{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"phi-4","slug":"phi-4","name":"Phi-4","type":"model","url":"https://azure.microsoft.com/en-us/products/phi","page_url":"https://unfragile.ai/phi-4","categories":["model-training","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"phi-4__cap_0","uri":"capability://planning.reasoning.high.efficiency.reasoning.via.data.quality.optimized.transformer","name":"high-efficiency reasoning via data-quality-optimized transformer","description":"Phi-4 achieves 84.8% MMLU and outperforms many 70B-parameter models through a 14B-parameter transformer architecture trained exclusively on carefully curated synthetic and filtered web data rather than raw internet scale. The model uses a data-quality-first training philosophy where dataset curation and filtering replaces parameter scaling, enabling strong reasoning performance on MATH, MMLU, and general reasoning benchmarks within a compact footprint suitable for resource-constrained inference.","intents":["Deploy a reasoning-capable LLM on edge devices or local hardware without 70B+ parameter overhead","Build reasoning-heavy applications (math solving, logic, multi-step inference) with minimal computational cost","Evaluate whether data quality optimization can replace model scaling for downstream task performance","Run inference at ultra-low latency in real-time guidance systems or autonomous agents"],"best_for":["Teams building edge AI and on-device reasoning systems with strict latency/power budgets","Developers prototyping reasoning-heavy applications before scaling to larger models","Organizations evaluating cost-per-inference tradeoffs between 14B and 70B+ models","Researchers studying data quality vs. model scale in LLM training"],"limitations":["16K token context window hard limit — unsuitable for long-document reasoning or multi-turn conversations exceeding context","Specific failure modes and hallucination characteristics undocumented — no published analysis of reasoning errors or edge cases","MATH and reasoning benchmark scores not quantified beyond 'strong performance' — exact performance gaps vs. 70B models unknown","Training data composition (synthetic vs. filtered web ratio, domain distribution) not publicly disclosed — reproducibility and bias analysis limited","No published ablation studies on data curation impact — unclear which data quality techniques drive performance gains"],"requires":["API key for Azure AI or Hugging Face inference (for cloud deployment)","GPU with sufficient VRAM for 14B parameter model (exact VRAM requirement undocumented; estimated 28-56GB for full precision, 7-14GB for 4-bit quantization)","Python 3.8+ or compatible runtime for local inference","16K token budget per inference call (context window constraint)"],"input_types":["text (natural language prompts, code, mathematical problems, reasoning tasks)"],"output_types":["text (generated responses, reasoning chains, code, mathematical solutions)"],"categories":["planning-reasoning","small-language-model"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-4__cap_1","uri":"capability://automation.workflow.multi.platform.inference.deployment.with.ultra.low.latency","name":"multi-platform inference deployment with ultra-low latency","description":"Phi-4 supports deployment across Azure AI Model-as-a-Service (MaaS) APIs, local on-device execution, and edge hardware through a unified model distribution strategy. The model is optimized for 'ultra-low latency' and 'blazing fast inference' via transformer architecture tuning and is available in multiple formats (GGUF, safetensors, ONNX availability inferred from Hugging Face distribution) enabling inference on CPUs, GPUs, and specialized edge accelerators without vendor lock-in.","intents":["Deploy Phi-4 to Azure cloud infrastructure with pay-as-you-go billing for variable workloads","Run Phi-4 locally on developer machines or on-premises servers for data privacy and latency control","Embed Phi-4 on edge devices (mobile, IoT, embedded systems) for real-time autonomous decision-making","Optimize inference latency for real-time guidance systems and interactive applications"],"best_for":["Teams requiring on-device or on-premises inference for data privacy compliance","Developers building latency-sensitive real-time applications (chatbots, autonomous agents, live guidance)","Organizations evaluating multi-cloud or hybrid deployment strategies","Edge AI teams deploying reasoning to resource-constrained hardware"],"limitations":["Specific latency benchmarks ('ultra-low,' 'blazing fast') not quantified — actual inference speed vs. competitors (Llama 2, Mistral) unknown","Quantization support and available model variants (FP32, FP16, 8-bit, 4-bit) not documented — unclear which formats