# paddlepaddle/paddlenlp

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12,953 stars · 3,036 forks · Python · Apache-2.0

## Links

- GitHub: https://github.com/PaddlePaddle/PaddleNLP
- Homepage: https://paddlenlp.readthedocs.io
- awesome-repositories: https://awesome-repositories.com/repository/paddlepaddle-paddlenlp.md

## Description

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.

## Tags

### Part of an Awesome List

- [LLM Frameworks and Libraries](https://awesome-repositories.com/f/awesome-lists/ai/llm-frameworks-and-libraries.md) — Serves as a foundational library for the entire lifecycle of training, fine-tuning, and deploying language models.
- [Language Model Development](https://awesome-repositories.com/f/awesome-lists/ai/language-model-development.md) — Development suite for LLMs based on PaddlePaddle.

### Artificial Intelligence & ML

- [Distributed Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-frameworks/distributed-training.md) — Implements distributed training tools to scale large language models across multiple hardware accelerators.
- [Language Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-fine-tuning.md) — Provides a framework for adjusting pre-trained language models using high-throughput operators and efficient tuning. ([source](https://github.com/paddlepaddle/paddlenlp#readme))
- [Large Language Model Serving](https://awesome-repositories.com/f/artificial-intelligence-ml/large-language-model-serving.md) — Implements a high-performance system for hosting and exposing large language models via APIs for real-time inference.
- [Large-Scale Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/large-scale-model-training.md) — Provides methodologies for training large-scale models that exceed the capacity of a single device. ([source](https://github.com/paddlepaddle/paddlenlp#readme))
- [High-Throughput Text Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/inference-runtimes/high-performance-ai-inference/high-throughput-text-inference.md) — Runs model predictions with optimized throughput and low latency for production-grade text generation. ([source](https://github.com/paddlepaddle/paddlenlp#readme))
- [Hardware-Agnostic Inference Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/inference-engines/hardware-agnostic-inference-layers.md) — Provides a hardware-agnostic inference layer to decouple model execution logic from specific compute backends.
- [Inference Acceleration Techniques](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/inference-optimization/inference-acceleration-techniques.md) — Increases token generation speed through operator merging and hardware-specific inference optimizations. ([source](https://github.com/paddlepaddle/paddlenlp#readme))
- [Model Loading Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-management/model-loading-interfaces.md) — Offers a standardized interface for loading and initializing diverse pre-trained models and tokenizers.
- [Large Language Model Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/training-frameworks/large-language-model-training-frameworks.md) — Provides a comprehensive framework for training large-scale transformer models across distributed GPU environments.
- [Natural Language Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-generation.md) — Produces natural language responses using large language model architectures to complete prompts. ([source](https://github.com/paddlepaddle/paddlenlp#readme))
- [Natural Language Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing.md) — Offers a full natural language processing pipeline for tasks including text generation and structured information extraction.
- [Parameter Efficient Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/parameter-efficient-fine-tuning.md) — Provides parameter-efficient fine-tuning methods to adapt large models with minimal compute and memory requirements.
- [Parameter-Efficient Training Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/parameter-efficient-training-toolkits.md) — Provides toolkits for parameter-efficient fine-tuning and distributed parallel training to reduce memory usage.
- [Pre-trained Model Application](https://awesome-repositories.com/f/artificial-intelligence-ml/pre-trained-model-application.md) — Provides tools for serving pre-trained language models across various hardware backends for real-time inference. ([source](https://github.com/paddlepaddle/paddlenlp#readme))
- [Transformer-Based NLP Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-based-nlp-libraries.md) — Implements a comprehensive library for transformer-based NLP tasks including model training, fine-tuning, and deployment.
- [Transformer Inference Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-inference-engines.md) — Ships a high-performance inference engine optimized for transformer models using operator fusion and backend-agnostic execution.
- [Distributed Model Checkpointing](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-model-checkpointing.md) — Supports saving and loading large-scale model states across compute nodes in a sharded format. ([source](https://github.com/paddlepaddle/paddlenlp#readme))
- [Fine-Tuning Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/fine-tuning-toolkits.md) — Supplies a set of tools for adapting pre-trained models using efficient tuning methods and high-throughput operators.
- [Information Extraction](https://awesome-repositories.com/f/artificial-intelligence-ml/information-extraction.md) — Extracts structured information and key relationships from unstructured text using language models. ([source](https://github.com/paddlepaddle/paddlenlp#readme))
- [Model Merging Strategies](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/architecture-and-operations/model-architecture/model-merging-strategies.md) — Implements strategies to combine different model weights or adaptations to improve overall performance. ([source](https://github.com/paddlepaddle/paddlenlp#readme))
- [Pre-trained Model Zoos](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning/pre-trained-model-zoos.md) — Manages a standardized zoo of pre-trained models and tokenizers to accelerate the development of AI features.
- [Model Deployment](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning/pre-trained-model-zoos/model-deployment.md) — Loads pre-trained architectures and weights into production environments for high-performance real-time inference.
- [Model Checkpointing](https://awesome-repositories.com/f/artificial-intelligence-ml/model-checkpointing.md) — Includes a unified protocol for saving and restoring model states during training and deployment.
- [Model Compression Suites](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/compression-techniques/model-pruning/model-compression-suites.md) — Includes a specialized suite for reducing memory footprints via weight precision conversion and lossless size optimization.
- [Mixed-Precision Quantization](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/compression-techniques/model-pruning/model-compression-suites/half-precision-compression/mixed-precision-quantization.md) — Compresses model memory footprints by converting high-precision weights into smaller numerical formats.
- [Model Serving Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/model-serving-engines.md) — Implements an optimized inference engine for serving language models with high throughput and low latency.
- [Model Compression](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-networks/model-compression.md) — Reduces model file size and complexity using lossless compression techniques to maintain accuracy. ([source](https://github.com/paddlepaddle/paddlenlp#readme))
- [Weight Quantization](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes/weight-quantization.md) — Compresses model weights into lower-precision formats to reduce memory usage and speed up inference. ([source](https://github.com/paddlepaddle/paddlenlp#readme))
- [Zero and Few-Shot Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/zero-and-few-shot-learning.md) — Enables zero-shot extraction of entities and relationships from documents without task-specific training.

### Programming Languages & Runtimes

- [Kernel Fusion Operations](https://awesome-repositories.com/f/programming-languages-runtimes/runtime-execution-environments/runtime-environments/runtimes/graph-symbolic-execution-engines/operation-kernels/kernel-fusion-operations.md) — Utilizes kernel fusion to combine multiple operations into single compute kernels for faster token generation.