{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"phi-3-5-mini","slug":"phi-3-5-mini","name":"Phi-3.5 Mini","type":"model","url":"https://azure.microsoft.com/en-us/products/phi","page_url":"https://unfragile.ai/phi-3-5-mini","categories":["model-training","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"phi-3-5-mini__cap_0","uri":"capability://text.generation.language.long.context.text.generation.with.128k.token.window","name":"long-context text generation with 128k token window","description":"Generates coherent text across extended contexts up to 128K tokens using a standard transformer architecture optimized for efficient attention computation. Unlike typical 4K-32K context models, Phi-3.5 Mini achieves this extended window through training on synthetic data specifically designed to leverage long-range dependencies, enabling document-level understanding and multi-turn conversations without context truncation. The model processes input through standard transformer layers with optimized attention patterns to maintain inference speed despite the large context size.","intents":["I need to process and generate responses based on entire documents or long conversation histories without losing context","I want to build a chatbot that remembers extended conversation history without manual summarization","I need to analyze and summarize long technical documentation or research papers in a single pass"],"best_for":["developers building edge-deployed chatbots with long conversation requirements","teams creating document analysis tools for resource-constrained environments","mobile app developers needing on-device long-context reasoning"],"limitations":["128K token limit is absolute maximum input size; exceeding this requires chunking or summarization","Actual usable context may be lower depending on deployment hardware (mobile devices may not efficiently use full 128K)","Long context processing increases latency compared to shorter contexts; exact latency scaling unknown","No documented performance degradation patterns at different context lengths"],"requires":["Input text tokenized to maximum 128K tokens using compatible tokenizer","Sufficient memory for model weights (3.8B parameters) plus KV cache for full context length","For mobile deployment: device with minimum 4GB RAM (estimated, not officially specified)"],"input_types":["text (raw strings, documents, conversation histories)"],"output_types":["text (generated continuations, responses, summaries)"],"categories":["text-generation-language","long-context-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-3-5-mini__cap_1","uri":"capability://text.generation.language.multilingual.text.generation.and.understanding","name":"multilingual text generation and understanding","description":"Processes and generates text across multiple languages through a shared transformer embedding space trained on high-quality synthetic and filtered multilingual data. The model learns language-agnostic representations that enable cross-lingual understanding and generation without language-specific branches or adapters. Specific supported languages are not documented, but the training data composition suggests coverage of major languages with emphasis on high-quality sources rather than broad web crawl.","intents":["I need a single model that can handle customer support in multiple languages without separate deployments","I want to build a translation-aware chatbot that understands context across language boundaries","I need to process and generate text in non-English languages on edge devices"],"best_for":["international teams building multilingual edge applications","developers creating global customer service bots with limited deployment resources","organizations needing language-agnostic content processing on mobile devices"],"limitations":["Specific supported languages not documented; language coverage unknown","No documented performance parity across languages; some languages may have degraded quality","No explicit cross-lingual transfer or zero-shot translation capability mentioned","Multilingual training may reduce per-language performance compared to language-specific models"],"requires":["Input text in supported language (specific language list not provided)","Tokenizer compatible with multilingual vocabulary"],"input_types":["text (in any supported language)"],"output_types":["text (in same or different language)"],"categories":["text-generation-language","multilingual-support"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-3-5-mini__cap_10","uri":"capability://planning.reasoning.benchmark.driven.performance.validation.on.mmlu.and.reasoning.tasks","name":"benchmark-driven performance validation on mmlu and reasoning tasks","description":"Demonstrates quantified performance on Massive Multitask Language Understanding (MMLU) benchmark with 69% accuracy, validating reasoning and knowledge capabilities across diverse domains. The model is evaluated on reasoning benchmarks (specific benchmarks not named) with claimed competitive results. Benchmark scores provide objective performance metrics for comparison with other models and validation of capability claims. However, comprehensive benchmark suite coverage