optimize for which hardware","VRAM and CPU requirements for different deployment targets not specified — developers must estimate or empirically test","No published inference cost comparison between Azure MaaS and local deployment — cost-benefit analysis requires manual calculation","Edge deployment optimization techniques (distillation, pruning, quantization) not disclosed — unclear if model is pre-optimized or requires post-training"],"requires":["Azure subscription and API credentials for cloud deployment via MaaS","Python 3.8+ runtime for local inference with transformers, vLLM, or llama.cpp","GPU (NVIDIA, AMD) or CPU with sufficient memory (28-56GB FP32, 7-14GB 4-bit quantized estimated)","Hugging Face account or direct model download for local deployment","Edge hardware with sufficient compute (ARM processors, mobile GPUs, TPUs for on-device deployment)"],"input_types":["text (prompts, code, reasoning tasks)"],"output_types":["text (generated responses, streaming tokens supported via inference APIs)"],"categories":["automation-workflow","deployment-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-4__cap_2","uri":"capability://code.generation.editing.domain.specific.fine.tuning.for.customized.reasoning.tasks","name":"domain-specific fine-tuning for customized reasoning tasks","description":"Phi-4 supports domain-specific customization through fine-tuning on downstream tasks, allowing developers to adapt the base 14B model to specialized reasoning domains (e.g., medical diagnosis, financial analysis, code generation) without retraining from scratch. Fine-tuning leverages the model's strong reasoning foundation and 16K context window to efficiently learn domain-specific patterns with reduced data requirements compared to training larger models, enabling rapid iteration on domain adaptation.","intents":["Fine-tune Phi-4 on proprietary domain data (medical, legal, financial) to build specialized reasoning agents","Adapt Phi-4 for code generation or code reasoning tasks specific to internal codebases or frameworks","Create domain-specific versions of Phi-4 with reduced hallucination and improved accuracy on specialized benchmarks","Evaluate fine-tuning efficiency and data requirements for domain adaptation at 14B scale"],"best_for":["Teams with domain-specific datasets (100s to 1000s of examples) seeking rapid model customization","Organizations building vertical AI applications (healthcare, finance, legal) requiring specialized reasoning","Developers optimizing inference cost by fine-tuning smaller Phi-4 instead of using larger base models","Researchers studying transfer learning and domain adaptation in small language models"],"limitations":["Fine-tuning methodology and recommended hyperparameters not documented — developers must rely on standard transformer fine-tuning practices","No published guidance on data requirements, convergence time, or performance gains from fine-tuning — empirical testing required","Fine-tuning infrastructure (hardware, frameworks, cost) not specified — unclear if Azure provides managed fine-tuning or requires custom setup","No comparison of fine-tuned Phi-4 vs. fine-tuned larger models (Llama 2 70B) — unclear when fine-tuning Phi-4 is preferable","Catastrophic forgetting and base model performance degradation risks not addressed — no published mitigation strategies"],"requires":["Domain-specific training dataset (minimum 100-1000 examples, exact requirement unknown)","GPU with sufficient VRAM for fine-tuning (estimated 40-80GB for full fine-tuning, 16-32GB with LoRA/QLoRA)","Fine-tuning framework (Hugging Face Transformers, Azure ML, or custom training loop)","Python 3.8+ and PyTorch or TensorFlow","Evaluation metrics and validation dataset for domain-specific performance assessment"],"input_types":["text (domain-specific examples, instruction-response pairs, reasoning chains)"],"output_types":["fine-tuned model weights (in GGUF, safetensors, or PyTorch format)","text (domain-adapted reasoning responses)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-4__cap_3","uri":"capability://planning.reasoning.mathematical.reasoning.and.symbolic.problem.solving","name":"mathematical reasoning and symbolic problem-solving","description":"Phi-4 demonstrates strong performance on mathematical reasoning tasks (MATH benchmark) and symbolic problem-solving through transformer architecture trained on curated synthetic mathematical data and filtered web sources. The model handles multi-step mathematical reasoning, equation solving, and logical inference within the 16K context window, enabling applications requiring step-by-step mathematical derivation and proof generation.","intents":["Build AI tutoring systems that solve and explain mathematical problems step-by-step","Create