datasocial/tiktok-5.6B-videos
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liked a dataset 14 days ago
datasocial/tiktok-5.6B-videos reacted to tardellirs's post with 👍 15 days ago
Crivo-150M is now on the Pareto frontier of the Decision Index 🎯
Scored by the maintainers on the private tests: Full 22.21, the best of all submissions at or below 150M and higher than every model below 0.75B, including models up to 4× its size. Median latency on the board's GPU: 13 ms.
It is a 149M typed-decision encoder (Ettin / ModernBERT): a state plus choice, noul or score questions in, a probability for every option out, in one forward pass. Trained on open data only.
Next: tasks from new domains, the weak spot of every small encoder on the board.
https://huggingface.co/tardellirs/crivo-150m
https://huggingface.co/spaces/multimodalart/jev-decision-index
Thanks to @multimodalart for the Decision Index and its open kit, @orionweller and the Ettin team, @tomaarsen for the Ettin rerankers, @hotchpotch for the Bekko System One data and @sileod for tasksource. liked a model 15 days ago
dmis-lab/UltraBERT-Embed-150MOrganizations
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dataset 14 days ago
reacted to tardellirs's post with 👍 15 days ago
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Crivo-150M is now on the Pareto frontier of the Decision Index 🎯
Scored by the maintainers on the private tests: Full 22.21, the best of all submissions at or below 150M and higher than every model below 0.75B, including models up to 4× its size. Median latency on the board's GPU: 13 ms.
It is a 149M typed-decision encoder (Ettin / ModernBERT): a state plus choice, noul or score questions in, a probability for every option out, in one forward pass. Trained on open data only.
Next: tasks from new domains, the weak spot of every small encoder on the board.
tardellirs/crivo-150m
multimodalart/jev-decision-index
Thanks to @multimodalart for the Decision Index and its open kit, @orionweller and the Ettin team, @tomaarsen for the Ettin rerankers, @hotchpotch for the Bekko System One data and @sileod for tasksource.
Scored by the maintainers on the private tests: Full 22.21, the best of all submissions at or below 150M and higher than every model below 0.75B, including models up to 4× its size. Median latency on the board's GPU: 13 ms.
It is a 149M typed-decision encoder (Ettin / ModernBERT): a state plus choice, noul or score questions in, a probability for every option out, in one forward pass. Trained on open data only.
Next: tasks from new domains, the weak spot of every small encoder on the board.
tardellirs/crivo-150m
multimodalart/jev-decision-index
Thanks to @multimodalart for the Decision Index and its open kit, @orionweller and the Ettin team, @tomaarsen for the Ettin rerankers, @hotchpotch for the Bekko System One data and @sileod for tasksource.
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reacted to mohit67890's post with 🔥 27 days ago
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🥇 Imajev-4b is #1 of 50 on Image JevBench (v0.1.4, 29 Sep 2026), the leaderboard for AI models that make decisions from images.
A small demo built on imajev-4b: a closet stylist 👗
Request you to star it here so we can make it better - https://github.com/mohit67890/imajev.
Tap a piece and it reads the photo (red 75%, checked 99%), then the app picks bottoms, shoes and a bag from your own closet in the colours you like. Change your colours and the outfit changes.
Under the hood it's one request with one photo and 4 typed questions. Every option gets a probability, so the app applies its rules (one pattern per outfit) and ranks what's left. About 1.1 s per outfit on a Mac (MLX). Every % in the video is the model's real answer.
Also, thanks to @zenmagnets for the FP8 version for Blackwell GPUs: same calibration, 0.1 pt less accuracy on all 23,900 DecisionBench rows, ~1.6× faster 🙏
zenmagnets/Imajev-4B-FP8-SM120
🧠 Weights: mohit67890/imajev-4b
🚀 Demo: mohit67890/imajev
📊 Leaderboard: https://benchmarkheaven.com/image-jev-bench
A small demo built on imajev-4b: a closet stylist 👗
Request you to star it here so we can make it better - https://github.com/mohit67890/imajev.
Tap a piece and it reads the photo (red 75%, checked 99%), then the app picks bottoms, shoes and a bag from your own closet in the colours you like. Change your colours and the outfit changes.
Under the hood it's one request with one photo and 4 typed questions. Every option gets a probability, so the app applies its rules (one pattern per outfit) and ranks what's left. About 1.1 s per outfit on a Mac (MLX). Every % in the video is the model's real answer.
Also, thanks to @zenmagnets for the FP8 version for Blackwell GPUs: same calibration, 0.1 pt less accuracy on all 23,900 DecisionBench rows, ~1.6× faster 🙏
zenmagnets/Imajev-4B-FP8-SM120
🧠 Weights: mohit67890/imajev-4b
🚀 Demo: mohit67890/imajev
📊 Leaderboard: https://benchmarkheaven.com/image-jev-bench
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