chunlicui/gemma.cpp

lightweight, standalone C++ inference engine for Google's Gemma models.

gemma.cpp

gemma.cpp is a lightweight, standalone C++ inference engine for the Gemma foundation models from Google.

For additional information about Gemma, see ai.google.dev/gemma. Model weights, including gemma.cpp specific artifacts, are available on kaggle.

Who is this project for?

Modern LLM inference engines are sophisticated systems, often with bespoke capabilities extending beyond traditional neural network runtimes. With this comes opportunities for research and innovation through co-design of high level algorithms and low-level computation. However, there is a gap between deployment-oriented C++ inference runtimes, which are not designed for experimentation, and Python-centric ML research frameworks, which abstract away low-level computation through compilation.

gemma.cpp provides a minimalist implementation of Gemma 2B and 7B models, focusing on simplicity and directness rather than full generality. This is inspired by vertically-integrated model implementations such as ggml, llama.c, and llama.rs.

gemma.cpp targets experimentation and research use cases. It is intended to be straightforward to embed in other projects with minimal dependencies and also easily modifiable with a small ~2K LoC core implementation (along with ~4K LoC of supporting utilities). We use the Google Highway Library to take advantage of portable SIMD for CPU inference.

For production-oriented edge deployments we recommend standard deployment pathways using Python frameworks like JAX, Keras, PyTorch, and Transformers (all model variations here).

Community contributions large and small are welcome. This project follows Google's Open Source Community Guidelines.

Quick Start

System requirements

Before starting, you should have installed:

• CMake
• Clang C++ compiler, supporting at least C++17.
• tar for extracting archives from Kaggle.

Step 1: Obtain model weights and tokenizer from Kaggle

Visit the Gemma model page on Kaggle and select Model Variations |> Gemma C++. On this tab, the Variation dropdown includes the options below. Note bfloat16 weights are higher fidelity, while 8-bit switched floating point weights enable faster inference.

2B instruction-tuned (it) and pre-trained (pt) models:

Model name	Description
2b-it	2 billion parameter instruction-tuned model, bfloat16
2b-it-sfp	2 billion parameter instruction-tuned model, 8-bit switched floating point
2b-pt	2 billion parameter pre-trained model, bfloat16
2b-pt-sfp	2 billion parameter pre-trained model, 8-bit switched floating point

7B instruction-tuned (it) and pre-trained (pt) models:

Model name	Description
7b-it	7 billion parameter instruction-tuned model, bfloat16
7b-it-sfp	7 billion parameter instruction-tuned model, 8-bit switched floating point
7b-pt	7 billion parameter pre-trained model, bfloat16
7b-pt-sfp	7 billion parameter pre-trained model, 8-bit switched floating point

Note

We recommend starting with 2b-it-sfp to get up and running.

Step 2: Extract Files

After filling out the consent form, the download should proceed to retrieve a tar archive file archive.tar.gz. Extract files from archive.tar.gz (this can take a few minutes):

tar -xf archive.tar.gz

This should produce a file containing model weights such as 2b-it-sfp.sbs and a tokenizer file (tokenizer.spm). You may want to move these files to a convenient directory location (e.g. the build/ directory in this repo).

Step 3: Build

The build system uses CMake. To build the gemma inference runtime, create a build directory and generate the build files using cmake from the top-level project directory:

(cd build && cmake ..)

Then run make to build the ./gemma executable:

cd build
make -j [number of parallel threads to use] gemma

For example, make -j 8 gemma. If this is successful, you should now have a gemma executable in the build/ directory.

Note

On Windows Subsystem for Linux (WSL) users should set the number of parallel threads to 1. Using a larger number may result in errors.

Step 4: Run

You can now run gemma from inside the build/ directory.

gemma has the following required arguments:

Argument	Description	Example value
--model	The model type.	2b-it, 2b-pt, 7b-it, 7b-pt, ... (see above)
--compressed_weights	The compressed weights file.	2b-it-sfp.sbs, ... (see above)
--tokenizer	The tokenizer file.	tokenizer.spm

gemma is invoked as:

./gemma \
--tokenizer [tokenizer file] \
--compressed_weights [compressed weights file] \
--model [2b-it or 2b-pt or 7b-it or 7b-pt or ...]

Example invocation for the following configuration:

• Compressed weights file 2b-it-sfp.sbs (2B instruction-tuned model, 8-bit switched floating point).
• Tokenizer file tokenizer.spm.

./gemma \
--tokenizer tokenizer.spm \
--compressed_weights 2b-it-sfp.sbs \
--model 2b-it

Usage

gemma has different usage modes, controlled by the verbosity flag.

All usage modes are currently interactive, triggering text generation upon newline input.

Verbosity	Usage mode	Details
--verbosity 0	Minimal	Only prints generation output. Suitable as a CLI tool.
--verbosity 1	Default	Standard user-facing terminal UI.
--verbosity 2	Detailed	Shows additional developer and debug info.

Interactive Terminal App

By default, verbosity is set to 1, bringing up a terminal-based interactive interface when gemma is invoked:

$ ./gemma [...]
  __ _  ___ _ __ ___  _ __ ___   __ _   ___ _ __  _ __
 / _` |/ _ \ '_ ` _ \| '_ ` _ \ / _` | / __| '_ \| '_ \
| (_| |  __/ | | | | | | | | | | (_| || (__| |_) | |_) |
 \__, |\___|_| |_| |_|_| |_| |_|\__,_(_)___| .__/| .__/
  __/ |                                    | |   | |
 |___/                                     |_|   |_|

tokenizer                     : tokenizer.spm
compressed_weights            : 2b-it-sfp.sbs
model                         : 2b-it
weights                       : [no path specified]
max_tokens                    : 3072
max_generated_tokens          : 2048

*Usage*
  Enter an instruction and press enter (%Q quits).