symbolic reasoning agents for automated theorem proving or mathematical verification","Develop educational tools that generate mathematical explanations and problem solutions","Evaluate mathematical reasoning capability in small language models vs. larger alternatives"],"best_for":["EdTech companies building AI tutoring and homework assistance systems","Researchers studying mathematical reasoning in small language models","Teams building symbolic reasoning agents with constrained computational budgets","Developers creating domain-specific tools for STEM education and scientific computing"],"limitations":["Specific MATH benchmark score not disclosed — only 'strong performance' stated, exact accuracy unknown","No analysis of failure modes on complex multi-step problems — unclear where mathematical reasoning breaks down","No comparison with specialized math models (e.g., Minerva, MathGLM) or larger models (GPT-4) on mathematical benchmarks","Symbolic reasoning capability (formal logic, theorem proving) not explicitly confirmed — inferred from 'reasoning' claims","No published evaluation on domain-specific math (calculus, linear algebra, abstract algebra) — general MATH benchmark only"],"requires":["Text input with mathematical problem or reasoning task (within 16K token context)","API access via Azure AI or Hugging Face for inference","Optional: domain-specific fine-tuning data for specialized mathematical domains"],"input_types":["text (mathematical problems, equations, symbolic reasoning tasks, step-by-step prompts)"],"output_types":["text (mathematical solutions, step-by-step derivations, symbolic reasoning chains)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-4__cap_4","uri":"capability://text.generation.language.general.knowledge.and.multitask.language.understanding","name":"general knowledge and multitask language understanding","description":"Phi-4 achieves 84.8% accuracy on MMLU (Massive Multitask Language Understanding), a comprehensive benchmark spanning 57 diverse knowledge domains (science, history, law, medicine, etc.), demonstrating broad general knowledge and multitask reasoning capability. The model's performance on MMLU indicates strong transfer learning across domains and ability to handle knowledge-intensive tasks within the 16K context window, enabling general-purpose AI assistants and knowledge-based applications.","intents":["Build general-purpose AI assistants that answer questions across diverse knowledge domains","Create knowledge-based chatbots and Q&A systems with strong factual accuracy","Evaluate general knowledge and reasoning capability in small language models","Deploy knowledge-intensive applications (customer support, technical documentation, educational content) with minimal computational overhead"],"best_for":["Teams building general-purpose AI assistants with edge or on-device deployment requirements","Organizations evaluating knowledge-intensive applications with cost and latency constraints","Researchers studying multitask learning and transfer learning in small language models","Developers seeking alternatives to larger models (70B+) for knowledge-based tasks"],"limitations":["MMLU 84.8% score is strong but not state-of-the-art — GPT-4 and other large models achieve 86-92%+","No domain-specific breakdown of MMLU performance — unclear which knowledge domains are stronger or weaker","No evaluation on knowledge-intensive benchmarks beyond MMLU (e.g., TriviaQA, Natural Questions) — generalization to other knowledge tasks unknown","Hallucination and factual accuracy not quantified — no published analysis of false claims or confabulation rates","No comparison with other 14B models (Mistral 7B, Llama 2 13B) on MMLU — relative performance vs. similar-scale models unclear"],"requires":["Text input with knowledge-based questions or reasoning tasks (within 16K token context)","API access via Azure AI or Hugging Face for inference","Optional: domain-specific fine-tuning for specialized knowledge domains"],"input_types":["text (knowledge questions, reasoning tasks, multidomain queries)"],"output_types":["text (factual answers, explanations, reasoning chains)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-4__cap_5","uri":"capability://planning.reasoning.real.time.autonomous.system.guidance.and.decision.making","name":"real-time autonomous system guidance and decision-making","description":"Phi-4 is explicitly designed for 'real-time guidance and autonomous systems' through ultra-low latency inference and strong reasoning capability, enabling deployment in time-sensitive applications requiring immediate decision-making. The model's 14B-parameter size and optimized inference enable sub-second response times suitable for autonomous agents, robotics, real-time recommendation