Using o-series Reasoning Models in PromptFlow

azure ai openai promptflow

If you have tried to use the OpenAI o-series reasoning models, such as o1 or o3, with PromptFlow recently, you certainly ran into a nasty surprise. While PromptFlow supports a wide range of models and providers, the o-series models are not among them. This is of course quite a shame, especially if you’d like to benchmark or evaluate your flows against those models.

In this short post, we will look at a workaround.

Running Q# code on Quokka

q-sharp quantum computing

Quokka (or “Quokka Puck”) is a brand new quantum educational device from Chris Ferrie and Simon Devitt, originally funded by a Kickstarter campaign. It is a beautifully designed, standalone, plug and play, 30-qubit quantum computer emulator. Sure you can simulate quantum programs on your laptop, but Quokka is a dedicated device that is designed to make quantum computing education accessible and fun. It is a great way to learn quantum programming and quantum algorithms, experience the interaction flow typical for real quantum hardware, and it is a perfect gift for anyone interested in quantum computing.

In today’s post, we will have a look at how you can run Q# on a Quokka Puck device.

Running Phi models on iOS with Apple MLX Framework

ios phi ai

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

ai rust ios swift phi

Last summer, I launched Strathweb Phi Engine โ€” 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.

ML-KLEM and ML-DSA Post-Quantum Cryptography in .NET

security quantum computing c-sharp cryptography

Some time ago, I wrote a post about post-quantum cryptography in .NET, where I introduced the concept of post-quantum cryptography and discussed the early BouncyCastle.NET implementation of Kyber and Dilithium. Today I would like to revisit this post, as both of these algorithms have been officially standardized as ML-KEM and ML-DSA.

Fine tuning Phi models with MLX

ai phi mac

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

ai phi csharp net

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.

Decorating a Quantum Christmas Tree with Q# and Qiskit

q-sharp quantum computing qiskit

For a few years in a row now, around this time of the year, I have been writing a festive Q# quantum computing post. This year I would like to keep the tradition going and explore another fun topic .

Ever wondered what would happen if we let quantum mechanics decorate a ๐ŸŽ„ Christmas tree ? Let’s explore a quantum program - in both my favorite quantum programming language, Q#, as well as in Qiskit - that makes quantum effects visible through festive decorations.

Generating OpenQASM from Q# code

q-sharp quantum computing

In the summer of 2024, I announced the Q# Bridge library, which allows you to run Q# simulations from many popular high-level languages - C#, Swift, Python and Kotlin. Today, I would like to write about a brand new feature in the library, an ability to generate OpenQASM 2.0 code from Q# source.

This is a feature that Q# toolchain does not natively supports, and it adds to the value proposition of Q# Bridge - acting as a literal bridge between Q# and other ecosystems (traditional languages or, in this case, quantum).

Simplifying the AI workflow: Access different types of model deployments with Azure AI Inference

python azure ai

In this post, we will explore the flexibility behind Azure AI Inference, a new library from Azure, which allows us to run inference against a wide range of AI model deployments - both in Azure and, as we will see in this notebook, in other places as well.

It is available for Python and for .NET - in this post, we will focus on the Python version.

About

Hi! I'm Filip W., a software architect from Zรผrich ๐Ÿ‡จ๐Ÿ‡ญ. I like Toronto Maple Leafs ๐Ÿ‡จ๐Ÿ‡ฆ, Rancid and quantum computing. Oh, and I love the Lowlands ๐Ÿด๓ ง๓ ข๓ ณ๓ ฃ๓ ด๓ ฟ.

You can find me on Github, on Mastodon and on Bluesky.

My Introduction to Quantum Computing with Q# and QDK book
Microsoft MVP

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