What is trending in Hugging Face on Microsoft Foundry? Feb, 2, 2026
Open‑source AI is moving fast, with important breakthroughs in reasoning, agentic systems, multimodality, and efficiency emerging every day. Hugging Face has been a leading platform where researchers, startups, and developers share and discover new models. Microsoft Foundry brings these trending Hugging Face models into a production‑ready experience, where developers can explore, evaluate, and deploy them within their Azure environment. Our weekly Model Monday’s series highlights Hugging Face models available in Foundry, focusing on what matters most to developers: why a model is interesting, where it fits, and how to put it to work quickly. This week’s Model Mondays edition highlights three Hugging Face models, including a powerful Mixture-of-Experts model from Z. AI designed for lightweight deployment, Meta’s unified foundation model for image and video segmentation, and MiniMax’s latest open-source agentic model optimized for complex workflows. Models of the week Z.AI’s GLM-4.7-flash Model Basics Model name: zai-org/GLM-4.7-Flash Parameters / size: 30B total -3B active Default settings: 131,072 max new tokens Primary task: Agentic, Reasoning and Coding Why this model matters Why it’s interesting: It utilizes a Mixture-of-Experts (MoE) architecture (30B total parameters and 3B active parameters) to offer a new option for lightweight deployment. It demonstrates strong performance on logic and reasoning benchmarks, outperforming similar sized models like gpt-oss-20b on AIME 25 and GPQA benchmarks. It supports advanced inference features like "Preserved Thinking" mode for multi-turn agentic tasks. Best‑fit use cases: Lightweight local deployment, multi-turn agentic tasks, and logical reasoning applications. What’s notable: From the Foundry catalog, users can deploy on a A100 instance or unsloth/GLM-4.7-Flash-GGUF on a CPU. ource SOTA scores among models of comparable size. Additionally, compared to similarly sized models, GLM-4.7-Flash demonstrates superior frontend and backend development capabilities. Click to see more: https://docs.z.ai Try it Use case Best‑practice prompt pattern Agentic coding (multi‑step repo work, debugging, refactoring) Treat the model as an autonomous coding agent, not a snippet generator. Explicitly require task decomposition and step‑by‑step execution, then a single consolidated result. Long‑context agent workflows (local or low‑cost autonomous agents) Call out long‑horizon consistency and context preservation. Instruct the model to retain earlier assumptions and decisions across turns. Now that you know GLM‑4.7‑Flash works best when you give it a clear goal and let it reason through a bounded task, here’s an example prompt that a product or engineering team might use to identify risks and propose mitigations: You are a software reliability analyst for a mid‑scale SaaS platform. Review recent incident reports, production logs, and customer issues to uncover edge‑case failures outside normal usage (e.g., rare inputs, boundary conditions, timing/concurrency issues, config drift, or unexpected feature interactions). Prioritize low‑frequency, high‑impact risks that standard testing misses. Recommend minimal, low‑cost fixes (validation, guardrails, fallback logic, or documentation). Deliver a concise executive summary with sections: Observed Edge Cases, Root Causes, User Impact, Recommended Lightweight Fixes, and Validation Steps. Meta's Segment Anything 3 (SAM3) Model Basics Model name: facebook/sam3 Parameters / size: 0.9B Primary task: Mask Generation, Promptable Concept Segmentation (PCS) Why this model matters Why it’s interesting: It handles a vastly larger set of open-vocabulary prompts than SAM 2, and unifies image and video segmentation capabilities. It includes a "SAM 3 Tracker" mode that acts as a drop-in replacement for SAM 2 workflows with improved performance. Best‑fit use cases: Open-vocabulary object detection, video object tracking, and automatic mask generation What’s notable: Introduces Promptable Concept Segmentation (PCS), allowing users to find all matching objects (e.g., "dial") via text prompt rather than just single instances. Try it This model enables users to identify specific objects within video footage and isolate them over extended periods. With just one line of code, it is possible to detect multiple similar objects simultaneously. The accompanying GIF demonstrates how SAM3 efficiently highlights players wearing white on the field as they appear and disappear from view. Additional examples are available at the following repository: https://github.com/facebookresearch/sam3/blob/main/assets/player.gif Use case Best‑practice prompt pattern Agentic coding (multi‑step repo work, debugging, refactoring) Treat SAM 3 as a concept detector, not an interactive click tool. Use short, concrete noun‑phrase concept prompts instead of describing the scene or asking questions. Example prompt: “yellow school bus” or “shipping containers”. Avoid verbs or full sentences. Video segmentation + object tracking Specify the same concept prompt once, then apply it across the video sequence. Do not restate the prompt per frame. Let