Summary of Changes

Hello @Sam-Deciga, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a new official Jupyter notebook designed to guide users through the process of deploying and interacting with NVIDIA's Llama Nemotron models on Google Cloud's Vertex AI platform. It provides comprehensive instructions and code examples for model registration, endpoint creation, model deployment, and real-time prediction, utilizing both direct API calls and the Vertex AI Python SDK. The notebook focuses on the Llama-3.3-Nemotron-Super-49B-v1.5 model, showcasing its advanced reasoning and efficiency features.

Highlights

• New Model Integration: Introduced a new official Jupyter notebook (llama_nemotron_intro.ipynb) to demonstrate the deployment and usage of NVIDIA Llama Nemotron models on Google Cloud Vertex AI.
• Comprehensive Deployment Guide: The notebook provides step-by-step instructions for setting up the Vertex AI environment, uploading the NVIDIA NIM model to the Model Registry, creating and deploying to a Vertex AI Endpoint, and performing real-time predictions.
• Dual API and SDK Support: Examples are provided for both direct Vertex AI API calls (using curl) and the Vertex AI Python SDK, offering flexibility for users to interact with the deployed models.
• Prediction Capabilities Showcase: The notebook demonstrates both unary and streaming prediction requests, including the ability to toggle 'reasoning on/off' for the Llama Nemotron model.
• Model Specifics: Specifically highlights the Llama-3.3-Nemotron-Super-49B-v1.5 model, detailing its capabilities for reasoning, human chat, agentic tasks, 128K token context length, and efficiency gains from a novel Neural Architecture Search (NAS) approach.

Checked with the partner and they say they are OK

Thanks for the clear PR template and checklists.

The required summary (“why the change is needed,” “what was changed,” and design alternatives), along with CODEOWNERS attribution and environment verification (e.g., Colab runs), creates a strong authorship and accountability signal that’s easy for reviewers to reason about.

The explicit cleanup requirements for generated artifacts are also helpful for maintaining long-term repo and resource hygiene.

Overall, this structure makes review intent, ownership, and execution boundaries much clearer up front. Appreciate the rigor.

Since MODEL_ID already contains the fully qualified resource path, re-concatenating it with projects/{PROJECT_ID}/locations/{LOCATION}/models/ would indeed produce a malformed identifier.

Using the existing MODEL_ID value directly for the "model" field keeps the payload aligned with the expected resource name format and avoids duplication.

Appreciate the clear explanation in the review comment — it makes the correction unambiguous.