Getting Started with Gemma Models

• Keras

  78 61,044 9.9 Python

  Deep Learning for humans

  

After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow.

• stylegan2-projecting-images

  136 288 10.0 Jupyter Notebook

  Projecting images to latent space with StyleGAN2.

A Colab notebook.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• efficientnet

  9 2,061 0.0 Python

  Implementation of EfficientNet model. Keras and TensorFlow Keras.

Examples of lightweight models include MobileNet, a computer vision model designed for mobile and embedded vision applications, EfficientDet, an object detection model, and EfficientNet, a CNN that uses compound scaling to enable better performance. All these are lightweight models from Google.

• gemma_pytorch

  6 5,059 7.7 Python

  The official PyTorch implementation of Google's Gemma models

  

Gemma is a family of lightweight, open-source machine learning models developed by Google AI. These models are designed to be accessible and efficient, making AI development more available for a broad range of users. Released on February 21st, 2024, Gemma is built from the same research and technology that was used to create the Gemini models. Amongst the key features, which are being lightweight and open-source, Gemma is also text-based. It excels in tasks like text summarization, question answering, and reasoning.

Suggest a related project