systems, and interactive guidance applications that cannot tolerate multi-second latencies of larger models.","intents":["Deploy reasoning-capable agents in autonomous systems (robotics, autonomous vehicles) requiring real-time decision-making","Build real-time recommendation and guidance systems with reasoning-based personalization","Create interactive AI assistants for live customer support or technical guidance with minimal latency","Develop edge-deployed autonomous agents for IoT and embedded systems with strict latency budgets"],"best_for":["Robotics and autonomous systems teams requiring on-device reasoning with <1s latency","Real-time recommendation and personalization platforms with strict SLA requirements","Edge AI teams deploying autonomous agents to resource-constrained hardware","Organizations building interactive AI assistants where latency directly impacts user experience"],"limitations":["Specific latency benchmarks not published — 'ultra-low latency' and 'blazing fast' are qualitative claims without quantified millisecond targets","No comparison of inference latency vs. competitors (Llama 2 7B, Mistral 7B, GPT-3.5) on standard hardware","Latency optimization techniques (batching, caching, quantization) not documented — unclear if latency claims assume specific deployment configurations","Real-time guidance use cases not detailed — unclear which autonomous system types are validated or recommended","No published SLA or latency guarantees for Azure MaaS deployment — cloud inference may not meet strict real-time requirements"],"requires":["Real-time inference infrastructure (GPU or optimized CPU for <1s response times)","Edge hardware with sufficient compute for on-device deployment (GPU, TPU, or high-performance CPU)","Integration framework for autonomous system control (ROS, custom agent framework)","Latency monitoring and optimization tools (profiling, quantization, batching)"],"input_types":["text (sensor data, user queries, system state descriptions, decision prompts)"],"output_types":["text (decisions, recommendations, guidance, reasoning chains)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-4__cap_6","uri":"capability://automation.workflow.mit.licensed.commercial.deployment.without.vendor.lock.in","name":"mit-licensed commercial deployment without vendor lock-in","description":"Phi-4 is distributed under the MIT license, explicitly permitting commercial use, redistribution, and modification without restrictions or attribution requirements beyond license inclusion. This licensing model enables developers to deploy Phi-4 in proprietary applications, create commercial derivatives, and avoid vendor lock-in by running the model locally or on any cloud provider without licensing fees or usage restrictions, contrasting with proprietary models (GPT-4, Claude) or restricted licenses (Llama 2 Community License).","intents":["Build commercial AI products and services using Phi-4 without licensing fees or usage restrictions","Deploy Phi-4 in proprietary applications with full IP ownership and no vendor lock-in","Create commercial derivatives or fine-tuned versions of Phi-4 for resale or internal use","Evaluate open-source LLM licensing and commercial viability of MIT-licensed models"],"best_for":["Startups and small teams building commercial AI products with minimal licensing overhead","Organizations seeking to avoid vendor lock-in and maintain deployment flexibility","Teams building proprietary AI applications requiring full IP ownership","Enterprises evaluating open-source LLM licensing for cost and compliance benefits"],"limitations":["MIT license provides no warranty or liability protection — commercial users assume all risk","No commercial support or SLA from Microsoft — support must come from community or custom contracts","No indemnification for IP claims — commercial users are responsible for ensuring model outputs don't infringe third-party IP","Training data composition not disclosed — unclear if model inadvertently memorizes copyrighted content","No guarantee of model stability or long-term maintenance — Microsoft could discontinue support or updates"],"requires":["MIT license compliance (include license text in distributions)","No API key or subscription required for local deployment","Optional: Azure subscription for cloud-hosted inference via MaaS"],"input_types":["model weights (GGUF, safetensors, PyTorch formats)"],"output_types":["deployed model (local, on-premises, or cloud-hosted)"],"categories":["automation-workflow","deployment-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-4__cap_7","uri":"capability://automation.workflow.efficient.inference.on.resource.constrained.hardware","name":"efficient inference on resource-constrained hardware","description":"Phi-4's 