Understanding Small Language Modes
Small Language Models (SLMs) bring AI from the cloud to your device. Unlike Large Language Models that require massive compute and energy, SLMs run locally, offering speed, privacy, and efficiency. They’re ideal for edge applications like mobile, robotics, and IoT.Phi-4: Small Language Models That Pack a Punch
What Are Small Language Models, and Why Should You Care? If you've been following AI development, you can probably recall "bigger is better" being the mantra for years. GPT-3.5 was 175 billion parameters, GPT-4 is even larger, and everyone seemed to be in an arms race to build the biggest model possible. But here's the thing: bigger models are expensive to run, slow to respond, and often overkill for what you actually need. Small Language Models (SLMs) flip this script. These are models with fewer parameters (typically 1-15 billion) that are trained really thoughtfully on high-quality data. The outcome of this is models that can run on your laptop, respond instantly, and still handle complex reasoning tasks. You can extrapolate from this, increased speed, privacy, and cost-effectiveness. Microsoft's been exploring this space for a while. It started with Phi-1, which showed that small models trained on carefully curated "textbook-like" data could punch way above their weight class. Then came Phi-2 and Phi-3, each iteration getting better at reasoning and problem-solving. Now we have Phi-4, and it's honestly impressive. At 14 billion parameters, it outperforms models that are 5 times its size on math and reasoning tasks. Microsoft trained it on 9.8 trillion tokens over three weeks, using a mix of synthetic data (generated by larger models like GPT-4o) and high-quality web content. The key innovation isn't just throwing more data at it but they were incredibly selective about what to include, focusing on teaching reasoning patterns rather than memorizing facts. The Phi family has also expanded recently. There's Phi-4-mini at 3.8 billion parameters for even lighter deployments, and Phi-4-multimodal at 5.6 billion parameters that can handle text, images, and audio all at once. Pretty cool if you're building something that needs to understand screenshots or transcribe audio. How Well Does It Actually Perform? Let's talk numbers, because that's where Phi-4 really shines. On MMLU (a broad test of knowledge across 57 subjects), Phi-4 scores 84.8%. That's better than Phi-3's 77.9% and competitive with models like GPT-4o-mini. On MATH (competition-level math problems), it hits 56.1%, which is significantly higher than Phi-3's 42.5%. For code generation on HumanEval, it achieves 82.6%. Model Parameters MMLU MATH HumanEval Phi-3-medium 14B 77.9% 42.5% 62.5% Phi-4 14B 84.8% 56.1% 82.6% Llama 3.3 70B 86.0% ~51% ~73% GPT-4o-mini Unknown ~82% 52.2% 87.2% Microsoft tested Phi-4 on the November 2024 AMC-10 and AMC-12 math competitions. These are tests that over 150,000 high school students take each year, and the questions appeared after all of Phi-4's training data was collected. Phi-4 beat not just similar-sized models, but also much larger ones. That suggests it's actually learned to reason, not just memorize benchmark answers. The model also does well on GPQA (graduate-level science questions) and even outperforms its teacher model GPT-4o on certain reasoning tasks. That's pretty remarkable for a 14 billion parameter model. If you're wondering about practical performance, Phi-4 runs about 2-4x faster than comparable larger models and uses significantly less memory. You can run it on a single GPU or even on newer AI-capable laptops with NPUs. That makes it practical for real-time applications where latency matters. Try Phi-4 Yourself You can start experimenting with Phi-4 right now without any complicated setup. Azure AI Foundry Microsoft's Azure AI Foundry is probably the quickest way to get started. Once you're logged in: Go to the Model Catalog and search for "Phi-4" Click "Use this Model" Select an active subscription in the subsequent pop-up and confirm Deploy and start chatting or testing prompts The playground lets you adjust parameters like temperature and see how the model responds. You can test it on math problems, coding questions, or reasoning tasks without writing any code. There's also a code view that shows you how to integrate it into your own applications. Hugging Face (for open-source enthusiasts) If you prefer to work with open-source tools, the model weights are available on Hugging Face. You can run it locally or use their hosted inference API: # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="microsoft/phi-4") messages = [ {"role": "user", "content": "What's the derivative of x²?"}, ] pipe(messages) Other Options The Phi Cookbook on GitHub has tons of examples for different use cases like RAG (retrieval-augmented generation), function calling, and multimodal inputs. If you want to run it locally with minimal setup, you can use Ollama (ollama pull phi-4) or LM Studio, which provides a nice GUI. The Azure AI Foundry Labs also has experimental features where you can test Phi-4-multimodal with audio and image inputs. What's Next? Phi-4 is surprisingly capable for its size, and it's practical enough to run almost anywhere. Whether you're building a chatbot, working on educational software, or just experimenting with AI, it's worth checking out. We might explore local deployment in more detail later, including how to build multi-agent systems where several SLMs work together, and maybe even look at fine-tuning Phi-4 for specific tasks. But for now, give it a try and see what you can build with it. The model weights are MIT licensed, so you're free to use them commercially. Microsoft's made it pretty easy to get started, so there's really no reason not to experiment. Resources: Azure AI Foundry Phi-4 on Hugging Face Phi Cookbook Phi-4 Technical ReportFrom Cloud to Chip: Building Smarter AI at the Edge with Windows AI PCs
