gemma Documentation

Index

``{eval-rst}

.. toctree:: :hidden: :caption: Guides

colab_sampling colab_multimodal colab_finetuning colab_tool_use colab_sharding colab_tokenizer colab_lora_sampling colab_lora_finetuning peft checkpoints research

.. toctree:: :hidden: :caption: Links

GitHub <https://github.com/google-deepmind/gemma> Issues <https://github.com/google-deepmind/gemma/issues> Ecosystem <https://ai.google.dev/gemma>

.. toctree:: :hidden: :caption: API

api/gm/index api/peft/index

text

{include} ../README.md
text
---
Checkpoints 
Checkpoints
Kaggle Hub 
Gemma models are available on Kaggle Hub for various formats:
*   Gemma 4
*   Gemma 3
*   Gemma 2
*   Gemma 1
To manually download the model:
*   Select one of the Flax model variations
*   Click the "Download" button to download the model archive
*   Extract the model files from the archiveThe archive contains both the model weights and the tokenizer, like:

2b/              # Directory containing model weights
tokenizer.model  # Tokenizer
text
To programmatically download the model:
python
import kagglehub
kagglehub.login()
weights_dir = kagglehub.model_download(f'google/gemma-3/flax/gemma3-4b-it')
text
v4 
NOTE: Gemma 4 Flax checkpoints will be updated on Kaggle when possible.
<section class="zippy" markdown="1">
v3 
| Size | Variant     | Quantized | Name                                     |
| ---- | ----------- | --------- | ---------------------------------------- |
| 1B   | Pre-trained |           | google/gemma-3/flax/gemma3-1b          |
:      : (PT)        :           :                                          :
| 4B   | Pre-trained |           | google/gemma-3/flax/gemma3-4b          |
:      : (PT)        :           :                                          :
| 12B  | Pre-trained |           | google/gemma-3/flax/gemma3-12b         |
:      : (PT)        :           :                                          :
| 27B  | Pre-trained |           | google/gemma-3/flax/gemma3-27b         |
:      : (PT)        :           :                                          :
| 1B   | Instruction |           | google/gemma-3/flax/gemma3-1b-it       |
:      : Tuned (IT)  :           :                                          :
| 4B   | Instruction |           | google/gemma-3/flax/gemma3-4b-it       |
:      : Tuned (IT)  :           :                                          :
| 12B  | Instruction |           | google/gemma-3/flax/gemma3-12b-it      |
:      : Tuned (IT)  :           :                                          :
| 27B  | Instruction |           | google/gemma-3/flax/gemma3-27b-it      |
:      : Tuned (IT)  :           :                                          :
| 1B   | Instruction | Y         | google/gemma-3/flax/gemma3-1b-it-int4  |
:      : Tuned (IT)  :           :                                          :
| 4B   | Instruction | Y         | google/gemma-3/flax/gemma3-4b-it-int4  |
:      : Tuned (IT)  :           :                                          :
| 12B  | Instruction | Y         | google/gemma-3/flax/gemma3-12b-it-int4 |
:      : Tuned (IT)  :           :                                          :
| 27B  | Instruction | Y         | google/gemma-3/flax/gemma3-27b-it-int4 |
:      : Tuned (IT)  :           :                                          :
</section>
<section class="zippy" markdown="1">
v2 
Size | Variant                | Name
---- | ---------------------- | -----------------------------------
2.6B | Pre-trained (PT)       | google/gemma-2/flax/gemma2-2b
9B   | Pre-trained (PT)       | google/gemma-2/flax/gemma2-9b
27B  | Pre-trained (PT)       | google/gemma-2/flax/gemma2-27b
2.6B | Instruction Tuned (IT) | google/gemma-2/flax/gemma2-2b-it
9B   | Instruction Tuned (IT) | google/gemma-2/flax/gemma2-9b-it
27B  | Instruction Tuned (IT) | google/gemma-2/flax/gemma2-27b-it
</section>
---
Research 
Research
Custom transformer 
For experimentation, you might want to create smaller custom versions of
Transformer, rather than using the default official "Gemma" pre-trained ones.
Here is an example to create a text-only, 12 layers transformer:
python
class MyTinyTransformer(gm.nn.Transformer):
  config: gm.nn.config.TransformerConfig = gm.nn.config.TransformerConfig(
      final_logit_softcap=None,
      num_embed=262144,  # Vocab size, matching the tokenizer
      embed_dim=896,
      hidden_dim=4 * 896,
      num_heads=4,
      head_dim=256,
      num_kv_heads=1,
      use_post_attn_norm=True,
      use_post_ffw_norm=True,
      use_qk_norm=True,
      attention_types=gm.nn.config.make_attention_layers_types(
          pattern=gm.nn.config.GEMMA3_ATTENTION_PATTERN,
          num_layers=12,
      ),
      query_pre_attn_norm=gm.nn.config.QueryPreAttentionNormalisation.BY_ONE_OVER_SQRT_HEAD_DIM,
      attn_logits_soft_cap=None,
      sliding_window_size=512,
      transpose_gating_einsum=True,
      local_base_frequency=10_000,
      global_base_frequency=1_000_000,
      vision_encoder=None,  # Text-only
  )
  INFO = gm.nn.config.ModelInfo(
      tokenizer_version=3,  # Auto-select the tokenizer in the sampler
  )
text
Gemma-related projects 
The Gemma repository also contain various non-official research projects around
Gemma, located in the research/
directory:*   t5gemma:
    Encoder/decoder Gemma architecture, based on Gemma 2.
---
CHANGELOG 
Changelog
[Unreleased] 
[4.1.0] - 2026-06-11 
- Release Diffusion Gemma
[4.0.1] - 2026-05-20 
- Fix dialog dependency requirement to be >= 1.1.0.
[4.0.0] - 2026-04-17 
* Add Gemma 4.
[1.0.0] - 2022-01-01 
* Initial release
[Unreleased]: https://github.com/google-deepmind/gemma/compare/v4.1.0...HEAD
[4.1.0]: https://github.com/google-deepmind/gemma/compare/v4.0.1...v4.1.0
[4.0.1]: https://github.com/google-deepmind/gemma/compare/v4.0.0...v4.0.1
[4.0.0]: https://github.com/google-deepmind/gemma/compare/v0.1.0...v4.0.0
[1.0.0]: https://github.com/google-deepmind/gemma/releases/tag/v0.1.0
---
CONTRIBUTING 
How to Contribute
We would love to accept your patches and contributions to this project.
Before you begin 
Sign our Contributor License Agreement 
Contributions to this project must be accompanied by a
Contributor License Agreement (CLA).
You (or your employer) retain the copyright to your contribution; this simply
gives us permission to use and redistribute your contributions as part of the
project.
If you or your current employer have already signed the Google CLA (even if it
was for a different project), you probably don't need to do it again.
Visit <https://cla.developers.google.com/> to see your current agreements or to
sign a new one.
Review our Community Guidelines 
This project follows Google's Open Source Community
Guidelines.
Contribution process 
Code Reviews 
All submissions, including submissions by project members, require review. We
use GitHub pull requests
for this purpose.
---
README 
Gemma
[](https://github.com/google-deepmind/gemma/actions/workflows/pytest_and_autopublish.yml)
[](https://badge.fury.io/py/gemma)
[](https://gemma-llm.readthedocs.io/en/latest/?badge=latest)
Gemma is a family of open-weights Large Language
Model (LLM) by Google DeepMind, based on Gemini
research and technology.
This repository contains the implementation of the
gemma PyPI package. A
JAX library to use and fine-tune Gemma.
For examples and use cases, see our
documentation. Please
report issues and feedback in
our GitHub.
Installation 
1.  Install JAX for CPU, GPU or TPU. Follow the instructions on
    the JAX website.
1.  Run
sh
    pip install gemma
    text
Examples 
Here is a minimal example to have a multi-turn, multi-modal conversation with
Gemma:
python
from gemma import gm
Model and parameters (Gemma 4)

model = gm.nn.Gemma4_E4B()
params = gm.ckpts.load_params(gm.ckpts.CheckpointPath.GEMMA4_E4B_IT)
Example of multi-turn conversation

sampler = gm.text.ChatSampler(
    model=model,
    params=params,
    multi_turn=True,
)
prompt = """Which of the 2 images do you prefer ?
Image 1: <|image|>
Image 2: <|image|>
Write your answer as a poem."""
out0 = sampler.chat(prompt, images=[image1, image2])

out1 = sampler.chat('What about the other image ?')`

The same ChatSampler` API works with all Gemma versions (2, 3, 3n, 4).

