base on 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](https://ai.google.dev/gemma). Model weights, including gemma.cpp specific artifacts, are [available on kaggle](https://www.kaggle.com/models/google/gemma-2). ## 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-2, Gemma-3, and PaliGemma-2 models, focusing on simplicity and directness rather than full generality. This is inspired by vertically-integrated model implementations such as [ggml](https://github.com/ggerganov/ggml), [llama.c](https://github.com/karpathy/llama2.c), and [llama.rs](https://github.com/srush/llama2.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](https://github.com/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](https://www.kaggle.com/models/google/gemma)). ## Contributing Community contributions large and small are welcome. See [DEVELOPERS.md](https://github.com/google/gemma.cpp/blob/main/DEVELOPERS.md) for additional notes contributing developers and [join the discord by following this invite link](https://discord.gg/H5jCBAWxAe). This project follows [Google's Open Source Community Guidelines](https://opensource.google.com/conduct/). > [!NOTE] 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. ## What's inside? - LLM - CPU-only inference for: Gemma 2-3, Griffin(SSM), PaliGemma 2. - Sampling with TopK and temperature. - Backward pass (VJP) and Adam optimizer for Gemma research. - Optimizations - Mixed-precision (fp8, bf16, fp32, fp64 bit) GEMM: - Designed for BF16 instructions, can efficiently emulate them. - Automatic runtime autotuning 7 parameters per matrix shape. - Weight compression integrated directly into GEMM: - Custom fp8 format with 2..3 mantissa bits; tensor scaling. - Also bf16, f32 and non-uniform 4-bit (NUQ); easy to add new formats. - Infrastructure - SIMD: single implementation via Highway. Chooses ISA at runtime. - Tensor parallelism: CCX-aware, multi-socket thread pool. - Disk I/O: memory map or parallel read (heuristic with user override). - Custom format with forward/backward-compatible metadata serialization. - Model conversion from Safetensors, not yet open sourced. - Portability: Linux, Windows/OS X supported. CMake/Bazel. 'Any' CPU. - Frontends - C++ APIs with streaming for single query and batched inference. - Basic interactive command-line app. - Basic Python bindings (pybind11). ## Quick Start ### System requirements Before starting, you should have installed: - [CMake](https://cmake.org/) - [Clang C++ compiler](https://clang.llvm.org/get_started.html), supporting at least C++17. - `tar` for extracting archives from Kaggle. Building natively on Windows requires the Visual Studio 2012 Build Tools with the optional Clang/LLVM C++ frontend (`clang-cl`). This can be installed from the command line with [`winget`](https://learn.microsoft.com/en-us/windows/package-manager/winget/): ```sh winget install --id Kitware.CMake winget install --id Microsoft.VisualStudio.2022.BuildTools --force --override "--passive --wait --add Microsoft.VisualStudio.Workload.VCTools;installRecommended --add Microsoft.VisualStudio.Component.VC.Llvm.Clang --add Microsoft.VisualStudio.Component.VC.Llvm.ClangToolset" ``` ### Step 1: Obtain model weights and tokenizer from Kaggle or Hugging Face Hub Visit the [Kaggle page for Gemma-2](https://www.kaggle.com/models/google/gemma-2/gemmaCpp) 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. > [!NOTE] **Important**: We strongly recommend starting off with the > `gemma2-2b-it-sfp` model to get up and running. Gemma 2 models are named `gemma2-2b-it` for 2B and `9b-it` or `27b-it`. See the `ModelPrefix` function in `configs.cc`. ### 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](https://cmake.org/). To build the gemma inference runtime, create a build directory and generate the build files using `cmake` from the top-level project directory. Note if you previous ran `cmake` and are re-running with a different setting, be sure to delete all files in the `build/` directory with `rm -rf build/*`. #### Unix-like Platforms ```sh cmake -B build ``` After running `cmake`, you can enter the `build/` directory and run `make` to build the `./gemma` executable: ```sh # Configure `build` directory cmake --preset make # Build project using make cmake --build --preset make -j [number of parallel threads to use] ``` Replace `[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 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. If the build is successful, you should now have a `gemma` executable in the `build/` directory. #### Windows ```sh # Configure `build` directory cmake --preset windows # Build project using Visual Studio Build Tools cmake --build --preset windows -j [number of parallel threads to use] ``` If the build is successful, you should now have a `gemma.exe` executable in the `build/` directory. #### Bazel ```sh bazel build -c opt --cxxopt=-std=c++20 :gemma ``` If the build is successful, you should now have a `gemma` executable in the `bazel-bin/` directory. #### Make If you prefer Makefiles, @jart has made one available here: https://github.com/jart/gemma3/blob/main/Makefile ### Step 4: Run You can now run `gemma` from inside the `build/` directory. `gemma` has the following required arguments: Argument | Description | Example value ------------- | ---------------------------- | --------------- `--weights` | The compressed weights file. | `2b-it-sfp.sbs` `--tokenizer` | The tokenizer file. | `tokenizer.spm` Example invocation for the following configuration: - weights file `gemma2-2b-it-sfp.sbs` (Gemma2 2B instruction-tuned model, 8-bit switched floating point). - Tokenizer file `tokenizer.spm` (can omit for single-format weights files created after 2025-05-06, or output by migrate_weights.cc). ```sh ./gemma \ --tokenizer tokenizer.spm --weights gemma2-2b-it-sfp.sbs ``` ### RecurrentGemma This repository includes a version of Gemma based on Griffin ([paper](https://arxiv.org/abs/2402.19427), [code](https://github.com/google-deepmind/recurrentgemma)). Its architecture includes both recurrent layers and local attention, thus it is more efficient for longer sequences and has a smaller memory footprint than standard Gemma. We here provide a C++ implementation of this model based on the paper. To use the recurrent version of Gemma included in this repository, build the gemma binary as noted above in Step 3. Download the compressed weights and tokenizer from the RecurrentGemma [Kaggle](https://www.kaggle.com/models/google/recurrentgemma/gemmaCpp) as in Step 1, and run the binary as follows: `./gemma --tokenizer tokenizer.spm --model gr2b-it --weights 2b-it-sfp.sbs` ### PaliGemma Vision-Language Model This repository includes a version of the PaliGemma 2 VLM ([paper](https://arxiv.org/abs/2412.03555)). We provide a C++ implementation of the PaliGemma 2 model here. To use the version of PaliGemma included in this repository, build the gemma binary as noted above in Step 3. Download the compressed weights and tokenizer from [Kaggle](https://www.kaggle.com/models/google/paligemma-2/gemmaCpp/paligemma2-3b-mix-224) and run the binary as follows: ```sh ./gemma \ --tokenizer paligemma_tokenizer.model \ --weights paligemma2-3b-mix-224-sfp.sbs \ --image_file paligemma/testdata/image.ppm ``` Note that the image reading code is very basic to avoid depending on an image processing library for now. We currently only support reading binary PPMs (P6). So use a tool like `convert` to first convert your images into that format, e.g. `convert image.jpeg -resize 224x224^ image.ppm` (As the image will be resized for processing anyway, we can already resize at this stage for slightly faster loading.) The interaction with the image (using the mix-224 checkpoint) may then look something like this: ``` > Describe the image briefly A large building with two towers in the middle of a city. > What type of building is it? church > What color is the church? gray > caption image A large building with two towers stands tall on the water's edge. The building has a brown roof and a window on the side. A tree stands in front of the building, and a flag waves proudly from its top. The water is calm and blue, reflecting the sky above. A bridge crosses the water, and a red and white boat rests on its surface. The building has a window on the side, and a flag on top. A tall tree stands in front of the building, and a window on the building is visible from the water. The water is green, and the sky is blue. ``` ### Migrating to single-file format There is now a new format for the weights file, which is a single file that allows to contain the tokenizer (and the model type) directly. A tool to migrate from the multi-file format to the single-file format is available. ```sh io/migrate_weights \ --tokenizer .../tokenizer.spm --weights .../gemma2-2b-it-sfp.sbs \ --output_weights .../gemma2-2b-it-sfp-single.sbs ``` After migration, you can omit the tokenizer argument like this: ```sh ./gemma --weights .../gemma2-2b-it-sfp-single.sbs ``` ### Troubleshooting and FAQs **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 (`gemma2-2b-it-sfp`) and not a pre-trained model (any model with a `-pt` suffix). **What sequence lengths are supported?