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1. A Coding Implementation on Microsoft’s Phi-4-Mini for Quantized Inference Reasoning Tool Use RAG and LoRA Fine-Tuning

   This tutorial provides a comprehensive coding walkthrough for building an advanced AI pipeline using Microsoft's Phi-4-mini language model. The guide demonstrates how to leverage this compact model for high-performance tasks within resource-constrained environments like Google Colab.
   Key topics covered include:
   - Setting up 4-bit quantized inference to optimize GPU memory usage.
   - Implementing streaming chat and multi-step chain-of-thought reasoning.
   - Executing native tool calling and function calling for agentic interactions.
   - Building a retrieval-augmented generation (RAG) pipeline using FAISS and sentence transformers.
   - Performing lightweight LoRA fine-tuning to inject new knowledge into the model.

   2026-04-26 Tags: microsoft phi-4-mini, quantized inference, llm tutorial, rag, lora fine-tuning, tool use, chain-of-thought reasoning, small language models, llm, hallux by klotz

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