Large language models (LLMs) can potentially democratize access to medical knowledge.
Ranked #1 on Multiple Choice Question Answering (MCQA) on MedMCQA (Dev Set (Acc-%) metric)
To address these challenges, we introduce StyleCrafter, a generic method that enhances pre-trained T2V models with a style control adapter, enabling video generation in any style by providing a reference image.
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.
A lifelike talking head requires synchronized coordination of subject identity, lip movements, facial expressions, and head poses.
Additionally, experiments on 18 datasets further demonstrate that Monkey surpasses existing LMMs in many tasks like Image Captioning and various Visual Question Answering formats.
The key contributions of SparseDC are two-fold.