PaddleFormers is a framework for the training, fine-tuning, and deployment of large language models. It provides a full lifecycle pipeline for executing large-scale model training and applying adaptation methods to align models with specialized tasks.
The project focuses on scaling model operations through distributed training and hardware accelerator integration. It employs pipeline parallelism and mixed-precision training to manage memory and increase throughput across multiple hardware devices.
The library includes a curated model zoo for serving pre-trained architectures and tools for production inference integration. It also provides data preparation utilities for chat templates and supports exporting model weights into standardized tensor formats for compatibility with external deployment engines.