As AI engineers, we’ve spent years optimizing models for the cloud, scaling inference, wrangling latency, and chasing compute across clusters. But the frontier is shifting. With the rise of Windows AI PCs and powerful local accelerators, the edge is no longer a constraint it’s now a canvas. Whether you're deploying vision models to industrial cameras, optimizing speech interfaces for offline assistants, or building privacy-preserving apps for healthcare, Edge AI is where real-world intelligence meets real-time performance. Why Edge AI, Why Now? Edge AI isn’t just about running models locally, it’s about rethinking the entire lifecycle: - Latency: Decisions in milliseconds, not round-trips to the cloud. - Privacy: Sensitive data stays on-device, enabling HIPAA/GDPR compliance. - Resilience: Offline-first apps that don’t break when the network does. - Cost: Reduced cloud compute and bandwidth overhead. With Windows AI PCs powered by Intel and Qualcomm NPUs and tools like ONNX Runtime, DirectML, and Olive, developers can now optimize and deploy models with unprecedented efficiency. What You’ll Learn in Edge AI for Beginners The Edge AI for Beginners curriculum is a hands-on, open-source guide designed for engineers ready to move from theory to deployment. Multi-Language Support This content is available in over 48 languages, so you can read and study in your native language. What You'll Master This course takes you from fundamental concepts to production-ready implementations, covering: Small Language Models (SLMs) optimized for edge deployment Hardware-aware optimization across diverse platforms Real-time inference with privacy-preserving capabilities Production deployment strategies for enterprise applications Why EdgeAI Matters Edge AI represents a paradigm shift that addresses critical modern challenges: Privacy & Security: Process sensitive data locally without cloud exposure Real-time Performance: Eliminate network latency for time-critical applications Cost Efficiency: Reduce bandwidth and cloud computing expenses Resilient Operations: Maintain functionality during network outages Regulatory Compliance: Meet data sovereignty requirements Edge AI Edge AI refers to running AI algorithms and language models locally on hardware, close to where data is generated without relying on cloud resources for inference. It reduces latency, enhances privacy, and enables real-time decision-making. Core Principles: On-device inference: AI models run on edge devices (phones, routers, microcontrollers, industrial PCs) Offline capability: Functions without persistent internet connectivity Low latency: Immediate responses suited for real-time systems Data sovereignty: Keeps sensitive data local, improving security and compliance Small Language Models (SLMs) SLMs like Phi-4, Mistral-7B, Qwen and Gemma are optimized versions of larger LLMs, trained or distilled for: Reduced memory footprint: Efficient use of limited edge device memory Lower compute demand: Optimized for CPU and edge GPU performance Faster startup times: Quick initialization for responsive applications They unlock powerful NLP capabilities while meeting the constraints of: Embedded systems: IoT devices and industrial controllers Mobile devices: Smartphones and tablets with offline capabilities IoT Devices: Sensors and smart devices with limited resources Edge servers: Local processing units with limited GPU resources Personal Computers: Desktop and laptop deployment scenarios Course Modules & Navigation Course duration. 10 hours of content Module Topic Focus Area Key Content Level Duration 📖 00 Introduction to EdgeAI Foundation & Context EdgeAI Overview • Industry Applications • SLM Introduction • Learning Objectives Beginner 1-2 hrs 📚 01 EdgeAI Fundamentals Cloud vs Edge AI comparison EdgeAI Fundamentals • Real World Case Studies • Implementation Guide • Edge Deployment Beginner 3-4 hrs 🧠 02 SLM Model Foundations Model families & architecture Phi Family • Qwen Family • Gemma Family • BitNET • μModel • Phi-Silica Beginner 4-5 hrs 🚀 03 SLM Deployment Practice Local & cloud deployment Advanced Learning • Local Environment • Cloud Deployment Intermediate 4-5 hrs ⚙️ 04 Model Optimization Toolkit Cross-platform optimization Introduction • Llama.cpp • Microsoft Olive • OpenVINO • Apple MLX • Workflow Synthesis Intermediate 5-6 hrs 🔧 05 SLMOps Production Production operations SLMOps Introduction • Model Distillation • Fine-tuning • Production Deployment Advanced 5-6 hrs 🤖 06 AI Agents & Function Calling Agent frameworks & MCP Agent Introduction • Function Calling • Model Context Protocol Advanced 4-5 hrs 💻 07 Platform Implementation Cross-platform samples AI Toolkit • Foundry Local • Windows Development Advanced 3-4 hrs 🏭 08 Foundry Local Toolkit Production-ready samples Sample applications (see details below) Expert 8-10 hrs Each module includes Jupyter notebooks, code samples, and deployment walkthroughs, perfect for engineers who learn by doing. Developer Highlights - 🔧 Olive: Microsoft's optimization toolchain for quantization, pruning, and acceleration. - 🧩 ONNX Runtime: Cross-platform inference engine with support for CPU, GPU, and NPU. - 🎮 DirectML: GPU-accelerated ML API for Windows, ideal for gaming and real-time apps. - 🖥️ Windows AI PCs: Devices with built-in NPUs for low-power, high-performance inference. Local AI: Beyond the Edge Local AI isn’t just about inference, it’s about autonomy. Imagine agents that: - Learn from local context - Adapt to user behavior - Respect privacy by design With tools like Agent Framework, Azure AI Foundry and Windows Copilot Studio, and Foundry Local developers can orchestrate local agents that blend LLMs, sensors, and user preferences, all without cloud dependency. Try It Yourself Ready to get started? Clone the Edge AI for Beginners GitHub repo, run the notebooks, and deploy your first model to a Windows AI PC or IoT devices Whether you're building smart kiosks, offline assistants, or industrial monitors, this curriculum gives you the scaffolding to go from prototype to production.Essential Microsoft Resources for MVPs & the Tech Community from the AI Tour
Unlock the power of Microsoft AI with redeliverable technical presentations, hands-on workshops, and open-source curriculum from the Microsoft AI Tour! Whether you’re a Microsoft MVP, Developer, or IT Professional, these expertly crafted resources empower you to teach, train, and lead AI adoption in your community. Explore top breakout sessions covering GitHub Copilot, Azure AI, Generative AI, and security best practices—designed to simplify AI integration and accelerate digital transformation. Dive into interactive workshops that provide real-world applications of AI technologies. Take it a step further with Microsoft’s Open-Source AI Curriculum, offering beginner-friendly courses on AI, Machine Learning, Data Science, Cybersecurity, and GitHub Copilot—perfect for upskilling teams and fostering innovation. Don’t just learn—lead. Access these resources, host impactful training sessions, and drive AI adoption in your organization. Start sharing today! Explore now: Microsoft AI Tour Resources.Join Us for a Technical Deep Dive and Q&A on Foundry Local - LLMs on device
