3 citations · 3 across the 5 of their papers we have counts for
5 papers
dots.tts Technical Report
Shi Lian, Changtao Li, Bohan Li +6
We present dotstts, a 2B-parameter continuous autoregressive text-to-speech (TTS) foundation model that models speech in a continuous latent space. Compared with existing contin…
dots.llm1 Technical Report
Bi Huo, Bin Tu, Cheng Qin +24
Mixture of Experts (MoE) models have emerged as a promising paradigm for scaling language models efficiently by activating only a subset of parameters for each input token. In this…
EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations
Nuowei Liu, Changzhi Sun, Tao Ji +4
Current Large Language Models (LLMs) for understanding proteins primarily treats amino acid sequences as a text modality. Meanwhile, Protein Language Models (PLMs), such as ESM-2,…
Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models
Junfeng Tian, Da Zheng, Yang Cheng +3
Large language models (LLM) have prioritized expanding the context window from which models can incorporate more information. However, training models to handle long contexts prese…
Nyonic Technical Report
Junfeng Tian, Rui Wang, Cong Li +3
This report details the development and key achievements of our latest language model designed for custom large language models. The advancements introduced include a novel Online…