---
library_name: keras-nlp
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
license: gemma
pipeline_tag: text-generation
---

# Gemma Model Card

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

This model card corresponds to the latest 2B version of the Gemma 1.1 model in Keras.


Keras models can be used with JAX, PyTorch or TensorFlow as numerical backends.
JAX is recommended for large models because it supports SPMD model paralellism.
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)

**Authors**: Google

## Loading the model

```python
import keras_nlp

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