Join us for an Ask Me Anything with the Foundry Local team on October 14th, 2025! Discover how Foundry Local is redefining edge AI with powerful features like on-device inference, enabling you to run models directly on your hardware, cutting costs and keeping your data secure. Whether you're customizing models to fit unique use cases or integrating seamlessly via SDKs, APIs, or CLI, Foundry Local offers scalable pathways to Azure AI Foundry as your needs evolve. It's the perfect solution for environments with limited connectivity, sensitive data requirements, low-latency demands, or early-stage experimentation before cloud deployment. If you're building smarter, leaner, and more private AI workflows, this AMA is your chance to dive deep with the team behind it all. What is Foundry Local? Foundry Local is a set of development tools designed to help you build and evaluate LLM applications on your local machine. It provides a curated collection of production-quality tools, including evaluation and prompt engineering capabilities, that are fully compatible with Azure AI. This allows for a seamless transition of your work from your local environment to the cloud. Don't miss this opportunity to connect with our experts and enhance your understanding of local LLM development. Foundry Local is an on-device AI inference solution offering performance, privacy, customization, and cost advantages. It integrates seamlessly into your existing workflows and applications through an intuitive CLI, SDK, and REST API. Key features On-Device Inference: Run models locally on your own hardware, reducing your costs while keeping all your data on your device. Model Customization: Select from preset models or use your own to meet specific requirements and use cases. Cost Efficiency: Eliminate recurring cloud service costs by using your existing hardware, making AI more accessible. Seamless Integration: Connect with your applications through an SDK, API endpoints, or the CLI, with easy scaling to Azure AI Foundry as your needs grow. How to Join: Register to Join the Azure AI Foundry Discord Community Event 14th Oct 2025 9am Pacific Time UTC−08:00 Unlock Accelerated Local LLM Development Discover how Foundry Local can enhance your development process and explore the possibilities for building robust LLM applications. Whether you're a seasoned AI developer or just getting started, this session is your chance to get hands-on insights into the innovative world of Azure AI Foundry. Event Highlights: An in-depth overview of the Foundry Local CLI and SDK. Interactive demo with step-by-step examples. Best practices for local AI Inference and models Transitioning your local development to cloud solutions or vice-versa Why Attend? Gain expert insights into Foundry Local, and ask questions about using Foundry Local Network with fellow AI professionals and developers in the Azure AI Foundry community. Enhance your AI development skills with practical examples. Stay at the forefront of LLM application development. Speakers Product Manager Foundry Local Maanav Dalal Product Manager |Foundry Local Microsoft Maanav Dalal is a PM on the AI Frameworks team. He's super inquisitive about the ways you use AI in daily life, so be encouraged to strike up a conversation with him about that. LinkedIn ProfileLeveraging the power of NPU to run Gen AI tasks on Copilot+ PCs
Thanks to their massive scale and impressive technical evolution, large language models (LLMs) have become the public face of Generative AI innovation. However, bigger isn’t always better. While LLMs like the ones behind Microsoft Copilot are incredibly capable at a wide range of tasks, less-discussed small language models (SLMs) expand the utility of Gen AI for real-time and edge applications. SLMs can run efficiently on a local device with low power consumption and fast performance, enabling new scenarios and cost models. SLMs can run on universally available chips like CPUs and GPUs, but their potential really comes alive running on Neural Processing Units (NPUs), such as the ones found in Microsoft Surface Copilot+ PCs. NPUs are specifically designed for processing machine learning workloads, leading to high performance per watt and thermal efficiency compared to CPUs or GPUs [1]. SLMs and NPUs together support running quite powerful Gen AI workloads efficiently on a laptop, even when running on battery power or multitasking. In this blog, we focus on running SLMs on Snapdragon® X Plus processors on the recently launched Surface Laptop 13-inch, using the Qualcomm® AI Hub, leading to efficient local inference, increased hardware utilization and minimal setup complexity. This is only one of many methods available - before diving into this specific use case, let’s first provide an overview of the possibilities for deploying small language models on Copilot+ PC NPUs. Qualcomm AI Engine Direct (QNN) SDK: This process requires converting SLMs into QNN binaries that can be executed through the NPU. The Qualcomm AI Hub provides a convenient way to compile any PyTorch, TensorFlow, or ONNX-converted models into QNN binaries executable by the Qualcomm AI Engine Direct SDK. Various precompiled models are directly available in the Qualcomm AI Hub, their collection of over 175 pre-optimized models, ready for download and integration into your application. ONNX Runtime: ONNX Runtime is an open-source inference engine from Microsoft designed to run models in the ONNX format. The QNN Execution Provider (EP) by Qualcomm Technologies optimizes inference on Snapdragon processors using AI acceleration hardware, mainly for mobile and embedded use. ONNX Runtime Gen AI is a specialized version optimized for generative AI tasks, including transformer-based models, aiming for high-performance inference in applications like large language models. Although ONNX Runtime with QNN EP can run models on Copilot+ PCs, some operator support is missing for Gen AI workloads. ONNX Runtime Gen AI is not yet publicly available for NPU; a private beta is currently out with an unclear ETA on public release at the time of releasing this blog. Here is the link to the Git repo for more info on upcoming releases microsoft/onnxruntime-genai: Generative AI extensions for onnxruntime Windows AI Foundry: Windows AI Foundry provides AI-supported features and APIs for Copilot+ PCs. It includes pre-built models such as Phi-Silica that can be inferred using Windows AI APIs. Additionally, it offers the capability to download models from the cloud for local inference on the device using Foundry Local. This feature is still in preview. You can learn more about Windows AI Foundry here: Windows AI Foundry | Microsoft Developer AI Toolkit for VS Code: The AI Toolkit for Visual Studio Code (VS Code) is a VS Code extension that simplifies generative AI app development by bringing together cutting-edge AI development tools and models from the Azure AI Foundry catalog and other catalogs like Hugging Face. This platform allows users to download multiple models either from the cloud or locally. It currently houses several models optimized to run on CPU, with support for NPU-based models forthcoming, starting with Deepseek R1. Comparison between different approaches Feature Qualcomm AI Hub ONNX Runtime (ORT) Windows AI Foundry AI Toolkit for VS code Availability of Models Wide set of AI models (vision, Gen AI, object detection, and audio). Any models can be integrated. NPU