Fine-Tuning ESM2
This sample walks through setting up a Google Cloud GKE environment to fine-tune ESM2 (Evolutionary Scale Modeling) using NVIDIA BioNeMo Framework 2.0
Tags: Fine-Tuning
Fine-tuning Gemma 3-1B-it on L4
This tutorial guides you through fine-tuning the Gemma 3-1B-it language model on Google Kubernetes Engine (GKE) using L4 GPU, leveraging Parameter Efficient Fine Tuning (PEFT) and LoRA. It covers setting up a GKE cluster, containerizing the fine-tuning code, running the fine-tuning job, and uploading the resulting model to Hugging Face. Finally, it demonstrates how to deploy and interact with the fine-tuned model using vLLM on GKE.
Fine-Tuning Gemma 2-9B on GKE using Metaflow and Argo Workflows
This tutorial will provide instructions on how to deploy and use the Metaflow framework on GKE (Google Kubernetes Engine) and operate AI/ML workloads using Argo-Workflows.
Fine-tune gemma-2-9b and track as an experiment in MLFlow
In this tutorial we will fine-tune gemma-2-9b using LoRA as an experiment in MLFlow. We will deploy MLFlow on a GKE cluster and set up MLFlow to store artifacts inside a GCS bucket. In the end, we will deploy a fine-tuned model using KServe.
Building a Multi-Agent Code Development Flow with Flowise on GKE
This tutorial will provide instructions on how to deploy and use FlowiseAI on GKE (Google Kubernetes Engine) to build and operate AI applications using a low-code/no-code approach.