In this video, we introduce the IBM Granite 4.1 family, a new set of highly optimized ai models designed to run efficiently on personal computers. We also show how to build a powerful multi agent AI researcher using these models alongside Google's gemma 4 and ollama. This setup allows for local AI research capabilities, keeping you updated with the latest ai news without needing expensive hardware.

Discover how to use IBM Granite 4.1, the best enterprise AI model for machine learning, and see why it beats Google Gemma 4 and Claude Code. In this IBM Granite 4.0 review, we setup local AI coding via Hugging Face.

IBM Granite AI models are changing the landscape of artificial intelligence. By utilizing a multi-stage reinforcement learning pipeline, these dense models completely outperform massive MoE architectures. If you are comparing gemma 4 vs claude or looking for a gemma 4 tutorial, you need to see how Granite handles complex instructions and tool-calling without slow reasoning tags.

Chapters:
0:00 Intro
1:11 Model Basics
2:17 Training Phases
4:04 Fine Tuning
5:11 RL Pipeline
6:26 Model Performance
7:17 Local Setup
10:03 Python Coding
12:32 Live Demo
15:13 Use Cases

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💻 Codes used in Video: https://github.com/47thtechcorner/RayCodes_GraniteResearcher

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