Rohan Sardar

Rohan Sardar

he/him

ML Student

Text Generation using Gemma library for JAX

less than 1 minute read

Published:

Text generation is one of the most exciting applications of modern Large Language Models (LLMs), enabling tasks like content creation, code assistance, summarization, and conversational AI. With the rise of efficient open-weights models, developers can now experiment with advanced text-generation systems directly on their own hardware.

Gemma, a family of open-weights LLMs introduced by Google DeepMind, brings the power of Gemini research to the broader community. Built for flexibility and efficiency, Gemma is especially appealing when paired with JAX—Google’s high-performance numerical computing library.

The Gemma library provides a lightweight Python interface on top of JAX, combining its advantages—accelerated computation, just-in-time compilation, and automatic differentiation—with a simplified, developer-friendly API. This dramatically cuts down on boilerplate code and makes experimenting with LLMs in JAX both intuitive and fast.

This article walks you through using the Gemma library for text generation, highlighting how easily you can run and customize advanced LLM inference within the JAX ecosystem.

👉 Read more: Text Generation using Gemma Library for JAX

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