Jev-guided sparse attention for MiniMax H3
Jev selects a sparse-attention rate for each layer during video generation. The author reports a run falling from 6m07s to 3m34s on an RTX 4070.
Jev selects a sparse-attention rate for each layer during video generation. The author reports a run falling from 6m07s to 3m34s on an RTX 4070.
A worked guide to Jev's Score primitive: writing a request, defining rubric levels, reading recorded probabilities and the weighted-score math.
Jev Trader · Research & data
Jev Trader
Guide
Research & data
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Sends a detected bot's last 48 request paths to Jev, which picks what it's after (prices, articles…) and how it crawls (pagination, IDs…), or unknown.
WebDecoy · Research & data
WebDecoy
Resource
Research & data
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Paste an article or post link. Fake / Real extracts its claims, finds outside evidence, and has Jev judge whether the evidence supports or contradicts each one.
@DansiDanutz · Research & data
@DansiDanutz
Site
Research & data
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Combines rubric science and LLM-as-a-judge research to grade outputs with AI judges: LLMs, decision models like Jev, or both.
@deliprao · Research & data
@deliprao
Site
Research & data
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流行のJevをMiniMax H3に組み込んで、動画生成を高速化してみた!Attention処理のスパース化にJevを使用。 ・層ごとにJevが重要度を判定(4step 49層が対象) ・Jevがスパース率1%, 3%, 5%, 10%を選択 RTX4070で6分7秒→3分34秒で41.7%短縮!動画生成中にJevクラウドに問合せしているのに速い!