llama.cpp

Roadmap / Project status / Manifesto / ggml

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

Recent API changes

Hot topics

A new binary llama-mtmd-cli is introduced to replace llava-cli, minicpmv-cli and gemma3-cli ggml-org#13012, libllava will be deprecated
How to use MTLResidencySet to keep the GPU memory active? ggml-org#11427
VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode
Universal tool call support in llama-server ggml-org#9639
Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
Introducing GGUF-my-LoRA ggml-org#10123
Hugging Face Inference Endpoints now support GGUF out of the box! ggml-org#9669
Hugging Face GGUF editor: discussion | tool

Description

The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud.

Plain C/C++ implementation without any dependencies
Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
AVX, AVX2, AVX512 and AMX support for x86 architectures
1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads MTT GPUs via MUSA)
Vulkan and SYCL backend support
CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity

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

Models

Typically finetunes of the base models below are supported as well.

Instructions for adding support for new models: HOWTO-add-model.md

Text-only

Multimodal

Bindings

Python: abetlen/llama-cpp-python
Go: go-skynet/go-llama.cpp
Node.js: withcatai/node-llama-cpp
JS/TS (llama.cpp server client): lgrammel/modelfusion
JS/TS (Programmable Prompt Engine CLI): offline-ai/cli
JavaScript/Wasm (works in browser): tangledgroup/llama-cpp-wasm
Typescript/Wasm (nicer API, available on npm): ngxson/wllama
Ruby: yoshoku/llama_cpp.rb
Rust (more features): edgenai/llama_cpp-rs
Rust (nicer API): mdrokz/rust-llama.cpp
Rust (more direct bindings): utilityai/llama-cpp-rs
Rust (automated build from crates.io): ShelbyJenkins/llm_client
C#/.NET: SciSharp/LLamaSharp
C#/VB.NET (more features - community license): LM-Kit.NET
Scala 3: donderom/llm4s
Clojure: phronmophobic/llama.clj
React Native: mybigday/llama.rn
Java: kherud/java-llama.cpp
Zig: deins/llama.cpp.zig
Flutter/Dart: netdur/llama_cpp_dart
Flutter: xuegao-tzx/Fllama
PHP (API bindings and features built on top of llama.cpp): distantmagic/resonance (more info)
Guile Scheme: guile_llama_cpp
Swift srgtuszy/llama-cpp-swift
Swift ShenghaiWang/SwiftLlama
Delphi Embarcadero/llama-cpp-delphi

UIs

(to have a project listed here, it should clearly state that it depends on llama.cpp)

AI Sublime Text plugin (MIT)
cztomsik/ava (MIT)
Dot (GPL)
eva (MIT)
iohub/collama (Apache-2.0)
janhq/jan (AGPL)
johnbean393/Sidekick (MIT)
KanTV (Apache-2.0)
KodiBot (GPL)
llama.vim (MIT)
LARS (AGPL)
Llama Assistant (GPL)
LLMFarm (MIT)
LLMUnity (MIT)
LMStudio (proprietary)
LocalAI (MIT)
LostRuins/koboldcpp (AGPL)
MindMac (proprietary)
MindWorkAI/AI-Studio (FSL-1.1-MIT)
Mobile-Artificial-Intelligence/maid (MIT)
Mozilla-Ocho/llamafile (Apache-2.0)
nat/openplayground (MIT)
nomic-ai/gpt4all (MIT)
ollama/ollama (MIT)
oobabooga/text-generation-webui (AGPL)
PocketPal AI (MIT)
psugihara/FreeChat (MIT)
ptsochantaris/emeltal (MIT)
pythops/tenere (AGPL)
ramalama (MIT)
semperai/amica (MIT)
withcatai/catai (MIT)
Autopen (GPL)

Tools

akx/ggify – download PyTorch models from HuggingFace Hub and convert them to GGML
akx/ollama-dl – download models from the Ollama library to be used directly with llama.cpp
crashr/gppm – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption
gpustack/gguf-parser - review/check the GGUF file and estimate the memory usage
Styled Lines (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers and a model example)

Infrastructure

Paddler - Stateful load balancer custom-tailored for llama.cpp
GPUStack - Manage GPU clusters for running LLMs
llama_cpp_canister - llama.cpp as a smart contract on the Internet Computer, using WebAssembly
llama-swap - transparent proxy that adds automatic model switching with llama-server
Kalavai - Crowdsource end to end LLM deployment at any scale
llmaz - ☸️ Easy, advanced inference platform for large language models on Kubernetes.

