Microsoft's 41K-star BitNet project is now doing something particularly interesting for…
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Microsoft's 41K-star BitNet project is now doing something particularly interesting for RAG and local AI. It has brought 1.58-bit weights to text embedding models. And in Microsoft's CPU benchmarks, its 600M-parameter embedding model achieves up to 2.28× the throughput of its FP16 baseline. This builds on the same BitNet research that produced the 2B-scale language model trained on 4 trillion tokens. The original idea was to train neural networks whose linear-layer weights take only three values: -1, 0, and +1. With specialized inference kernels, this can substantially reduce weight stora
Posted by Tech with Mak (48.3k followers) 15 h ago · 25 likes · 1.7k views · view the original post on X. Kept by the Dev Radar as AI dev tools. Tools mentioned: bitnet.
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