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Today we’re releasing the Allora Forge Builder Kit, a streamlined way to build, train, and deploy ML models directly on the Allora Network 🔥⚒️ Now within a single notebook, users can move from setup to live predictions in minutes and start earning with Allora. Many contributors struggle with fragmented...

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Build worlds and forge Legendary NFTs. 🛠️⚡ Zogi Labs World of Bezogia has minted it’s first NFTs on Enjin Blockchain 🙌 To celebrate this milestone, the game is hosting its first ever Builders Tournament for the Enjin community. Learn more about the Builders Tournament: With the Creator Toolkit, anyone can become a world-builder. 🌎 No coding skills required - simply drag and drop 3D models. Create exciting challenges and experiences for friends and other players. World of Bezogia is out now on Epic Games Store store and mobile app stores! The Builders Tournament launches on February 15, 2024! Mark your calendars 👀

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22,455 Aufrufe • vor 2 Jahren

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PolyAI

380,754 Aufrufe • vor 3 Monaten

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📣 It's TIME! We are proud to announce that the XRPL EVM Sidechain is now live on Mainnet! 🚀 After 3+ years of research and development with Ripple, the network is live providing native #XRP with unlimited new capacities to flow across the entire web3, deploy and interact with EVM smart contracts and interoperate with the entire crypto ecosystem 🤝🌐 Now, it's your turn, developers🫵🏻 You can now deploy, integrate, and list XRP in your protocols. The #XRPArmy is waiting for it ✅ This is just day 1. Expect wallets, infra providers and dapps to join progressively. We'll be posting updates. 🙏🏼 Many thanks to all the contributors who have made this possible like Axelar Network, Common Prefix and Cosmos - The Interchain ⚛️ among many others on collaborating to open a new era of possibilities for #XRPLedger You can see all the details on 👉

Peersyst Technology

459,078 Aufrufe • vor 1 Jahr

We are excited to announce OctoMCP (beta) — our Solana Programs Model Context Protocol (MCP) that allows anyone to build, test, and deploy Solana programs within minutes. Solana With OctoMCP, users can easily download all essential files including idl.json (for frontend integration), program .so (for deployment), and the full source code. After that, you can generate a custom UI using just a couple of prompts. In the coming days, we’ll be releasing detailed tutorials on how to use OctoMCP and the UI generator to quickly build powerful Solana programs and dApps. We’ll also be hosting dApp Generation Competitions, where users can showcase their creativity and win exciting rewards. Huge thanks to the boltdiy team for making the Stackblitz open-source version — our UI generator is built on top of boltdiy.
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We are excited to announce OctoMCP (beta) — our Solana Programs Model Context Protocol (MCP) that allows anyone to build, test, and deploy Solana programs within minutes. Solana With OctoMCP, users can easily download all essential files including idl.json (for frontend integration), program .so (for deployment), and the full source code. After that, you can generate a custom UI using just a couple of prompts. In the coming days, we’ll be releasing detailed tutorials on how to use OctoMCP and the UI generator to quickly build powerful Solana programs and dApps. We’ll also be hosting dApp Generation Competitions, where users can showcase their creativity and win exciting rewards. Huge thanks to the boltdiy team for making the Stackblitz open-source version — our UI generator is built on top of boltdiy.

OctonetAI

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Introducing NEO: The first Agentic Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on the MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:
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Introducing NEO: The first Agentic Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on the MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Neo AI

19,571,775 Aufrufe • vor 1 Jahr

New Course: Post-training of LLMs Learn to post-train and customize an LLM in this short course, taught by Banghua Zhu, Assistant Professor at the University of Washington University of Washington, and co-founder of @NexusflowX. Training an LLM to follow instructions or answer questions has two key stages: pre-training and post-training. In pre-training, it learns to predict the next word or token from large amounts of unlabeled text. In post-training, it learns useful behaviors such as following instructions, tool use, and reasoning. Post-training transforms a general-purpose token predictor—trained on trillions of unlabeled text tokens—into an assistant that follows instructions and performs specific tasks. Because it is much cheaper than pre-training, it is practical for many more teams to incorporate post-training methods into their workflows than pre-training. In this course, you’ll learn three common post-training methods—Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning (RL)—and how to use each one effectively. With SFT, you train the model on pairs of input and ideal output responses. With DPO, you provide both a preferred (chosen) and a less preferred (rejected) response and train the model to favor the preferred output. With RL, the model generates an output, receives a reward score based on human or automated feedback, and updates the model to improve performance. You’ll learn the basic concepts, common use cases, and principles for curating high-quality data for effective training. Through hands-on labs, you’ll download a pre-trained model from Hugging Face and post-train it using SFT, DPO, and RL to see how each technique shapes model behavior. In detail, you’ll: - Understand what post-training is, when to use it, and how it differs from pre-training. - Build an SFT pipeline to turn a base model into an instruct model. - Explore how DPO reshapes behavior by minimizing contrastive loss—penalizing poor responses and reinforcing preferred ones. - Implement a DPO pipeline to change the identity of a chat assistant. - Learn online RL methods such as Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO), and how to design reward functions. - Train a model with GRPO to improve its math capabilities using a verifiable reward. Post-training is one of the most rapidly developing areas of LLM training. Whether you’re building a high-accuracy context-specific assistant, fine-tuning a model's tone, or improving task-specific accuracy, this course will give you experience with the most important techniques shaping how LLMs are post-trained today. Please sign up here:
2:52

New Course: Post-training of LLMs Learn to post-train and customize an LLM in this short course, taught by Banghua Zhu, Assistant Professor at the University of Washington University of Washington, and co-founder of @NexusflowX. Training an LLM to follow instructions or answer questions has two key stages: pre-training and post-training. In pre-training, it learns to predict the next word or token from large amounts of unlabeled text. In post-training, it learns useful behaviors such as following instructions, tool use, and reasoning. Post-training transforms a general-purpose token predictor—trained on trillions of unlabeled text tokens—into an assistant that follows instructions and performs specific tasks. Because it is much cheaper than pre-training, it is practical for many more teams to incorporate post-training methods into their workflows than pre-training. In this course, you’ll learn three common post-training methods—Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning (RL)—and how to use each one effectively. With SFT, you train the model on pairs of input and ideal output responses. With DPO, you provide both a preferred (chosen) and a less preferred (rejected) response and train the model to favor the preferred output. With RL, the model generates an output, receives a reward score based on human or automated feedback, and updates the model to improve performance. You’ll learn the basic concepts, common use cases, and principles for curating high-quality data for effective training. Through hands-on labs, you’ll download a pre-trained model from Hugging Face and post-train it using SFT, DPO, and RL to see how each technique shapes model behavior. In detail, you’ll: - Understand what post-training is, when to use it, and how it differs from pre-training. - Build an SFT pipeline to turn a base model into an instruct model. - Explore how DPO reshapes behavior by minimizing contrastive loss—penalizing poor responses and reinforcing preferred ones. - Implement a DPO pipeline to change the identity of a chat assistant. - Learn online RL methods such as Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO), and how to design reward functions. - Train a model with GRPO to improve its math capabilities using a verifiable reward. Post-training is one of the most rapidly developing areas of LLM training. Whether you’re building a high-accuracy context-specific assistant, fine-tuning a model's tone, or improving task-specific accuracy, this course will give you experience with the most important techniques shaping how LLMs are post-trained today. Please sign up here:

Andrew Ng

125,146 Aufrufe • vor 1 Jahr