You can get started in 4 ways:

## Run it locally

Select which TxGemma variant you want to use, download it from [Hugging
Face](https://huggingface.co/collections/google/health-ai-developer-foundations-hai-def-6744dc060bc19b6cf631bb0f)
and run it locally.

This is the recommended option, if you want to experiment with the model and
don't need to handle a high volume of data. Our GitHub repository includes a
[notebook](https://github.com/google-gemini/gemma-cookbook/blob/main/TxGemma/%5BTxGemma%5DQuickstart_with_Hugging_Face.ipynb)
that you can use to get started with TxGemma.

We have two other notebooks:

- [Finetune with Hugging
  Face](https://github.com/google-gemini/gemma-cookbook/blob/main/TxGemma/%5BTxGemma%5DFinetune_with_Hugging_Face.ipynb) which shows how you can finetune TxGemma.
- [Agentic-Tx
  Demo](https://github.com/google-gemini/gemma-cookbook/blob/main/TxGemma/%5BTxGemma%5DAgentic_Demo_with_Hugging_Face.ipynb) which has an example agentic implementation of TxGemma. You'll need a Gemini API key to run this.

## Deploy your own online service

TxGemma can be deployed as a highly available and scalable HTTPS endpoint on
[Vertex AI](https://cloud.google.com/vertex-ai). The easiest way is through
[Model
Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/txgemma).

This option is ideal for production-grade, online applications with low latency,
high scalability and availability requirements. Refer to [Vertex AI's service
level agreement (SLA)](https://cloud.google.com/vertex-ai/sla) and [pricing
model](https://cloud.google.com/vertex-ai/pricing) for online predictions.

A sample
[notebook](https://github.com/google-gemini/gemma-cookbook/blob/main/TxGemma/%5BTxGemma%5DQuickstart_with_Model_Garden.ipynb)
is available to help you get started quickly.

## Launch a batch job

For larger dataset in a batch workflow, it's best to launch TxGemma as a [Vertex
AI batch prediction
job](https://cloud.google.com/vertex-ai/docs/predictions/get-batch-predictions#request_a_batch_prediction).
Note that [Vertex AI's SLA](https://cloud.google.com/vertex-ai/sla) and [pricing
model](https://cloud.google.com/vertex-ai/pricing) are different for batch
prediction jobs.

Refer to the "Get batch predictions" section in the [Quick start with Model
Garden](https://github.com/google-gemini/gemma-cookbook/blob/main/TxGemma/%5BTxGemma%5DQuickstart_with_Model_Garden.ipynb)
notebook to get started.

## Engage with us

Here are the best ways to engage with our team and the community:

- **Feature your work** on the [HAI-DEF
  Showcase](https://developers.google.com/health-ai-developer-foundations/showcase) using the [intake
  form](https://services.google.com/fb/forms/hai-def-showcase/).
- **Help shape our roadmap** by sharing your use cases using our [feedback
  form](https://services.google.com/fb/forms/hai-def-feedback/). This helps us align our engineering efforts with the industry's most common needs.
- **Stay in the know** on new tools and models by signing up for [our
  newsletter](https://services.google.com/fb/forms/hai-def-news/).
- **Seek technical support** on the [HAI-DEF developer
  forum](https://discuss.ai.google.dev/c/hai-def/).
- **File technical issues** directly on [GitHub](https://github.com/google-gemini/gemma-cookbook/issues).