To bridge this gap, we present Nexus-Gen, a unified model that synergizes the language reasoning capabilities of LLMs with the image synthesis power of diffusion models.
Eliminating non-stationarity is essential for avoiding spurious regressions and capturing local dependencies in short-term modeling, while preserving it is crucial for revealing long-term cointegration across variates.
We introduce INTELLECT-2, the first globally distributed reinforcement learning (RL) training run of a 32 billion parameter language model.
Contrastive Language-Image Pre-training (CLIP) excels in multimodal tasks such as image-text retrieval and zero-shot classification but struggles with fine-grained understanding due to its focus on coarse-grained short captions.
The advent of 1-bit large language models (LLMs), led by BitNet b1. 58, has spurred interest in ternary LLMs.
We present FlashInfer: a customizable and efficient attention engine for LLM serving.
Recently, large language model (LLM) based text-to-speech (TTS) systems have gradually become the mainstream in the industry due to their high naturalness and powerful zero-shot voice cloning capabilities. Here, we introduce the IndexTTS system, which is mainly based on the XTTS and Tortoise model.
Third, to effectively train the framework, we construct a large-scale character dataset containing 10-million-level samples.
The era of intelligent agents is upon us, driven by revolutionary advancements in large language models.
Recent advances in deep generative models (e. g., GANs, diffusion models) and 3D representations (e. g., NeRF, 3D Gaussians) have enabled the learning of real-world scene distributions, improving fidelity, diversity, and view consistency.