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  "text": "Are scaling laws finally working for time series foundation models?\n\nToday, @datadoghq is releasing Toto 2.0 weights in Apache 2.0 on @huggingface. It's a family of open-weights TSFMs from 4M to 2.5B parameters, where every size beats the last from a single hyperparameter config. First across the leading benchmarks: BOOM, GIFT-Eval, and TIME.\n\nMost TSFM families ship multiple sizes that all perform roughly the same. This one doesn't.\n\nWhy it matters: scaling laws gave language and vision a predictable relationship between compute, data, parameters, and downstream performance. Time series hasn't had that curve until now. Once you have it, you can scale data and compute with confidence, and start asking which new capabilities emerge at the next order of magnitude.\n\n2.5B open-source weights: https://t.co/prpcGoCw0U\n4M open-source weights: https://t.co/5d6rw5NYL2\n\nBlogpost: https://t.co/xKazgTMh1I",
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