{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-phi3","slug":"phi3","name":"Phi 3 (3.8B, 7B, 14B)","type":"model","url":"https://ollama.com/library/phi3","page_url":"https://unfragile.ai/phi3","categories":["text-writing"],"tags":["ollama","open-source","microsoft"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-phi3__cap_0","uri":"capability://text.generation.language.instruction.following.text.generation.with.4k.context.window","name":"instruction-following text generation with 4k context window","description":"Generates coherent, instruction-aligned text responses using a decoder-only transformer architecture trained via supervised fine-tuning (SFT) and Direct Preference Optimization (DPO). Processes user messages in standard chat format (role/content structure) and produces contextually relevant outputs within a 4,096-token context window, optimized for latency-bound scenarios where model size and inference speed are critical constraints.","intents":["Build a lightweight chatbot that runs locally without GPU acceleration","Deploy an instruction-following assistant in memory-constrained environments (edge devices, embedded systems)","Create a conversational AI that prioritizes response latency over maximum reasoning depth","Integrate a small language model into applications where bandwidth and compute costs must be minimized"],"best_for":["solo developers building local-first AI applications","teams deploying models on edge devices or resource-constrained servers","organizations prioritizing inference latency and cost over maximum capability","developers building chatbots for low-bandwidth environments"],"limitations":["4K context window limits ability to process long documents or maintain extended conversation history without truncation","English-focused training means non-English language quality is unknown and likely degraded","No specific benchmark scores provided, making performance comparison against alternatives difficult","Post-training safety measures documented but specific failure modes and bias characteristics not disclosed","Instruction-tuning approach may reduce zero-shot capability compared to larger base models"],"requires":["Ollama 0.1.39+ for local execution","2.2GB disk space for 3.8B variant, 7.9GB for 14B variant","Python 3.7+ or Node.js 14+ for SDK usage","macOS, Windows, Linux, or Docker runtime"],"input_types":["text (chat messages with role/content structure)","multi-turn conversation history"],"output_types":["text (streaming or complete generation)","structured JSON via REST API"],"categories":["text-generation-language","chatbots-assistants"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi3__cap_1","uri":"capability://text.generation.language.extended.context.text.generation.with.128k.token.window","name":"extended-context text generation with 128k token window","description":"Extends the standard 4K context window to 128K tokens, enabling processing of long documents, extended conversation histories, and complex multi-document reasoning tasks. Accessed via specific model variant (phi3:medium-128k) requiring Ollama 0.1.39+, allowing developers to trade off some inference speed for dramatically increased context capacity without changing model weights or architecture.","intents":["Process entire research papers, books, or long documents in a single inference pass","Maintain extended multi-turn conversations without losing early context","Perform document summarization, comparison, or analysis across multiple long texts","Build RAG-adjacent systems that retrieve and process large document chunks without external chunking"],"best_for":["developers building document analysis or long-form content processing systems","teams implementing extended conversation memory without external vector databases","applications requiring in-context learning with large example sets or documentation"],"limitations":["Requires Ollama 0.1.39+ — older versions default to 4K context and cannot access 128K variant","Inference latency increases substantially with longer contexts due to quadratic attention complexity","No published benchmarks showing quality degradation or performance metrics at 128K tokens","Memory requirements scale with context length — exact VRAM requirements not documented","Extended context may amplify model biases or hallucinations over very long sequences"],"requires":["Ollama 0.1.39 or later","Explicit model selection: phi3:medium-128k (not default phi3:latest)","Sufficient system RAM or VRAM to hold 128K token context in memory","Application-level handling of token counting to avoid exceeding 128K limit"],"input_types":["text (up to 128,000 tokens)","multi-document chat with long context"],"output_types":["text (streaming or complete generation)","structured JSON via REST