What limitations do today’s LLMs face, what makes Microsoft’s BitNet model so different, and could 1-bit quantisation be the future of sustainable AI? Artificial Intelligence has taken the world by ...

There's a strong thread of the occult running through the 1-bit adventure: the calendar you mark the passage of time with has a pentagram on it, and that's just for starters. And while The Oregon ...

Microsoft Research has introduced a new “1-bit” LLM with a two-billion parameter scale that can run on a CPU. Microsoft’s 1-bit LLM is trained on a corpus of 4 trillion tokens and offers performance ...

The 1-bit LLM (1.58-bit, to be more precise) uses -1, 0, and 1 to indicate weights, which could be useful for running LLMs on small devices, such as smartphones. Microsoft put BitNet b1.58 2B4T on ...

In recent years, the most extreme quantization efforts have focused on so-called "BitNets" that represent each weight in a single bit (representing +1 or -1). The new BitNet b1.58b model doesn't ...

Add support for multiple languages other than English. Integrate 1-bit models into multimodal architectures. Better understand the theory behind why 1-bit training at scale produced efficiencies.

1}.” Activations are quantised to 8-bit integers with an “absolute maximum (absmax) quantisation strategy, applied per token”. Subln normalisation is incorporated to further enhance training stability ...

Researchers from ByteDance have introduced the 1.58-bit FLUX model, a quantized version of the FLUX Vision Transformer. This model reduces 99.5% of its parameters (11.9 billion in total) to 1.58 bits, ...

Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant.

But the mansion's layout is randomized each time you play, and trying to find clues in each room ends up feeling like a fun 1-bit version of an escape room, only with a lot more beeping (the sound ...

This necessity has made deploying LLMs expensive and energy-intensive. At their core, 1-bit LLMs use extreme quantization techniques to represent model weights using only three possible values: -1, 0, ...