Muhammad Thabsheer

@MThabsheerNP

Software Engineer🧑‍💻 | Machine Learning💻 & Data Science📈

India

Joined March 2018

91 Following

21 Followers

33 Posts

Muhammad Thabsheer @MThabsheerNP

8 days ago

@Kautukkundan Cool. When is the next edition happening?

Muhammad Thabsheer @MThabsheerNP

about 2 years ago

@notso_vivekkk @KMbappe @Cristiano @realmadriden @KMbappe x @Cristiano 🤍🥹

MThabsheerNP retweeted

@_akhaliq

over 2 years ago

Microsoft presents The Era of 1-bit LLMs All Large Language Models are in 1.58 Bits Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and training tokens in terms of both perplexity and end-task performance, while being significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. More profoundly, the 1.58-bit LLM defines a new scaling law and recipe for training new generations of LLMs that are both high-performance and cost-effective. Furthermore, it enables a new computation paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs.

$_akhaliq's tweet photo. Microsoft presents The Era of 1-bit LLMs All Large Language Models are in 1.58 Bits Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and training tokens in terms of both perplexity and end-task performance, while being significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. More profoundly, the 1.58-bit LLM defines a new scaling law and recipe for training new generations of LLMs that are both high-performance and cost-effective. Furthermore, it enables a new computation paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs.$