API"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi3__cap_10","uri":"capability://safety.moderation.safety.aligned.instruction.following.with.dpo.post.training","name":"safety-aligned instruction-following with dpo post-training","description":"Phi-3 models undergo Direct Preference Optimization (DPO) post-training to improve instruction adherence and incorporate safety measures, reducing harmful outputs and improving alignment with user intent. DPO uses preference pairs (preferred vs. dispreferred responses) to fine-tune the model without requiring explicit reward models, enabling instruction-following behavior that better matches user expectations while maintaining model efficiency.","intents":["Deploy models with reduced risk of harmful or off-topic outputs","Build applications requiring instruction-following without extensive prompt engineering","Use smaller models (3.8B) with safety properties comparable to larger alternatives","Reduce need for external content filtering or guardrails"],"best_for":["applications requiring safety-aligned models (customer-facing chatbots, educational tools)","teams building systems where instruction-following is critical","developers wanting smaller models without sacrificing safety properties"],"limitations":["Specific safety measures and failure modes not documented — unclear what types of harmful outputs are prevented","No published safety benchmarks or red-teaming results — safety claims unverified","DPO training data composition unknown — unclear what preferences were optimized for","Safety alignment may degrade on adversarial inputs or jailbreak attempts — robustness unknown","Developers still responsible for evaluating safety for specific use cases per Microsoft documentation"],"requires":["Ollama 0.1.39+ with instruction-tuned Phi-3 variant","Clear, specific instructions in prompts (instruction-tuned models perform better with explicit guidance)","Application-level safety evaluation for high-risk scenarios (per Microsoft documentation)"],"input_types":["text (instructions, prompts, user queries)"],"output_types":["text (instruction-aligned responses)","reduced harmful or off-topic outputs (compared to base models)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi3__cap_11","uri":"capability://planning.reasoning.synthetic.data.augmentation.for.reasoning.capability","name":"synthetic data augmentation for reasoning capability","description":"Phi-3 training incorporates synthetic data generation to create high-quality reasoning examples (math, logic, code), enabling the small 3.8B model to achieve reasoning performance comparable to 7B-13B models trained on natural data alone. Synthetic data augmentation compensates for parameter count disadvantage by providing dense, reasoning-focused training examples rather than relying on scale.","intents":["Use small models (3.8B) for reasoning tasks typically requiring 7B+ parameters","Deploy reasoning-capable models in latency-sensitive or resource-constrained environments","Build applications requiring math/logic reasoning without large model overhead","Achieve competitive reasoning performance with minimal inference cost"],"best_for":["developers building reasoning-heavy applications with strict latency/cost constraints","teams deploying models on edge devices or embedded systems","applications requiring math or logic reasoning in resource-constrained environments"],"limitations":["Synthetic data composition and generation process not documented — unclear what reasoning types are covered","No published benchmarks comparing synthetic vs. natural data training — quality tradeoffs unknown","Reasoning capability may degrade on out-of-distribution problems not covered by synthetic data","Synthetic data may introduce artifacts or biases not present in natural reasoning examples","Reasoning performance still limited by 4K context window and 3.8B-14B parameter count"],"requires":["Ollama 0.1.39+ with Phi-3 model","Clear problem statements or reasoning prompts (synthetic training optimizes for explicit instructions)","Realistic expectations about reasoning complexity (not equivalent to 70B models)"],"input_types":["text (math problems, logic puzzles, code tasks)"],"output_types":["text (step-by-step reasoning, solutions, code)"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi3__cap_2","uri":"capability://tool.use.integration.local.first.inference.via.ollama.cli.and.rest.api","name":"local-first inference via ollama cli and rest api","description":"Executes Phi-3 models entirely on local hardware (macOS, Windows, Linux, Docker) without sending data to external servers, using Ollama's runtime which handles model downloading, quantization format management, and GPU/CPU inference orchestration. Exposes both CLI interface (ollama run phi3) and HTTP REST API (localhost:11434) for programmatic access, enabling zero-latency, privacy-preserving inference