support for Gen AI tasks and ONNX Gen AI Runtime are not yet generally available. Phi Silica model is available through Windows AI APIs, additional AI models from cloud can be downloaded for local inference using Foundry Local Access to models from sources such as Azure AI Foundry and Hugging Face. Currently only supports Deepseek R1 and Phi 4 Mini models for NPU inference. Ease of development The API is user-friendly once the initial setup and end-to-end replication are complete. Simple setup, developer-friendly; however, limited support for custom operators means not all models deploy through ORT. Easiest framework to adopt—developers familiar with Windows App SDK face no learning curve. Intuitive interface for testing models via prompt-response, enabling quick experimentation and performance validation. Is processor or SoC independent No. Supports Qualcomm Technologies processors only. Models must be compiled and optimized for the specific SOC on the device. A list of supported chipsets is provided, and the resulting .bin files are SOC-specific. Limitations exist with QNN EP’s HTP backend: only quantized models and those with static shapes are currently supported. Yes. The tool can operate independently of SoC. It is part of the broader Windows Copilot Runtime framework, now rebranded as the Windows AI Foundry. Model-dependent. Easily deployable on-device; model download and inference are straightforward. As of writing this article and based on our team's research, we found Qualcomm AI Hub to be the most user-friendly and well-supported solution available at this time. In contrast, most other frameworks are still under development and not yet generally available. Before we dive into how to use Qualcomm AI Hub to run Small Language Models (SLMs), let’s first understand what Qualcomm AI Hub is. What is Qualcomm AI Hub? Qualcomm AI Hub is a platform designed to simplify the deployment of AI models for vision, audio, speech, and text applications on edge devices. It allows users to upload, optimize, and validate their models for specific target hardware—such as CPU, GPU, or NPU—within minutes. Models developed in PyTorch or ONNX are automatically converted for efficient on-device execution using frameworks like TensorFlow Lite, ONNX Runtime, or Qualcomm AI Engine Direct. The Qualcomm AI Hub offers access to a collection of over 100 pre-optimized models, with open-source deployment recipes available on GitHub and Hugging Face. Users can also test and profile these models on real devices with Snapdragon and Qualcomm platforms hosted in the cloud. In this blog we will be showing how you can use Qualcomm AI Hub to get a QNN context binary for models and use Qualcomm AI Engine to run those context binaries. The context binary is a SoC-specific deployment mechanism. When compiled for a device, it is expected that the model will be deployed to the same device. The format is operating system agnostic so the same model can be deployed on Android, Linux, or Windows. The context binary is designed only for the NPU. For more details on how to compile models in other formats, please visit the documentation here Overview of Qualcomm AI Hub — qai-hub documentation. The following case study details the efficient execution of the Phi-3.5 model using optimized, hardware-specific binaries on a Surface Laptop 13-inch powered by the Qualcomm Snapdragon X Plus processor, Hexagon™ NPU, and Qualcomm Al Hub. Microsoft Surface Engineering Case Study: Running Phi-3.5 Model Locally on Snapdragon X Plus on Surface Laptop 13-inch This case study details how the Phi-3.5 model was deployed on a Surface Laptop 13-inch powered by the Snapdragon X Plus processor. The study was developed and documented by the Surface DASH team, which specializes in delivering AI/ML solutions to Surface devices and generating data-driven insights through advanced telemetry. Using Qualcomm AI Hub, we obtained precompiled QNN context binaries tailored to the target SoC, enabling efficient local inference. This method maximizes hardware utilization and minimizes setup complexity. We used a Surface Laptop 13-inch with the Snapdragon X Plus processor as our test device. The steps below apply to the Snapdragon X Plus processor; however, the process remains similar for other Snapdragon X Series processors and devices as well. For the other processors, you may need to download different model variants of the desired models from Qualcomm AI Hub. Before you begin to follow along, please check the make and models of your NPU by navigating to Device Manager --> Neural Processors. We also used Visual Studio Code and Python (3.10.3.11, 3.12). We used the 3.11 version to run these steps below and recommend using the same, although there should be no difference in using a higher Python version. Before starting, let's create a new virtual environment in Python as a best practice. Follow the steps to create a new virtual environment here: https://code.visualstudio.com/docs/python/environments?from=20423#_creating-environments Create a folder named ‘genie_bundle’ store config and bin files. Download the QNN context binaries specific to your NPU and place the config files into the genie_bundle folder. Copy the .dll files from QNN SDK into the genie_bundle folder. Finally, execute the test prompt through genie-sdk in the required format for Phi-3.5. Setup steps in details Step 1: Setup local development environment Download QNN SDK: Go to the Qualcomm Software Center Qualcomm Neural Processing SDK | Qualcomm Developer and download the QNN SDK by clicking on Get Software (by default latest version of SDK gets downloaded). For the purpose of this demo, we used latest version available (2.34) . You may need to make an account on the Qualcomm website to access it. Step 2: Download QNN Context Binaries from Qualcomm AI Hub Models Download Binaries: Download the context binaries (.bin files) for the Phi-3.5-mini-instruct model from (Link to Download Phi-3.5 context binaries). Clone AI Hub Apps repo: Use the Genie SDK (Generative Runtime built on top of Qualcomm AI Direct Engine), and leverage the sample provided in https://github.com/quic/ai-hub-apps Setup folder structure to follow along the code: Create a folder named "genie_bundle" outside of the folder where AI Hub Apps repo was cloned. Selectively copy configuration files from AI Hub sample repo to 'genie_bundle' Step 3: Copy config files and edit files Copy config files to genie_bundle folder from ai-hub-apps. You will need two config files. You can use the PowerShell script below to copy the config files from repo to local genie folder created in previous steps. You also need to copy HTP backend config file as well as the genie config file from the repo # Define the source paths $sourceFile1 = "ai-hub-apps/tutorials/llm_on_genie/configs/htp/htp_backend_ext_config.json.template" $sourceFile2 = "ai-hub-apps/tutorials/llm_on_genie/configs/genie/phi_3_5_mini_instruct.json" # Define the local folder path $localFolder = "genie_bundle" # Define the destination file paths using the local folder $destinationFile1 = Join-Path -Path $localFolder -ChildPath "htp_backend_ext_config.json" $destinationFile2 = Join-Path -Path $localFolder -ChildPath "genie_config.json" # Create the local folder if it doesn't exist if (-not (Test-Path -Path $localFolder)) { New-Item -ItemType Directory -Path $localFolder } # Copy the files to the local folder Copy-Item -Path $sourceFile1 -Destination $destinationFile1 -Force Copy-Item -Path $sourceFile2 -Destination $destinationFile2 -Force Write-Host "Files have been successfully copied to the genie_bundle folder with updated names." After copying the files, you will need to make sure to change the default values of the parameters provided with template files copied. Edit HTP backend file in the newly pasted location - Change dsp_arch and soc_model to match with your configuration pdate soc model and dsp arch in HTP backend config files Edit genie_config file to include the downloaded binaries for Phi 3 models in previous steps Step 4: Download the tokenizer file from Hugging Face Visit the Hugging Face Website: Open your web browser and go to https://huggingface.co/microsoft/Phi-3.5-mini-instruct/tree/main Locate the Tokenizer File: On the Hugging Face page, find the tokenizer file for the Phi-3.5-mini-instruct model Download the File: Click on the download button to save the tokenizer file to your computer Save the File: Navigate to your genie_bundle folder and save the downloaded tokenizer file there. Note: There is an issue with the tokenizer.json file for the Phi 3.5 mini instruct model, where the output does not break words using spaces. To resolve this, you need to delete lines #192-197 in the tokenizer.json file. Download tokenizer files from the hugging face repo (Image Source - Hugging Face) Step 5: Copy files from QNN SDK Locate the QNN SDK Folder: Open the folder where you have installed the QNN SDK in step 1 and identify the required files. You need to copy the files from the below mentioned folder. Exact folder naming may change based on SDK version <QNN-SDK ROOT FOLDER>/qairt/2.34.0.250424/lib/hexagon-v75/unsigned <QNN-SDK ROOT FOLDER> /qairt/2.34.0.250424/lib/aarch64-windows-msvc <QNN-SDK ROOT FOLDER> /qairt/2.34.0.250424/bin/aarch64-windows-msvc Navigate to your genie_bundle folder and paste the copied files there. Step 6: Execute the Test Prompt Open Your Terminal: Navigate to your genie_bundle folder using your terminal or command prompt. Run the Command: Copy and paste the following command into your terminal: ./genie-t2t-run.exe -c genie_config.json -p "<|system|>\nYou are an assistant. Provide helpful and brief responses.\n<|user|>What is an NPU? \n<|end|>\n<|assistant|>\n" Check the Output: After running the command, you should see the response from the assistant in your terminal. This case study demonstrates the process of deploying a small language model (SLM) like Phi-3.5 on a Copilot+ PC using the Hexagon NPU and Qualcomm AI Hub. It outlines the setup steps, tooling, and configuration required for local inference using hardware-specific binaries. As deployment methods mature, this approach highlights a viable path toward efficient, scalable Gen AI execution directly on edge devices. Snapdragon® and Qualcomm® branded products are products of Qualcomm Technologies, Inc. and/or its subsidiaries. Qualcomm, Snapdragon and Hexagon™ are trademarks or registered trademarks of Qualcomm Incorporated.JS AI Build‑a‑thon: Wrapping Up an Epic June 2025!
After weeks of building, testing, and learning — we’re officially wrapping up the first-ever JS AI Build-a-thon 🎉. This wasn't your average coding challenge. This was a hands-on journey where JavaScript and TypeScript developers dove deep into real-world AI concepts — from local GenAI prototyping to building intelligent agents and deploying production-ready apps. Whether you joined from the start or hopped on midway, you built something that matters — and that’s worth celebrating. Replay the Journey No worries if you joined late or want to revisit any part of the journey. The JS AI Build-a-thon was designed to let you learn at your own pace, so whether you're starting now or polishing up your final project, here’s your complete quest map: Build-a-thon set up guide: https://aka.ms/JSAIBuildathonSetup Quest 1: 🔧 Build your first GenAI app locally with GitHub Models 👉🏽 https://aka.ms/JSAIBuildathonQuest1 Quest 2: ☁️ Move your AI prototype to Azure AI Foundry 👉🏽 https://aka.ms/JSAIBuildathonQuest Quest 3: 🎨 Add a chat UI using Vite + Lit 👉🏽 https://aka.ms/JSAIBuildathonQuest3 Quest 4: 📄 Enhance your app with RAG (Chat with Your Data) 👉🏽 https://aka.ms/JSAIBuildathonQuest4 Quest 5: 🧠 Add memory and context to your AI app 👉🏽 https://aka.ms/JSAIBuildathonQuest5 Quest 6: ⚙️ Build your first AI Agent using AI Foundry 👉🏽 https://aka.ms/JSAIBuildathonQuest6 Quest 7: 🧩 Equip your agent with tools from an MCP server 👉🏽 https://aka.ms/JSAIBuildathonQuest7 Quest 8: 💬 Ground your agent with real-time search using Bing 👉🏽 https://aka.ms/JSAIBuildathonQuest8 Quest 9: 🚀 Build a real-world AI project with full-stack templates 👉🏽 https://aka.ms/JSAIBuildathonQuest9 Link to our space in the AI Discord Community: https://aka.ms/JSAIonDiscord Project Submission Guidelines 📌 Quest 9 is where it all comes together. Participants chose a problem, picked a template, customized it, submitted it, and rallied their community for support! 🏅 Claim Your Badge! Whether you completed select quests or went all the way, we celebrate your learning. If you participated in the June 2025 JS AI Build-a-thon, make sure to Submit the Participation Form to receive your participation badge recognizing your commitment to upskilling in AI with JavaScript/ TypeScript. What’s Next? We’re not done. In fact, we’re just getting started. We’re already cooking up JS AI Build-a-thon v2, which will introduce: Running everything locally with Foundry Local Real-world RAG with vector databases Advanced agent patterns with remote MCPs And much more based on your feedback Want to shape what comes next? Drop your ideas in the participation form and in our Discord. In the meantime, add these resources to your JavaScript + AI Dev Pack: 🔗 Microsoft for JavaScript developers 📚 Generative AI for Beginners with JavaScript Wrap-Up This build-a-thon showed what’s possible when developers are empowered to learn by doing. You didn’t just follow tutorials — you shipped features, connected services, and created working AI experiences. We can’t wait to see what you build next. 👉 Bookmark the repo 👉 Join the community on Join the Azure AI Foundry Discord Server! 👉 Stay building Until next time — keep coding, keep shipping!Bringing AI to the edge: Hackathon Windows ML
