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 ReportStep-by-Step: Setting Up GitHub Student and GitHub Copilot as an Authenticated Student Developer
To become an authenticated GitHub Student Developer, follow these steps: create a GitHub account, verify student status through a school email or contact GitHub support, sign up for the student developer pack, connect to Copilot and activate the GitHub Student Developer Pack benefits. The GitHub Student Developer Pack offers 100s of free software offers and other benefits such as Azure credit, Codespaces, a student gallery, campus experts program, and a learning lab. Copilot provides autocomplete-style suggestions from AI as you code. Visual Studio Marketplace also offers GitHub Copilot Labs, a companion extension with experimental features, and GitHub Copilot for autocomplete-style suggestions. Setting up your GitHub Student and GitHub Copilot as an authenticated Github Student DeveloperModel Mondays S2:E7 · AI-Assisted Azure Development
Welcome to Episode 7! This week, we explore how AI is transforming Azure development. We’ll break down two key tools—Azure MCP Server and GitHub Copilot for Azure—and see how they make working with Azure resources easier for everyone. We’ll also look at a real customer story from SightMachine, showing how AI streamlines manufacturing operations.Announcing GitHub Universe Cloud Skills Challenge!
Join the GitHub Universe Cloud Skills Challenge and start your exiting journey in AI! Whether you’re beginning or looking to change your career, this learning experience is designed to introduce you to some of the most requested GitHub tools for AI beginners, and to explore new opportunities. Join the GitHub Universe Cloud Skills Challenge and start your exciting journey in AI!Importing and Exporting a List Template (.stp file) in SharePoint
Developers often need to import the same data for reuse across SharePoint sites. They want an effective way to import and manage this data across sites and want to preserve environment settings, rather than having to start from scratch each time. As a developer, if you’re using the modern experience in SharePoint, the option to upload a SharePoint list template (.stp) file isn’t obvious in your settings. Also, if you try to upload it, you might get a message that says, “Sorry, you don’t have access.”Exploring Generative AI: A Hands-on Course on Prompt Engineering for non-tech students - Part 1
Introduction Generative Artificial Intelligence (AI) has transformed the digital landscape through "intent-based outcome specification," a paradigm where users describe desired outcomes via detailed prompts instead of traditional commands. This course - targeting a non-developer audience - delved into the foundational principles of Generative AI and Large Language Models (LLMs), focusing on their core mechanisms and capabilities. Students learned and practiced effective prompting techniques, essential for navigating this powerful yet complex method. The course included the analysis and discussion of recent research on prompt engineering, keeping students abreast of the latest developments. The structure of the course balanced theoretical understanding and practical application, with 30% dedicated to traditional lectures and 70% to hands-on workshops and collaborative group projects. Practical exercises using models like GPT allowed students to apply their theoretical knowledge in real-world scenarios. Group projects focused on specific application domains – including music, literature and cuisine - leading to presentations, peer reviews, and instructor feedback. The course - comprising 20 hours of direct instruction - was conducted at the Fondazione Bruno Kessler (FBK) campuses in Povo. Instructors Antonio Bucchiarone and Nadia Mana guided the learning journey. Additionally, Carlotta Castelluccio from Microsoft conducted a seminar on Responsible AI, emphasizing ethical considerations in AI applications. In this first part of the blog series, we are going to present the methodological framework and the tools used throughout the course. In the second part, we are going to cover the student projects’ main outcomes and key takeaways. The Card Model Template and the Flow of Cards One of the primary goals of the course was to provide a clear and comprehensive understanding of prompt engineering. This was achieved by introducing a structured framework known as the "Card Model" to define and organize generative AI tasks. In the context of this course, a card refers to a structured format or template used to define a specific task or objective for generating content or output using generative AI techniques. The Card Model serves as a conceptual framework that outlines the structure, components, and relationships involved in generating content or output using generative AI techniques. It provides a high-level abstraction of the task, capturing its essential elements and defining their interactions. Here’s a simplified model of a generative AI task: Objective: This is the overarching goal or purpose of the generative AI task, defining what needs to be achieved through the content generation process. Input: Information provided to the generative AI technique to guide the content generation process. This includes: Prompt: A starting point or stimulus to generate content, such as a partial sentence, a question, an instruction, or other forms of input. Context: Additional information or constraints that provide context for the generation task, such as background knowledge, relevant data sources, or specific requirements. Generative Model: The AI model responsible for generating content based on the input provided. Examples include pre-trained language models like OpenAI GPT-3.5Turbo, neural network architectures for text generation, or other generative AI systems. Output: The generated content produced by the generative model in response to the input, including: Generated Text: The actual output, which could be in the form of text, images, or other media. Evaluation Metrics: Criteria used to assess the quality and relevance of the generated content, including measures of coherence, relevance, fluency, and other factors depending on the specific task requirements. Feedback Loop: A mechanism for iteratively improving the generative AI model based on feedback from users or evaluators. This may involve refining the input prompts, adjusting