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

MarkTechPost / 4/21/2026

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Key Points

The article provides a tutorial for building an end-to-end LLM workflow using Microsoft’s Phi-4-mini within a single notebook.
It covers setting up a stable environment and loading Phi-4-mini-instruct using efficient 4-bit quantization to enable lightweight inference.
The workflow is extended with RAG (Retrieval-Augmented Generation) and demonstrates how to integrate tool use for reasoning tasks.
It also walks through LoRA fine-tuning as part of the pipeline, showing how to adapt the compact model for specific needs.
Overall, it focuses on enabling modern LLM use cases on smaller, quantized models with practical implementation steps.

In this tutorial, we build a pipeline on Phi-4-mini to explore how a compact yet highly capable language model can handle a full range of modern LLM workflows within a single notebook. We begin by setting up a stable environment, loading Microsoft’s Phi-4-mini-instruct in efficient 4-bit quantization, and then move step by step through streaming […]

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