Games

Lucy's Labyrinth - A simple maze game where agents controlled by an AI model will try to trick you.

Supported backends

Backend	Target devices
Metal	Apple Silicon
BLAS	All
BLIS	All
SYCL	Intel and Nvidia GPU
MUSA	Moore Threads MTT GPU
CUDA	Nvidia GPU
HIP	AMD GPU
Vulkan	GPU
CANN	Ascend NPU
OpenCL	Adreno GPU
RPC	All

Building the project

The main product of this project is the llama library. Its C-style interface can be found in include/llama.h. The project also includes many example programs and tools using the llama library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server. Possible methods for obtaining the binaries:

Clone this repository and build locally, see how to build
On MacOS or Linux, install llama.cpp via brew, flox or nix
Use a Docker image, see documentation for Docker
Download pre-built binaries from releases

Obtaining and quantizing models

The Hugging Face platform hosts a number of LLMs compatible with llama.cpp:

You can either manually download the GGUF file or directly use any llama.cpp-compatible models from Hugging Face or other model hosting sites, such as ModelScope, by using this CLI argument: -hf <user>/<model>[:quant].

By default, the CLI would download from Hugging Face, you can switch to other options with the environment variable MODEL_ENDPOINT. For example, you may opt to downloading model checkpoints from ModelScope or other model sharing communities by setting the environment variable, e.g. MODEL_ENDPOINT=https://www.modelscope.cn/.

After downloading a model, use the CLI tools to run it locally - see below.

llama.cpp requires the model to be stored in the GGUF file format. Models in other data formats can be converted to GGUF using the convert_*.py Python scripts in this repo.

The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with llama.cpp:

Use the GGUF-my-repo space to convert to GGUF format and quantize model weights to smaller sizes
Use the GGUF-my-LoRA space to convert LoRA adapters to GGUF format (more info: ggml-org#10123)
Use the GGUF-editor space to edit GGUF meta data in the browser (more info: ggml-org#9268)
Use the Inference Endpoints to directly host llama.cpp in the cloud (more info: ggml-org#9669)

To learn more about model quantization, read this documentation

`llama-cli`

A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality.

Run in conversation mode

Models with a built-in chat template will automatically activate conversation mode. If this doesn't occur, you can manually enable it by adding -cnv and specifying a suitable chat template with --chat-template NAME

llama-cli -m model.gguf

# > hi, who are you?
# Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today?
#
# > what is 1+1?
# Easy peasy! The answer to 1+1 is... 2!

Run in conversation mode with custom chat template

# use the "chatml" template (use -h to see the list of supported templates)
llama-cli -m model.gguf -cnv --chat-template chatml

# use a custom template
llama-cli -m model.gguf -cnv --in-prefix 'User: ' --reverse-prompt 'User:'

Run simple text completion

To disable conversation mode explicitly, use -no-cnv

llama-cli -m model.gguf -p "I believe the meaning of life is" -n 128 -no-cnv

# I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey.

Constrain the output with a custom grammar
```
llama-cli -m model.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'

# {"appointmentTime": "8pm", "appointmentDetails": "schedule a a call"}
```
The grammars/ folder contains a handful of sample grammars. To write your own, check out the GBNF Guide.

For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/

`llama-server`

A lightweight, OpenAI API compatible, HTTP server for serving LLMs.

