Twitter)

教育科学技术新闻政治

Anya Rossi• Live Now

Private livecam show

9 条评论

rushi 的头像

rushi1 年前

AI MOVE

UserInterface 的头像

UserInterface2 年前

How to Make Sales on UserInterface Freelance Marketplace #freelance

Gorilla Furkan | Integrated 🦍⚡️ 的头像

Gorilla Furkan | Integrated 🦍⚡️1 年前

@rushimanche GMOVE

TOP SIGNAL🔝 的头像

TOP SIGNAL🔝1 年前

"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."

dekompoza_Ng🪀⛽️ 的头像

dekompoza_Ng🪀⛽️1 年前

We know it’s all about #Openledger

Chopin Frédéric 的头像

Chopin Frédéric1 年前

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

Novastro | RWA L2 的头像

Novastro | RWA L21 年前

AI on MOVE

KRMC | SUPRA 的头像

KRMC | SUPRA1 年前

GN #openledger #opnup.

Tameryus | INTEGRATED 🦍 ⚡ 的头像

Tameryus | INTEGRATED 🦍 ⚡1 年前

AI MOVE

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Vibe code like never before Unlocking exclusive Solidity data from top devs. Fine-tuned with OpenLedger to build specialized models. Now, anyone can create expert AI agents for Solidity! This model is a testament to the power of community data. This is your chance to help us refine it further by participating in our upcoming testnet. Hugging Face link shared below

Vibe code like never before Unlocking exclusive Solidity data from top devs. Fine-tuned with OpenLedger to build specialized models. Now, anyone can create expert AI agents for Solidity! This model is a testament to the power of community data. This is your chance to help us refine it further by participating in our upcoming testnet. Hugging Face link shared below

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Excited to share a glimpse of what’s possible with specialized models on OpenLedger🐙 In this video, you’ll see a chat agent in action—think of it as the Perplexity of crypto. Powered by tool calling, this specialized model showcases how OpenLedger enables domain-specific intelligence for AI-driven apps. Vision of these specialised models is to use english to interact, query, and transact on-chain.🐙

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OpenLedger

87,878 次观看 • 1 年前

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Santiago

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0G Labs (Ø,G) - AI L1

69,917 次观看 • 1 年前

Episode 8 - OpenLedger Openledger with Ram Ram An amazing interview hosted by Jack D, this time with Ram Core Contributor at Openledger, huge moves are being made, it is a must watch episode! Enjoy! Youtube: Summary: In this episode of DEGN Talk, host Jack Degen Jack D interviews Ram Kumar Ram, a core contributor at Openledger, discussing the innovative blockchain platform designed for data contribution and AI model development. Ram explains the current focus on testing the product in its Testnet phase, the importance of specialized AI models, and the introduction of the OpenLedger Model Factory and OpenLoRA to reduce costs and democratize AI model training. The conversation also touches on the current landscape of the crypto market and the role of meme coins. Timecodes: 00:00 Introduction to OpenLedger and Web3 Journey 00:43 Current Focus and Future Roadmap of OpenLedger 03:29 Building Specialized AI Models on OpenLedger 06:10 The Role of Data Contributors and Attribution 08:46 Community Engagement and Upcoming Features 11:35 OpenLedger's Model Factory Explained 14:21 OpenLoRA: Decentralized Compute for AI Models 17:03 Support for Developers and Builders on OpenLedger 20:00 Market Insights and Future of Crypto Important Links: OpenLedger: Openledger Ram Kumar: Ram OpenLedger Website: #chain #web3 #podcast #degn #talk #blockchain #ai #gaming #focg #crypto #openledger #openLoRA

Episode 8 - OpenLedger Openledger with Ram Ram An amazing interview hosted by Jack D, this time with Ram Core Contributor at Openledger, huge moves are being made, it is a must watch episode! Enjoy! Youtube: Summary: In this episode of DEGN Talk, host Jack Degen Jack D interviews Ram Kumar Ram, a core contributor at Openledger, discussing the innovative blockchain platform designed for data contribution and AI model development. Ram explains the current focus on testing the product in its Testnet phase, the importance of specialized AI models, and the introduction of the OpenLedger Model Factory and OpenLoRA to reduce costs and democratize AI model training. The conversation also touches on the current landscape of the crypto market and the role of meme coins. Timecodes: 00:00 Introduction to OpenLedger and Web3 Journey 00:43 Current Focus and Future Roadmap of OpenLedger 03:29 Building Specialized AI Models on OpenLedger 06:10 The Role of Data Contributors and Attribution 08:46 Community Engagement and Upcoming Features 11:35 OpenLedger's Model Factory Explained 14:21 OpenLoRA: Decentralized Compute for AI Models 17:03 Support for Developers and Builders on OpenLedger 20:00 Market Insights and Future of Crypto Important Links: OpenLedger: Openledger Ram Kumar: Ram OpenLedger Website: #chain #web3 #podcast #degn #talk #blockchain #ai #gaming #focg #crypto #openledger #openLoRA

DEGN Talk

29,914 次观看 • 1 年前

The difference between namesake partnerships and meaningful ones? Real results. In this clip, Ram explains how io.net provides Openledger with enterprise-grade compute for cost-effective fine-tuning of AI models. = empowering builders and driving decentralized AI innovation. 👇

