Nathan Lambert on Ripple-65B: Small Teams Can Also Build Solid Models
Nathan Lambert commented on Ricursive Intelligence's release of Ripple-65B Preview, calling it an ambitious project and expressing hope that more U.S. labs will follow suit by building solid models with small, focused teams. He noted that the model's approach continues the olmo line, and that seeing a project he was involved in scaled up is meaningful. The quoted tweet states that Ripple-65B is a 65B dense large language model pretrained from scratch, surpassing larger models such as Muse Spark Open-816B, Beam-501B, Inkling-975B, and Nemotron 3 Ultra-550B on agentic coding tasks among U.S. open-weight models.
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Writing from the perspective of someone who has been involved in the olmo project, the author places Ripple-65B within the lineage of pretraining efforts by small teams, offering background context for understanding this release.
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View original postToday we release Ripple-65B Preview, our 65B dense LLM we pretrained from scratch. Ripple sets a new state-of-the-art for U.S. open-weight models in agentic coding tasks, surpassing significantly larger models including Muse Spark Open-816B, Beam-501B, Inkling-975B, and Nemotron 3 Ultra-550B. Learn more: https://t.co/Pe04ZKhkE9
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Today we release Ripple-65B Preview, our 65B dense LLM we pretrained from scratch. Ripple sets a new state-of-the-art for U.S. open-weight models in agentic coding tasks, surpassing significantly larger models including Muse Spark Open-816B, Beam-501B, Inkling-975B, and Nemotron 3 Ultra-550B. Learn more: https://t.co/Pe04ZKhkE9
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Love this scrappy project. Hoping more US labs follow suit, showing they can also make solid models with small focused teams. This also intellectually follows on the olmo lineage, so super cool to see what our baby looks like scaled up.