the model maintain identity continuity. Example: “person wearing a red jersey”. Hard‑to‑name or visually subtle objects Use exemplar‑based prompts (image region or box) when text alone is ambiguous. Optionally combine positive and negative exemplars to refine the concept. Avoid over‑constraining with long descriptions. Using the GIF above as a leading example, here is a prompt that shows how SAM 3 turns raw sports footage into structured, reusable data. By identifying and tracking players based on visual concepts like jersey color so that sports leagues can turn tracked data into interactive experiences where automated player identification can relay stats, fun facts, etc when built into a larger application. Here is a prompt that will allow you to start identifying specific players across video: Act as a sports analytics operator analyzing football match footage. Segment and track all football players wearing blue jerseys across the video. Generate pixel‑accurate segmentation masks for each player and assign persistent instance IDs that remain stable during camera movement, zoom, and player occlusion. Exclude referees, opposing team jerseys, sidelines, and crowd. Output frame‑level masks and tracking metadata suitable for overlays, player statistics, and downstream analytics pipelines. MiniMax AI's MiniMax-M2.1 Model Basics Model name: MiniMaxAI/MiniMax-M2.1 Parameters / size: 229B-10B Active Default settings: 200,000 max new tokens Primary task: Agentic and Coding Why this model matters Why it’s interesting: It is optimized for robustness in coding, tool use, and long-horizon planning, outperforming Claude Sonnet 4.5 in multilingual scenarios. It excels in full-stack application development, capable of architecting apps "from zero to one”. Previous coding models focused on Python optimization, M2.1 brings enhanced capabilities in Rust, Java, Golang, C++, Kotlin, Objective-C, TypeScript, JavaScript, and other languages. The model delivers exceptional stability across various coding agent frameworks. Best‑fit use cases: Lightweight local deployment, multi-turn agentic tasks, and logical reasoning applications. What’s notable: The release of open-source weights for M2.1 delivers a massive leap over M2 on software engineering leaderboards. https://www.minimax.io/ Try it Use case Best‑practice prompt pattern End‑to‑end agentic coding (multi‑file edits, run‑fix loops) Treat the model as an autonomous coding agent, not a snippet generator. Explicitly require task decomposition and step‑by‑step execution, then a single consolidated result. Long‑horizon tool‑using agents (shell, browser, Python) Explicitly request stepwise planning and sequential tool use. M2.1’s interleaved thinking and improved instruction‑constraint handling are designed for complex, multi‑step analytical tasks that require evidence tracking and coherent synthesis, not conversational back‑and‑forth. Long‑context reasoning & analysis (large documents / logs) Declare the scope and desired output structure up front. MiniMax‑M2.1 performs best when the objective and final artifact are clear, allowing it to manage long context and maintain coherence. Because MiniMax‑M2.1 is designed to act as a long‑horizon analytical agent, it shines when you give it a clear end goal and let it work through large volumes of information—here’s a prompt a risk or compliance team could use in practice: You are a financial risk analysis agent. Analyze the following transaction logs and compliance policy documents to identify potential regulatory violations and systemic risk patterns. Plan your approach before executing. Work through the data step by step, referencing evidence where relevant. Deliver a final report with the following sections: Key Risk Patterns Identified, Supporting Evidence, Potential Regulatory Impact, Recommended Mitigations. Your response should be a complete, executive-ready report, not a conversational draft. Getting started You can deploy open‑source Hugging Face models directly in Microsoft Foundry by browsing the Hugging Face collection in the Foundry model catalog and deploying to managed endpoints in just a few clicks. You can also start from the Hugging Face Hub. First, select any supported model and then choose "Deploy on Microsoft Foundry", which brings you straight into Azure with secure, scalable inference already configured. Learn how to discover models and deploy them using Microsoft Foundry documentation. Follow along the Model Mondays series and access the GitHub to stay up to date on the latest Read Hugging Face on Azure docs Learn about one-click deployments from the Hugging Face Hub on Microsoft Foundry Explore models in Microsoft FoundryBeyond the Model: Empower your AI with Data Grounding and Model Training
Discover how Microsoft Foundry goes beyond foundational models to deliver enterprise-grade AI solutions. Learn how data grounding, model tuning, and agentic orchestration unlock faster time-to-value, improved accuracy, and scalable workflows across industries.Unlocking Efficient and Secure AI for Android with Foundry Local
