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QVAC first demo (android mobile) of its local on-device fine-tuning generalized framework, which aims to bring LORA fine-tuning to any device, scaling from the smallest embedded device to any smartphone, laptop, server, operating system. In combination with WIP multi-node features, we expect QVAC to become the standard for local... show more

Paolo Ardoino 🤖

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142,836 次观看 • 9 个月前 •via X (Twitter)

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Demo of Tether's QVAC running local inference on a mobile device at incredible speed, via llama.cpp + LLAMA 3.2 1B parameters. QVAC is a generalized inference and fine-tuning runtime able to adapt to any device, from smartphones, to laptops and servers. Lots of models being supported already. More to come. No limits. Infinite intelligence. Coming soon

Demo of Tether's QVAC running local inference on a mobile device at incredible speed, via llama.cpp + LLAMA 3.2 1B parameters. QVAC is a generalized inference and fine-tuning runtime able to adapt to any device, from smartphones, to laptops and servers. Lots of models being supported already. More to come. No limits. Infinite intelligence. Coming soon

Paolo Ardoino 🤖

42,120 次观看 • 1 年前

The era of Stable Intelligence is here 🤖 Tether’s QVAC Fabric just released the world’s first cross-platform 1-bit LLM LoRA fine-tuning framework. QVAC Fabric extends Microsoft's ultra-efficient BitNet architecture, allowing fine-tuning and inference of LLMs directly on your smartphone—no NVIDIA GPU/CUDA lock-in or expensive server required. The Breakthrough: - Total Sovereignty: LoRA fine-tune ultra-efficient models locally on any smartphone, including iPhones, Pixel phones, Samsung Galaxy phones and any desktop/laptop operating systems using Vulkan and Metal backends. - Extreme Efficiency: 1-bit architecture uses up to 90% less memory and runs up to 11x faster than traditional models. - Universal Access: What used to require a data center now runs on the chip in your pocket. Own your intelligence. The era of stable, local AI is here. 📱🧠 Read the full details on Hugging Face and grab the binaries to build on your own hardware.

The era of Stable Intelligence is here 🤖 Tether’s QVAC Fabric just released the world’s first cross-platform 1-bit LLM LoRA fine-tuning framework. QVAC Fabric extends Microsoft's ultra-efficient BitNet architecture, allowing fine-tuning and inference of LLMs directly on your smartphone—no NVIDIA GPU/CUDA lock-in or expensive server required. The Breakthrough: - Total Sovereignty: LoRA fine-tune ultra-efficient models locally on any smartphone, including iPhones, Pixel phones, Samsung Galaxy phones and any desktop/laptop operating systems using Vulkan and Metal backends. - Extreme Efficiency: 1-bit architecture uses up to 90% less memory and runs up to 11x faster than traditional models. - Universal Access: What used to require a data center now runs on the chip in your pocket. Own your intelligence. The era of stable, local AI is here. 📱🧠 Read the full details on Hugging Face and grab the binaries to build on your own hardware.

QVAC

108,835 次观看 • 5 个月前

Ready to build the future of stable private on-device AI? 🧠 Our latest tutorial shows you how to build a sovereign mobile app in minutes using the QVAC SDK and Expo. Start from a blank template and deploy in minutes a local Llama 3.2 inference running directly on your own devices. What you’ll learn: Modular Setup: Use the QVAC CLI to tree-shake and keep your mobile bundle lean. Local-First Flow: Initialize the SDK, download weights, and run high-speed inference without a cloud uplink. Cross-Platform Power: See the smoke test in action on a physical Samsung S25. No rented clouds. No API keys. Build local, on-device, unstoppable intelligence in your pocket. Watch the full guide and start building:

