Georgi Gerganov - ggml - llama.cpp - whisper.cpp
- [1. Georgi Gerganov](#1. Georgi Gerganov)
-
- [1.1. Projects](#1.1. Projects)
- [2. `ggml`](#2.
ggml) - [3. `llama.cpp`](#3.
llama.cpp) - [4. `whisper.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
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
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
References
[1] Yongqiang Cheng, https://yongqiang.blog.csdn.net/
[2] Introduction to ggml, https://huggingface.co/blog/introduction-to-ggml