The ability to run advanced AI models directly on smartphones is transforming the mobile landscape. Foundry Local for Android simplifies the integration of generative AI models, allowing teams to deliver sophisticated, secure, and low-latency AI experiences natively on mobile devices. This post highlights Foundry Local for Android as a compelling solution for Android developers, helping them efficiently build and deploy powerful on-device AI capabilities within their applications. The Challenges of Deploying AI on Mobile Devices On-device AI offers the promise of offline capabilities, enhanced privacy, and low-latency processing. However, implementing these capabilities on mobile devices introduces several technical obstacles: Limited computing and storage: Mobile devices operate with constrained processing power and storage compared to traditional PCs. Even the most compact language models can occupy significant space and demand substantial computational resources. Efficient solutions for model and runtime optimization are critical for successful deployment. Concerns about the app size: Integrating large AI models and libraries can dramatically increase application size, reducing install rates and degrading other app features. It remains a challenge to provide advanced AI capabilities while keeping the application compact and efficient. Complexity of development and integration: Most mobile development teams are not specialized in machine learning. The process of adapting, optimizing, and deploying models for mobile inference can be resource intensive. Streamlined APIs and pre-optimized models simplify integration and accelerate time to market. Introducing Foundry Local for Android Foundry Local is designed as a comprehensive on-device AI solution, featuring pre-optimized models, a cross-platform inference engine, and intuitive APIs for seamless integration. Initially announced at //Build 2025 with support for Windows and MacOS desktops, Foundry Local now extends its capabilities to Android in private preview. You can sign up for the private preview https://aka.ms/foundrylocal-androidprp for early evaluation and feedback. To meet the demands of production deployments, Foundry Local for Android is architected as a dedicated Android app paired with an SDK. The app manages model distribution, hosts the AI runtime, and operates as a specialized background service. Client applications interface with this service using a lightweight Foundry Local Android SDK, ensuring minimal overhead and streamlined connectivity. One Model, Multiple Apps: Foundry Local centralizes model management, ensuring that if multiple applications utilize the same model in Foundry Local, it is downloaded and stored only once. This approach optimizes storage and streamlines resource usage. Minimal App Footprint: Client applications are freed from embedding bulky machine learning libraries and models. This avoids ballooning app size and memory usage. Run Separately from Client Apps: The Foundry Local operates independently of client applications. Developers benefit from continuous enhancements without the need for frequent app releases. Customer Story: PhonePe PhonePe, one of India's largest consumer payments platforms that enables access to payments and financial services to hundreds of millions of people across the country. With Foundry Local, PhonePe is enabling AI that allows their users to gain deeper insights into their transactions and payments behavior directly on their mobile device. And because inferencing happens locally, all data stays private and secure. This collaboration addresses PhonePe's key priority of delivering an AI experience that upholds privacy. Foundry Local enables PhonePe to differentiate their app experience in a competitive market using AI while ensuring compliance with privacy commitments. Explore their journey here: PhonePe Product Showcase at Microsoft Ignite 2025 Call to Action Foundry Local equips Android apps with on-device AI, supporting the development of smarter applications for the future. Developers are able to build efficient and secure AI capabilities into their apps, even without extensive expertise in artificial intelligence. See more about Foundry Local in action in this episode of Microsoft Mechanics: https://aka.ms/FL_IGNITE_MSMechanics We look forward to seeing you light up AI capabilities in your Android app with Foundry Local. Don’t miss our private preview: https://aka.ms/foundrylocal-androidprp. We appreciate your feedback, as it will help us make our product better. Thanks to the contribution from NimbleEdge which delivers real-time, on-device personalization for millions of mobile devices. NimbleEdge's mobile technology expertise helps Foundry Local deliver a better experience for Android users.Get to know the core Foundry solutions