Ready to build the future of stable private on-device AI? 🧠 Our latest tutorial shows you how to build a sovereign mobile app in minutes using the QVAC SDK and Expo. Start from a blank template and deploy in minutes a local Llama 3.2 inference running directly on your own devices. What you’ll learn: Modular Setup: Use the QVAC CLI to tree-shake and keep your mobile bundle lean. Local-First Flow: Initialize the SDK, download weights, and run high-speed inference without a cloud uplink. Cross-Platform Power: See the smoke test in action on a physical Samsung S25. No rented clouds. No API keys. Build local, on-device, unstoppable intelligence in your pocket. Watch the full guide and start building:

QVAC

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The QVAC SDK is the "LEGO block" of the next era of computing. It’s a modular, local-first framework designed to turn anything—from a simple robot to an industrial server—into a sovereign, autonomous mind. Why build with QVAC? Atomic Intelligence: AI as a raw material embedded directly into your hardware. No Cloud Dependency: 0 latency and total privacy. If the internet breaks, your world keeps thinking. Infinite Scale: A single API for local AI that runs on any device, anywhere. From a child’s toy to the fabric of the universe, if you can dream it, you can build it. Start building the future: 🚀

The QVAC SDK is the "LEGO block" of the next era of computing. It’s a modular, local-first framework designed to turn anything—from a simple robot to an industrial server—into a sovereign, autonomous mind. Why build with QVAC? Atomic Intelligence: AI as a raw material embedded directly into your hardware. No Cloud Dependency: 0 latency and total privacy. If the internet breaks, your world keeps thinking. Infinite Scale: A single API for local AI that runs on any device, anywhere. From a child’s toy to the fabric of the universe, if you can dream it, you can build it. Start building the future: 🚀

QVAC

4,707,376 次观看 • 4 个月前

Two islands. Two futures. 🏝️ One chose to trust its people with intelligence. The other turned them into the product. QVAC is the foundation for a sovereign future. No central servers, no "Department of Truth," and no surveillance. Just local-first AI that lives on your device, learns with you, and belongs to you. Your data. Your device. Your freedom. Build the right choice:

Two islands. Two futures. 🏝️ One chose to trust its people with intelligence. The other turned them into the product. QVAC is the foundation for a sovereign future. No central servers, no "Department of Truth," and no surveillance. Just local-first AI that lives on your device, learns with you, and belongs to you. Your data. Your device. Your freedom. Build the right choice:

QVAC

3,599,437 次观看 • 4 个月前

Intelligence should not be a service you rent; it is a foundational element you possess. At Tether, we see AI as a new element of the periodic table - a raw material that can be embedded into the very fabric of the universe. Today, the QVAC SDK is officially live - the atomic unit for the next era of compute. From your smartphone today to the edge of the galaxy tomorrow, we are building the decentralized mind that doesn't require an uplink to function. Infinite Stable Intelligence: - Local-First: Runs privately on any device without permission or central servers. - Single API: A complete SDK for Vision, RAG, P2P networking, and LLM fine-tuning. - Unstoppable: No central point of failure if the internet breaks, your world keeps thinking. - Decentralized: Evolve through Peer-to-Peer Swarms of Infinite Intelligence. The era of Stable Intelligence has begun. Start building the future at

Intelligence should not be a service you rent; it is a foundational element you possess. At Tether, we see AI as a new element of the periodic table - a raw material that can be embedded into the very fabric of the universe. Today, the QVAC SDK is officially live - the atomic unit for the next era of compute. From your smartphone today to the edge of the galaxy tomorrow, we are building the decentralized mind that doesn't require an uplink to function. Infinite Stable Intelligence: - Local-First: Runs privately on any device without permission or central servers. - Single API: A complete SDK for Vision, RAG, P2P networking, and LLM fine-tuning. - Unstoppable: No central point of failure if the internet breaks, your world keeps thinking. - Decentralized: Evolve through Peer-to-Peer Swarms of Infinite Intelligence. The era of Stable Intelligence has begun. Start building the future at

