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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...

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87,959 görüntüleme • 1 yıl önce •via X (Twitter)

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KRMC | SUPRA profil fotoğrafı
KRMC | SUPRA1 yıl önce

Güzel anlatım. Teşekkürler. #openledger #opnup

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Lab4crypto1 yıl önce

🚀 Don't gamble with your portfolio! Use our advanced hybrid quant risk tool using on/off-chain data and make informed decisions. 📈 Acess to 1000+ charts for your crypto journey. 📚Join our Premium Telegram for daily alerts. 📊+21 projects supported. 🏗️ Beginners and experts.

Ozzi.sol (Ø,G) (✸,✸) profil fotoğrafı
Ozzi.sol (Ø,G) (✸,✸)1 yıl önce

IS THE @OpenledgerHQ DEVELOPING THIS, DUDE?

S a n t o s 𓊈𒆜🐙𒆜𓊉 profil fotoğrafı
S a n t o s 𓊈𒆜🐙𒆜𓊉1 yıl önce

cooking ❤️‍🔥 #Openledger #Opnup

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UV1 yıl önce

Something Deep?! you’re Digging Guys. 👌

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Noah (Ø,G)1 yıl önce

Wow! Already looking great, looking forward to the final product #Opnup

꧁IP꧂ crypto king CLONE (✸,✸) (Ø,G) profil fotoğrafı
꧁IP꧂ crypto king CLONE (✸,✸) (Ø,G)1 yıl önce

Great project

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Lilremedy1 yıl önce

Something is cooking @OpenledgerHQ , so sorry for faders 😂

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DigitalNomad 𓊈𒆜🐙𒆜𓊉꧁IP꧂1 yıl önce

Revolutionize everything AI! #Opnup

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Mazher Shahzad (web3cryptoworld)1 yıl önce

to the moon #Openledger #Opnup

Vicky Sharma (✸,✸) CLONE ⛺ Kaisar profil fotoğrafı
Vicky Sharma (✸,✸) CLONE ⛺ Kaisar1 yıl önce

cooking ❤️‍🔥 #Openledger #Opnup

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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 agents using our no-code model fine-tuning platform. This is the future of AI and Web3 innovation. Watch this space to see more specialised models and data feeds being built for next generation agents on top of OpenLedger #Move
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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 agents using our no-code model fine-tuning platform. This is the future of AI and Web3 innovation. Watch this space to see more specialised models and data feeds being built for next generation agents on top of OpenLedger #Move

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OpenLora is the designated deployment engine for specialized models in the OpenLedger ecosystem. By leveraging just-in-time adapter switching, OpenLora enables the efficient serving of thousands of fine-tuned LoRA adapters on a single GPU, drastically reducing deployment costs. Unlike generic models, OpenLora-powered specialized models require fewer input tokens and produce more optimized, precise outputs - minimizing both input and output token size while delivering task-specific performance at scale. Faster, Smarter, and Specialized.

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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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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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Chain-of-thought reasoning is a powerful tool to enable language models to work through complex problems. Can we use this with robots? With embodied chain-of-thought, vision-language-action (VLA) models can think through perception and planning! A 🧵👇
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Chain-of-thought reasoning is a powerful tool to enable language models to work through complex problems. Can we use this with robots? With embodied chain-of-thought, vision-language-action (VLA) models can think through perception and planning! A 🧵👇

Sergey Levine

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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.
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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.

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Today, we’re excited to announce the general availability of the Contextual AI Platform. This is the first enterprise platform designed for building specialized RAG agents to support expert knowledge work. What is a specialized RAG agent? First, a general-purpose AI agent is one designed to automate simple daily tasks like scheduling a meeting or responding to an email. On the other hand, a specialized RAG agent is one designed to augment subject-matter experts performing complex domain-specific work. The Contextual AI Platform allows you to create these agents easily and achieve SOTA accuracy right out of the box. Check out what the Contextual AI Platform can do.

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