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.
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.
Active development is currently done on the dev branch. Please open pull
requests targeting dev branch instead of main, which is intended to be more
stable.
Before starting, you should have installed:
- CMake
- Clang C++ compiler, supporting at least C++17.
tarfor extracting archives 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. In general, we recommend starting with the
-sfp checkpoints.
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
Important: We strongly recommend starting off with the 2b-it-sfp model to
get up and running.
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).
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. For the 8-bit switched floating point
weights (sfp), run cmake with no options:
cmake -B buildor if you downloaded bfloat16 weights (any model without -sfp in the name),
instead of running cmake with no options as above, run cmake with WEIGHT_TYPE
set to highway's hwy::bfloat16_t type
(this will be simplified in the future, we recommend using -sfp weights
instead of bfloat16 for faster inference):
cmake -B build -DWEIGHT_TYPE=hwy::bfloat16_tAfter running whichever of the above cmake invocations that is appropriate for
your weights, you can enter the build/ directory and run make to build the
./gemma executable:
cd build
make -j [number of parallel threads to use] gemmaReplace [number of parallel threads to use] with a number - the number of
cores available on your system is a reasonable heuristic.
For example, make -j4 gemma will build using 4 threads. If this is successful,
you should now have a gemma executable in the build/ directory. If the
nproc command is available, you can use make -j$(nproc) gemma as a
reasonable default for the number of threads.
If you aren't sure of the right value for the -j flag, you can simply run
make gemma instead and it should still build the ./gemma executable.
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.
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-itRunning ./gemma fails with "Failed to read cache gating_ein_0 (error 294) ..."
The most common problem is that cmake was built with the wrong weight type and
gemma is attempting to load bfloat16 weights (2b-it, 2b-pt, 7b-it,
7b-pt) using the default switched floating point (sfp) or vice versa. Revisit
step #3 and check that the cmake command used to build gemma was correct for
the weights that you downloaded.
In the future we will handle model format handling from compile time to runtime to simplify this.
Problems building in Windows / Visual Studio
Currently if you're using Windows, we recommend building in WSL (Windows Subsystem for Linux). We are exploring options to enable other build configurations, see issues for active discussion.
Model does not respond to instructions and produces strange output
A common issue is that you are using a pre-trained model, which is not
instruction-tuned and thus does not respond to instructions. Make sure you are
using an instruction-tuned model (2b-it-sfp, 2b-it, 7b-it-sfp, 7b-it)
and not a pre-trained model (any model with a -pt suffix).
How do I convert my fine-tune to a .sbs compressed model file?
We're working on a python script to convert a standard model format to .sbs,
and hope have it available in the next week or so. Follow this
issue for updates.
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. |
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.
...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: " | gemma2bNote
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**:
* `kSeqLen`: Stores the length of the sequence to be processed. It's set to 7168.
* `kVocabSize`: Stores the size of the vocabulary, which is 256128.
* `kLayers`: Number of layers in the deep learning model. It's set to 28.
* `kModelDim`: Dimension of the model's internal representation. It's set to 3072.
* `kFFHiddenDim`: Dimension of the feedforward and recurrent layers' hidden representations. It's set to 16 * 3072 / 2.
**ConfigGemma2B**:
* `kSeqLen`: Stores the length of the sequence to be processed. It's also set to 7168.
* `kVocabSize`: Size of the vocabulary, which is 256128.
* `kLayers`: Number of layers in the deep learning model. It's set to 18.
* `kModelDim`: Dimension of the model's internal representation. It's set to 2048.
* `kFFHiddenDim`: 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.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})
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:
cmake -B buildThen, run make with the libgemma target:
cd build
make -j [number of parallel threads to use] libgemmaIf this is successful, you should now have a libgemma library file in the
build/ directory. On Unix platforms, the filename is libgemma.a.
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.