QVAC

13,344,999 次观看 • 4 个月前

$QVAC SDK will support in 0.9.0 (gonna be release in ~10 days) LoRA fine-tuning directly on-device, letting developers customize LLMs with their own data without sending anything to the cloud. You just load a base model, point it at your training dataset, and get a lightweight LoRA adapter back — all running locally. The fine-tuned model can then be used for inference immediately, with no extra setup. Why it matters: LoRA (Low-Rank Adaptation) fine-tuning lets you specialize a general-purpose language model for your specific use case — like matching a brand's tone, mastering domain terminology, or following a particular output format — using a fraction of the compute a full fine-tune would require. QVAC handles the entire workflow locally: dataset preparation, training with configurable hyperparameters, checkpoint saving, and seamless inference with the resulting adapter. Your data never leaves the device. The developer experience: Fine-tuning with QVAC is as simple as calling "sdk.finetune()" with your dataset and a few hyperparameters. Training runs entirely on your local hardware, produces a compact LoRA adapter file, and supports pause/resume so you can stop a job and pick it back up without losing progress. The result plugs straight into QVAC's inference pipeline — no model conversion, no deployment step, just immediate local completions with your fine-tuned model.$

QVAC SDK will support in 0.9.0 (gonna be release in ~10 days) LoRA fine-tuning directly on-device, letting developers customize LLMs with their own data without sending anything to the cloud. You just load a base model, point it at your training dataset, and get a lightweight LoRA adapter back — all running locally. The fine-tuned model can then be used for inference immediately, with no extra setup. Why it matters: LoRA (Low-Rank Adaptation) fine-tuning lets you specialize a general-purpose language model for your specific use case — like matching a brand's tone, mastering domain terminology, or following a particular output format — using a fraction of the compute a full fine-tune would require. QVAC handles the entire workflow locally: dataset preparation, training with configurable hyperparameters, checkpoint saving, and seamless inference with the resulting adapter. Your data never leaves the device. The developer experience: Fine-tuning with QVAC is as simple as calling "sdk.finetune()" with your dataset and a few hyperparameters. Training runs entirely on your local hardware, produces a compact LoRA adapter file, and supports pause/resume so you can stop a job and pick it back up without losing progress. The result plugs straight into QVAC's inference pipeline — no model conversion, no deployment step, just immediate local completions with your fine-tuned model.

Paolo Ardoino 🤖

42,413 次观看 • 4 个月前

The world of tomorrow cannot run on a rented cloud. 🚫 With 10 billion humans and 10 billion autonomous agents, intelligence must be embedded at the edge - not centralized in a server farm. The QVAC SDK is the invisible engine for this transition. We’ve built the foundational toolkit for the next era: highly efficient, fully modular, and 100% sovereign. From a single light to an industrial grid, the power to build local-first AI is now in your hands. The revolution will not be hosted. It will be local. Learn more:

The world of tomorrow cannot run on a rented cloud. 🚫 With 10 billion humans and 10 billion autonomous agents, intelligence must be embedded at the edge - not centralized in a server farm. The QVAC SDK is the invisible engine for this transition. We’ve built the foundational toolkit for the next era: highly efficient, fully modular, and 100% sovereign. From a single light to an industrial grid, the power to build local-first AI is now in your hands. The revolution will not be hosted. It will be local. Learn more:

QVAC

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Yesterday at Lugano Plan ₿ I presented a preview of Tether's Local AI development kit, privacy focused, powered by peer-to-peer technology 🍐🍐 Write the code once, and run everywhere, from a 40$ phone, to a flagship smartphone, from a laptop to a mainframe with many H100, from a smart fridge to a car entertainment system. Based on 🐻 Terher's AI SDK is extremely modular, it uses supports any model (Marian, Llama, ...) and loads/stores weights and fine-tuning data in P2P data structures. Open source once battle tested. Demo of a sample local and 100% private translation app working on any device 👇

Paolo Ardoino 🤖

127,950 次观看 • 1 年前

Elon Musk says he is not working on making a phone, and shares his thoughts on the future of telecommunications: “I am not working on a phone. A phone will really be an edge node for Al inference, for Al video inference. With, you know, with some radios to obviously connect to, but essentially you'll. have Al on the server-side, community communicating to an Al on your device. I think that there won't be operating systems. There won't be apps in the future. There will be operating systems or apps; it'll just be you've got a device that is there for the screen and audio, and for, and, and to put as much Al on the on on the device as possible, so as to minimize the amount of bandwidth that's needed between your edge node device, formally known as a phone, and the servers.”

