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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 β€œtextbook-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.

πŸ‘‰ 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.

ReguLattice - Local Sovereign GRC Engine

ReguLattice - Local Sovereign GRC Engine

ReguLattice is a sovereign GRC (Governance, Risk, and Compliance) platform designed for enterprises in highly regulated, high-stakes, or national security sectors. Modern automated compliance tools are built on cloud-first SaaS models. They require organizations to connect their live databases and code repositories to third-party public clouds, which violates data localization regulations such as US CMMC 2.0, Saudi SAMA, and Pakistan's SBP. ReguLattice solves this by operating as a fully air-gapped compliance engine that runs locally inside the client's secure virtual private cloud. The system operates on three core principles: Private Local Compliance: All document analysis, mapping, and audit logging occur fully offline. Sensitive files never leave the organization's network, ensuring absolute protection of corporate intellectual property and data sovereignty. Cross-Compliance Graph: Our architecture maps a single technical evidence record to overlapping controls across multiple frameworks (like ISO 27001, ISO 42001, and regional banking guidelines), eliminating redundant audit efforts and speeding up verification. Custom Governance Console: The platform allows risk teams to align private offline intelligence with their company's custom internal policies and historical audit registries. By automating evidence gathering and compliance scoring locally, ReguLattice brings modern automation to the defense, financial, and critical infrastructure sectors without compromising security or sovereignty.

AI-Powered ISO 27001 Audit Intelligence Platform

AI-Powered ISO 27001 Audit Intelligence Platform

ISMS 2026 β€” 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 β€” including policies, procedures, control frameworks, risk registers, and mandatory ISO documentation β€” 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 β€” automatically ingesting newly added files and purging deleted ones, keeping the index perpetually current. All three services β€” backend, frontend, and Ollama β€” 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 β€” for example, an orphan metric without an actor β€” 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.