ppc64le/linux/: xgrammar-0.1.33+ppc64le1 metadata and description

Efficient, Flexible and Portable Structured Generation

author	MLC Team
classifiers	License :: OSI Approved :: Apache Software License Development Status :: 4 - Beta Intended Audience :: Developers Intended Audience :: Education Intended Audience :: Science/Research Environment :: MetaData :: IBM Python Ecosystem
description_content_type	text/markdown
keywords	machine learning,inference
license	Apache 2.0
metadata_version	2.1
project_urls	Homepage, https://xgrammar.mlc.ai/ GitHub, https://github.com/mlc-ai/xgrammar
provides_extras	test metal
requires_dist	pydantic torch>=1.10.0 transformers>=4.38.0 triton; platform_system == "Linux" and platform_machine == "x86_64" numpy typing-extensions>=4.9.0 huggingface-hub[cli]; extra == "test" protobuf; extra == "test" pytest; extra == "test" sentencepiece; extra == "test" tiktoken; extra == "test" transformers<4.50.0; platform_system == "Darwin" and extra == "test" mlx-lm; platform_system == "Darwin" and platform_machine == "arm64" and extra == "metal"
requires_python	<4,>=3.8

File	Tox results	History
xgrammar-0.1.33+ppc64le1-cp311-cp311-manylinux_2_34_ppc64le.whl Size 41 MB Type Python Wheel Python 3.11		Uploaded to ppc64le/linux by ppc64le 2026-05-05 15:46:51
xgrammar-0.1.33+ppc64le1-cp312-cp312-manylinux_2_34_ppc64le.whl Size 41 MB Type Python Wheel Python 3.12		Uploaded to ppc64le/linux by ppc64le 2026-05-05 15:46:53

Efficient, Flexible and Portable Structured Generation

Get Started | Documentation | Blogpost | Technical Report

News

[2025/12] XGrammar has been officially integrated into Mirai
[2025/09] XGrammar has been officially integrated into OpenVINO GenAI
[2025/02] XGrammar has been officially integrated into Modular's MAX
[2025/01] XGrammar has been officially integrated into TensorRT-LLM.
[2024/12] XGrammar has been officially integrated into vLLM.
[2024/12] We presented research talks on XGrammar at CMU, UC Berkeley, MIT, THU, SJTU, Ant Group, LMSys, Qingke AI, Camel AI. The slides can be found here.
[2024/11] XGrammar has been officially integrated into SGLang.
[2024/11] XGrammar has been officially integrated into MLC-LLM.
[2024/11] We officially released XGrammar v0.1.0!

Overview

XGrammar is an open-source library for efficient, flexible, and portable structured generation.

It leverages constrained decoding to ensure 100% structural correctness of the output. It supports general context-free grammar to enable a broad range of structures, including JSON, regex, custom context-free grammar, etc.

XGrammar uses careful optimizations to achieve extremely low overhead in structured generation. It has achieved near-zero overhead in JSON generation, making it one of the fastest structured generation engines available.

XGrammar features universal deployment. It supports:

Platforms: Linux, macOS, Windows
Hardware: CPU, NVIDIA GPU, AMD GPU, Apple Silicon, TPU, etc.
Languages: Python, C++, and JavaScript APIs
Models: Qwen, Llama, DeepSeek, Phi, Gemma, etc.

XGrammar is very easy to integrate with LLM inference engines. It is the default structured generation backend for most LLM inference engines, including vLLM, SGLang, TensorRT-LLM, and MLC-LLM, as well as many other companies. You can also try out their structured generation modes!

Get Started

Install XGrammar:

pip install xgrammar

For use with MPS on Apple Silicon, install with:

pip install "xgrammar[metal]"

Import XGrammar:

import xgrammar as xgr

Please visit our documentation to get started with XGrammar.

Third-Party Bindings

Rust: xgrammar-rs — Community Rust bindings for XGrammar.

Collaborators

XGrammar has been widely adopted in industry, open-source projects, and academia. Our collaborators include:

WebLLM

Citation

If you find XGrammar useful in your research, please consider citing our paper:

@article{dong2024xgrammar,
  title={Xgrammar: Flexible and efficient structured generation engine for large language models},
  author={Dong, Yixin and Ruan, Charlie F and Cai, Yaxing and Lai, Ruihang and Xu, Ziyi and Zhao, Yilong and Chen, Tianqi},
  journal={Proceedings of Machine Learning and Systems 7},
  year={2024}
}

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