AI Developer Hackathon Windows ML Hosted by Qualcomm on SnapDragonX We’re excited to announce our support and participation for the upcoming global series of Edge AI hackathons, hosted by Qualcomm Technologies. The first is on June 14-15 in Bangalore. We see a world of hybrid AI, developing rapidly as new generation of intelligent applications get built for diverse scenarios. These range from mobile, desktop, spatial computing and extending all the way to industrial and automotive. Mission critical workloads oscillate between decision-making in the moment, on device, to fine tuning models on the cloud. We believe we are in the early stages of development of agentic applications that efficiently run on the edge for scenarios needing local deployment and on-device inferencing. Microsoft Windows ML Windows ML – a cutting-edge runtime optimized for performant on-device model inference and simplified deployment, and the foundation of Windows AI Foundry. Windows ML is designed to support developers creating AI-infused applications with ease, harnessing the incredible strength of Windows’ diverse hardware ecosystem whether it’s for entry-level laptops, Copilot+ PCs or top-of-the-line AI workstations. It’s built to help developers leverage the client silicon best suited for their specific workload on any given device whether it’s an NPU for low-power and sustained inference, a GPU for raw horsepower or CPU for the broadest footprint and flexibility. Introducing Windows ML: The future of machine learning development on Windows - Windows Developer Blog Getting Started To get started, install AI Toolkit, leverage one of our conversion and optimization templates, or start building your own. Explore documentation and code samples available on Microsoft Learn, check out AI Dev Gallery (install, documentation) for demos and more samples to help you get started with Windows ML. Microsoft and Qualcomm Technologies: A strong collaboration Microsoft and Qualcomm Technologies’ collaboration bring new advanced AI features into Copilot+ PCs, leveraging the Snapdragon X Elite. Microsoft Research has played a pivotal role by optimizing new lightweight LLMs, such as Phi Silica, specifically for on-device execution with the Hexagon NPU. These models are designed to run efficiently on Hexagon NPUs, enabling multimodal AI experiences like vision-language tasks directly on Copilot+ PCs without relying on the cloud. Additionally, Microsoft has made DeepSeek R1 7B and 14B distilled models available via Azure AI Foundry, further expanding the AI ecosystem on the edge. This collaboration marks a significant step in democratizing AI by making powerful, efficient models accessible on everyday devices Windows AI Foundry expands AI capabilities by providing high-performance built-in models and supports developers' custom models with silicon performance. This developer platform plays a key role in this collaboration. Windows ML enables Windows 11 and Copilot+ PCs to use the Hexagon NPU for power efficient inference. Scaling optimization through Olive toolchain The Windows ML foundation of the Windows AI Foundry provides a unified platform for AI development across various hardware architectures and brings silicon performance using QNN Execution provider. This stack includes Windows ML and toolchains like Olive, easily accessible in AI Toolkit for VS Code, which streamlines model optimization and deployment. Qualcomm Technologies has contributed to Microsoft’s Olive, an open-source model optimization tool that enhances AI performance by optimizing models for efficient inference on client systems. This tool is particularly beneficial for running LLMs and GenAI workloads on Qualcomm Technologies’ platforms. Real-World Applications Through Qualcomm Technologies and Microsoft’s collaboration we have partnered with top developers to adopt Windows ML and have demonstrated impressive performance for their AI features. Independent Solution Vendors (ISVs) such as Powder, Topaz Labs, Camo and McAfee, Join us at the Hackathon With the recent launch of Qualcomm Snapdragon® X Elite-powered Windows laptops, developers can now take advantage of powerful NPUs (Neural Processing Units) to deploy AI applications that are both responsive and energy-efficient. These new devices open up a world of opportunities for developers to rethink how applications are built from productivity tools to creative assistants and intelligent agents all running directly on the device. Our mission has always been to enable high-quality AI experiences using compact, optimized models. These models are tailor-made for edge computing, offering faster inference, lower memory usage, and enhanced privacy without compromising performance. We encourage all application developers whether you’re building with open-source SLMs (small language models), working on smart assistants, or exploring new on-device AI use cases to join us at the event. You can register here: https://www.qualcomm.com/support/contact/forms/edge-ai-developer-hackathon-bengaluru-proposal-submission Dive deeper into these innovative developer solutions: Windows AI Foundry & Windows ML on Qualcomm NPU Microsoft and Qualcomm Technologies collaborate on Windows 11, Copilot+ PCs and Windows AI Foundry | Qualcomm Unlocking the power of Qualcomm QNN Execution Provider GPU backen Introducing Windows ML: The future of machine learning development on Windows - Windows Developer BlogExploring Azure AI Model Inference: A Comprehensive Guide
Azure AI model inference provides access to a wide range of flagship models from leading providers such as AI21 Labs, Azure OpenAI, Cohere, Core42, DeepSeek, Meta, Microsoft, Mistral AI, and NTT Data https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models . These models can be consumed as APIs, allowing you to integrate advanced AI capabilities into your applications seamlessly. Model Families and Their Capabilities Azure AI Foundry categorises its models into several families, each offering unique capabilities: AI21 Labs: Known for the Jamba family models, which are production-grade large language models (LLMs) using AI21's hybrid Mamba-Transformer architecture. These models support chat completions, tool calling, and multiple languages including English, French, Spanish, Portuguese, German, Arabic, and Hebrew. https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models Azure OpenAI: Offers diverse models designed for tasks such as reasoning, problem-solving, natural language understanding, and code generation. These models support text and image inputs, multiple languages, and tool calling https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models Cohere: Provides models for embedding and command tasks, supporting multilingual capabilities and various response formats https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models