is limited; only MMLU explicitly reported.","intents":["I need to evaluate whether Phi-3.5 Mini meets performance requirements for my use case","I want to compare Phi-3.5 Mini performance against other models on standard benchmarks","I need quantified evidence of reasoning and knowledge capabilities before deployment"],"best_for":["teams evaluating models for production deployment","researchers comparing model performance across architectures","organizations with specific performance requirements (e.g., 'must achieve >70% on MMLU')"],"limitations":["Only MMLU score (69%) explicitly reported; other reasoning benchmarks mentioned but not named or scored","No comprehensive benchmark suite; missing standard evaluations (HellaSwag, ARC, TruthfulQA, HumanEval, etc.)","No domain-specific benchmark results; performance on specialized tasks (code, math, medical) unknown","69% MMLU is substantially below larger models; unclear if performance is sufficient for specific applications","No error analysis or failure mode documentation; unclear where model struggles","Benchmark evaluation methodology not documented; potential for non-standard evaluation protocols"],"requires":["Understanding of MMLU benchmark and its limitations","Access to benchmark evaluation code and datasets for independent validation"],"input_types":["benchmark questions and evaluation datasets"],"output_types":["performance metrics (accuracy, F1, etc.)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-3-5-mini__cap_2","uri":"capability://planning.reasoning.reasoning.and.multi.step.problem.solving","name":"reasoning and multi-step problem solving","description":"Performs logical reasoning and multi-step problem decomposition through transformer-based chain-of-thought patterns learned during training on synthetic reasoning datasets. The model generates intermediate reasoning steps before final answers, enabling performance on benchmarks like MMLU (69%) and other reasoning tasks. The approach relies on learned patterns from training data rather than explicit reasoning algorithms, with performance constrained by the 3.8B parameter budget.","intents":["I need a lightweight model that can answer complex questions requiring multi-step reasoning","I want to deploy a model on edge devices that can solve math or logic problems","I need to extract reasoning traces from model outputs for interpretability or verification"],"best_for":["developers building lightweight reasoning agents for edge deployment","teams creating educational tools that explain problem-solving steps","organizations needing interpretable AI on resource-constrained hardware"],"limitations":["69% MMLU score is substantially below larger models (GPT-3.5: ~86%, GPT-4: ~92%), indicating reasoning capability ceiling","No documented performance on specialized reasoning benchmarks (ARC, HellaSwag, TruthfulQA)","Reasoning quality degrades on complex multi-step problems compared to 7B+ models","No explicit chain-of-thought prompting guidance or best practices documented","Hallucination rates and factuality on reasoning tasks unknown"],"requires":["Input formatted as question or problem statement","Sufficient context window to accommodate reasoning steps (uses portion of 128K context)"],"input_types":["text (questions, problems, prompts)"],"output_types":["text (answers with intermediate reasoning steps)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-3-5-mini__cap_3","uri":"capability://automation.workflow.edge.device.and.mobile.deployment.with.onnx.and.gguf.formats","name":"edge device and mobile deployment with onnx and gguf formats","description":"Deploys across heterogeneous hardware (iOS, Android, browsers, edge devices) through dual format support: ONNX (Open Neural Network Exchange) for cross-platform inference optimization and GGUF (quantized format) for efficient local inference. The model is pre-converted to these formats, eliminating custom conversion steps. ONNX enables hardware-specific optimizations (CPU, GPU, NPU) while GGUF provides quantized variants for memory-constrained devices. Both formats support offline inference without cloud connectivity.","intents":["I need to run a language model directly on iOS or Android without sending data to the cloud","I want to embed AI inference in a web browser without server-side processing","I need to deploy a model on edge devices with minimal memory footprint and no internet dependency"],"best_for":["mobile app developers building on-device AI features for iOS/Android","web developers creating browser-based AI applications","IoT and edge computing teams deploying models on resource-constrained hardware","organizations with privacy requirements preventing cloud data transmission"],"limitations":["ONNX format requires ONNX Runtime (additional dependency); specific version requirements unknown","GGUF quantization reduces model precision; exact quantization levels and accuracy impact not documented","Mobile inference latency not benchmarked; actual performance on iPhone/Android devices unknown","Browser deployment requires WebAssembly or WebGPU support; compatibility matrix not provided","No documented battery impact or thermal characteristics for mobile devices","Format conversion