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Phi-3 Model Family

The Phi-3 model family, developed by Microsoft, encompasses a range of small language models (SLMs) designed to offer high-quality AI capabilities with a focus on efficiency and accessibility. These models are particularly suited for applications where computational resources are limited, such as mobile devices or edge deployments. The Phi models balance performance and size, making them ideal for a variety of use cases, from natural language understanding to coding tasks.

General
Relese dateJune 2023
AuthorMicrosoft
WebsitePhi-3 open models
TypeSmall Language Models

Key Models and Features

  • Phi-3 Mini: A compact model with 3.8 billion parameters, trained on 3.3 trillion tokens. It provides strong performance across various benchmarks, including MMLU and GSM-8K, and is capable of running locally on smartphones and other edge devices.

  • Phi-3 Small: Featuring 7 billion parameters, this model includes additional capabilities for handling longer context lengths (up to 128K tokens). It offers enhanced performance in reasoning tasks and is fine-tuned with supervised and preference optimization techniques.

  • Phi-3 Medium: A larger variant with 14 billion parameters, designed for more complex applications that require robust reasoning and data analysis capabilities.

Training and Data

The training data for Phi models is meticulously curated, combining publicly available high-quality documents, synthetic “textbook-like” data, and chat-format supervised data. This approach ensures the models have a strong foundation in reasoning, coding, and general knowledge while maintaining efficiency in processing and storage requirements.

Applications and Use Cases

  • Edge and Mobile Deployment: The small size and efficient design of the Phi models make them suitable for deployment on devices with limited computational power, such as smartphones or IoT devices. They can operate offline, which is crucial for applications in remote or disconnected environments.

  • High-Risk Scenarios: While the models are designed to minimize biases and handle sensitive data responsibly, they are not recommended for high-stakes applications like legal advice or financial decision-making without additional safeguards.

Availability and Licensing

The Phi models are available on platforms like Hugging Face and Microsoft Azure AI Model Catalog. They are released under open licenses, allowing developers to integrate them into various applications while adhering to responsible AI practices.

👉 For more detailed information, you can refer to the technical report and resources available on Microsoft Research and Hugging Face.

Microsoft Phi-3 AI technology Hackathon projects

Discover innovative solutions crafted with Microsoft Phi-3 AI technology, developed by our community members during our engaging hackathons.

AI-Powered ISO 27001 Audit Intelligence Platform

AI-Powered ISO 27001 Audit Intelligence Platform

ISMS 2026 — AI-Powered ISO 27001 Document Intelligence Platform ISMS 2026 is a full-stack, locally-hosted document management and AI assistant platform built for the company to support ISO 27001:2022 Information Security Management System compliance. The system consolidates 824+ ISMS documents — including policies, procedures, control frameworks, risk registers, and mandatory ISO documentation — into a searchable, AI-queryable knowledge base that operates entirely on-premises without any external API dependencies. The platform consists of three core components: a Next.js 14 frontend (port 3001) providing a modern dark/light-mode UI with global document search, an AI chat panel, and real-time sync controls; a Node.js/Express backend (port 3000) handling document ingestion, semantic search via a metadata index, and REST API endpoints; and a local Ollama instance running the phi3 language model for AI-powered Q&A grounded in actual ISMS documents through Retrieval-Augmented Generation (RAG). Key features include debounced full-text search with relevance scoring across all document types (PDF, DOCX, XLSX, TXT), an AI assistant that references specific ISO control identifiers (e.g., A.5.1, A.8.2) and actual document names in its answers, and a one-click Sync Docs button that intelligently diffs the repository folder against the search index — automatically ingesting newly added files and purging deleted ones, keeping the index perpetually current. All three services — backend, frontend, and Ollama — start automatically at Windows boot via PM2 process manager and Windows Task Scheduler, with crash recovery and auto-restart built in. No manual terminal intervention is ever required.