with full control over model execution.","intents":["Run language models locally without cloud API costs or data transmission to third parties","Build privacy-first applications where model inputs/outputs never leave the user's machine","Develop offline-capable AI features that work without internet connectivity","Prototype and test models locally before deploying to production infrastructure"],"best_for":["solo developers and small teams building privacy-sensitive applications","organizations with strict data residency or compliance requirements (HIPAA, GDPR)","developers in bandwidth-constrained environments or offline-first scenarios","teams evaluating models before committing to cloud infrastructure costs"],"limitations":["Inference speed depends entirely on local hardware — no access to cloud GPU acceleration unless using Ollama cloud (paid tier)","Model download and initial setup requires significant disk I/O and bandwidth (2.2GB-7.9GB per variant)","Ollama runtime adds abstraction layer — exact quantization format and optimization details not documented","No built-in load balancing or horizontal scaling — single machine deployment only","Requires manual model management (updates, variant switching) via CLI commands"],"requires":["Ollama 0.1.39+ installed and running as daemon","2.2GB disk space (3.8B variant) or 7.9GB (14B variant)","macOS 11+, Windows 10+, Linux (Ubuntu 20.04+), or Docker","For GPU acceleration: NVIDIA CUDA 11.8+ or compatible GPU (exact VRAM requirements not specified)"],"input_types":["text (via CLI stdin or REST JSON payload)","chat message arrays with role/content structure"],"output_types":["text (streaming via Server-Sent Events or complete JSON response)","structured JSON with model metadata and generation parameters"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi3__cap_3","uri":"capability://tool.use.integration.cloud.hosted.inference.via.ollama.pro.max.subscription","name":"cloud-hosted inference via ollama pro/max subscription","description":"Deploys Phi-3 models to Ollama's managed cloud infrastructure (separate from local execution), enabling remote inference without maintaining local hardware while retaining API compatibility with local Ollama instances. Subscription tiers (Pro: $20/mo, Max: $100/mo) determine concurrent model capacity (1, 3, or 10 concurrent models), with identical REST API and SDK interfaces to local execution, allowing seamless switching between local and cloud deployment.","intents":["Scale inference beyond local hardware capacity without managing cloud infrastructure","Use GPU acceleration without owning or provisioning GPUs","Run multiple model variants concurrently for A/B testing or ensemble approaches","Transition from local prototyping to production deployment with minimal code changes"],"best_for":["teams needing GPU acceleration without infrastructure management overhead","applications requiring concurrent model execution (multi-variant inference)","developers wanting to scale from local to cloud without API refactoring","organizations with variable inference load that benefits from managed scaling"],"limitations":["Requires paid subscription ($20/mo minimum) — no free tier for cloud inference","Concurrent model limits (1 free, 3 Pro, 10 Max) restrict multi-model deployments on lower tiers","No published SLA, uptime guarantees, or latency metrics for cloud tier","Data transmission to Ollama servers — not suitable for applications with strict data residency requirements","Vendor lock-in to Ollama platform — API compatibility with local Ollama may diverge over time"],"requires":["Ollama Pro ($20/mo) or Max ($100/mo) subscription","API key for authentication to Ollama cloud","Network connectivity to Ollama cloud endpoints","Same SDK/API compatibility as local Ollama (Python, JavaScript, REST)"],"input_types":["text (via REST JSON or SDK)","chat message arrays"],"output_types":["text (streaming or complete)","structured JSON responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi3__cap_4","uri":"capability://code.generation.editing.code.generation.and.reasoning.for.mathematical.logical.tasks","name":"code generation and reasoning for mathematical/logical tasks","description":"Phi-3 models are instruction-tuned and benchmarked on code generation, mathematical reasoning, and logical problem-solving tasks, leveraging synthetic training data and DPO post-training to improve reasoning capability. The 3.8B Mini variant achieves competitive performance on code and math benchmarks despite its small size, making it suitable for code completion, algorithm explanation, and structured problem-solving without requiring 7B+ parameter models.","intents":["Generate code snippets or complete functions from natural language descriptions","Explain mathematical concepts or solve step-by-step math problems","Debug code