jp6/cu128/: xgrammar-0.1.15 metadata and description
Efficient, Flexible and Portable Structured Generation
| author | MLC Team |
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| description_content_type | text/markdown |
| keywords | machine learning, inference |
| license | Apache 2.0 |
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| provides_extras | test |
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| requires_python | <4,>=3.9 |
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xgrammar-0.1.15-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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xgrammar-0.1.15-cp312-cp312-linux_aarch64.whl
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xgrammar-0.1.15-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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XGrammar
Efficient, Flexible and Portable Structured Generation
News
- [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 Catalyst, Berkeley SkyLab, MIT HANLAB, THU IIIS, SJTU, Ant Group, SGLang Meetup, 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 supports general context-free grammar to enable a broad range of structures while bringing careful system optimizations to enable fast executions. XGrammar features a minimal and portable C++ backend that can be easily integrated into multiple environments and frameworks, and is co-designed with the LLM inference engine and enables zero-overhead structured generation in LLM inference.
Get Started
Please visit our documentation to get started with XGrammar.