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ERNIE is an open-source large-model toolkit and model family from the PaddlePaddle ecosystem that focuses on training, fine-tuning, compression, and practical application of ERNIE large language models. The repository positions ERNIEKit as an industrial-grade development toolkit, emphasizing end-to-end workflows that span high-performance pre-training, supervised fine-tuning, and alignment. It supports both full-parameter training and parameter-efficient approaches so teams can choose between maximum quality and lower-cost adaptation depending on their constraints. The project also emphasizes optimization techniques for large-scale training, including mixed-precision and hybrid-parallel strategies that are commonly needed for multi-node GPU clusters. In addition to training, it includes guidance and example materials intended to help developers adopt ERNIE models for real product scenarios rather than only research demonstrations.
Features
- High-performance pre-training workflows for large ERNIE models
- Full-parameter supervised fine-tuning for instruction and task adaptation
- Preference alignment pipelines including Direct Preference Optimization
- Parameter-efficient fine-tuning options such as SFT-LoRA and DPO-LoRA
- Quantization support including Quantization-Aware Training and Post-Training Quantization
- Practical examples and guidance for deploying ERNIE models in real applications
Project Samples
