PaddlePaddle/PaddleFormers

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.

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