Foundry includes specialized services for vision, language, documents, and search, plus Microsoft Foundry for orchestration and governance. Here’s what each does and why it matters: Azure Vision With Azure Vision, you can detect common objects in images, generate captions, descriptions, and tags based on image contents, and read text in images. Example: Automate visual inspections or extract text from scanned documents. Azure Language Azure Language helps organizations understand and work with text at scale. It can identify key information, gauge sentiment, and create summaries from large volumes of content. It also supports building conversational experiences and question-answering tools, making it easier to deliver fast, accurate responses to customers and employees. Example: Understand customer feedback or translate text into multiple languages. Azure Document IntelligenceWith Azure Document Intelligence, you can use pre-built or custom models to extract fields from complex documents such as invoices, receipts, and forms. Example: Automate invoice processing or contract review. Azure SearchAzure Search helps you find the right information quickly by turning your content into a searchable index. It uses AI to understand and organize data, making it easier to retrieve relevant insights. This capability is often used to connect enterprise data with generative AI, ensuring responses are accurate and grounded in trusted information. Example: Help employees retrieve policies or product details without digging through files. Microsoft FoundryActs as the orchestration and governance layer for generative AI and AI agents. It provides tools for model selection, safety, observability, and lifecycle management. Example: Coordinate workflows that combine multiple AI capabilities with compliance and monitoring. Business leaders often ask: Which Foundry tool should I use? The answer depends on your workflow. For example: Are you trying to automate document-heavy processes like invoice handling or contract review? Do you need to improve customer engagement with multilingual support or sentiment analysis? Or are you looking to orchestrate generative AI across multiple processes for marketing or operations? Connecting these needs to the right Foundry solution ensures you invest in technology that delivers measurable results.The Future of AI: The Model is Key, but the App is the Doorway
This post explores the real-world impact of GPT-5 beyond benchmark scores, focusing on how application design shapes user experience. It highlights early developer feedback, common integration challenges, and practical strategies for adapting apps to leverage the advanced capabilities of GPT-5 in Foundry Models. From prompt refinement to fine-tuning to new API controls, learn how to make the most of this powerful model.The Future of AI: Building Weird, Warm, and Wildly Effective AI Agents
Discover how humor and heart can transform AI experiences. From the playful Emotional Support Goose to the productivity-driven Penultimate Penguin, this post explores why designing with personality matters—and how Azure AI Foundry empowers creators to build tools that are not just efficient, but engaging.
The Future of AI: An Intern’s Adventure Turning Hours of Video into Minutes of Meaning
This blog post, part of The Future of AI series by Microsoft’s AI Futures team, follows an intern’s journey in developing AutoHighlight—a tool that transforms long-form video into concise, narrative-driven highlight reels. By combining Azure AI Content Understanding with OpenAI reasoning models, AutoHighlight bridges the gap between machine-detected moments and human storytelling. The post explores the challenges of video summarization, the technical architecture of the solution, and the lessons learned along the way.The Future of AI: Structured Vibe Coding - An Improved Approach to AI Software Development
In this post from The Future of AI series, the author introduces structured vibe coding, a method for managing AI agents like a software team using specs, GitHub issues, and pull requests. By applying this approach with GitHub Copilot, they automated a repetitive task—answering Microsoft Excel-based questionnaires—while demonstrating how AI can enhance developer workflows without replacing human oversight. The result is a scalable, collaborative model for AI-assisted software development.GPT-5 Model Family Now Powers Azure AI Foundry Agent Service
The GPT-5 model family is now available in Azure AI Foundry Agent Service, which is generally available for enterprise customers. This means developers and enterprises can move beyond “just models” to build production-ready AI agents with: GPT-5’s advanced reasoning, coding, and multimodal intelligence Enterprise-grade trust, governance, and AgentOps built in Open standards and multi-agent orchestration for real-world workflows From insurance claims to supply chain optimization, Foundry enterprise agents are ready to power mission-critical AI at scale.The Future of AI: Maximize your fine-tuned model performance with the new Azure AI Evaluation SDK
In this article, we will explore how to effectively evaluate fine-tuned AI models using the new Azure AI Evaluation SDK. This comprehensive guide is the fourth part of our series on making large language model distillation easier. We delve into the importance of model evaluation, outline a systematic process for assessing the performance of a distilled student model against a baseline model, and demonstrate the use of advanced metrics provided by Azure's SDK. Join us as we navigate the intricacies of AI evaluation and provide insights for continuous model improvement and operational efficiency.