pipeline not exposed; users cannot create custom quantization variants"],"requires":["ONNX Runtime for ONNX format deployment (version not specified)","GGUF-compatible inference engine (llama.cpp, Ollama, or equivalent)","For iOS: minimum iOS version not specified; likely 13+ based on typical ML frameworks","For Android: minimum Android API level not specified; likely 21+ (Android 5.0+)","For browser: WebAssembly support (all modern browsers) or WebGPU (Chrome 113+, Firefox experimental)"],"input_types":["text (raw strings, tokenized input)"],"output_types":["text (generated completions, responses)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-3-5-mini__cap_4","uri":"capability://data.processing.analysis.synthetic.and.filtered.training.data.quality.optimization","name":"synthetic and filtered training data quality optimization","description":"Achieves competitive performance on reasoning and language understanding benchmarks through training on curated high-quality synthetic data and filtered web data rather than raw web crawl. The training pipeline emphasizes data quality over quantity, using synthetic data generation and filtering heuristics to remove low-quality, toxic, or irrelevant content. This approach trades dataset size for signal quality, enabling strong performance in a small parameter budget. Specific filtering criteria, synthetic data generation methods, and data composition percentages are not documented.","intents":["I need a model trained on high-quality data that avoids common web biases and toxicity","I want a small model that performs like larger models trained on raw web data","I need to understand data quality tradeoffs in model training for my own fine-tuning"],"best_for":["teams fine-tuning Phi-3.5 Mini on domain-specific data and wanting to apply similar quality principles","organizations concerned about model bias and toxicity from web-trained models","researchers studying data efficiency and quality-vs-quantity tradeoffs in language models"],"limitations":["Specific filtering criteria not documented; reproducibility of training approach unknown","Synthetic data generation methods not disclosed; potential for synthetic data artifacts unknown","Data composition percentages (synthetic vs. filtered web) not provided","No documented bias audit or toxicity evaluation results","Training data sources not fully disclosed; potential licensing or attribution issues unknown","No guidance on applying similar quality principles to custom fine-tuning data"],"requires":["Understanding of data quality concepts for fine-tuning applications","Access to model weights and training documentation (limited; primarily marketing materials available)"],"input_types":["training data (text documents, synthetic examples)"],"output_types":["trained model weights"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-3-5-mini__cap_5","uri":"capability://tool.use.integration.azure.model.as.a.service.maas.inference.api.with.pay.as.you.go.pricing","name":"azure model-as-a-service (maas) inference api with pay-as-you-go pricing","description":"Provides cloud-hosted inference through Azure's managed API endpoint with consumption-based billing (pay-per-token or pay-per-request). The model is deployed on Microsoft's infrastructure with automatic scaling, eliminating infrastructure management. Integration occurs through standard REST/HTTP APIs compatible with OpenAI API format or Azure-specific SDKs. Inference is processed server-side with results returned asynchronously or synchronously depending on endpoint configuration. No explicit rate limiting, quota, or SLA documentation provided.","intents":["I want to use Phi-3.5 Mini without managing servers or GPUs","I need a cost-effective cloud inference endpoint for low-to-medium volume applications","I want to prototype with Phi-3.5 Mini before committing to on-device deployment"],"best_for":["startups and small teams without infrastructure expertise","developers prototyping applications before optimizing for edge deployment","organizations with variable inference load that benefit from auto-scaling"],"limitations":["Pricing structure not documented in provided materials; exact cost per token/request unknown","Latency and throughput SLAs not specified; cloud inference slower than optimized local deployment","Data transmission to cloud may violate privacy requirements for sensitive applications","Dependency on Azure service availability; no on-premises fallback option","Rate limiting and quota policies not documented","No documented data retention or deletion policies for inference requests"],"requires":["Azure account with billing configured","API key for authentication (provided by Azure)","Network connectivity to Azure endpoints","Familiarity with REST APIs or Azure SDK (Python, C#, JavaScript available)"],"input_types":["text (via HTTP POST request with JSON payload)"],"output_types":["text (via HTTP response with JSON payload)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-3-5-mini__cap_6","uri":"capability://tool.use.integration.microsoft.foundry.free.tier.access.and.deployment","name":"microsoft foundry free tier access and deployment","description":"Provides free access to Phi-3.5 Mini through Microsoft