---
library_name: keras-nlp
license: gemma
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
  agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging
  Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
---

# Gemma 1

**Google Model Page**: [Gemma](https://ai.google.dev/gemma/docs)

This model card corresponds to the latest 2B version of the Gemma model in Keras.


Keras models can be used with JAX, PyTorch or TensorFlow as numerical backends.
JAX, with its support for SPMD model paralellism, is recommended for large models.
For more information: [distributed training with Keras and JAX](https://keras.io/guides/distribution/).


You can find other models in the Gemma family here:

|    | Base                                               | Instruct                                                             |
|----|----------------------------------------------------|----------------------------------------------------------------------|
| 2B | [**gemma-2b-keras**](https://huggingface.co/google/gemma-2b-keras) | [gemma-1.1-2b-it-keras](https://huggingface.co/google/gemma-1.1-2b-it-keras) |
| 7B | [gemma-7b-keras](https://huggingface.co/google/gemma-7b-keras) | [gemma-1.1-7b-it-keras](https://huggingface.co/google/gemma-1.1-7b-it-keras)     |


For more information about the model, visit https://huggingface.co/google/gemma-2b.

**Resources and Technical Documentation**:

* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-2b-gg-hf)

**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent/verify/huggingface?returnModelRepoId=google/gemma-2b-keras)

**Authors**: Google

## Loading the model

```python
import keras_nlp

gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset("hf://google/gemma-2b-keras")
```