PaddlePaddle/PaddleFormers
PaddleFormers
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.
Features
- Language Model Fine-Tuning - Provides a comprehensive framework for training and fine-tuning large language models using specialized adaptation workflows.
- Distributed Training - Scales model training across multiple hardware accelerators using data and model parallelism strategies.
- Hardware Acceleration - Integrates specialized hardware accelerators into training routines to improve computational throughput and memory efficiency.
- Large Language Model Serving - Enables the hosting and serving of large language models via APIs for text generation and inference tasks.
- Hardware Acceleration - Interfaces with specialized hardware accelerators to optimize tensor operations and reduce model training time.
- Large Language Model Training Frameworks - Provides a specialized framework for the pre-training and fine-tuning of transformer-based large language models.
- Parameter Efficient Fine-Tuning - Provides adaptation methods to update a small subset of model weights, aligning large models with specialized tasks efficiently.
- Pipeline Parallelism Partitioners - Implements pipeline parallelism to partition large model tensors across multiple GPUs to manage memory and throughput.
- Mixed Precision Training - Employs mixed-precision training to reduce memory consumption and accelerate computation during the training process.
- Model Export Formats - Supports converting trained model weights into standardized industry formats for compatibility with external deployment engines.
- Pre-trained Model Zoos - Maintains a curated model zoo of pre-trained architectures and weights for standardized loading and inference.
- Model Weight Export Formats - Saves trained model weights into standardized formats to ensure compatibility across different deep learning frameworks.
- Inference Runtime Integrations - Integrates with specialized inference engines to optimize the execution of models in production environments.