by analyzing error messages and suggesting fixes","Solve logical reasoning tasks or constraint satisfaction problems"],"best_for":["developers building code-assistant features in resource-constrained environments","educational applications teaching programming or mathematics","applications requiring lightweight reasoning without full LLM inference overhead","teams needing code generation in offline or edge-device scenarios"],"limitations":["No specific benchmark scores provided — performance vs. Copilot, Claude, or GPT-4 unknown","Code generation quality likely degrades on complex multi-file refactoring or architectural tasks","Mathematical reasoning limited by 4K context window — cannot process very long derivations or proofs","No explicit support for multiple programming languages — training data composition unknown","Reasoning capability constrained by 3.8B-14B parameter count — complex logical chains may fail"],"requires":["Ollama 0.1.39+ with Phi-3 model downloaded","Code or math problem as text input","For best results: clear, specific problem statements (instruction-tuned models perform better with explicit instructions)"],"input_types":["text (code snippets, math problems, natural language descriptions)","multi-turn conversation with code context"],"output_types":["text (code, explanations, step-by-step solutions)","structured code blocks or formatted mathematical notation"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi3__cap_5","uri":"capability://text.generation.language.multi.turn.conversation.with.role.based.message.formatting","name":"multi-turn conversation with role-based message formatting","description":"Supports multi-turn conversations using standard chat message format (role: user/assistant, content: text), enabling stateless conversation management where each API call includes full conversation history. Ollama REST API and SDKs handle message serialization and streaming responses, allowing developers to build chatbot interfaces without managing conversation state or session persistence.","intents":["Build conversational chatbot interfaces with multi-turn dialogue","Maintain conversation context across multiple user messages without external session storage","Implement assistant-like interactions where the model responds to follow-up questions","Create interactive debugging or tutoring systems with back-and-forth exchanges"],"best_for":["developers building chatbot UIs or conversational interfaces","applications requiring stateless conversation (no database required)","teams prototyping conversational AI without session management infrastructure"],"limitations":["Conversation history must be sent with every request — scales poorly with very long conversations (4K context limit)","No built-in conversation persistence — developers must implement external storage if conversation history needs to survive application restarts","Role-based formatting (user/assistant) is rigid — no support for system prompts or custom roles","Streaming responses require Server-Sent Events (SSE) support — not all HTTP clients handle streaming natively","No automatic conversation summarization or context compression — developers must manually truncate old messages"],"requires":["Ollama 0.1.39+ with REST API enabled (default localhost:11434)","Message array with role (user/assistant) and content fields","For streaming: HTTP client supporting Server-Sent Events or chunked transfer encoding","Application-level conversation history management (array of message objects)"],"input_types":["JSON array of message objects: [{role: 'user'|'assistant', content: 'text'}, ...]"],"output_types":["text (streaming via SSE or complete JSON response)","structured JSON with message, model, and generation metadata"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi3__cap_6","uri":"capability://text.generation.language.streaming.text.generation.with.server.sent.events","name":"streaming text generation with server-sent events","description":"Generates text incrementally using HTTP Server-Sent Events (SSE), streaming tokens to the client as they are produced rather than waiting for complete generation. Reduces perceived latency and enables real-time UI updates (token-by-token display) without buffering entire responses, implemented via Ollama REST API with stream=true parameter.","intents":["Display text generation in real-time as tokens are produced (typewriter effect)","Reduce perceived latency by showing partial results immediately","Build responsive chatbot UIs that update incrementally rather than blocking on complete generation","Monitor generation progress and allow user interruption mid-generation"],"best_for":["web-based chatbot interfaces requiring real-time user feedback","applications where perceived latency is critical to user experience","developers building interactive AI