Our documentation contains various Colabs and tutorials, including:

* Sampling
* Multi-modal
* Fine-tuning
* LoRA
* ...

Additionally, our
examples/ folder
contain additional scripts to fine-tune and sample with Gemma.

Learn more about Gemma

* To use this library: Gemma documentation
* Technical reports for metrics and model capabilities:
* Gemma 1
* Gemma 2
* Gemma 3
* Gemma 4
* Other Gemma implementations and doc on the
Gemma ecosystem

Downloading the models

To download the model weights. See
our documentation.

System Requirements

Gemma can run on a CPU, GPU and TPU. For GPU, we recommend 8GB+ RAM on GPU for
The 2B checkpoint and 24GB+ RAM on GPU are used for the 7B checkpoint.

Contributing

We welcome contributions! Please read our Contributing Guidelines before submitting a pull request.

This is not an official Google product.

---

gemma

Index

Checkpoints

Checkpoints

Kaggle Hub

v4

v3

`v2`

`Research`

`Research`

`Custom transformer`

`CHANGELOG`

`Changelog`

`[Unreleased]`

`[4.1.0] - 2026-06-11`

`[4.0.1] - 2026-05-20`

`[4.0.0] - 2026-04-17`

`[1.0.0] - 2022-01-01`

`CONTRIBUTING`

`How to Contribute`

`Before you begin`

`Sign our Contributor License Agreement`

`Review our Community Guidelines`

`Contribution process`

`Code Reviews`

`README`

`Gemma`

`Installation`

Examples

Model and parameters (Gemma 4)

Example of multi-turn conversation

Learn more about Gemma

Downloading the models

System Requirements

Contributing

Table of Contents

Index Copy Section

Checkpoints Copy Section

Checkpoints

Kaggle Hub Copy Chapter

v4 Copy Section

v3 Copy Section

v2 Copy Section

Research Copy Section

Research

Custom transformer Copy Chapter

Gemma-related projects Copy Chapter

CHANGELOG Copy Section

Changelog

[Unreleased] Copy Chapter

[4.1.0] - 2026-06-11 Copy Chapter

[4.0.1] - 2026-05-20 Copy Chapter

[4.0.0] - 2026-04-17 Copy Chapter

[1.0.0] - 2022-01-01 Copy Chapter

CONTRIBUTING Copy Section

How to Contribute

Before you begin Copy Chapter

Sign our Contributor License Agreement Copy Section

Review our Community Guidelines Copy Section

Contribution process Copy Chapter

Code Reviews Copy Section

README Copy Section

Gemma

Installation Copy Section

Examples Copy Section

Model and parameters (Gemma 4)

Example of multi-turn conversation

Learn more about Gemma Copy Section

Downloading the models Copy Section

System Requirements Copy Section

Contributing Copy Section

Explore Related Technologies & Topics

Index

Checkpoints

Kaggle Hub

v4

v3

`v2`

`Research`

`Research`

`Custom transformer`

Gemma-related projects

`CHANGELOG`

`Changelog`

`[Unreleased]`

`[4.1.0] - 2026-06-11`

`[4.0.1] - 2026-05-20`

`[4.0.0] - 2026-04-17`

`[1.0.0] - 2022-01-01`

`CONTRIBUTING`

`How to Contribute`

`Before you begin`

`Sign our Contributor License Agreement`

`Review our Community Guidelines`

`Contribution process`

`Code Reviews`

`README`

`Gemma`

`Installation`

Examples

Learn more about Gemma

Downloading the models

System Requirements

Contributing