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 <h3>📰 News</h3>
 
-<strong>07/20/2026:</strong> 📣 We released <a href="https://huggingface.co/microsoft/BitNet-embedding-0.6B"><strong>BitNet-embedding-0.6B</strong></a> and <a href="https://huggingface.co/microsoft/BitNet-embedding-270M"><strong>BitNet-embedding-270M</strong></a> on Hugging Face — the first 1-bit embedding models that deliver competitive embedding quality with significantly faster inference on CPUs. ![NEW](https://img.shields.io/badge/NEW-red)
+<strong>07/23/2026:</strong> 📣 We released <a href="https://github.com/microsoft/VibeASR.cpp"><strong>VibeASR.cpp</strong></a> — a real-time multilingual ASR inference engine on CPU using BitNet I2_S quantization, achieving RTF < 1 with very few threads on x86 (AVX2) and ARM (NEON) platforms. [<a href="https://github.com/microsoft/VibeASR.cpp">Code</a>] [<a href="https://huggingface.co/microsoft/VibeVoice-ASR-BitNet">Models</a>] [<a href="https://arxiv.org/abs/2607.21075">Report</a>] ![NEW](https://img.shields.io/badge/NEW-red)
+
+<strong>07/20/2026:</strong> 📣 We released <a href="https://huggingface.co/microsoft/BitNet-embedding-0.6B"><strong>BitNet-embedding-0.6B</strong></a> and <a href="https://huggingface.co/microsoft/BitNet-embedding-270M"><strong>BitNet-embedding-270M</strong></a> on Hugging Face — the first 1-bit embedding models that deliver competitive embedding quality with significantly faster inference on CPUs.
 - **1.42x to 2.28x speedup** over F16 on BitNet-embedding-0.6B prefill (8 threads)
 - **1.32x to 1.74x speedup** over F16 on BitNet-embedding-270M prefill (8 threads)
 - Supports I2_S conversion with optimized kernels on x86 CPUs