Finetuning the FunctionGemma model is made fast and easy using the lightweight JAX-based Tunix library on Google TPUs, a process demonstrated here using LoRA for supervised finetuning. This approach delivers significant accuracy improvements with high TPU efficiency, culminating in a model ready for deployment.
Easy FunctionGemma finetuning with Tunix on Google TPUs
Related Posts
An Introduction to AWS Batch
AWS Batch is a fully managed service that helps us developers run batch computing workloads on the cloud.…
JSX: The Secret Sauce Behind React’s Success
React is a popular JavaScript library for building user interfaces. It was created by Facebook and released to…
Building Container Images Securely on AWS EKS with Kaniko
Introduction In the world of Kubernetes, building container images securely and efficiently is a common challenge. This is…
