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2025/4/27 4:26:06 来源:https://blog.csdn.net/chengyq116/article/details/144940458  浏览:    关键词:凡科网首页_网站基本建设_新网站应该怎么做seo_上海搜索引擎优化公司排名
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Georgi Gerganov - ggml - llama.cpp - whisper.cpp

  • 1. Georgi Gerganov
    • 1.1. Projects
  • 2. `ggml`
  • 3. `llama.cpp`
  • 4. `whisper.cpp`
  • References

1. Georgi Gerganov

https://github.com/ggerganov
https://ggerganov.com/

ggml-org
https://github.com/ggml-org

GGML - AI at the edge
https://ggml.ai/

ggml.ai is a company founded by Georgi Gerganov to support the development of ggml.

ggml is a tensor library for machine learning to enable large models and high performance on commodity hardware. It is used by llama.cpp and whisper.cpp.

1.1. Projects

  • whisper.cpp

https://github.com/ggerganov/whisper.cpp

High-performance inference of OpenAI’s Whisper automatic speech recognition model

The project provides a high-quality speech-to-text solution that runs on Mac, Windows, Linux, iOS, Android, Raspberry Pi, and Web

  • llama.cpp

https://github.com/ggerganov/llama.cpp

Inference of Meta’s LLaMA model (and others) in pure C/C++

The project provides efficient inference across a wide range of hardware and serves as the foundation for numerous LLM-based applications

2. ggml

https://github.com/ggerganov/ggml

Tensor library for machine learning

Some of the development is currently happening in the llama.cpp and whisper.cpp repos.

sync : llama.cpp
sync : whisper.cpp

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3. llama.cpp

https://github.com/ggerganov/llama.cpp

Inference of Meta’s LLaMA model (and others) in pure C/C++

The llama.cpp project is the main playground for developing new features for the ggml library.

sync : ggml

在这里插入图片描述

4. whisper.cpp

https://github.com/ggerganov/whisper.cpp

High-performance inference of OpenAI’s Whisper automatic speech recognition (ASR) model.

The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library.

sync : ggml
sync : ggml + llama.cpp
sync : ggml and llama.cpp

在这里插入图片描述

References

[1] Yongqiang Cheng, https://yongqiang.blog.csdn.net/
[2] Introduction to ggml, https://huggingface.co/blog/introduction-to-ggml

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