Bi-Mamba
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The curator’s take
a scalable 1-bit Mamba architecture designed for more efficient LLMs with multiple sizes across 780M, 1.3B, and 2.7B; Bi-Mamba achieves performance comparable to its full-precision counterparts (e.g., FP16 or BF16); it significantly reduces memory footprint with better accuracy than posttraining-binarization Mamba baselines.
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