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"Microsoft released BitNet b1.58 2B4T, a native 1.58-bit large language model trained on 4 trillion tokens. The model matches the performance of similar-sized full-precision models across language understanding, math reasoning, coding, and conversational tasks, while dramatically reducing resource requirements.
BitNet b1.58 uses just 0.4GB of memory compared to 2-4.8GB for comparable models, consumes up to 90 percent less energy, and offers faster inference speeds. ..."
From the abstract:
"We introduce BitNet b1.58 2B4T, the first open-source, native 1-bit Large Language Model (LLM) at the 2-billion parameter scale. Trained on a corpus of 4 trillion tokens, the model has been rigorously evaluated across benchmarks covering language understanding, mathematical reasoning, coding proficiency, and conversational ability.
Our results demonstrate that BitNet b1.58 2B4T achieves performance on par with leading open-weight, full-precision LLMs of similar size, while offering significant advantages in computational efficiency, including substantially reduced memory footprint, energy consumption, and decoding latency. ..."
Figure 1:BitNet b1.58 2B4T advances the Pareto frontier defined by leading open-weight LLMs under 3B parameters in terms of performance versus memory, demonstrating superior efficiency.
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