Core42: Features the Jais-30B-chat model, designed for chat completions https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models DeepSeek: Includes models like DeepSeek-V3 and DeepSeek-R1, focusing on advanced AI tasks https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models Meta: Offers the Llama series models, which are instruction-tuned for various AI tasks https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models Microsoft: Provides the Phi series models, supporting multimodal instructions and vision tasks https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models Mistral AI: Features models like Ministral-3B and Mistral-large, designed for high-performance AI tasks https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models NTT Data: Offers the Tsuzumi-7b model, focusing on specific AI capabilities https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models Deployment and Integration Azure AI model inference supports global standard deployment, ensuring consistent throughput and performance. Models can be deployed in various configurations, including regional deployments and sovereign clouds such as Azure Government, Azure Germany, and Azure China https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models To integrate these models into your applications, you can use the Azure AI model inference API, which supports multiple programming languages including Python, C#, JavaScript, and Java. This flexibility allows you to deploy models multiple times under different configurations, providing a robust and scalable solution for your AI needs https://learn.microsoft.com/en-us/azure/ai-foundry/model-inference/overview Conclusion Azure AI model inference in Azure AI Foundry offers a comprehensive solution for integrating advanced AI models into your applications. With a wide range of models from leading providers, flexible deployment options, and robust API support, Azure AI Foundry empowers you to leverage cutting-edge AI capabilities without the complexity of hosting and managing the infrastructure. Explore the Azure AI model catalog today and unlock the potential of AI for your business. Join the Conversation on Azure AI Foundry Discussions! Have ideas, questions, or insights about AI? Don't keep them to yourself! Share your thoughts, engage with experts, and connect with a community that’s shaping the future of artificial intelligence. 👉 Click here to join the discussion!Make Phi-4-mini-reasoning more powerful with industry reasoning on edge devices
In situations with limited computing, Phi-4-mini-reasoning will is an excellent model choice. We can use Microsoft Olive or Apple MLX Framework to quantize Phi-4-mini-reasoning and deploy it on edge terminals such as IoT, Laotop and mobile devices. Quantization In order to solve the problem that the model is difficult to deploy directly to specific hardware, we need to reduce the complexity of the model through model quantization. Undertaking the quantization process will inevitably cause precision loss. Quantize Phi-4-mini-reasoning using Microsoft Olive Microsoft Olive is an AI model optimization toolkit for ONNX Runtime. Given a model and target hardware, Olive (short for Onnx LIVE) will combine the most appropriate optimization techniques to output the most efficient ONNX model for inference in the cloud or on the edge. We can combine Microsoft Olive and Phi-4-mini-reasoning on Azure AI Foundry's Model Catalog to quantize Phi-4-mini-reasoning to an ONNX format model. Create your Notebook on Azure ML Install Microsoft Olive pip install git+https://github.com/Microsoft/Olive.git Quantize using Microsoft Olive olive auto-opt --model_name_or_path {Azure Model Catalog path ,such as azureml://registries/azureml/models/Phi-4-mini-reasoning/versions/1 }--device cpu --provider CPUExecutionProvider --use_model_builder --precision int4 --output_path ./phi-4-14b-reasoninig-onnx --log_level 1 Register your quantized Model ! python -m mlx_lm.generate --model ./phi-4-mini-reasoning --adapter-path ./adapters --max-token 4096 --prompt "A 54-year-old construction worker with a long history of smoking presents with swelling in his upper extremity and face, along with dilated veins in this region. After conducting a CT scan and venogram of the neck, what is the most likely diagnosis for the cause of these symptoms?" --extra-eos-token "<|end|>" Download to local and run Download the onnx model to local device ml_client.models.download("phi-4-mini-onnx-int4-cpu", 1) Running onnx model with onnxruntime-genai Install onnxruntime-genai (This is CPU version) pip install onnxruntime-genai Run it import onnxruntime_genai as og model_folder = "Your ONNX Model Path" model = og.Model(model_folder) tokenizer = og.Tokenizer(model) tokenizer_stream = tokenizer.create_stream() search_options = {} search_options['max_length'] = 32768 chat_template = "<|user|>{input}<|end|><|assistant|>" text = 'A school arranges dormitories for students. If each dormitory accommodates 5 people, 4 people cannot live there; if each dormitory accommodates 6 people, one dormitory only has 4 people, and two dormitories are empty. Find the number of students in this grade and the number of dormitories.' prompt = f'{chat_template.format(input=text)}' input_tokens = tokenizer.encode(prompt) params = og.GeneratorParams(model) params.set_search_options(**search_options) generator = og.Generator(model, params) generator.append_tokens(input_tokens) while not generator.is_done(): generator.generate_next_token() new_token = generator.get_next_tokens()[0] print(tokenizer_stream.decode(new_token), end='', flush=True) Get Notebook from Phi Cookbook : https://aka.ms/phicookbook Quantize Phi-4-mini-reasoning model using Apple MLX Install Apple MLX Framework pip install -U mlx-lm Convert Phi-4-mini-reasoning model through Apple MLX quantization python -m mlx_lm.convert --hf-path {Phi-4-mini-reasoning Hugging face id} -q Run Phi-4-mini-reasoning with Apple MLX in terminal python -m mlx_lm.generate --model ./mlx_model --max-token 2048 --prompt "A school arranges dormitories for students. If each dormitory accommodates 5 people, 4 people cannot live there; if each dormitory accommodates 6 people, one dormitory only has 4 people, and two dormitories are empty. Find the number of students in this grade and the number of dormitories." --extra-eos-token "<|end|>" --temp 0.0 Fine-tuning We can fine-tune the CoT data of different scenarios to enable Phi-4-mini-reasoning to have reasoning capabilities for different scenarios. Here we use the Medical CoT data from a public Huggingface datasets as our example (this is just an example. If you need rigorous medical reasoning, please seek more professional data support) We can fine-tune our CoT data in Azure ML Fine-tune Phi-4-mini-reasoning using Microsoft Olive in Azure ML