12 month cliff is standard in the industry though even at larger, established companies
5 karma · joined February 29, 2024
> 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
based on (an admittedly rapid and indulgent reading of the paper), it seems like they're not increasing the parameter size. Do you mind pointing out where the blowup is occurring?