PaddleNLP | Awesome Repository

PaddleNLP is a development library and toolkit for training, fine-tuning, and deploying large and small language models using the PaddlePaddle framework. It provides a comprehensive suite for the entire natural language processing lifecycle, from model development to high-performance inference.

The project features a standardized model zoo for loading and managing pre-trained models and tokenizers through a unified interface. It distinguishes itself with a specialized model compression framework that reduces memory footprints via weight precision conversion and lossless size optimization, alongside an inference engine that utilizes operator fusion and backend-agnostic execution to increase token generation speed.

The library covers a broad range of capabilities including distributed parallel training, parameter-efficient fine-tuning, and model weight merging. It also supports a full natural language processing pipeline for tasks such as text generation and zero-shot structured information extraction.

Features

LLM Frameworks and Libraries - Serves as a foundational library for the entire lifecycle of training, fine-tuning, and deploying language models.
Distributed Training - Implements distributed training tools to scale large language models across multiple hardware accelerators.
Language Model Fine-Tuning - Provides a framework for adjusting pre-trained language models using high-throughput operators and efficient tuning.
Large Language Model Serving - Implements a high-performance system for hosting and exposing large language models via APIs for real-time inference.

Features

LLM Frameworks and Libraries - Serves as a foundational library for the entire lifecycle of training, fine-tuning, and deploying language models.
Distributed Training - Implements distributed training tools to scale large language models across multiple hardware accelerators.
Language Model Fine-Tuning - Provides a framework for adjusting pre-trained language models using high-throughput operators and efficient tuning.
Large Language Model Serving - Implements a high-performance system for hosting and exposing large language models via APIs for real-time inference.