Jiaxuan You
@youjiaxuan • 4,820 subscribers
Assist. Prof. @ UIUC (@siebelschool), directing U Lab on LLM Agent infra and application @Stanford CS PhD ex NVIDIA Senior Scientist
Videos
Open-sourcing the first unified LLM routing library 🔥 Meet LLMRouter 16+ routers in ONE framework. Stop reimplementing routing papers from scratch, simply run pip install llmrouter-lib And instantly deploy SOTA LLM routing tailored to your exact needs! 🔗 Get Started with LLMRouter 🚀 Code: 🌐 Project Page: 💸 Why LLMRouter? Why pay GPT-5 prices for "What's the weather?" Smart routing = Significant Savings Simple query → Cheap model Complex query → Powerful model Save 30–50% on inference costs without sacrificing quality 🧭 The 16+ Router Arsenal Switch between state-of-the-art methods with a single flag change. We cover every paradigm: ✅ Single-Round: From classic ML (KNN, SVM) to neural approaches (Graph, Contrastive). ✅ Multi-Round: RL-powered reasoning that "thinks" before routing. ✅ Agentic: Breaks down complex tasks and routes step-by-step. ✅ Personalized: Learns from user history to fit unique needs. 🛠️ Complete "Out-of-the-Box" Toolchain We didn't just ship the code; we shipped the pipeline. ✅ Unified CLI: Train, infer, and chat with any router. ✅ Interactive UI: Full Gradio interface included. ✅ Benchmarks: 11 datasets ready to use + full data generation pipeline. From LLM routing research to production in minutes. 🚀
Jiaxuan You75,077 görüntüleme • 6 ay önce
🚨 RL for LLMs is finally accessible. Introducing OpenTinker: The first community-driven, open-source framework designed to democratize Reinforcement Learning for LLMs. Inspired by Thinking Machines's amazing Tinker, we realize the biggest bottleneck in agentic LLM research isn’t the math—it’s the setup. Current RL pipelines are messy. Configuring VeRL for every single experiment is a productivity killer. OpenTinker fixed it. 🛠 How OpenTinker Works: Decoupled Design of Server and Client - Setup Once, Run Forever: Configure the OpenTinker backend on your GPU cluster once. - Develop Locally: Define your RL environments directly on your laptop. - Train on the Cloud: Simply point your local client to the backend. The cluster handles the compute; you handle the science. 📉 The 10x Development Efficiency Thanks to our elegant architectural decomposition, OpenTinker reduces the time to develop a new RL training pipeline by at least an order of magnitude. ⚡ Turn Idle GPU Compute into Gold Small labs often have underutilized hardware. OpenTinker turns your idle GPUs into an internal/external API service for - RL Training - SFT - Inference 🎯 Who needs OpenTinker? - Researchers tired of infrastructure hell. - Labs needing to standardize workflows. - Teams wanting to maximize hardware ROI. Thanks my amazing PhD student Siqi Zhu for leading the project. We are building the future of open RL infra. Be the first to build with us. 👇 Start Building with OpenTinker Now 🚀 Repo: 🌐 Blog: If you believe RL should be accessible to everyone, give us a star, repost this 🔄 post, and let us know what agents you plan to build!
Jiaxuan You58,120 görüntüleme • 6 ay önce
Love OpenClaw but hate the token burn? 💸 Running a 24/7 agent on GPT-4/Claude is overkill. You don't need SOTA reasoning to handle a greeting or a simple lookup. LLMRouter 🩷 OpenClaw The first production-ready, agentic router designed to plug directly into OpenClaw. LLMRouter fully supports Multimodal, Memory-Equipped routing that adapts 100% to your needs—compatible with FREE open-source models. The Logic is Simple:🔹 Simple query → Cheap/Local model 🔹 Complex reasoning → SOTA model (GPT-4/Claude 3.5) 🔹 Multimodal input → Vision/Audio specialized model Why this isn't just a switch: 📉 30–50% drop in inference costs 🧠 Zero loss in response quality 🔓 100% compatible with OpenAI-style APIs 🚀 Deploy in Seconds General Usage: Get the library and serve any model: pip install llmrouter-lib llmrouter serve OpenClaw Native Integration: Want the full agent experience? LLMRouter built a dedicated integration for OpenClaw users: LLMRouter Resources: 🔗 Repo: 📦 PyPI: 🤝 Works with: Route smarter. Train your own. Pay less. More on LLMRouter: Most routers are static if/else. LLMRouter is an intelligent, learning system. 🤖 Agentic & Memory-Aware: Decisions aren't stateless. We use RAG-powered memory to route based on context and history. 👤 Fully Personalized: It learns from your usage patterns via RL feedback loops. 🔬 Research-Grade: Switch between 16+ routing strategies (KNN, SVM, BERT, Graph, RL) with a single flag.
Jiaxuan You31,303 görüntüleme • 4 ay önce
Daha fazla içerik yok.