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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... show more

OpenLedger

405,708 subscribers

76,275 views • 1 year ago •via X (Twitter)

Science & Technology

Anya Rossi• Live Now

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11 Comments

Alireza1 year ago

Now that’s the kind of innovation the AI space needs. OpenLoRA isn’t just efficient—it’s purpose-built for real-world tasks. Smarter models, lower costs, and scalable precision? Game changer. #Openledger #OpenLoRA #Opnup #DeAI

Chargeblast1 year ago

Accept more payments by reducing your chargeback rate to 0%. See how you can increase sales by up to 5% with one simple change.

Cristian Petrescu1 year ago

ayo OpenLedger, OpenLora sounds like it drank 3 red bulls, read your mind, and deployed 500 models before you even blinked. 😂😂

Utkarsh0x 🐙1 year ago

Crazy stuff. Who can skip these type of overpowered product - Faster, smarter, affordable, specialized. Make DeAI Big Again

Hosea_xx.🧞‍♂️1 year ago

Talking more on OpenLoRA

Cełestine1 year ago

OpenLoRa is a game-changer efficient, specialized, and cost-effective. It’s optimizing AI deployment like never before, bringing precision at scale. Faster, smarter, and more efficient all the way. #Opnup #OpenLedger

RIch KId.𝕏1 year ago

A product one can't fade! You fade @OpenledgerHQ's OpenLoRA, then.. you're missing out! Check out what's in stock! Massive one

Official_Chi 💎🚀 𝕏 (Ø,G) 🐙🐙1 year ago

Keep building buddy

Crypto Muslima ☪️1 year ago

OpenLora sounds like a game-changer—efficient, scalable, and laser-focused on performance. This is how you bring real-world impact to AI deployment.

Crypton ∞ (🫰,✨)1 year ago

Faster, Smarter, and Specialized

𝖇𝖑𝖆𝖓𝖈𝖔 |🐉1 year ago

This is incredible, @OpenledgerHQ! OpenLora’s just in time adapter switching is a game changer serving thousands of LoRA adapters on a single GPU while slashing costs? That’s the kind of efficiency AI needs! Loving the "Faster, Smarter, Specialized" vibe. #OpenLedger #OpenLora

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Slash AI costs by 99%, run 1000+ models on a single GPU! OpenLoRA lets you run thousands of models on one GPU instead of one per model, drastically cutting costs and boosting flexibility. Excited to work with Aethir, Hyperbolic for infrastructure and for verifiability. Watch the demo:

Slash AI costs by 99%, run 1000+ models on a single GPU! OpenLoRA lets you run thousands of models on one GPU instead of one per model, drastically cutting costs and boosting flexibility. Excited to work with Aethir, Hyperbolic for infrastructure and for verifiability. Watch the demo:

OpenLedger

155,244 views • 1 year ago

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 views • 1 year ago

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.🐙

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.🐙

OpenLedger

87,875 views • 1 year ago

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

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

OpenLedger

61,662 views • 1 year ago

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

164,162 views • 1 year ago

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.

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.

OpenLedger

17,229 views • 1 year ago

Highlights of the OpenLedger journey: OpenLedger is the AI Blockchain unlocking liquidity for data, models, and agents, backed by Polychain, Balaji Srinivasan (ex-Coinbase CTO), and more. $14.7M+ in revenue from global enterprise projects and $91M+ in investment commitments secured. Worked with global enterprises like Walmart, Sony, Meta LLaMA, Viacom, and others. Built on Stanford’s data attribution research paper, enabling verifiable, fair rewards for contributors. 40+ projects, including ex-Google DeepMind teams and top DeFi builders, are building specialized AI models. 1M+ AI nodes, 25M+ transactions, and 20K+ models, real-world usage at scale.

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OpenLedger

727,397 views • 9 months ago

OpenAI just dropped a completely new kind of model gpt-realtime-translate takes in speech audio from any language and outputs speech in your target language LLMs are great, but you need specialized models for specialized use cases We're running this on our smart glasses

OpenAI just dropped a completely new kind of model gpt-realtime-translate takes in speech audio from any language and outputs speech in your target language LLMs are great, but you need specialized models for specialized use cases We're running this on our smart glasses

cayden 凯登

100,395 views • 11 days ago

OpenAI's Greg Brockman says the AGI future looks less like a monolith - and more like a menagerie of specialized agents. Models that call other models. “We're heading to a world where the economy is fundamentally powered by AI.” The goal is to unlock 10x more activity, output, and opportunity. AI lowers the barrier to entry. Domain expertise becomes leverage.

OpenAI's Greg Brockman says the AGI future looks less like a monolith - and more like a menagerie of specialized agents. Models that call other models. “We're heading to a world where the economy is fundamentally powered by AI.” The goal is to unlock 10x more activity, output, and opportunity. AI lowers the barrier to entry. Domain expertise becomes leverage.

