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llama.cpp is an open source software library that performs inference on various large language models such as Llama. It is co-developed alongside the GGML project, a general-purpose tensor library.
Command-line tools are included with the library, alongside a server with a simple web interface.




Background


Towards the end of September 2022, Georgi Gerganov started work on the GGML library, a C library implementing tensor algebra. Gerganov developed the library with the intention of strict memory management and multi-threading. The creation of GGML was inspired by Fabrice Bellard's work on LibNC.
Before llama.cpp, Gerganov worked on a similar library called whisper.cpp which implemented Whisper, a speech to text model by OpenAI.
Gerganov has a background in medical physics, and was part of the Faculty of Physics in Sofia University. In 2006 he won a silver medal in the International Physics Olympiad. In 2008 he won a programming competition organized by the Bulgarian Association of Software Companies, PC Magazine and Musala Soft, a Bulgarian software services company.




Development


llama.cpp began development in March 2023 by Georgi Gerganov as an implementation of the Llama inference code in pure C/C++ with no dependencies. This improved performance on computers without GPU or other dedicated hardware, which was a goal of the project. llama.cpp gained traction with users who lacked specialized hardware as it could run on just a CPU including on Android devices. While initially designed for CPUs, GPU inference support was later added. As of November 2024 it has more than 67,000 stars on GitHub.
In March 2024 Justine Tunney introduced new optimized matrix multiplication kernels for x86 and ARM CPUs, improving prompt evaluation performance for FP16 and 8-bit quantized data types. These improvements were committed upstream to llama.cpp. Tunney also created a tool called llamafile that bundles models and llama.cpp into a single file that runs on multiple operating systems via the Cosmopolitan Libc library also created by Tunney which allows C/C++ to be more portable across operating systems.




Architecture


llama.cpp supports multiple hardware targets including x86, ARM, CUDA, Metal, Vulkan and SYCL. These back-ends make up the GGML tensor library which is used by the front-end model-specific llama.cpp code. llama.cpp supports ahead of time model quantization as opposed to on-the-fly quantization. llama.cpp makes use of several CPU extensions for optimization: AVX, AVX2 and AVX-512 for X86-64, and Neon on ARM. Apple silicon is an important target for the project. It supports grammar-based output formatting as JSON. It also supports speculative decoding.




GGUF file format



The GGUF (GGML Universal File) file format is a binary format that stores both tensors and metadata in a single file, and is designed for fast saving, and loading of model data. It was introduced in August 2023 by the llama.cpp project to better maintain backwards compatibility as support was added for other model architectures. It succeeded previous formats used by the project such as GGML.
GGUF files are typically created by converting models developed with a different machine learning library such as PyTorch.




= Design

=
The format focuses on quantization, the act of reducing precision in the model weights. This can lead to reduced memory usage, and increased speed at the expense of lower model accuracy.
GGUF supports 2-bit to 8-bit quantized integer types; common floating-point data formats such as float32, float16, and bfloat16; and 1.56 bit quantization.
This file format contains information necessary for running a GPT-like language model such as the tokenizer vocabulary, context length, tensor info and other attributes.




Supported models






References

Kata Kunci Pencarian:

• Llama.cpp
• Llama (language model)
• SYCL
• ROCm
• Akbayan
• Bongbong Marcos
• Presidency of Benigno Aquino III
• Gregorio Catapang Jr.
• Tramadol
• Deaths in June 2014

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• How to install LLaMA: 8-bit and 4-bit : r/LocalLLaMA - Reddit
• A simple guide on how to use llama.cpp with the server GUI
• Llama.cpp GGUF Wrapper : r/LocalLLaMA - Reddit
• Detailed performance numbers and Q&A for llama.cpp GPU …
• GGML Flash Attention support merged into llama.cpp : …
• Guide: build llama.cpp on windows with AMD GPUs, and using …
• As of about 4 minutes ago, llama.cpp has been released with
• Current, comprehensive guide to to installing llama.cpp and llama …
• llama.cpp CPU optimization : r/LocalLLaMA - Reddit
• Early AWQ support added to llama.cpp! : r/LocalLLaMA - Reddit

How to install LLaMA: 8-bit and 4-bit : r/LocalLLaMA - Reddit

A self contained distributable from Concedo that exposes llama.cpp function bindings, allowing it to be used via a simulated Kobold API endpoint. You get llama.cpp with a fancy UI, persistent stories, editing tools, save formats, memory, world info, author's note, characters, scenarios and everything Kobold and Kobold Lite have to offer.

A simple guide on how to use llama.cpp with the server GUI

The llama.cpp server interface is an underappreciated, but simple & lightweight way to interface with local LLMs quickly. I hope this helps anyone looking to get models running quickly. P.S: the batch script I made should support re-launching the models with the same settings as …

Llama.cpp GGUF Wrapper : r/LocalLLaMA - Reddit

Currently llama.cpp is where it's at for non-python based LLM inference. It has really flexible and solid model support, gets new stuff quickly and the community is awesome. There are some rust llama.cpp wrapper libraries that seem promising, and probably not too much hassle to …

Detailed performance numbers and Q&A for llama.cpp GPU …

May 14, 2023 · It would invoke llama.cpp fresh for each prompt. So it would concat all the prompts together to maintain context. That made it progressively slower. With just a few rounds of prompts, it was taking minutes just to product simple output. That's why I switched to using llama.cpp raw. It's a much faster experience.

GGML Flash Attention support merged into llama.cpp : …

This supposes ollama uses the llama.cpp server example under the hood. I went to dig into the ollama code to prove this wrong and... actually you're completely right that llama.cpp servers are a subprocess under ollama. They could absolutely improve parameter handling to allow user-supplied llama.cpp parameters around here. This might not play ...

Guide: build llama.cpp on windows with AMD GPUs, and using …

Atlast, download the release from llama.cpp. At the time of writing, the recent release is llama.cpp-b1198. Unzip and enter inside the folder. I downloaded and unzipped it to: C:\llama\llama.cpp-b1198\llama.cpp-b1198, after which I created a directory called build, so my final path is this: C:\llama\llama.cpp-b1198\llama.cpp-b1198\build

As of about 4 minutes ago, llama.cpp has been released with

Also llama-cpp-python is probably a nice option too since it compiles llama.cpp when you do the pip install, and you can set a few environment variables before that to configure BLAS support and these things. Probably needs that Visual Studio stuff installed too, don't really know since I …

Current, comprehensive guide to to installing llama.cpp and llama …

Jul 18, 2023 · Okay, so you're trying to use this with ooba. I wouldn't be surprised if you can't just update ooba's llama-cpp-python but Idk, maybe it works with some version jumps. Also you probably only compiled/updated llama.cpp (which is included in llama-cpp-python) so you didn't even have matching python bindings (which is what llama-cpp-python provides).

llama.cpp CPU optimization : r/LocalLLaMA - Reddit

Llama.cpp is not touching the disk after loading the model, like a video transcoder does. Basically everything it is doing is in RAM. It is sometimes RAM IO bound, but this always shows up as 100% utilization in most performance monitors.

Early AWQ support added to llama.cpp! : r/LocalLLaMA - Reddit

In summary, the addition of AWQ to llama.cpp is a significant step forward in making LLMs more efficient, accurate, and accessible. Its ability to reduce model size while maintaining or even improving accuracy and inference speed is particularly exciting.