GitHub Actions toolkit automates GPU fine-tuning and model deployment on Nebius AI Cloud

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A software engineer has released nebius-actions, a set of composable GitHub Actions designed to automate the full lifecycle of fine-tuning and deploying AI models on Nebius AI Cloud GPU infrastructure. The pipeline is triggered by a single workflow_dispatch click in GitHub and runs across five sequential jobs: submit, wait, deploy, try, and cleanup. In a demo workflow, it QLoRA-fine-tunes the Qwen2.5-0.5B model on wikitext using Axolotl, packages the resulting LoRA adapters into a vLLM serving image, and deploys it to a live Nebius endpoint. The system uses short-lived IAM tokens rather than long-lived credentials, and includes automatic cancellation of cloud GPU jobs if the GitHub workflow itself is interrupted, preventing runaway billing. The project aims to eliminate manual steps such as SSH access or ad-hoc Jupyter notebooks from the model training and deployment process.

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