TridenGuard

TridenGuard

TridenGuard is a deterministic validation system for LLM outputs in legal and financial contracts. It addresses a critical gap in enterprise AI adoption: LLMs hallucinate, but contracts don't forgive. The system enforces a neuro-symbolic isolation architecture. First, Lobster Trap (Veea) blocks prompt injections, PII leaks, and data exfiltration at the ingress layer. Second, a local Phi-4-mini model extracts 8 atomic radicals: Actor, Deontic, Action, Object, Temporal, Spatial, Metric, and Condition. Third, a deterministic validator applies 8 exclusion rules (R1-R8) and a grounding check that verifies every radical exists literally in the source text. If a structural failure is detected — for example, an orphan metric without an actor — the case is quarantined in a forensic panel. A human lawyer reviews the case, approves or discards it, and that decision becomes training data for a sovereign local LoRA model. The system also exports audit reports in CSV for regulatory compliance. TridenGuard is designed to run on Veea Edge Nodes: low-latency, air-gapped, and fully sovereign. No cloud. No data leakage. The enterprise owns its intelligence. Benchmark results (Phase 1, 20 cases): 100% block rate for prompt injections and PII, 100% interception of real-world court hallucinations (Lacey v. State Farm, Russell v. Mells, Lexos Media, Baidu AI), 87.5% structural validator accuracy, and 85% overall pipeline accuracy. A 64-case benchmark matrix is designed for V2. Roadmap: V2 adds TOON + GBNF token-level governance and Fisher's Exact Test for statistical threat hunting. V3 adds sovereign local LoRA fine-tuning from human decisions. Built for Veea Edge Nodes.

Qubic Liquidation Guardian

Qubic Liquidation Guardian

Qubic Liquidation Guardian is a hybrid Track 1 + Track 2 project built by CrewX that brings real-time liquidation protection, institutional-grade risk analysis, and automated alerting to the Qubic Network. The problem is simple: DeFi liquidations happen instantly, but users do not get instant signals. As a result, borrowers lose capital, protocols lose liquidity, and investors hesitate to adopt new systems without safety infrastructure. Inspired by this gap, Qubic Liquidation Guardian provides a complete safety layer over lending protocols deployed on the Nostromo Launchpad. At its core, the system includes an on-chain event listener and a real-time risk scoring engine, which analyzes: • Health Factor • Liquidation Proximity • Total Debt Exposure • Active Positions These metrics are combined into a 0–100 Risk Score, dynamically updated for each borrower. Based on the score, users are automatically classified into Low, Medium, High, and Critical risk tiers, enabling rapid decision-making. The platform also includes advanced features such as: • Whale Watch: Detect large-value transactions to anticipate market shifts • Smart Alerts: Severity-based notifications connected to any tool • Auto-Airdrop: Rewards for users who resolve high-risk positions • Crash Simulator: A built-in testing environment to simulate -70% market dumps, rebounds, and full resets to verify protocol safety Qubic Liquidation Guardian is designed to strengthen the Nostromo ecosystem by improving investor confidence, increasing protocol safety, and enabling risk-aware liquidity management. With over 35 production-ready API endpoints, an edge-distributed database, and a Next.js 15 architecture, the application is fully deployable and already live for testing. Ultimately, this project delivers exactly what new chains and protocols need: speed, stability, transparency, and automation—making Qubic safer for everyone.

The Intelligent Home

The Intelligent Home

An Intelligent Home is a modern living environment where everyday household systems—lighting, climate control, security, entertainment, and appliances—are interconnected through a network of smart devices and sensors. These components communicate seamlessly, enabling the home to monitor its own state and respond to user needs automatically. The goal is to create a living space that enhances comfort, convenience, and safety while reducing manual effort. At the center of an Intelligent Home is a smart home hub, which acts as the system’s brain. It manages communication between devices, processes real-time sensor data, and allows users to interact with the environment through voice commands, mobile apps, or automated routines. Through machine learning, the home can recognize patterns—such as daily schedules or common behaviors—and adjust settings automatically, like dimming lights in the evening or pre-cooling before residents arrive. A defining feature of an Intelligent Home is its ability to be context-aware. Using sensors that track motion, temperature, occupancy, and environmental changes, the home adapts to real-time conditions. For example, lighting can adjust based on natural sunlight, thermostats can adapt to user comfort levels, and security systems can differentiate between routine activity and potential threats. This awareness enables the home to evolve and provide increasingly personalized experiences. Another key aspect is connectivity and interoperability. Intelligent Homes support a broad ecosystem of devices and technologies using standards such as Wi-Fi, Zigbee, Z-Wave, and Matter, ensuring that products from different manufacturers work together seamlessly. This flexibility allows homeowners to expand, upgrade, or customize their setup without being locked into a single brand. A unified network enables synchronized automation—like having lights, security, and climate systems work in harmony based on a single trigger or routine.