For visual generation, discrete autoregressive models often struggle with poor tokenizer reconstruction, difficulties in sampling from large vocabularies, and slow token-by-token generation speeds. We ...

Abstract: The foundation of current large language model applications lies in the generative language model, which typically employs an autoregressive token generation approach. However, this model ...

According to @krea_ai, the company has open-sourced Krea Realtime, a 14 billion parameter autoregressive AI model that is 10 times larger than any other open-source equivalent. This breakthrough model ...

Abstract: The pre-training architectures of large language models encompass various types, including autoencoding models, autoregressive models, and encoder-decoder models. We posit that any modality ...

Recent advancements in training large multimodal models have been driven by efforts to eliminate modeling constraints and unify architectures across domains. Despite these strides, many existing ...

Autoregressive LLMs are complex neural networks that generate coherent and contextually relevant text through sequential prediction. These LLms excel at handling large datasets and are very strong at ...

Large language models (LLMs) based on autoregressive Transformer Decoder architectures have advanced natural language processing with outstanding performance and scalability. Recently, diffusion ...