Elon Musk says he is not working on making a phone, and shares his thoughts on the future of telecommunications: “I am not working on a phone. A phone will really be an edge node for Al inference, for Al video inference. With, you know, with some radios to obviously connect to, but essentially you'll. have Al on the server-side, community communicating to an Al on your device. I think that there won't be operating systems. There won't be apps in the future. There will be operating systems or apps; it'll just be you've got a device that is there for the screen and audio, and for, and, and to put as much Al on the on on the device as possible, so as to minimize the amount of bandwidth that's needed between your edge node device, formally known as a phone, and the servers.”

Nic Cruz Patane

17,796 次观看 • 10 个月前

Elon Musk says smartphones won't exist in 5 years. "We're not going to have a phone in the traditional sense. What we call a phone will really be an edge node for AI inference, with some radios to obviously connect, but essentially you'll have AI on the server side communicating to an AI on your device." "There won't be operating systems, there won't be apps in the future. You've got a device that is there for the screen and audio to put as much AI on the device as possible so as to minimize the amount of bandwidth needed between your edge node device and the servers." "You'll get everything through AI. Whatever you can think of, or really whatever the AI can anticipate you can think of, it'll show you." "It's probably five or six years, something like that."

Elon Musk says smartphones won't exist in 5 years. "We're not going to have a phone in the traditional sense. What we call a phone will really be an edge node for AI inference, with some radios to obviously connect, but essentially you'll have AI on the server side communicating to an AI on your device." "There won't be operating systems, there won't be apps in the future. You've got a device that is there for the screen and audio to put as much AI on the device as possible so as to minimize the amount of bandwidth needed between your edge node device and the servers." "You'll get everything through AI. Whatever you can think of, or really whatever the AI can anticipate you can think of, it'll show you." "It's probably five or six years, something like that."

Metav3rse

645,806 次观看 • 2 个月前

Hyra AI Reborn. Stronger. Faster. Decentralized. 🔥 Reimagined. A new direction. A new era of decentralized intelligence. From privacy-first edge compute → to a global AI mesh owned by the people. From inference on your device → to digital sovereignty at scale. ✨ Every device is now a node. ✨ Every user is a Hyrasapien. ✨ Every action builds the future of AI. We’re not just rebranding ~ we’re reshaping the narrative of AI. Together, let’s build an AI infrastructure that’s secure, verifiable, and community-owned. 🔑 Your Device. Your Power. Your Sovereignty. 🌐 Explore the new era:

Hyra AI Reborn. Stronger. Faster. Decentralized. 🔥 Reimagined. A new direction. A new era of decentralized intelligence. From privacy-first edge compute → to a global AI mesh owned by the people. From inference on your device → to digital sovereignty at scale. ✨ Every device is now a node. ✨ Every user is a Hyrasapien. ✨ Every action builds the future of AI. We’re not just rebranding ~ we’re reshaping the narrative of AI. Together, let’s build an AI infrastructure that’s secure, verifiable, and community-owned. 🔑 Your Device. Your Power. Your Sovereignty. 🌐 Explore the new era:

Hyra AI

96,712 次观看 • 11 个月前

The QVAC SDK puts the "brain" directly into your pocket. From real-time on-device translation to multimodal understanding, build apps that work everywhere, even 30,000 feet in the air. Local AI is here: 💡Offline-First: No cloud, no latency, no "Department of Truth". 💻 Universal API: One codebase for iOS, Android, macOS, and Linux. 🔍 Multimodal: Understanding text, audio, and images without a server. If you can dream it, you can build it. The era of Stable Intelligence has begun. Start building:

The QVAC SDK puts the "brain" directly into your pocket. From real-time on-device translation to multimodal understanding, build apps that work everywhere, even 30,000 feet in the air. Local AI is here: 💡Offline-First: No cloud, no latency, no "Department of Truth". 💻 Universal API: One codebase for iOS, Android, macOS, and Linux. 🔍 Multimodal: Understanding text, audio, and images without a server. If you can dream it, you can build it. The era of Stable Intelligence has begun. Start building:

QVAC

36,652 次观看 • 4 个月前

QVAC SDK 0.10.0 is now live, bringing advanced local compute capabilities and specialized hardware optimization directly to your device Key Features and Updates: - Image-to-Image Diffusion: Transform and edit images using simple prompts with 100% local compute—no cloud uploads or external servers required - Dynamic Tooling & KV Cache Management:Your local LLM now receives a tailored toolbox for every interaction, with automatic KV cache clearing to maintain high-speed inference - Doctor CLI: A new diagnostic tool that analyzes your hardware and memory, providing specific instructions on how to optimize your GPU for local AI - Suspend & Resume API: Specifically designed for mobile environments, this allows apps to pause P2P swarms and RAG workspaces to meet background rules without losing model state - GPT-OSS Compatibility: Added support for the latest GPT-OSS models loaded externally, expanding the range of open-source intelligence available on the platform Build the future of private, unstoppable AI:

QVAC SDK 0.10.0 is now live, bringing advanced local compute capabilities and specialized hardware optimization directly to your device Key Features and Updates: - Image-to-Image Diffusion: Transform and edit images using simple prompts with 100% local compute—no cloud uploads or external servers required - Dynamic Tooling & KV Cache Management:Your local LLM now receives a tailored toolbox for every interaction, with automatic KV cache clearing to maintain high-speed inference - Doctor CLI: A new diagnostic tool that analyzes your hardware and memory, providing specific instructions on how to optimize your GPU for local AI - Suspend & Resume API: Specifically designed for mobile environments, this allows apps to pause P2P swarms and RAG workspaces to meet background rules without losing model state - GPT-OSS Compatibility: Added support for the latest GPT-OSS models loaded externally, expanding the range of open-source intelligence available on the platform Build the future of private, unstoppable AI:

QVAC

34,508 次观看 • 4 个月前

Superior methodology beats raw parameter count. 🧠 Introducing QVAC MedPsy: Local-first medical AI that redefines the possible. 1/ Unprecedented Power: MedPsy 1.7B model outperforms Google’s MedGemma 4B by 11 points and our 4B model beats MedGemma 27B on real-world health benchmarks. 2/ Extreme Efficiency: 3.2x fewer tokens means near-instant inference on your phone or wearable. 3/ Absolute Privacy: Expert-level reasoning running 100% locally. No data leaves your device. We aren’t simply shrinking models; we’re anchoring intelligence where it matters most. High-level medical logic is now a sovereign right. The future of healthcare is local. Learn more:

Superior methodology beats raw parameter count. 🧠 Introducing QVAC MedPsy: Local-first medical AI that redefines the possible. 1/ Unprecedented Power: MedPsy 1.7B model outperforms Google’s MedGemma 4B by 11 points and our 4B model beats MedGemma 27B on real-world health benchmarks. 2/ Extreme Efficiency: 3.2x fewer tokens means near-instant inference on your phone or wearable. 3/ Absolute Privacy: Expert-level reasoning running 100% locally. No data leaves your device. We aren’t simply shrinking models; we’re anchoring intelligence where it matters most. High-level medical logic is now a sovereign right. The future of healthcare is local. Learn more:

QVAC

2,416,342 次观看 • 3 个月前

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

61,693 次观看 • 1 年前

Meet Hyperlink, the first AI super assistant that lives inside your computer. Your computer stores all your files and personal context. Hyperlink deeply understands them and gives cited answers instantly — like Perplexity for your local files. It turns your computer into a true AI OS, with 100% private on-device AI agent. Welcome to the new interface for personal intelligence.