The difference between namesake partnerships and meaningful ones? Real results. In this clip, Ram explains how io.net provides Openledger with enterprise-grade compute for cost-effective fine-tuning of AI models. = empowering builders and driving decentralized AI innovation. 👇

io.net

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OpenLedger

172,303 次观看 • 10 个月前

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You can now fine-tune Llama 3 without writing a single line of code! We are moving at breakneck speed. I recorded a video to show you how to fine-tune any open-source model in a few minutes. I'm using a GPT capable of taking a problem and turning it into a fine-tuned model that will solve it. You don't have to write any code. You only need to explain to a GPT what problem you want to solve and tell it you want to use Llama 3. For example, "fine-tune Llama 3" or "deploy zephyr." It feels magic. The system will recommend a dataset and fine-tune the model for you. I'm using Monster API, a platform that specializes in making fine-tuning and deploying open-source models easy and fast. Their stack is well-optimized to maximize fine-tuning efficiency using techniques like Q-Lora and vLLM. They are behind the GPT. Here is what you need to do: 1. Create an account at 2. Load the GPT with the link below This is as simple as it gets. When you are done, you can click a button to deploy the model and start using it. I have 10,000 free credits for anyone using the code "SANTIAGO" in the dashboard. You can use these credits to access, fine-tune, and deploy these open-source models. You can also keep up with their latest updates, and get free credits and special offers on their Discord server:

Santiago

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RedPajama-INCITE-3B, an LLM for everyone: We are excited to share llama.cpp support! Efficiently run RedPajama on commodity CPUs! This, plus new resources to fine-tune the model using LoRA, instruction-tuning tips and more on our blog:

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New short course on Fine-tuning LLMs! Many developers are moving beyond only prompting, to also fine-tuning LLMs - that is, taking a pre-trained model and training it further on your own data, which can deliver superior results inexpensively. In this course, Sharon Zhou, CEO of Lamini (disclosure: I’m a minor shareholder) shows you how to recognize when fine-tuning can be help, and how to train an open-source LLM on your own data. I hope you enjoy the course!

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Tony Blair and Oracle co-founder Larry Ellison plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" their AI models. "We have to take all of this data... and move it into a single... unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Tony Blair and Oracle co-founder Larry Ellison plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" their AI models. "We have to take all of this data... and move it into a single... unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Wide Awake Media

54,084 次观看 • 7 个月前

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We’re excited to announce our partnership with Use Morpheus as they develop a specialized AI model on OpenLedger. Leveraging our decentralized infrastructure, Morpheus will create a fine-tuned Web3 LLM focused on secure code generation and autonomous workflows. This collaboration will push the boundaries of intelligent contract creation, enabling decentralized applications to scale smarter, faster, and more securely. The next wave of AI-powered smart contracts is here, stay tuned for more updates! Hear adamzhao.eth , founder of Use Morpheus, break it down in the video.

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17,229 次观看 • 1 年前

Excited to introduce DeepSeek R1 fine-tuning with Firecrawl 🔥 Learn how to fine-tune DeepSeek's R1 model on custom instruction datasets generated by Firecrawl's web data extraction. Train your own AI expert to answer domain-specific questions about your website!

Excited to introduce DeepSeek R1 fine-tuning with Firecrawl 🔥 Learn how to fine-tune DeepSeek's R1 model on custom instruction datasets generated by Firecrawl's web data extraction. Train your own AI expert to answer domain-specific questions about your website!

Eric Ciarla (hiring)

40,189 次观看 • 1 年前

Learn to train an LLM with distributed data while ensuring privacy using federated learning in a new two-part short course, Intro to Federated Learning and Federated Fine-tuning of LLMs with Private Data, created with Flower and taught by Daniel J. Beutel and nic lane. Federated learning allows a single model to be trained across multiple devices, such as phones, or multiple organizations, such as hospitals, without the need to share data to a central server. This two-part course gives you an introduction to federated learning, and then teaches you how to fine-tune your large language model with distributed data using Flower Lab’s open source federated learning framework. You’ll learn: - How to use federated learning to train a variety of models, ranging from speech and vision models to LLMs, across distributed data while offering data privacy options to users and organizations. - Privacy Enhancing Technologies like differential privacy (DP), which obscures individual data by adding calibrated noise to query results. - Two variants of differential privacy - Central and Local - and how to choose depending on your use case. - How to measure and decrease bandwidth usage to make federated learning more practical and efficient with techniques like using pre-trained models and Parameter-Efficient Fine-Tuning - How federated LLM fine-tuning reduces the risk of leaking training data. Sign up here!

Learn to train an LLM with distributed data while ensuring privacy using federated learning in a new two-part short course, Intro to Federated Learning and Federated Fine-tuning of LLMs with Private Data, created with Flower and taught by Daniel J. Beutel and nic lane. Federated learning allows a single model to be trained across multiple devices, such as phones, or multiple organizations, such as hospitals, without the need to share data to a central server. This two-part course gives you an introduction to federated learning, and then teaches you how to fine-tune your large language model with distributed data using Flower Lab’s open source federated learning framework. You’ll learn: - How to use federated learning to train a variety of models, ranging from speech and vision models to LLMs, across distributed data while offering data privacy options to users and organizations. - Privacy Enhancing Technologies like differential privacy (DP), which obscures individual data by adding calibrated noise to query results. - Two variants of differential privacy - Central and Local - and how to choose depending on your use case. - How to measure and decrease bandwidth usage to make federated learning more practical and efficient with techniques like using pre-trained models and Parameter-Efficient Fine-Tuning - How federated LLM fine-tuning reduces the risk of leaking training data. Sign up here!

Andrew Ng

64,538 次观看 • 1 年前

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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105,497 次观看 • 1 年前

We got our robots to wash pans, clean windows, make peanut butter sandwiches, and more! Fine-tuning our latest model enables all of these tasks, and this has interesting implications for robotics, Moravec's paradox, and the future of large models in embodied AI. More below!

We got our robots to wash pans, clean windows, make peanut butter sandwiches, and more! Fine-tuning our latest model enables all of these tasks, and this has interesting implications for robotics, Moravec's paradox, and the future of large models in embodied AI. More below!

Physical Intelligence

542,237 次观看 • 5 个月前