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In a Training Loop
92.0
TFLOPS
Sweaterdog
Sweaterdog
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webxos's profile picture
alei707's profile picture
SirBubblesIII's profile picture
50 followers
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43 following
https://www.skinnertopia.com
Sweaterdog
AI & ML interests
Agentic AI for self exploration
Recent Activity
reacted
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savoji
's
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with 🔥
1 day ago
Idol, a new Apache-2.0 image-to-3D model from @speridlabs, makes a 3D Gaussian splat from one image in 7.2 GB of peak VRAM. It is the #1 trending model on the Hub with 1,057 likes. It writes a standard 3DGS .ply with up to 1,048,576 Gaussians, including sides the picture never showed, and does background removal and framing itself. The catch: the only quality evidence is the authors' own benchmark, on a repo created 2026-10-28. 📊 The numbers • Authors' benchmark, 1 x H100: best in 6 of 8 columns vs TripoSplat, TRELLIS and LGM (SSIM 0.726, LPIPS 0.213, DINOv2 0.781, CLIP 0.885) • LGM wins PSNR (12.46 vs 12.25) and speed (1.1 s). Idol takes 6.8 s vs 7.1 s for TRELLIS and 9.9 s for TripoSplat • 262,144 Gaussians, 50 steps: 3.4 s on B300, 6.7 s on H100, 19.1 s on RTX 4090, 46.2 s on a 10 GB RTX 3080 🛠️ Try it • Install idol-sdk, accept the terms of the gated DINOv3 and FLUX.2-dev models, run hf auth login • python -m idol.sample --image photo.jpg --mode real • --num_gaussians runs from 32,768 (0.4 MB) to 1,048,576 (12.2 MB) ⚠️ Caveats • Test set and size are not named, and no independent evaluation exists yet • The card says "under 2 seconds", but the timing table shows 3.4 s at best • Single objects only, NVIDIA CUDA required, no failure cases shown https://huggingface.co/speridlabs/idol https://speridlabs.com/research/idol If you run it on your own messy photos, how do the unseen sides hold up? 📚 Read it elsewhere • The deep dive on Medium: https://medium.com/p/5f660ae6ae56 • The long read on Substack: https://sajjadpakdamansavoji.substack.com/p/speridlabs-idol-builds-3d-splats • The thread on X: https://x.com/saavoji/status/2119137739580858545 • Every version on sajjadpsavoji.github.io: https://sajjadpsavoji.github.io/blog#speridlabs-idol
reacted
to
DedeProGames
's
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9 days ago
Hi everyone! I want to share a few updates about GRM development. *GRM-3.4 has been canceled* Although GRM-3.4 showed strong reasoning and coding capabilities, testing revealed several issues. It tended to overthink, and its code often contained errors that GRM-3.2-Sky handled correctly. Its tool use also needed improvement, and its terminal performance did not reach the level we were aiming for. We repeatedly saw it rewrite files from scratch instead of using editing tools, and use shell commands to create or modify files when dedicated tools were available. *Looking ahead to GRM-3.6* GRM-3.6 is a possible next generation for the GRM models. We are still deciding whether it will be a single large model or a complete model family. The flagship is set to be **GRM-3.6-Moon**, a **35B-A3B** model. *Experimental models* Orion LLM Labs may release experimental models more regularly from now on. These experiments will help us research and test ideas for future GRM models. Thanks for following the project!
liked
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11 days ago
mistralai/Mistral-Large-4-1T-A52B
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Sweaterdog
's datasets
35
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Sweaterdog/Step-3.5-SFT-ShareGPT
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Sweaterdog/The-Trellis
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Sweaterdog/website-html-2k
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Sweaterdog/temp3
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Sweaterdog/500-websites-html
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Sweaterdog/image-to-svg-10k
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Sweaterdog/chess-gameplay-20k
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Sweaterdog/music-theory-images-20k
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Sweaterdog/Music-Lyrics
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Sweaterdog/GRaPE-preview
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Sweaterdog/Andy-correct-v1
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Sweaterdog/Andy-correct-v0
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Sweaterdog/Mindcraft-CE-cloud-logging-anonomized
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Sweaterdog/Andy-gen-dataset-base
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Sweaterdog/Andy-gen-dataset
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Sweaterdog/Andy-4-reasoning-FT
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Sweaterdog/Andy-4-FT
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Sweaterdog/Smol-reason2.1-GRPO
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Sweaterdog/Andy-base-2
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Sweaterdog/Andy-4-base
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Sweaterdog/Smol-reason2.1
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Sweaterdog/Smol-reason2.1-base
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