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Mirendil-Base-Large

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  1. Mirendil-Base trained by one researcher and AI agents

    Mirendil says one researcher used AI agents to pretrain the 100B–500B-parameter, code-focused Mirendil-Base from scratch in 3.5 months for less than $10 million, including agent token usage, data processing, and research experiments. The company says agents wrote more than 99% of the code, while the researcher set direction, evaluated results, and intervened at key points; training used public data without purchased datasets, distillation, or synthetic data. Mirendil’s tests reported that Mirendil-Base ranked second behind Kimi K3 on cross-entropy across seven held-out code repositories, ahead of Nemotron-550B-Base, GLM-5.3, and DeepSeek-V4-Pro-Base.

    36氪 人工智能 · 🔥 0

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