Foundry platform for real-time deployment and experimentation. The Foundry platform abstracts infrastructure management, offering pre-configured deployment templates and monitoring dashboards. Free tier enables developers to test the model without Azure credits or payment setup. Specific free tier quotas, rate limits, and feature restrictions are not documented.","intents":["I want to try Phi-3.5 Mini without setting up Azure billing or payment methods","I need a quick way to deploy and test the model in a managed environment","I want to evaluate Phi-3.5 Mini before committing to production deployment"],"best_for":["individual developers and researchers experimenting with Phi-3.5 Mini","students and academic projects with no budget","teams evaluating the model before production rollout"],"limitations":["Free tier quotas and rate limits not documented; unclear if suitable for production use","Feature parity with paid Azure tier unknown; some features may be restricted","No SLA or uptime guarantees for free tier (typical for free services)","Unclear if free tier includes all deployment options (ONNX, GGUF, cloud inference)","Data retention and privacy policies for free tier not specified","Upgrade path and pricing for exceeding free tier limits unknown"],"requires":["Microsoft account (free to create)","Access to Microsoft Foundry platform (availability and geographic restrictions unknown)"],"input_types":["text (via Foundry UI or API)"],"output_types":["text (via Foundry UI or API)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-3-5-mini__cap_7","uri":"capability://tool.use.integration.hugging.face.model.hub.distribution.and.community.access","name":"hugging face model hub distribution and community access","description":"Distributes Phi-3.5 Mini through Hugging Face Model Hub with free download and community access. The model is available in multiple formats (ONNX, GGUF, and likely PyTorch/safetensors) for direct download without authentication. Community features include model cards with documentation, discussion forums, and integration with Hugging Face inference APIs. The model can be loaded directly into Hugging Face Transformers library or other compatible frameworks.","intents":["I want to download Phi-3.5 Mini and use it with Hugging Face Transformers or other open-source frameworks","I need to access community discussions and documentation about the model","I want to integrate Phi-3.5 Mini into my existing Hugging Face-based pipeline"],"best_for":["open-source developers using Hugging Face ecosystem","researchers and academics building on community models","teams already invested in Hugging Face infrastructure"],"limitations":["Download bandwidth may be limited during peak usage","No guaranteed uptime or SLA for Hugging Face hosting","Community documentation quality varies; official documentation may be limited","Integration with Hugging Face inference API may have rate limits or quotas","Model card may not include all technical details needed for production deployment"],"requires":["Hugging Face account (free to create)","Hugging Face Transformers library (Python 3.8+) or compatible inference framework","Internet connectivity for model download (3.8B parameters ≈ 7-15GB depending on format)"],"input_types":["text (via Transformers pipeline or custom inference code)"],"output_types":["text (generated completions)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-3-5-mini__cap_8","uri":"capability://safety.moderation.mit.licensed.open.source.model.with.commercial.use.rights","name":"mit-licensed open-source model with commercial use rights","description":"Released under MIT license, permitting unrestricted commercial use, modification, and redistribution with minimal attribution requirements. The license enables businesses to build proprietary products on top of Phi-3.5 Mini without licensing fees or legal restrictions. Model weights, architecture, and deployment artifacts are all covered by MIT license. No additional commercial licensing or enterprise agreements required.","intents":["I need to build a commercial product using an open-source language model without licensing concerns","I want to fine-tune and redistribute a model variant for my business without legal restrictions","I need to ensure my AI product has clear IP rights and no licensing ambiguity"],"best_for":["commercial software companies building AI features","startups and enterprises with IP-sensitive requirements","teams needing clear licensing for regulatory compliance"],"limitations":["MIT license requires attribution (copyright notice and license text in distributions)","No warranty or liability protection beyond standard MIT terms","Training data licensing not covered by model license; potential data-related restrictions unknown","No explicit patent grant; potential patent risks from training data or architecture unknown","No commercial support or SLA included with MIT license"],"requires":["Inclusion of MIT license text in product distribution","Attribution to Microsoft and original authors (standard MIT requirement)"],"input_types":["model weights, code, documentation"],"output_types":["derivative works, commercial products, modified