PaddleFormers is a library of pre-trained large language models built on PaddlePaddle that provides transformer model interfaces and functionality comparable to Hugging Face Transformers.
The library addresses the need for efficient large language model and vision-language model training within the PaddlePaddle ecosystem. It leverages PaddlePaddle's built-in advantages in high-performance training by supporting distributed training strategies including tensor parallelism, pipeline parallelism, and expert parallelism, alongside acceleration techniques like automatic mixed precision. The tool aims to deliver high-performance training with low resource consumption, abstracting away complex optimization details so users can focus on model development rather than infrastructure tuning.
Developers working within the PaddlePaddle ecosystem who need to train large language models or vision-language models should consider this tool. It suits projects that require distributed training at scale and benefit from PaddlePaddle's training optimizations. The library is designed as a PaddlePaddle-native alternative to frameworks like Hugging Face Transformers, offering equivalent model interfaces and functionality tailored to PaddlePaddle's strengths.
The project maintains active development with recent releases introducing support for training large-scale models. Development activity shows ongoing feature expansion and optimization work focused on improving training performance and resource efficiency across distributed training scenarios.