Note- Please use Standard_NC24ads_A100_v4 to run this sample Get Data from Hugging face datasets pip install datasets run this script to get train data from datasets import load_dataset def formatting_prompts_func(examples): inputs = examples["Question"] cots = examples["Complex_CoT"] outputs = examples["Response"] texts = [] for input, cot, output in zip(inputs, cots, outputs): text = prompt_template.format(input, cot, output) + "<|end|>" # text = prompt_template.format(input, cot, output) + "<|endoftext|>" texts.append(text) return { "text": texts, } # Create the English dataset dataset = load_dataset("FreedomIntelligence/medical-o1-reasoning-SFT","en", split = "train",trust_remote_code=True) dataset = dataset.map(formatting_prompts_func, batched = True,remove_columns=["Question", "Complex_CoT", "Response"]) dataset.to_json("en_dataset.jsonl") Fine-tuning with Microsoft Olive olive finetune \ --method lora \ --model_name_or_path {Azure Model Catalog path , azureml://registries/azureml/models/Phi-4-mini-reasoning/versions/1} \ --trust_remote_code \ --data_name json \ --data_files ./en_dataset.jsonl \ --train_split "train[:16000]" \ --eval_split "train[16000:19700]" \ --text_field "text" \ --max_steps 100 \ --logging_steps 10 \ --output_path {Your fine-tuning save path} \ --log_level 1 Convert the model to ONNX with Microsoft Olive olive capture-onnx-graph \ --model_name_or_path {Azure Model Catalog path , azureml://registries/azureml/models/Phi-4-mini-reasoning/versions/1} \ --adapter_path {Your fine-tuning adapter path} \ --use_model_builder \ --output_path {Your save onnx path} \ --log_level 1 olive generate-adapter \ --model_name_or_path {Your save onnx path} \ --output_path {Your save onnx adapter path} \ --log_level 1 Run the model with onnxruntime-genai-cuda Install onnxruntime-genai-cuda SDK import onnxruntime_genai as og import numpy as np import os model_folder = "./models/phi-4-mini-reasoning/adapter-onnx/model/" model = og.Model(model_folder) adapters = og.Adapters(model) adapters.load('./models/phi-4-mini-reasoning/adapter-onnx/model/adapter_weights.onnx_adapter', "en_medical_reasoning") tokenizer = og.Tokenizer(model) tokenizer_stream = tokenizer.create_stream() search_options = {} search_options['max_length'] = 200 search_options['past_present_share_buffer'] = False search_options['temperature'] = 1 search_options['top_k'] = 1 prompt_template = """<|user|>{}<|end|><|assistant|><think>""" question = """ A 33-year-old woman is brought to the emergency department 15 minutes after being stabbed in the chest with a screwdriver. Given her vital signs of pulse 110\/min, respirations 22\/min, and blood pressure 90\/65 mm Hg, along with the presence of a 5-cm deep stab wound at the upper border of the 8th rib in the left midaxillary line, which anatomical structure in her chest is most likely to be injured? """ prompt = prompt_template.format(question, "") input_tokens = tokenizer.encode(prompt) params = og.GeneratorParams(model) params.set_search_options(**search_options) generator = og.Generator(model, params) generator.set_active_adapter(adapters, "en_medical_reasoning") generator.append_tokens(input_tokens) while not generator.is_done(): generator.generate_next_token() new_token = generator.get_next_tokens()[0] print(tokenizer_stream.decode(new_token), end='', flush=True) inference model with onnxruntime-genai cuda olive finetune \ --method lora \ --model_name_or_path {Azure Model Catalog path , azureml://registries/azureml/models/Phi-4-mini-reasoning/versions/1} \ --trust_remote_code \ --data_name json \ --data_files ./en_dataset.jsonl \ --train_split "train[:16000]" \ --eval_split "train[16000:19700]" \ --text_field "text" \ --max_steps 100 \ --logging_steps 10 \ --output_path {Your fine-tuning save path} \ --log_level 1 Fine-tune Phi-4-mini-reasoning using Apple MLX locally on MacOS Note- we recommend that you use devices with a minimum of 64GB Memory and Apple Silicon devices Get the DataSet from Hugging face datasets pip install datasets run this script to get train and valid data from datasets import load_dataset prompt_template = """<|user|>{}<|end|><|assistant|><think>{}</think>{}<|end|>""" def formatting_prompts_func(examples): inputs = examples["Question"] cots = examples["Complex_CoT"] outputs = examples["Response"] texts = [] for input, cot, output in zip(inputs, cots, outputs): # text = prompt_template.format(input, cot, output) + "<|end|>" text = prompt_template.format(input, cot, output) + "<|endoftext|>" texts.append(text) return { "text": texts, } dataset = load_dataset("FreedomIntelligence/medical-o1-reasoning-SFT","en", trust_remote_code=True) split_dataset = dataset["train"].train_test_split(test_size=0.2, seed=200) train_dataset = split_dataset['train'] validation_dataset = split_dataset['test'] train_dataset = train_dataset.map(formatting_prompts_func, batched = True,remove_columns=["Question", "Complex_CoT", "Response"]) train_dataset.to_json("./data/train.jsonl") validation_dataset = validation_dataset.map(formatting_prompts_func, batched = True,remove_columns=["Question", "Complex_CoT", "Response"]) validation_dataset.to_json("./data/valid.jsonl") Fine-tuning with Apple MLX python -m mlx_lm.lora --model ./phi-4-mini-reasoning --train --data ./data --iters 100 Running the model ! python -m mlx_lm.generate --model ./phi-4-mini-reasoning --adapter-path ./adapters --max-token 4096 --prompt "A 54-year-old construction worker with a long history of smoking presents with swelling in his upper extremity and face, along with dilated veins in this region. After conducting a CT scan and venogram of the neck, what is the most likely diagnosis for the cause of these symptoms?" --extra-eos-token "<|end|>" Get Notebook from Phi Cookbook : https://aka.ms/phicookbook We hope this sample has inspired you to use Phi-4-mini-reasoning and Phi-4-reasoning to complete industry reasoning for our own scenarios. Related resources Phi4-mini-reasoning Tech Report https://aka.ms/phi4-mini-reasoning/techreport Phi-4-Mini-Reasoning technical Report· microsoft/Phi-4-mini-reasoning Phi-4-mini-reasoning on Azure AI Foundry https://aka.ms/phi4-mini-reasoning/azure Phi-4 Reasoning Blog https://aka.ms/phi4-mini-reasoning/blog Phi Cookbook https://aka.ms/phicookbook Showcasing Phi-4-Reasoning: A Game-Changer for AI Developers | Microsoft Community Hub Models Phi-4 Reasoning https://huggingface.co/microsoft/Phi-4-reasoning Phi-4 Reasoning Plus https://huggingface.co/microsoft/Phi-4-reasoning-plus Phi-4-mini-reasoning Hugging Face https://aka.ms/phi4-mini-reasoning/hf Phi-4-mini-reasoning on Azure AI Foundry https://aka.ms/phi4-mini-reasoning/azure Microsoft (Microsoft) Models on Hugging Face Phi-4 Reasoning Models Azure AI Foundry Models Access Phi-4-reasoning models Phi Models at Azure AI Foundry Models Phi Models on Hugging Face Phi Models on GitHub Marketplace Models
