Phi on Strathweb. A free flowing tech monologue.

Fine-tuning Phi-4 with Azure ML

Mon, 23 Feb 2026 07:06:14 +0000

Recently, I dedicated quite a lot of room on this blog to the topic of running Phi locally. This time, I want to focus on a different aspect of adopting small language models like Phi - fine-tuning them. I already covered local fine-tuning in the past, so today we are going to do this with Azure Machine Learning (Azure ML).

Azure ML is a comprehensive cloud service for accelerating and managing the machine learning project lifecycle. While local fine-tuning is great, moving to Azure ML makes a lot of sense when you need to scale, and/or when you want to experience the Nvidia GPUs without investing in hardware.

We are going to do LoRA fine-tuning of a Phi-4 model, and then deploy it to a managed batch endpoint for inference.

SLM-default, LLM-fallback pattern with Agent Framework and Azure AI Foundry

Fri, 05 Dec 2025 08:00:00 +0000

When building AI workflows, we often face a choice: do we use a massive, expensive cloud model for everything (to ensure best reasoning capabilities), or do we cut costs with a smaller local model (and risk hallucinations)? In this post, we’ll explore a “best of both worlds” architecture, as described in the recent survey “Small Language Models for Agentic Systems” Sharma & Mehta, 2025.

We call this the “SLM-default, LLM-fallback” pattern. The premise is simple: route all queries to a fast, private, on-device Small Language Model (SLM) first. Only if that model cannot confidently answer the query, do we escalate the request to a paid cloud model (LLM).

LLM and SLM collaboration using the Minions pattern (with Phi-4-mini and Azure OpenAI)

Fri, 24 Oct 2025 07:06:14 +0000

In this post, we’ll explore a novel approach to optimizing AI workflows by strategically combining large language models (LLMs) with small language models (SLMs) using the “Minions pattern.” This technique, described in the research paper “Minions: Cost-efficient Collaboration Between On-device and Cloud Language Models” by Narayan et al., addresses one of the most pressing challenges in AI application development - the cost of processing large amounts of data with expensive, cloud-based language models. If you’ve ever built an AI system that needs to analyze extensive documents or datasets, you’ve probably felt the frustration of watching your API costs skyrocket as you process more and more content.

Using Phi Silica in Windows App SDK on a Copilot Plus PC

Fri, 25 Apr 2025 07:06:14 +0000

Last year, Microsoft announced the Copilot Plus PC, a new class of devices that are designed to run AI workloads locally. The flagship device of the line is of course the Surface Pro 11, which is powered by the Qualcomm Snapdragon X Elite ARM processor. Unfortunately, since the launch, the AI capabilities have been more than underwhelming, as few applications and workloads are able to take advantage of the integrated NPU hardware.

One of the milestones in this direction is the Phi Silica model, which is a small but powerful ONNX-Runtime-based on-device SLM (Small Language Model) that is designed to run on the Copilot Plus PC NPU, and that is built into the Windows Copilot Runtime. This removes a lot of the friction that developers have when trying to run models on-device, as they can now simply use the Windows App SDK to access the NPU and invoke the model just like ant other system API.

Today we will have a look at how to use the Phi Silica model in a Windows App SDK applications.

Running Phi models on iOS with Apple MLX Framework

Mon, 10 Mar 2025 08:30:12 +0000

As I previously blogged a few times, I have been working on the Strathweb Phi Engine, a cross-platform library for running Phi model inference via a simple, high-level API, from a number of high-level languages: C#, Swift, Kotlin and Python. This of course includes the capability of running Phi models on iOS devices, and the sample repo contains a demo SwiftUI application that demonstrates how to do this.

Today I wanted to show an alternative way of running Phi models on iOS devices, using Apple’s MLX framework. I previously blogged about fine-tuning Phi models on iOS using MLX, so that post is a good read if you want to learn more about the MLX framework and how to use it.

Strathweb Phi Engine - now with Phi-4 support

Mon, 24 Feb 2025 07:06:14 +0000

Last summer, I launched Strathweb Phi Engine — a cross-platform library for running Phi model inference via a simple, high-level API, from a number of high-level languages: C#, Swift, Kotlin and Python.

Today I am happy to announce support for Phi-4, the latest model in the Phi family, which Microsoft AI released in December 2024.

Fine tuning Phi models with MLX

Fri, 17 Jan 2025 07:06:14 +0000

Recently, I dedicated quite a lot of room on this blog to the topic of running Phi locally with the Strathweb Phi Engine. This time, I want to focus on a different aspect of adopting small language models like Phi - fine-tuning them. We are going to do this with Apple’s MLX library, which offers excellent performance for ML-related tasks on Apple Silicon.

We are going to do LoRA fine tuning of a Phi model, and then invoke it using Strathweb Phi Engine.

Running Phi Inference in .NET Applications with Strathweb Phi Engine

Fri, 20 Dec 2024 07:06:14 +0000

Local AI inference has become increasingly important for developers seeking to build robust, privacy-preserving applications. In this deep dive, I’ll show you how to leverage Strathweb Phi Engine multi-platform library to run Microsoft’s Phi-family models directly in your .NET applications, exploring both basic integration patterns and advanced features that make Phi inference more accessible than ever.

Strathweb Phi Engine - now with Safe Tensors support

Fri, 15 Nov 2024 07:06:14 +0000

This summer, I announced the Strathweb Phi Engine — a cross-platform library for running Phi inference anywhere. Up until now, the library only supported models in the quantized GGUF format. Today, I’m excited to share that the library now also supports the Safe Tensor model format.

This enhancement significantly expands the scope of use cases and interoperability for the Strathweb Phi Engine. With Safe Tensor support, you can now load and execute models in a format that is not only performant but also prioritizes security and memory safety. Notably, all the Phi models published by Microsoft use the Safe Tensor format by default.

Using Local Phi-3 Models in AutoGen with Strathweb Phi Engine

Fri, 06 Sep 2024 07:06:14 +0000

I recently announced Strathweb Phi Engine, a cross-platform library/toolset for conveniently running Phi-3 (almost) anywhere. Today I would like to show how to integrate a local Phi-3 model, orchestrated by Strathweb Phi Engine, into an agentic workflow built with AutoGen.

Announcing Strathweb Phi Engine - a cross-platform library for running Phi-3 anywhere

Thu, 25 Jul 2024 04:06:14 +0000

I recently wrote a blog post about using Rust to run Phi-3 model on iOS. The post received an overwhelmingly positive response, and I got a lot of questions about running Phi-3 using similar approach on other platforms, such as Android, Windows, macOS or Linux. Today, I’m excited to announce the project I have been working on recently - Strathweb Phi Engine, a cross-platform library for running Phi-3 (almost) anywhere.

Running Microsoft's Phi-3 Model in an iOS app with Rust

Thu, 09 May 2024 07:06:14 +0000

Last month, Microsoft released the exciting new minimal AI model, Phi-3 mini. It’s a 3.8B model that can outperform many other larger models, while still being small enough to run on a phone. In this post, we’ll explore how to run the Phi-3 model inside a SwiftUI iOS application using the minimalist ML framework for Rust, called candle, and built by the nice folks at HuggingFace.