Start a local HTTP server with default configuration on port 8080

llama-server -m model.gguf --port 8080

# Basic web UI can be accessed via browser: http://localhost:8080
# Chat completion endpoint: http://localhost:8080/v1/chat/completions

Support multiple-users and parallel decoding

# up to 4 concurrent requests, each with 4096 max context
llama-server -m model.gguf -c 16384 -np 4

Enable speculative decoding

# the draft.gguf model should be a small variant of the target model.gguf
llama-server -m model.gguf -md draft.gguf

Serve an embedding model

# use the /embedding endpoint
llama-server -m model.gguf --embedding --pooling cls -ub 8192

Serve a reranking model

# use the /reranking endpoint
llama-server -m model.gguf --reranking

Constrain all outputs with a grammar

# custom grammar
llama-server -m model.gguf --grammar-file grammar.gbnf

# JSON
llama-server -m model.gguf --grammar-file grammars/json.gbnf

`llama-perplexity`

A tool for measuring the perplexity ¹² (and other quality metrics) of a model over a given text.

Measure the perplexity over a text file

llama-perplexity -m model.gguf -f file.txt

# [1]15.2701,[2]5.4007,[3]5.3073,[4]6.2965,[5]5.8940,[6]5.6096,[7]5.7942,[8]4.9297, ...
# Final estimate: PPL = 5.4007 +/- 0.67339

Measure KL divergence
```
# TODO
```

`llama-bench`

Benchmark the performance of the inference for various parameters.

Run default benchmark

llama-bench -m model.gguf

# Output:
# | model               |       size |     params | backend    | threads |          test |                  t/s |
# | ------------------- | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |
# | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         pp512 |      5765.41 ± 20.55 |
# | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         tg128 |        197.71 ± 0.81 |
#
# build: 3e0ba0e60 (4229)

`llama-run`

A comprehensive example for running `llama.cpp` models. Useful for inferencing. Used with RamaLama ³.

Run a model with a specific prompt (by default it's pulled from Ollama registry)
```
llama-run granite-code
```

`llama-simple`

A minimal example for implementing apps with `llama.cpp`. Useful for developers.

Basic text completion

llama-simple -m model.gguf

# Hello my name is Kaitlyn and I am a 16 year old girl. I am a junior in high school and I am currently taking a class called "The Art of

Contributing

Contributors can open PRs
Collaborators can push to branches in the llama.cpp repo and merge PRs into the master branch
Collaborators will be invited based on contributions
Any help with managing issues, PRs and projects is very appreciated!
See good first issues for tasks suitable for first contributions
Read the CONTRIBUTING.md for more information
Make sure to read this: Inference at the edge
A bit of backstory for those who are interested: Changelog podcast

Other documentation

Development documentation

Seminal papers and background on the models

If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:

LLaMA:
- Introducing LLaMA: A foundational, 65-billion-parameter large language model
- LLaMA: Open and Efficient Foundation Language Models
GPT-3
- Language Models are Few-Shot Learners
GPT-3.5 / InstructGPT / ChatGPT:
- Aligning language models to follow instructions
- Training language models to follow instructions with human feedback

XCFramework

The XCFramework is a precompiled version of the library for iOS, visionOS, tvOS, and macOS. It can be used in Swift projects without the need to compile the library from source. For example:

// swift-tools-version: 5.10
// The swift-tools-version declares the minimum version of Swift required to build this package.

import PackageDescription

let package = Package(
    name: "MyLlamaPackage",
    targets: [
        .executableTarget(
            name: "MyLlamaPackage",
            dependencies: [
                "LlamaFramework"
            ]),
        .binaryTarget(
            name: "LlamaFramework",
            url: "https://github.com/ggml-org/llama.cpp/releases/download/b5046/llama-b5046-xcframework.zip",
            checksum: "c19be78b5f00d8d29a25da41042cb7afa094cbf6280a225abe614b03b20029ab"
        )
    ]
)

The above example is using an intermediate build b5046 of the library. This can be modified to use a different version by changing the URL and checksum.

Completions

Command-line completion is available for some environments.

Bash Completion

$ build/bin/llama-cli --completion-bash > ~/.llama-completion.bash
$ source ~/.llama-completion.bash

Optionally this can be added to your .bashrc or .bash_profile to load it automatically. For example:

$ echo "source ~/.llama-completion.bash" >> ~/.bashrc

References

License

Qitmeer/llama.cpp

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