Meet Hyperlink, the first AI super assistant that lives inside your computer. Your computer stores all your files and personal context. Hyperlink deeply understands them and gives cited answers instantly — like Perplexity for your local files. It turns your computer into a true AI OS, with 100% private on-device AI agent. Welcome to the new interface for personal intelligence.

NEXA AI

2,136,912 次观看 • 9 个月前

Perplexity CEO Aravind Srinivas on the biggest threat to the data center industry: It's not competition. It's not regulation. It's decentralisation. "The biggest threat to a data center is if the intelligence can be packed locally on a chip that's running on the device and then there's no need to inference all of it on like one centralized data center." He outlines how this could work in practice. Personalisation doesn't necessarily require on-device model training. Retrieval augmented generation, tool calls, and local data can already tailor AI to individual users. But the real unlock? Test time training. Aravind Srinivas describes a future where AI lives on your device, watches how you work and gradually automates your repetitive tasks. "Imagine we crack test time training where the AI watches tasks you repeatedly do on your local system, adapts to you over time and starts automating a lot of the things you do." The key insight: in this model, the intelligence belongs to you. It's your data, your device, your personalised AI brain. And if that future arrives, the economics of centralised infrastructure start to collapse. "That really disrupts the whole data center industry. It doesn't make sense to spend all this money, 500 billion, 5 trillion, whatever on building all the centralized data centers across the world that do a lot of the intelligence workloads for people." The companies spending trillions on centralised infrastructure may want to rethink where intelligence actually needs to live.

Perplexity CEO Aravind Srinivas on the biggest threat to the data center industry: It's not competition. It's not regulation. It's decentralisation. "The biggest threat to a data center is if the intelligence can be packed locally on a chip that's running on the device and then there's no need to inference all of it on like one centralized data center." He outlines how this could work in practice. Personalisation doesn't necessarily require on-device model training. Retrieval augmented generation, tool calls, and local data can already tailor AI to individual users. But the real unlock? Test time training. Aravind Srinivas describes a future where AI lives on your device, watches how you work and gradually automates your repetitive tasks. "Imagine we crack test time training where the AI watches tasks you repeatedly do on your local system, adapts to you over time and starts automating a lot of the things you do." The key insight: in this model, the intelligence belongs to you. It's your data, your device, your personalised AI brain. And if that future arrives, the economics of centralised infrastructure start to collapse. "That really disrupts the whole data center industry. It doesn't make sense to spend all this money, 500 billion, 5 trillion, whatever on building all the centralized data centers across the world that do a lot of the intelligence workloads for people." The companies spending trillions on centralised infrastructure may want to rethink where intelligence actually needs to live.

Big Brain AI

90,276 次观看 • 6 个月前

Gave UEFN its own blueprint graph. Python tool running inside the editor. Scans your level and parses your Verse code to draw a full visual map of your game logic. What it shows you: - Every device in your level as a node - Every @ editable reference between devices - Every event subscription and function call - Which Verse functions are actually being used vs sitting there doing nothing What it catches: - Broken connections where an @ editable points to nothing - Orphan devices with zero connections - Unassigned editables you forgot to wire up What you can do with it: - Click a node, and it selects the device in your viewport - Double-click to fly straight to it - Open the Verse file in VS Code with one button - Search and filter by device type - Zoom in and out - Add notes to any device - Export the whole graph as a doc

Gave UEFN its own blueprint graph. Python tool running inside the editor. Scans your level and parses your Verse code to draw a full visual map of your game logic. What it shows you: - Every device in your level as a node - Every @ editable reference between devices - Every event subscription and function call - Which Verse functions are actually being used vs sitting there doing nothing What it catches: - Broken connections where an @ editable points to nothing - Orphan devices with zero connections - Unassigned editables you forgot to wire up What you can do with it: - Click a node, and it selects the device in your viewport - Double-click to fly straight to it - Open the Verse file in VS Code with one button - Search and filter by device type - Zoom in and out - Add notes to any device - Export the whole graph as a doc

Immature

12,088 次观看 • 5 个月前

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 Nicholas 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 Nicholas 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

64,609 次观看 • 2 年前