Getting Started with Gemma Models

1. Keras

   1 88 63,200 9.8 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.

2. InfluxDB

   www.influxdata.com featured

   InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.

   

3. stylegan2-projecting-images

   2 158 299 10.0 Jupyter Notebook

   Projecting images to latent space with StyleGAN2.

   

   A Colab notebook.

4. efficientnet

   3 9 2,083 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.

5. gemma_pytorch

   4 15 5,498 5.3 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.

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