model parameters, or incorporating additional training data to enhance performance. The Card Model helps define the key components involved in the task and their relationships, facilitating the design, execution, and evaluation of generative AI tasks in various applications. Cards Flow Model The concept of flow was also introduced in the course to provide a formal representation of the relationships between different cards composing a generative AI task. This flow model helps in visualizing and understanding the sequential and conditional transitions between different stages of a generative AI task, ensuring a structured and systematic approach to designing, executing, and evaluating generative AI processes. In more details, cards can be combined together to create complex workflows by defining specific transitions and dependencies between them. By linking these cards through directed edges, students can create intricate flows that mirror real-world applications. This pattern also helps students to break down complex tasks into smaller subtasks, described through detailed prompts and potentially addressed by different specialized models, generally leading to a more accurate final outcome. For example, a card flow might begin with a card that generates an initial story prompt. The output of this card could then flow into a card that adds contextual details, which in turn flows into another card responsible for generating the story based on the enhanced prompt. Subsequent cards could be used to evaluate the generated content, refine the prompt based on evaluation metrics, and iterate the process. To ensure a thorough understanding of these flows, students were asked to evaluate different paths within a flow. This involved analyzing how changes in one card could affect the overall output and exploring alternative pathways to achieve the desired outcome. Students were tasked with: Mapping Out Flows: Students mapped out various flows, identifying all possible paths and transitions between cards. Evaluating Paths: They evaluated each path to understand how different sequences and combinations of tasks impacted the final output. Comparing Outcomes: Students compared outcomes from different paths to determine which flow produced the most coherent, relevant, and high-quality results. Feedback and Iteration: They incorporated feedback into their flows, refining cards and transitions to optimize the generative process. By engaging in these activities, students gained hands-on experience in managing complex generative AI tasks, learning to anticipate and handle the dependencies and contingencies that arise in practical applications. This exercise not only reinforced their understanding of prompt engineering but also highlighted the importance of structured planning and iterative improvement in generative AI projects. The Azure AI Proxy Playground Students learned to interact with OpenAI models through the GUI offered by the Azure AI Proxy Playground. The service is an open-source solution which provides a Playground-like experience to explore the Azure OpenAI chat completions using a time-bound event code with different models and parameters. It’s designed for educational scenarios (e.g., a course, a hackathon, or a workshop) where students might not have access to an Azure subscription enabled with Azure OpenAI service and/or are not familiar with the Azure ecosystem and how to provision and consume Azure AI resources. By leveraging this solution, we were able to provide students with a simplified lab environment, where all the complexity related to the Cloud resources provisioning and model deployments was hidden to the final user and managed through a single Azure subscription, connected to the Proxy Playground. This was particularly helpful in the context of a course whose audience was non-technical and whose focus was learning to interact with large language models through prompt engineering techniques. For the sake of the course, we provisioned a gpt-3.5 turbo instance, so all the students’ interactions via the playground happened with that specific model. Tool GUI and Card Model mapping The Playground GUI is composed of several elements. Most of them can be directly mapped with the Card Model components, ensuring consistency between the theoretical concepts and the actual experimentations. User prompt: free-form text field used to enter the user request to the model. It’s the prompt component of the input in the card model. System message: free-form text field used to enter additional information to use in responses, data sources and/or tone and style specifications. It maps with the context component of the input in the card model. Configuration: parameters to tune the degree of randomness of the responses. It also includes a dropdown menu to select the model to use as chat engine, what we call generative model in the card template. Assistant response: in the chat session the user can read the model’s response, aka the generated text component of the output in the card model. Summary In this article, we covered the methodological framework and tools used in the Prompt Engineering course at Fondazione Bruno Kessler, to teach non-tech students to effectively interact with generative AI models. We explored the "Card Model" - a structured approach to define and organize generative AI tasks - and the concept of the "flow", which further structures the relationships between tasks, aiding in the creation of complex workflows. Students utilized the Azure AI Proxy Playground, an open-source GUI, to interact with OpenAI models like GPT-3.5 Turbo, applying their theoretical knowledge in practical scenarios without needing extensive technical skills. In the second part of the blog series, we will delve into the main outcomes of the students' projects and the key takeaways from their practical applications.Revolutionize Education with Azure OpenAI: PolyGlot-Edu Generator
Explore the groundbreaking PolyGlot-Edu Generator, developed by Antonio Bucchiarone in collaboration with Microsoft Azure's OpenAI services. This cutting-edge tool empowers educators worldwide with AI-driven exercises, allowing customization of exercises, integration of materials, and precise evaluation using Semantic Kernel and Large Language Model. Join us in refining this innovative tool by trying the demo and sharing your feedback through our survey for continuous improvements
