Introduction to ggml
📰 Hugging Face Blog
Introduction to ggml, a machine learning library for Transformer inference
Action Steps
- Explore the ggml library on GitHub
- Understand the focus on Transformer inference
- Learn about the C and C++ implementation
- Check out the examples and documentation on the Hugging Face blog
Who Needs to Know This
Data scientists and machine learning engineers can benefit from ggml for efficient Transformer inference, while software engineers can appreciate its C and C++ implementation
Key Insight
💡 ggml is a lightweight library for efficient Transformer inference
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💡 Discover ggml, a ML library for Transformer inference in C and C++
Key Takeaways
Introduction to ggml, a machine learning library for Transformer inference
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Published Time: 2024-08-13T00:00:00.440Z
# Introduction to ggml
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# [](https://huggingface.co/blog/introduction-to-ggml#introduction-to-ggml) Introduction to ggml
Published August 13, 2024
[Update on GitHub](https://github.com/huggingface/blog/blob/main/introduction-to-ggml.md)
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[](https://huggingface.co/ggml-org "ggml-org")[ggml-org](https://huggingface.co/ggml-org)
* [Getting started](https://huggingface.co/blog/introduction-to-ggml#getting-started "Getting started")
* [Terminology and concepts](https://huggingface.co/blog/introduction-to-ggml#terminology-and-concepts "Terminology and concepts")
* [Simple example](https://huggingface.co/blog/introduction-to-ggml#simple-example "Simple example")
* [Example with a backend](https://huggingface.co/blog/introduction-to-ggml#example-with-a-backend "Example with a backend")
* [Printing the computational graph](https://huggingface.co/blog/introduction-to-ggml#printing-the-computational-graph "Printing the computational graph")
* [Conclusion](https://huggingface.co/blog/introduction-to-ggml#conclusion "Conclusion")
[ggml](https://github.com/ggerganov/ggml) is a machine learning (ML) library written in C and C++ with a focus on Transformer inference. The project
# Introduction to ggml
[Hugging Face](https://huggingface.co/)
* [Models](https://huggingface.co/models)
* [Datasets](https://huggingface.co/datasets)
* [Spaces](https://huggingface.co/spaces)
* [Buckets new](https://huggingface.co/storage)
* [Docs](https://huggingface.co/docs)
* [Enterprise](https://huggingface.co/enterprise)
* [Pricing](https://huggingface.co/pricing)
*
*
* * *
* [Log In](https://huggingface.co/login)
* [Sign Up](https://huggingface.co/join)
[Back to Articles](https://huggingface.co/blog)
# [](https://huggingface.co/blog/introduction-to-ggml#introduction-to-ggml) Introduction to ggml
Published August 13, 2024
[Update on GitHub](https://github.com/huggingface/blog/blob/main/introduction-to-ggml.md)
[- [x] Upvote 276](https://huggingface.co/login?next=%2Fblog%2Fintroduction-to-ggml)
* [](https://huggingface.co/julien-c "julien-c")
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* [](https://huggingface.co/victor "victor")
* [](https://huggingface.co/sugatoray "sugatoray")
* [](https://huggingface.co/osanseviero "osanseviero")
* [](https://huggingface.co/pcuenq "pcuenq")
* +270
[](https://huggingface.co/ngxson)
[Xuan-Son Nguyen ngxson Follow](https://huggingface.co/ngxson)
[](https://huggingface.co/ggerganov)
[Georgi Gerganov ggerganov Follow](https://huggingface.co/ggerganov)
[](https://huggingface.co/ggml-org "ggml-org")[ggml-org](https://huggingface.co/ggml-org)
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[slaren slaren Follow](https://huggingface.co/slaren)
[](https://huggingface.co/ggml-org "ggml-org")[ggml-org](https://huggingface.co/ggml-org)
* [Getting started](https://huggingface.co/blog/introduction-to-ggml#getting-started "Getting started")
* [Terminology and concepts](https://huggingface.co/blog/introduction-to-ggml#terminology-and-concepts "Terminology and concepts")
* [Simple example](https://huggingface.co/blog/introduction-to-ggml#simple-example "Simple example")
* [Example with a backend](https://huggingface.co/blog/introduction-to-ggml#example-with-a-backend "Example with a backend")
* [Printing the computational graph](https://huggingface.co/blog/introduction-to-ggml#printing-the-computational-graph "Printing the computational graph")
* [Conclusion](https://huggingface.co/blog/introduction-to-ggml#conclusion "Conclusion")
[ggml](https://github.com/ggerganov/ggml) is a machine learning (ML) library written in C and C++ with a focus on Transformer inference. The project
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