AI at Meta(@AIatMeta):Meta FAIR推出的新技术Byte Latent Transformer(BLT):Patch比Token更好地适应规模,首次在大规模上与基于tokenization的LLM性能相匹配,并且在推理效率和鲁棒性方面有显著改进。 梅塔的AI研究团队FAIR推出了一种名为Byte Latent Transformer(BLT)的新模型,这在语言模型领域是一项重大进展。BLT...
This is primarily due to the time consumed in data loading and processing, especially the tokenization takes a lot of time. The acceleration rate will be much closer to K if the streaming mode is disabled. Loss Curves Below are the loss curves obtained from our training on the Pile dataset...
Sun et al. (2022) adopted a Gaussian distribution weighted tokenization module within the spectral–spatial feature tokenization transformer (SSFTT) to improve sample separability. Roy et al. (2023) introduced a model called morphological transformer (morphFormer), which utilizes attention mechanism to...
In practice, the acceleration rate of patch-level training is lower than the patch size K. This is primarily due to the time consumed in data loading and processing, especially the tokenization takes a lot of time. The acceleration rate will be much closer to K if the streaming mode is di...
《Charformer: Fast Character Transformers via Gradient-based Subword Tokenization》(2021) GitHub:https:// github.com/lucidrains/charformer-pytorch [fig2]《Contrastive Representation Learning for Hand Shape Estimation》(2021) GitHub:https:// github.com/lmb-freiburg/contra-hand...
当前,多模态大语言模型(MLLMs)在视觉-语言理解任务中取得了令人瞩目的进展,其中视觉分词(vision tokenization)作为视觉与语言语义对齐的关键环节,发挥着至关重要的作用。 然而,现有方法往往采用将图像划分为规则网格(grid patch token)的方式,这种过度碎片化的分词策略破坏了视觉语义的完整性,导致视觉与语言表征之间难以实...