assistants with streaming responses"],"limitations":["Requires HTTP client with SSE support — not all frameworks/languages have native streaming support","Token-by-token streaming adds network overhead (one HTTP chunk per token) — may increase total latency vs. buffered response","No built-in error handling for mid-stream failures — partial responses may be incomplete if connection drops","Streaming responses cannot be retried or resumed — must restart generation from beginning","Client-side buffering still required for display — streaming doesn't eliminate need for response handling logic"],"requires":["Ollama 0.1.39+ with REST API enabled","HTTP client supporting Server-Sent Events (fetch API, axios, requests with streaming, etc.)","stream=true parameter in REST API request","Application-level handling of SSE chunks and token concatenation"],"input_types":["text (chat messages or prompts)","REST API request with stream=true"],"output_types":["Server-Sent Events stream (text/event-stream MIME type)","individual tokens as they are generated"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi3__cap_7","uri":"capability://tool.use.integration.python.and.javascript.sdk.integration.with.native.language.bindings","name":"python and javascript sdk integration with native language bindings","description":"Provides official Python and JavaScript SDKs that wrap Ollama REST API, enabling idiomatic language-specific code (async/await in JavaScript, context managers in Python) without manual HTTP request construction. SDKs handle message serialization, streaming response parsing, and error handling, reducing boilerplate and enabling integration into existing Python/JavaScript projects.","intents":["Integrate Phi-3 inference into Python data science or backend applications","Build JavaScript/Node.js chatbot servers or browser-based AI features","Use async/await patterns for non-blocking inference in JavaScript","Leverage language-specific error handling and type hints (Python type annotations)"],"best_for":["Python developers building ML pipelines or backend services","JavaScript/Node.js developers building web servers or Electron apps","teams already invested in Python/JavaScript ecosystems","developers preferring language-native APIs over raw HTTP"],"limitations":["SDKs are thin wrappers around REST API — no performance advantage over direct HTTP calls","Limited to Python 3.7+ and Node.js 14+ — older versions not supported","No type hints or TypeScript definitions documented — JavaScript SDK may lack IDE autocomplete","SDK documentation minimal — developers may need to reference REST API docs for advanced features","No async support documented for Python SDK — may block event loops in async applications"],"requires":["Python 3.7+ (for Python SDK) or Node.js 14+ (for JavaScript SDK)","pip install ollama (Python) or npm install ollama (JavaScript)","Ollama 0.1.39+ running locally or accessible via network","Import statements: from ollama import Client (Python) or import { Ollama } from 'ollama' (JavaScript)"],"input_types":["Python: message dicts or Chat objects","JavaScript: message objects with role/content"],"output_types":["Python: Response objects or generator for streaming","JavaScript: Promise<Response> or AsyncIterable for streaming"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi3__cap_8","uri":"capability://automation.workflow.docker.containerization.for.reproducible.deployment","name":"docker containerization for reproducible deployment","description":"Phi-3 models can be deployed via Docker containers running Ollama, enabling reproducible, isolated execution environments across development, testing, and production. Docker images include Ollama runtime, model weights, and all dependencies, eliminating 'works on my machine' issues and enabling orchestration via Kubernetes, Docker Compose, or other container platforms.","intents":["Deploy Phi-3 in containerized environments (Kubernetes, Docker Swarm, ECS)","Ensure reproducible model execution across development and production","Isolate model inference from host system dependencies","Enable horizontal scaling via container orchestration platforms"],"best_for":["teams using Kubernetes or container orchestration platforms","organizations requiring reproducible deployments across environments","developers building microservices architectures with AI components","teams needing to scale inference across multiple container instances"],"limitations":["Docker adds abstraction layer and startup overhead — slower cold-start than native execution","GPU support requires nvidia-docker or similar GPU-aware container runtime — not all cloud platforms support this","Model weights must be downloaded on first container startup — no pre-baked images with weights documented","Container image size large (2.2GB+ for model weights) — impacts deployment