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super().__init__(embed_dim, num_heads, dropout, bias, add_bias_kv, add_zero_attn, kdim, vdim, batch_first, device, dtype) def attention( self, q, k, v, batch_size=1, seqlen=77, softmax_scale=None, attention_dropout=0.0, causal=False, cu_seqlens=None, max_s=None, need_...
mPLUG-2 地址:https://github.com/X-PLUG/mPLUG-2 mPLUG-Owl 地址:https://github.com/X-PLUG/mPLUG-Owl 研究背景 ⼤规模预训练基础模型是⼈⼯智能领域的新兴范式,涉及语⾔、视觉和多模态等多个领域。随着 Transformer 体系结构的⼴泛成功,近年来已经出现了语⾔、视觉和多模态预训练的⼤融合趋势...
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mPLUG-2 地址:https://github.com/X-PLUG/mPLUG-2 mPLUG-Owl 地址:https://github.com/X-PLUG/mPLUG-Owl 研究背景 ⼤规模预训练基础模型是⼈⼯智能领域的新兴范式,涉及语⾔、视觉和多模态等多个领域。随着Transformer 体系结构的⼴泛成功,近年来已经出现了语⾔、视觉和多模态预训练的⼤融合趋势...
we present mPLUG-2, a new unified paradigm with modularized design for multi-modal pretraining, which can benefit from modality collaboration while addressing the problem of modality entanglement. In contrast to predominant paradigms of solely relying on sequence-to-sequence generation or encoder-based...
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video (ICML 2023) - X-PLUG/mPLUG-2
we present mPLUG-2, a new unified paradigm with modularized design for multi-modal pretraining, which can benefit from modality collaboration while addressing the problem of modality entanglement. In contrast to predominant paradigms of solely relying on sequence-to-sequence generation or encoder-based...