*Examples*
  - Write an email to grandma thanking her for the cookies.
  - What are some historical attractions to visit around Massachusetts?
  - Compute the nth fibonacci number in javascript.
  - Write a standup comedy bit about WebGPU programming.

> What are some outdoorsy places to visit around Boston?

[ Reading prompt ] .....................


**Boston Harbor and Islands:**

* **Boston Harbor Islands National and State Park:** Explore pristine beaches, wildlife, and maritime history.
* **Charles River Esplanade:** Enjoy scenic views of the harbor and city skyline.
* **Boston Harbor Cruise Company:** Take a relaxing harbor cruise and admire the city from a different perspective.
* **Seaport Village:** Visit a charming waterfront area with shops, restaurants, and a seaport museum.

**Forest and Nature:**

* **Forest Park:** Hike through a scenic forest with diverse wildlife.
* **Quabbin Reservoir:** Enjoy boating, fishing, and hiking in a scenic setting.
* **Mount Forest:** Explore a mountain with breathtaking views of the city and surrounding landscape.

...

Usage as a Command Line Tool

For using the gemma executable as a command line tool, it may be useful to create an alias for gemma.cpp with arguments fully specified:

alias gemma2b="~/gemma.cpp/build/gemma -- --tokenizer ~/gemma.cpp/build/tokenizer.spm --compressed_weights ~/gemma.cpp/build/2b-it-sfp.sbs --model 2b-it --verbosity 0"

Replace the above paths with your own paths to the model and tokenizer paths from the download.

Here is an example of prompting gemma with a truncated input file (using a gemma2b alias like defined above):

cat configs.h | tail -35 | tr '\n' ' ' | xargs -0 echo "What does this C++ code do: " | gemma2b

Note

CLI usage of gemma.cpp is experimental and should take context length limitations into account.

The output of the above command should look like:

$ cat configs.h | tail -35 | tr '\n' ' ' | xargs -0 echo "What does this C++ code do: " | gemma2b
[ Reading prompt ] ......................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
The code defines two C++ structs, `ConfigGemma7B` and `ConfigGemma2B`, which are used for configuring a deep learning model.

**ConfigGemma7B**:

* `seq_len`: Stores the length of the sequence to be processed. It's set to 7168.
* `vocab_size`: Stores the size of the vocabulary, which is 256128.
* `n_layers`: Number of layers in the deep learning model. It's set to 28.
* `dim_model`: Dimension of the model's internal representation. It's set to 3072.
* `dim_ffw_hidden`: Dimension of the feedforward and recurrent layers' hidden representations. It's set to 16 * 3072 / 2.

**ConfigGemma2B**:

* `seq_len`: Stores the length of the sequence to be processed. It's also set to 7168.
* `vocab_size`: Size of the vocabulary, which is 256128.
* `n_layers`: Number of layers in the deep learning model. It's set to 18.
* `dim_model`: Dimension of the model's internal representation. It's set to 2048.
* `dim_ffw_hidden`: Dimension of the feedforward and recurrent layers' hidden representations. It's set to 16 * 2048 / 2.

These structs are used to configure a deep learning model with specific parameters for either Gemma7B or Gemma2B architecture.

Incorporating gemma.cpp as a Library in your Project

The easiest way to incorporate gemma.cpp in your own project is to pull in gemma.cpp and dependencies using FetchContent. You can add the following to your CMakeLists.txt:

include(FetchContent)

FetchContent_Declare(sentencepiece GIT_REPOSITORY https://github.com/google/sentencepiece GIT_TAG 53de76561cfc149d3c01037f0595669ad32a5e7c)
FetchContent_MakeAvailable(sentencepiece)

FetchContent_Declare(gemma GIT_REPOSITORY https://github.com/google/gemma.cpp GIT_TAG origin/main)
FetchContent_MakeAvailable(gemma)

FetchContent_Declare(highway GIT_REPOSITORY https://github.com/google/highway.git GIT_TAG da250571a45826b21eebbddc1e50d0c1137dee5f)
FetchContent_MakeAvailable(highway)

Note for the gemma.cpp GIT_TAG, you may replace origin/main for a specific commit hash if you would like to pin the library version.

After your executable is defined (substitute your executable name for [Executable Name] below):

target_link_libraries([Executable Name] libgemma hwy hwy_contrib sentencepiece)
FetchContent_GetProperties(gemma)
FetchContent_GetProperties(sentencepiece)
target_include_directories([Executable Name] PRIVATE ${gemma_SOURCE_DIR})
target_include_directories([Executable Name] PRIVATE ${sentencepiece_SOURCE_DIR})

Building gemma.cpp as a Library

gemma.cpp can also be used as a library dependency in your own project. The shared library artifact can be built by modifying the make invocation to build the libgemma target instead of gemma.

Note

If you are using gemma.cpp in your own project with the FetchContent steps in the previous section, building the library is done automatically by cmake and this section can be skipped.

First, run cmake:

(cd build && cmake ..)

Then, run make with the libgemma target:

cd build
make -j [number of parallel threads to use] libgemma

If this is successful, you should now have a libgemma library file in the build/ directory. On linux the filename is libgemma.a.

Acknowledgements and Contacts

gemma.cpp was started in fall 2023 by Austin Huang and Jan Wassenberg, and subsequently released February 2024 thanks to contributions from Phil Culliton, Paul Chang, and Dan Zheng.

This is not an officially supported Google product.