speed and registry storage","No Kubernetes manifests or Helm charts provided — developers must write custom orchestration configs"],"requires":["Docker 20.10+ or compatible container runtime","For GPU: nvidia-docker or container runtime with GPU support","Sufficient disk space for model weights (2.2GB-7.9GB per variant)","Network access to download model weights on first run"],"input_types":["text (via REST API to containerized Ollama instance)","chat messages via HTTP"],"output_types":["text (streaming or complete)","structured JSON responses"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi3__cap_9","uri":"capability://tool.use.integration.model.variant.selection.and.version.management","name":"model variant selection and version management","description":"Ollama enables selection between Phi-3 variants (3.8B Mini, 14B Medium) and context window options (4K default, 128K extended) via model tag syntax (e.g., phi3:latest, phi3:medium-128k). Developers specify desired variant in API calls or CLI commands, and Ollama automatically downloads and caches the appropriate model weights, enabling A/B testing or context-aware variant selection without manual model management.","intents":["Choose between model sizes based on latency/quality tradeoffs (3.8B vs 14B)","Select context window size (4K vs 128K) based on application requirements","Test multiple model variants for performance comparison","Switch model variants without restarting application or managing weights manually"],"best_for":["developers evaluating model variants for specific use cases","applications requiring dynamic model selection based on input characteristics","teams A/B testing different model sizes for cost/quality optimization"],"limitations":["Variant switching requires downloading new model weights — adds latency and disk I/O on first access","No automatic variant selection based on input complexity — developers must implement selection logic","Limited variant documentation — unclear which variants are available or their exact differences","No version pinning — 'latest' tag may change, breaking reproducibility","Concurrent variant execution limited by subscription tier (1 free, 3 Pro, 10 Max)"],"requires":["Ollama 0.1.39+ with model tag support","Sufficient disk space for multiple variants (2.2GB + 7.9GB = 10.1GB for both)","Model tag specification in API calls: model='phi3:medium-128k' or similar","Network access to download variant weights on first use"],"input_types":["model tag string (e.g., 'phi3:3.8b', 'phi3:medium-128k')"],"output_types":["model metadata (name, size, context window)","text generation from selected variant"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Ollama 0.1.39+ for local execution","2.2GB disk space for 3.8B variant, 7.9GB for 14B variant","Python 3.7+ or Node.js 14+ for SDK usage","macOS, Windows, Linux, or Docker runtime","Ollama 0.1.39 or later","Explicit model selection: phi3:medium-128k (not default phi3:latest)","Sufficient system RAM or VRAM to hold 128K token context in memory","Application-level handling of token counting to avoid exceeding 128K limit","Ollama 0.1.39+ with instruction-tuned Phi-3 variant","Clear, specific instructions in prompts (instruction-tuned models perform better with explicit guidance)"],"failure_modes":["4K context window limits ability to process long documents or maintain extended conversation history without truncation","English-focused training means non-English language quality is unknown and likely degraded","No specific benchmark scores provided, making performance comparison against alternatives difficult","Post-training safety measures documented but specific failure modes and bias characteristics not disclosed","Instruction-tuning approach may reduce zero-shot capability compared to larger base models","Requires Ollama 0.1.39+ — older versions default to 4K context and cannot access 128K variant","Inference latency increases substantially with longer contexts due to quadratic attention complexity","No published benchmarks showing quality degradation or performance metrics at 128K tokens","Memory requirements scale with context length — exact VRAM requirements not documented","Extended context may amplify model biases or hallucinations over very long sequences","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.483Z","last_scraped_at":"2026-05-03T15:20:48.403Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=phi3","compare_url":"https://unfragile.ai/compare?artifact=phi3"}},"signature":"BKm7t9bwtImvILR1ig4EO0XEYtXIBdOME02ZVjybU3Z1QGQSjGMV6yCqWjbRI4OyFT4DGa78RhcX+HPQICE7AA==","signedAt":"2026-06-22T14:24:25.712Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/phi3","artifact":"https://unfragile.ai/phi3","verify":"https://unfragile.ai/api/v1/verify?slug=phi3","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}