Graybx

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Training an AI model is a bit like building habits. What happens in the first steps often determines the outcome🔥 Monitor early, understand faster, iterate smarter 👇

Proud to share that WeightsLab just received its first community PR (thanks, Srinivas V.)! 🔥 This year, the product has gained real visibility and momentum, and we’re excited to keep growing. Check it out and join the journey: https://lnkd.in/eumsU-2G   #OpenSource #AI #MachineLearning #DeveloperTools #Collaboration #WeightsLab #GrayboxTech

WeightsLab just passed 20 early adopters worldwide, across the US, Europe, and Asia.🔥 WeightsLab is an IDE for model development: one workspace where you monitor training at scale, inspect your data, and explain what your model is actually doing. Especially useful for training and fine-tuning. A gray box is the opposite of a black box, a model you can: ✅Train — plots board with live metrics and side-by-side run comparison. ✅Explore — per-sample signals, overlays, and metadata, so you can find the samples dragging your loss instead of guessing. ✅Act — discard, re-weight, or re-tag those samples and relaunch, without leaving the workspace. Our Github repository: https://lnkd.in/ezEfH4xQ #MachineLearning #Explainability #XAI #MLOps #ComputerVision #ModelTraining

Thrilled to announce a milestone for Graybox: our first external contributor PR has been merged into WeightsLab. Huge thanks to Srinivas V. for the contribution! 🔥 Building on the modeling work we've already established, this update unlocks enhanced programmatic foundation model editing, whether done manually or by an assistant in‑training. This contribution will be available in our next release 🚀; stay tuned. More to come. Follow the repo to track progress and join the conversation: https://lnkd.in/ezEfH4xQ Want to see more about modeling? 👉 https://lnkd.in/eN35Vc27 What data-to-model visualizations or programmatic editing features would you like to see next? 👇   #OpenSource #AI #MachineLearning #DeveloperTools #WeightsLab #GrayboxTech #Modeling #MLDev

🚀 New Release: WeightsLab v2.0 is here! WeightsLab delivers a major upgrade to agent‑driven monitoring, experiment tracking, and real‑time analysis. Faster run loading, smoother plotting, and leaner backend processes make training loops and integrated notebooks feel noticeably more responsive. ✨ What's new: 🤖 Integrated Agent (OpenCode): for training, monitoring, and automated experiment report generation 📊 Runs Management: organize, inspect, and compare experiments directly from our UI 📈 In-Training Outlier Highlighting: error bands for clearer, more actionable curve insights 🎥 Multimodal Support: images, videos, and metadata, all in one place ⚡ Experiment Resource Monitoring: keep track of what your runs are consuming, live 📝 Dynamic Report Generation: turn experiment data into shareable reports in seconds Plus quality-of-life improvements: ✅ Faster experiment history loading ✅ Brighter curve colors in light mode ✅ More stable decimation that preserves markers, notes, and outliers ✅ Updated documentation 🔗 Release notes & repo: https://lnkd.in/ezEfH4xQ

𝐖𝐞𝐢𝐠𝐡𝐭𝐬𝐥𝐚𝐛 𝐧𝐨𝐰 𝐫𝐮𝐧𝐬 𝐰𝐢𝐭𝐡 𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐨𝐥𝐚𝐛 Check it out 👇 https://lnkd.in/ezEfH4xQ ________

𝐌𝐢𝐱𝐭𝐮𝐫𝐞 𝐨𝐟 𝐄𝐱𝐩𝐞𝐫𝐭𝐬 (𝐌𝐨𝐄) 𝐭𝐡𝐞 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐛𝐞𝐡𝐢𝐧𝐝 𝐊𝐢𝐦𝐢 𝐊3 (𝐌𝐨𝐨𝐧𝐬𝐡𝐨𝐭 𝐀𝐈) Moonshot AI dropped Kimi K3 last week, and like many of today's frontier models, it's built on a Mixture of Experts (MoE) architecture. So what is MoE, exactly? Mixture of experts (MoE) is an AI model architecture that uses multiple, specialized submodels, called "experts". Instead of routing every input through one giant, monolithic model, MoE splits the network into many specialized subnetworks and only activates the ones relevant to the task at hand. The 4 𝐜𝐨𝐫𝐞 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐛𝐥𝐨𝐜𝐤𝐬: 🔹 𝘌𝘹𝘱𝘦𝘳𝘵𝘴: specialized subnetworks trained to excel at narrow domains 🔹 𝘌𝘹𝘱𝘦𝘳𝘵 𝘚𝘱𝘢𝘳𝘴𝘪𝘵𝘺: only a small subset of experts fire per token, not the whole network 🔹 𝘎𝘢𝘵𝘪𝘯𝘨 𝘕𝘦𝘵𝘸𝘰𝘳𝘬: a learned router decides which experts handle each input 🔹 𝘖𝘶𝘵𝘱𝘶𝘵 𝘊𝘰𝘮𝘣𝘪𝘯𝘢𝘵𝘪𝘰𝘯: the selected experts' outputs are fused (often weighted by the router's confidence) into the final result During training, experts naturally specialize in what they're best at, while the router learns the optimal routing of tokens. The payoff? By activating only a small subset of specialized experts for each token, MoEs achieve state-of-the-art results with inference costs and performance that rival smaller dense models. _______

𝐌𝐋 𝐭𝐨𝐨𝐥𝐢𝐧𝐠 𝐛𝐮𝐢𝐥𝐭 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐦𝐞𝐬𝐬𝐲 𝐫𝐞𝐚𝐥 𝐰𝐨𝐫𝐥𝐝 Weightslab Github [ https://lnkd.in/ezEfH4xQ ] __________

𝐍𝐨𝐭 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐬𝐡𝐨𝐮𝐥𝐝 𝐛𝐞 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝. At Graybx, we build for the last mile of model development, where human judgement cannot be replaced. [ github.com/GrayboxTech ] ______