vitrupo

383,214 views • 1 year ago

✈️ Starting the day in Istanbul. Let's talk AI. The future of AI won't be shaped by size, but by precision. Lately I've been diving into what OpenLedger has been building, and I think we're witnessing one of the most important hard forks in AI: 👉 From giant generalist models 👉 To focused, hightrust AI agents Here's why that shift matters , and why OpenLedger's vision makes perfect sense: ✅ Specialized > Generalized General AI can do many things. But specialized AI? It does one thing extraordinarily well. Tailored models don't waste compute on irrelevant context, every parameter is purposedriven. ✅ Explainability isn't optional anymore In highstakes sectors like finance or healthcare, because the model said so won't cut it. We need transparent reasoning paths. Models must show how they reached conclusions , not just what they concluded. ✅ Trust comes from traceability With OpenLedger's Proof of Attribution, each AI decision is traceable, verifiable, and tamperproof. We're talking onchain records of who contributed what , accountability by design. ✅ Less hallucination, more signal Smaller, specialized models trained on clean, domainspecific data are far less prone to hallucinations. Clear boundaries = higher reliability. ✅ Efficiency is the real scalability Deploying one massive model for everything? Expensive, slow, and unsustainable. Specialized AI is leaner, faster, and far more costeffective. In short, OpenLedger isn't just following a trend. They're laying the rails for the infrastructure layer of verifiable, domainaware AI. And in a space flooded with blackbox models and hype, that clarity hits different.

✈️ Starting the day in Istanbul. Let's talk AI. The future of AI won't be shaped by size, but by precision. Lately I've been diving into what OpenLedger has been building, and I think we're witnessing one of the most important hard forks in AI: 👉 From giant generalist models 👉 To focused, hightrust AI agents Here's why that shift matters , and why OpenLedger's vision makes perfect sense: ✅ Specialized > Generalized General AI can do many things. But specialized AI? It does one thing extraordinarily well. Tailored models don't waste compute on irrelevant context, every parameter is purposedriven. ✅ Explainability isn't optional anymore In highstakes sectors like finance or healthcare, because the model said so won't cut it. We need transparent reasoning paths. Models must show how they reached conclusions , not just what they concluded. ✅ Trust comes from traceability With OpenLedger's Proof of Attribution, each AI decision is traceable, verifiable, and tamperproof. We're talking onchain records of who contributed what , accountability by design. ✅ Less hallucination, more signal Smaller, specialized models trained on clean, domainspecific data are far less prone to hallucinations. Clear boundaries = higher reliability. ✅ Efficiency is the real scalability Deploying one massive model for everything? Expensive, slow, and unsustainable. Specialized AI is leaner, faster, and far more costeffective. In short, OpenLedger isn't just following a trend. They're laying the rails for the infrastructure layer of verifiable, domainaware AI. And in a space flooded with blackbox models and hype, that clarity hits different.

Crypto Sinan

13,636 views • 9 months ago

Introducing Computer for Enterprise Computer runs multi-step workflows across research, coding, design, and deployment. It routes tasks across 20 specialized models and connects to 400+ applications.

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Perplexity

202,446 views • 3 months ago

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Fireworks AI

17,619 views • 1 year ago

"It all comes down to compute." Dmitri Alperovitch from Silverado Policy Accelerator explains: "The single most important input to winning is compute the processing power used to train and run AI models. Let me say that plainly, because it is defining that everything that congress and this committee do on AI policy. The binding constraint in this competition is not talent, it is not data, and it is not cash. Both the United States and China have talented researchers and financial resources, and we have learned that frontier models trained on generic data routinely outperform smaller models, trained on more specialized inputs. It all comes down to compute."

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Select Committee on China

41,507 views • 1 month ago

Meet Tencent HY-MT1.5 — our latest open-source translation models, featuring 1.8B and 7B models designed for seamless on-device and cloud deployment. 🌍 The 1.8B version is already trending at #1 on Hugging Face! 🥇It’s optimized for consumer hardware, delivering 0.18s latency (50 tokens) with just a 1GB memory footprint. Meanwhile, the 7B model achieves exceptional performance, surpassing many mid-sized models and rivaling the 90th percentile of Gemini 3.0 Pro. 🚀🧠 👉🏻 Try it now: 🔗 GitHub: 🤗 Hugging Face:

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Tencent HY

17,105 views • 5 months ago

Last week we released Meta Chameleon: a new mixed-modal research model from Meta FAIR. Get the models ➡️ The 7B & 34B safety tuned models we’ve released can take any combination of text and images as input and produce text outputs using a new early fusion approach. While some LLMs have separate image and text encoders or decoders, Chameleon is one of the first publicly released approaches using a single unified architecture. We’re releasing Chameleon models under a research license to help democratize access to foundational mixed-modal models & further research on early fusion. Approach & training details in the paper ➡️

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54,409 views • 1 year ago

AGI is already on our doorstep. Stability AI's Emad joined @Devrelius & LJW.ip on a Space to break down: ・Why AGI requires a collection of specialized models ・Best type of models for creative output ・How to benchmark AGI (and why IQ isn’t enough) A highlight ↴

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17,451 views • 1 year ago

Introducing ERNIE X1 Turbo & ERNIE 4.5 Turbo! Building on the success of ERNIE X1 and 4.5, the upgraded ERNIE X1 Turbo and 4.5 Turbo deliver results faster and cheaper. Both models stand out for their multimodal capabilities, strong reasoning and low costs. For X1 Turbo, input and output prices start at $0.14 per 1M tokens and $0.55 per 1M tokens, respectively, priced at approximately 25% of DeepSeek R1. #BaiduCreate2025 (Exchange rate used: 1 RMB ≈ 0.14 USD)

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Baidu Inc.

136,265 views • 1 year ago

Agent swarm above single agents? The more data input to one agent, the more risk of hallucination. INFINIT's specialized agent swarms prevent this, each specialized only in their domain, coordinating like a high-performing team. Less confusion, more precision. Listen to Tascha (@Infinit_Labs) discuss in our podcast with blocmates.

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INFINIT

77,088 views • 6 months ago