PaddleMIX2.0 is a multi-modal large model development toolkit launched by Baidu, which integrates multi-modal data such as images, text, audio, and video, comprehensively covering various application scenarios including autonomous driving, smart healthcare, and search engines, thereby promoting innovation in AI applications. The release of PaddleMIX2.0 aims to reduce the development difficulty for developers in the multi-modal field, providing support for high-performance algorithms, convenient development, efficient training, and comprehensive deployment.
The three highlights of PaddleMIX2.0 include:
A rich multi-modal model library, covering image, text, video, and audio modalities, and introducing cutting-edge models such as the LLaVA series.
An end-to-end full-process development experience, including the multi-modal data processing toolbox DataCopilot and Auto module, simplifying the training process for large multi-modal models.
High-performance large-scale training and inference capabilities, with the DiT model supporting pre-training at a scale of 3 billion parameters, leading in performance, and introducing the MixToken training strategy, significantly enhancing training throughput.
PaddleMIX2.0 also offers the AppFlow tool, which constructs various multi-modal applications through a pipeline-style combination, and the ComfyUI plugin, supporting multi-modal capabilities and simplifying operations for AIGC tasks. Additionally, PaddleMIX2.0 has seen significant performance improvements in large-scale pre-training, efficient fine-tuning training, and high-performance inference.
Open Source Project Homepage: https://github.com/PaddlePaddle/PaddleMIX