models"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-3-5-mini__cap_9","uri":"capability://automation.workflow.efficient.inference.on.resource.constrained.hardware","name":"efficient inference on resource-constrained hardware","description":"Achieves competitive performance on language understanding and reasoning tasks with only 3.8B parameters, enabling inference on devices with limited compute and memory (mobile phones, edge devices, older laptops). The model is optimized through quantization formats (GGUF) and architecture design for low-latency inference without GPU acceleration. Inference speed and memory footprint vary by deployment format and hardware, but the small parameter count enables sub-second latency on modern mobile devices.","intents":["I need to run AI inference on a smartphone without cloud connectivity","I want to deploy a language model on IoT devices with limited RAM and CPU","I need to minimize latency and power consumption for real-time on-device AI"],"best_for":["mobile app developers building on-device AI features","IoT and embedded systems teams","organizations with strict latency requirements (sub-second response times)","applications requiring offline-first or privacy-first architecture"],"limitations":["Actual inference latency and memory usage not benchmarked; hardware-dependent performance unknown","Quantization (GGUF format) reduces precision; accuracy impact not documented","Performance on older or lower-end devices (e.g., mid-range Android phones) not characterized","Battery impact and thermal characteristics on mobile devices not documented","No explicit optimization for specific hardware (Apple Neural Engine, Qualcomm Hexagon, etc.)","Inference speed may degrade significantly with longer context (128K tokens) on resource-constrained devices"],"requires":["Device with minimum estimated 2-4GB RAM (exact requirement not specified)","CPU with support for quantized inference (most modern mobile CPUs)","Optional: GPU or NPU for accelerated inference (not required but beneficial)"],"input_types":["text (tokenized input)"],"output_types":["text (generated completions)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-3-5-mini__headline","uri":"capability://text.generation.language.compact.ai.language.model.with.long.context.window","name":"compact ai language model with long context window","description":"Phi-3.5 Mini is a compact AI language model featuring a unique 128K context window, optimized for edge devices and mobile applications, making it ideal for developers seeking efficient and powerful language processing capabilities.","intents":["best compact AI language model","AI model for mobile applications","high-performance language model for edge devices","AI model with long context window","best model for multilingual support"],"best_for":["edge devices","mobile applications"],"limitations":[],"requires":[],"input_types":["text"],"output_types":["text"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Input text tokenized to maximum 128K tokens using compatible tokenizer","Sufficient memory for model weights (3.8B parameters) plus KV cache for full context length","For mobile deployment: device with minimum 4GB RAM (estimated, not officially specified)","Input text in supported language (specific language list not provided)","Tokenizer compatible with multilingual vocabulary","Understanding of MMLU benchmark and its limitations","Access to benchmark evaluation code and datasets for independent validation","Input formatted as question or problem statement","Sufficient context window to accommodate reasoning steps (uses portion of 128K context)","ONNX Runtime for ONNX format deployment (version not specified)"],"failure_modes":["128K token limit is absolute maximum input size; exceeding this requires chunking or summarization","Actual usable context may be lower depending on deployment hardware (mobile devices may not efficiently use full 128K)","Long context processing increases latency compared to shorter contexts; exact latency scaling unknown","No documented performance degradation patterns at different context lengths","Specific supported languages not documented; language coverage unknown","No documented performance parity across languages; some languages may have degraded quality","No explicit cross-lingual transfer or zero-shot translation capability mentioned","Multilingual training may reduce per-language performance compared to language-specific models","Only MMLU score (69%) explicitly reported; other reasoning benchmarks mentioned but not named or scored","No comprehensive benchmark suite; missing standard evaluations (HellaSwag, ARC, TruthfulQA, HumanEval, etc.)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":null,"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=phi-3-5-mini","compare_url":"https://unfragile.ai/compare?artifact=phi-3-5-mini"}},"signature":"r206RNwr5H7dWP7g64/GSk89b2IsmZT7wjmw/RKtXO9Qvv8wvm0JDOuDYM+M///lUOGma5eNggRIa4KzM2CSDg==","signedAt":"2026-06-20T21:15:56.006Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/phi-3-5-mini","artifact":"https://unfragile.ai/phi-3-5-mini","verify":"https://unfragile.ai/api/v1/verify?slug=phi-3-5-mini","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"}}