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DeepNewz, mobile.
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Tech companies like Microsoft, Mobius Labs, and Fiddler Labs are introducing new 1-bit versions of LLMs, such as BitNet b1.58 and Llama models, which aim to enhance AI efficiency by reducing latency, memory usage, and energy consumption. These models leverage extreme quantization techniques to achieve full-precision performance while using ternary parameters {-1, 0, 1}. The advancements in 1-bit LLMs are expected to shape the future of AI applications.
What is this absolute insanity? 1-bit (binary) Llama 2-7B thanks to HQQ + LoRA. SFT greatly improves the quantized models. Quite impressed by the Colab notebook they released, comparing FP16 vs. 1-bit. I couldn't run theirs so here's my fixed version: https://t.co/xDlbCiNw4U https://t.co/QmEhBVkw0L
Super thrilled to release our work on extreme quantization (1-bit and 2-bit)! We're starting with the Llama2-7b since it's a well-understood model. Check out our detailed blog post: https://t.co/RkX8fR32L6
📌 The new BitNet b1.58 model is here, transforming #AI with 1-bit efficiency. Achieves full-precision performance with less latency, memory, & energy. How will it shape the future of AI applications? Paper 🔗: https://t.co/MkPkVOjH1R https://t.co/7jt1uydbqQ