** See `max_seq_len` in `configs.cc` and `InferenceArgs.seq_len`. For the Gemma 3 models larger than 1B, this is typically 32K but 128K would also work given enough RAM. Note that long sequences will be slow due to the quadratic cost of attention. **How do I convert my fine-tune to a `.sbs` compressed model file?** For PaliGemma 2 checkpoints, you can use python/convert_from_safetensors.py to convert from safetensors format (tested with building via bazel). For an adapter model, you will likely need to call merge_and_unload() to convert the adapter model to a single-file format before converting it. Here is how to use it using a bazel build of the compression library assuming locally installed (venv) torch, numpy, safetensors, absl-py, etc.: ```sh bazel build //compression/python:compression BAZEL_OUTPUT_DIR="${PWD}/bazel-bin/compression" python3 -c "import site; print(site.getsitepackages())" # Use your sites-packages file here: ln -s $BAZEL_OUTPUT_DIR [...]/site-packages/compression python3 python/convert_from_safetensors.py --load_path [...].safetensors.index.json ``` **What are some easy ways to make the model run faster?** 1. Make sure you are using the 8-bit switched floating point `-sfp` models. These are half the size of bf16 and thus use less memory bandwidth and cache space. 2. Due to auto-tuning, the second and especially third query will be faster. 3. If you're on a laptop, make sure power mode is set to maximize performance and saving mode is **off**. For most laptops, the power saving modes get activated automatically if the computer is not plugged in. 4. Close other unused cpu-intensive applications. 5. On macs, anecdotally we observe a "warm-up" ramp-up in speed as performance cores get engaged. We're also working on algorithmic and optimization approaches for faster inference, stay tuned. ## 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: ```sh $ ./gemma [...] __ _ ___ _ __ ___ _ __ ___ __ _ ___ _ __ _ __ / _` |/ _ \ '_ ` _ \| '_ ` _ \ / _` | / __| '_ \| '_ \ | (_| | __/ | | | | | | | | | | (_| || (__| |_) | |_) | \__, |\___|_| |_| |_|_| |_| |_|\__,_(_)___| .__/| .__/ __/ | | | | | |___/ |_| |_| ... *Usage* Enter an instruction and press enter (%C reset conversation, %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: ```sh alias gemma2b="~/gemma.cpp/build/gemma -- --tokenizer ~/gemma.cpp/build/tokenizer.spm --weights ~/gemma.cpp/build/gemma2-2b-it-sfp.sbs --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): ```sh cat configs.h | tail -n 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: ```sh [ Reading prompt ] [...] This C++ code snippet defines a set of **constants** used in a large language model (LLM) implementation, likely related to the **attention mechanism**. Let's break down the code: [...] ``` ### 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 92d327e841d78e11ae888757a3e16d291951cf64) 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`: ```sh cmake -B build ``` Then, run `make` with the `libgemma` target: ```sh 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 Unix platforms, the filename is `libgemma.a`. ## Independent Projects Using gemma.cpp Some independent projects using gemma.cpp: - [gemma-cpp-python - Python bindings](https://github.com/namtranase/gemma-cpp-python) - [lua-cgemma - Lua bindings](https://github.com/ufownl/lua-cgemma) - [Godot engine demo project](https://github.com/Rliop913/Gemma-godot-demo-project) If you would like to have your project included, feel free to get in touch or submit a PR with a `README.md` edit. ## Acknowledgements and Contacts gemma.cpp was started in fall 2023 by [Austin Huang](mailto:[email protected]) and [Jan Wassenberg](mailto:[email protected]), and subsequently released February 2024 thanks to contributions from Phil Culliton, Paul Chang, and Dan Zheng. Griffin support was implemented in April 2024 thanks to contributions by Andrey Mikhaylov, Eugene Kliuchnikov, Jan Wassenberg, Jyrki Alakuijala, Lode Vandevenne, Luca Versari, Martin Bruse, Phil Culliton, Sami Boukortt, Thomas Fischbacher and Zoltan Szabadka. Gemma-2 support was implemented in June/July 2024 with the help of several people. PaliGemma support was implemented in September 2024 with contributions from Daniel Keysers. [Jan Wassenberg](mailto:[email protected]) has continued to contribute many improvements, including major gains in efficiency, since the initial release. This is not an officially supported Google product. 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