14B-parameter size enables efficient inference on consumer-grade GPUs, CPUs, and edge hardware (mobile, IoT, embedded systems) through reduced memory footprint and computational requirements compared to 70B+ models. The model supports quantization (inferred from Hugging Face distribution) and is optimized for inference speed, allowing deployment on hardware with 8-16GB VRAM (estimated for 4-bit quantization) or CPU-only systems without specialized accelerators, making reasoning-capable AI accessible on resource-constrained devices.","intents":["Deploy reasoning-capable AI on consumer GPUs (RTX 3060, RTX 4070) without high-end hardware","Run Phi-4 on CPU-only systems or older hardware for cost-effective inference","Embed Phi-4 on mobile devices, IoT hardware, and embedded systems for on-device reasoning","Optimize inference cost and hardware requirements for large-scale deployments"],"best_for":["Developers building AI applications on consumer-grade hardware or laptops","Teams deploying AI to resource-constrained edge devices (mobile, IoT, embedded)","Organizations optimizing inference cost and hardware requirements for scale","Researchers studying efficient inference and model compression techniques"],"limitations":["Exact VRAM requirements for different quantization levels not documented — developers must estimate or empirically test","CPU inference performance not benchmarked — unclear if CPU-only deployment is practical for real-time applications","Quantization impact on reasoning performance not published — unclear if 4-bit or 8-bit quantization degrades MMLU/MATH scores","No published comparison of inference speed on different hardware (RTX 3060, RTX 4070, M1/M2, Snapdragon) — optimization for specific hardware unknown","Mobile and embedded deployment guidance not provided — unclear which mobile platforms and frameworks are supported"],"requires":["GPU with 8-16GB VRAM (estimated for 4-bit quantization) or CPU with 32GB+ RAM for FP32 inference","Inference framework (llama.cpp, vLLM, Ollama, or transformers library)","Python 3.8+ or compatible runtime","Optional: quantization tools (llama.cpp, GPTQ, bitsandbytes) for memory optimization"],"input_types":["text (prompts, reasoning tasks)"],"output_types":["text (generated responses)"],"categories":["automation-workflow","deployment-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-4__cap_8","uri":"capability://automation.workflow.free.and.open.source.model.distribution.via.hugging.face.and.microsoft.foundry","name":"free and open-source model distribution via hugging face and microsoft foundry","description":"Phi-4 is freely available through Hugging Face Model Hub and Microsoft Foundry without authentication, API keys, or subscription requirements for download and local deployment. The model is distributed in multiple formats (GGUF, safetensors, PyTorch) enabling compatibility with diverse inference frameworks (llama.cpp, vLLM, transformers, Ollama) and deployment platforms, with no usage restrictions or rate limits for local inference, contrasting with proprietary cloud APIs requiring subscriptions and rate limiting.","intents":["Download and deploy Phi-4 locally without authentication or subscription","Integrate Phi-4 into open-source projects and frameworks without licensing friction","Evaluate Phi-4 performance and capabilities before committing to production deployment","Build reproducible AI systems using freely available, open-source model weights"],"best_for":["Open-source developers and researchers building AI projects without budget constraints","Teams evaluating Phi-4 before committing to production or commercial deployment","Organizations prioritizing reproducibility and avoiding proprietary model dependencies","Developers building on Hugging Face or integrating with open-source inference frameworks"],"limitations":["No official support or SLA from Microsoft — community support only","Model updates and maintenance not guaranteed — Microsoft could discontinue support","No commercial support contract available — production deployments lack vendor backing","Hugging Face distribution depends on third-party infrastructure — potential availability or bandwidth limitations","No guaranteed model stability or backward compatibility across versions"],"requires":["Hugging Face account (free) or direct download from Hugging Face Model Hub","Internet connection for initial model download (14B model ~28GB for FP32)","Storage for model weights (28GB FP32, 7GB 4-bit quantized)","Inference framework (llama.cpp, vLLM, transformers, Ollama)"],"input_types":["model weights (GGUF, safetensors, PyTorch formats)"],"output_types":["deployed model (local or