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Magnus Lundstedt PRO

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https://tensorlens.dev

AI & ML interests

Local AI inference. Most recently built a tool for inspecting HF models: https://tensorlens.dev

Recent Activity

reacted to Banaxi-Tech's post with 🔥 about 6 hours ago
We're excited to release BananaMind 2 Micro, our smallest model yet. It fits a compact architecture in only 2.9M parameters achieving the highest parameter efficiency on BananaMind Base Bench against comparable models. It achieves comparable performance to GPT S2 5M and GPT S 5M at almost half the size while beating CMA 1M Mini. BananaMind 2 Micro achieved the #1 spot on the Open SLM Leaderboard for the sub 3M category (not added yet but it achieves #1) For the training we used Muon + the XSA Refresh Gate with a 5e-2 lr for Muon and 4e-3 for the 1D weights. Its score on our efficiency measure is 0.326 getting the first place with Syn 2.6M on the second place scoring 0.291 and GPT S 5M at 0.235* Check it out at https://huggingface.co/BananaMind/BananaMind-2-Micro and follow us at: @vovaRL @DedeProGames @Banaxi-Tech https://huggingface.co/BananaMind Our new releases aren't stopping 🚀 August 13-14 BananaMind 2 Pro
reacted to OppaAI's post with 🔥 about 6 hours ago
Yesterday I said I wanna make a UI for users to draw DAG workflow so my AI Waifu can use these workflows to run automated agentic tasks for me, so I ask Grok to make something that looks like the image shown in the demo. I wish I have the time and skill to make it like n8n-style and look more professional. Right now there are only 5 nodes to choose from and will as I need for my own workflow. But at least right now I can see if the graphs in my code are properly setup. This could potentially be a enterprise-level product, I just don't have the time and skills to scale it to a bigger level. I have just pushed this in my code yet. Not tested yet. Probably need sometimes to polish it. Github: https://github.com/OppaAI/Aiko-chan/
reacted to dronefreak's post with 👍 about 6 hours ago
🚀 Excited to open-source the GWHD Wheat Head Detection Model Zoo on Hugging Face. This release includes: - 🤖 YOLOv8, YOLOv11, YOLOv26 and RF-DETR object detection models trained on GWHD (Global Wheat Head Dataset), spanning nano through x-large variants across both architecture families. - 🌾 Benchmarked on GWHD's dense, single-class wheat-head detection task — ~45 annotated heads per image on average, captured across multiple countries, genotypes, and growth stages, a genuinely hard small/dense-object setting. - 📊 Detailed model cards with mAP/precision/recall, per-class breakdowns, PR/F1 curves and confusion matrices (YOLO), qualitative detection showcases, and full training configurations for reproducibility. Headline numbers: - 🏆 Best mAP@50: 74.25% (YOLOv11x), 34.92% mAP@50:95, 83.37% precision. - ⚡ Best efficiency tradeoff: YOLOv26s hits 70.49% mAP@50 at just 22.8 GFLOPs (10.0M params) — within ~4 points of the top YOewer FLOPs (196.0 GFLOPs). The goal is to make benchmarking and experimenting with agricultural computer vision easier by providing ready-to-use pretrained checkpoints, all trained and evaluated under one shared pipeline (DetectionBench: https://github.com/dronefreak/DetectionBench). Full credit for the underlying dataset goes to Etienne David, Mario Serouart, Simon Madec, and the Global Wheat Head Detection 2020/2021) — this release is anunofficial, YOLO-ready reformatting of their work, not a new dataset. If you're working on precision at detection, or just want areproducible detector benchmark, I hope these resources are useful. 📦 Dataset: dronefreak/GWHD 🤖 Model Collection: https://huggingface.co/collections/dronefreak/gwhd-wheat-head-detection-model-zoo-6a7aea28b5431918cc46cec1 Feedback, bug reports, and contributions are always welcome.
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