gemma.cpp is a lightweight pure C++ inference runtime implementation of the Gemma model.
For additional information about Gemma, see the model card. 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 C++ model implementations such as ggml, llama.c, and llama.rs.
gemma.cpp targets experimentation and research use cases – in particular, exploring the design space of CPU inference and inference algorithms using portable SIMD via the Google Highway library. 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).
For production-oriented edge deployments, we recommend standard deployment pathways using mature 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.
Quickstart
To complete this quickstart, you must clone or download gemma.cpp.
System requirements
Before starting, you should have installed:
- CMake
- Clang C++ Compiler
tarfor 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, theVariation` drop-down includes the following options below. Note bfloat16 weights options 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.
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 filename. | 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]
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 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 with the required arguments:
$ ./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 (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.