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Using Gemma Open Models | Vertex AI | Google Cloud

Using Gemma Open Models | Vertex AI | Google Cloud

 


Gemma is a set of lightweight generative artificial intelligence (AI) open models. Gemma models can run within applications, on hardware, mobile devices, or hosted services. You can also use tuning techniques to customize these models to better perform tasks important to administrators and users. The Gemma model is based on the Gemini model and is intended for the AI ​​development community to extend and evolve further.

Gemma models can be used for text generation. You can also tune them to improve performance on specific tasks. Tuned Gemma models help make generative AI solutions more targeted and efficient.

To test Gemma models and learn more, please see the Gemma Model Garden model card.

Here are some options for where you can use Gemma:

Use Gemma with Vertex AI

Vertex AI provides a managed platform to quickly build and scale machine learning projects without requiring in-house MLOps expertise. You can use Vertex AI as a downstream application that provides Gemma models. For example, you can port the weights from the Keras implementation of Gemma. You can then use Vertex AI to provide that version of his Gemma and get a prediction. If you want end-to-end MLOps capabilities, value-added ML capabilities, and a serverless experience to streamline your development, Vertex AI is the way to go.

To get started with Gemma, see the following notebooks:

Use Gemma with other Google Cloud products

Gemma can be used with other Google Cloud products such as Google Kubernetes Engine and Dataflow.

Using Gemma with GKE

Google Kubernetes Engine (GKE) is a Google Cloud solution for managed Kubernetes that provides scalability, security, resiliency, and cost efficiency. If you have existing Kubernetes investments, in-house MLOps expertise, or need fine-grained control over complex AI/ML workloads with unique security, data pipeline, and resource management requirements. We recommend this option. For more information, see the following tutorials in the GKE documentation:

Use Gemma with Dataflow

You can use Gemma models in Dataflow to perform sentiment analysis. Use Dataflow to run an inference pipeline that uses the Gemma model. For more information, see Run Inference Pipelines with Gemma Open Models.

Using Gemma with Colab

You can use Gemma and Colaboratory to create Gemma solutions. Colab allows you to use Gemma with her framework options such as PyTorch and JAX. See below for more information.

Gemma model size and features

Gemma models are available in several sizes, allowing you to build generative AI solutions based on your available computing resources, desired functionality, and where you run them. Each model is available in calibrated and uncalibrated versions.

Pretrained – This version of the model has not been trained on any specific tasks or instructions beyond the Gemma core data training set. We do not recommend using this model without tuning.

Tailored Instructions – This version of the model was trained on human language interactions so that it can participate in conversations like a simple chat bot.

If you're not sure which one to try, consider Gemma 2B. Smaller parameter sizes mean lower resource requirements and more deployment flexibility than Gemma 7B.

Model Name Parameter Size Input Output Tuned Version Target Platforms Gemma 2B 2.2 Billion Text Text Mobile Devices and Laptops Gemma 7B 7 Billion Text Text Desktop Computers and Small Servers

Gemma is tested using Google's dedicated v5e TPU hardware and NVIDIA's L4 (G2 standard), A100 (A2 standard), and H100 (A3 standard) GPU hardware.

Sources

1/ https://Google.com/

2/ https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma

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