Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module.
Magicoder models are trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets to generate high-quality instruction data for code.
We introduce a novel sequential modeling approach which enables learning a Large Vision Model (LVM) without making use of any linguistic data.
Furthermore, we significantly improve the naturalness and speaker similarity of synthetic speech even in zero-shot speech synthesis scenarios.
We also introduce latent DiffiT which consists of transformer model with the proposed self-attention layers, for high-resolution image generation.
Ranked #2 on Image Generation on ImageNet 256x256
Denoising diffusion models (DDMs) have attracted attention for their exceptional generation quality and diversity.