on-premises)"],"categories":["automation-workflow","deployment-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-4__cap_9","uri":"capability://text.generation.language.16k.token.context.window.for.extended.reasoning.and.multi.turn.conversations","name":"16k token context window for extended reasoning and multi-turn conversations","description":"Phi-4 supports a 16,384-token context window enabling processing of extended documents, long reasoning chains, and multi-turn conversations within a single inference call. The 16K context allows developers to maintain conversation history, include large code snippets or documents, and perform reasoning over longer sequences without context truncation, balancing context length against the model's 14B-parameter efficiency for practical applications requiring extended context.","intents":["Build multi-turn conversational AI systems with full conversation history within context","Process and reason over long documents (research papers, legal contracts, code files) in single inference","Create reasoning chains and step-by-step problem-solving with extended intermediate steps","Implement retrieval-augmented generation (RAG) with larger retrieved context windows"],"best_for":["Teams building conversational AI with multi-turn dialogue and context retention","Document analysis and reasoning applications requiring extended context","RAG systems where larger retrieved context improves reasoning accuracy","Developers building reasoning-heavy applications with complex multi-step inference"],"limitations":["16K token limit is smaller than larger models (GPT-4 128K, Claude 200K) — long documents may require chunking or summarization","No published analysis of context utilization or attention patterns — unclear how effectively model uses full 16K context","No comparison of reasoning quality with shorter context windows — unclear if 16K provides meaningful improvement over 8K or 4K","Token counting and context management overhead not documented — developers must implement careful token budgeting","No guidance on optimal context window size for different task types — empirical optimization required"],"requires":["Text input within 16K token budget (approximately 12,000-16,000 words depending on tokenization)","Token counting implementation to track context usage and avoid exceeding 16K limit","Optional: context management and summarization for inputs exceeding 16K tokens"],"input_types":["text (documents, conversation history, reasoning chains, code)"],"output_types":["text (responses, reasoning chains, document analysis)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"phi-4__headline","uri":"capability://model.training.data.optimized.language.model","name":"data-optimized language model","description":"Microsoft's Phi-4 is a 14B parameter language model that achieves performance comparable to larger models through superior data quality, making it ideal for resource-constrained environments needing strong reasoning capabilities.","intents":["best data-optimized language model","language model for reasoning tasks","14B parameter model comparison","language model for resource-constrained deployments","best model for MMLU and MATH benchmarks"],"best_for":["resource-constrained deployments","strong reasoning tasks"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["API key for Azure AI or Hugging Face inference (for cloud deployment)","GPU with sufficient VRAM for 14B parameter model (exact VRAM requirement undocumented; estimated 28-56GB for full precision, 7-14GB for 4-bit quantization)","Python 3.8+ or compatible runtime for local inference","16K token budget per inference call (context window constraint)","Azure subscription and API credentials for cloud deployment via MaaS","Python 3.8+ runtime for local inference with transformers, vLLM, or llama.cpp","GPU (NVIDIA, AMD) or CPU with sufficient memory (28-56GB FP32, 7-14GB 4-bit quantized estimated)","Hugging Face account or direct model download for local deployment","Edge hardware with sufficient compute (ARM processors, mobile GPUs, TPUs for on-device deployment)","Domain-specific training dataset (minimum 100-1000 examples, exact requirement unknown)"],"failure_modes":["16K token context window hard limit — unsuitable for long-document reasoning or multi-turn conversations exceeding context","Specific failure modes and hallucination characteristics undocumented — no published analysis of reasoning errors or edge cases","MATH and reasoning benchmark scores not quantified beyond 'strong performance' — exact performance gaps vs. 70B models unknown","Training data composition (synthetic vs. filtered web ratio, domain distribution) not publicly disclosed — reproducibility and bias analysis limited","No published ablation studies on data curation impact — unclear which data quality techniques drive performance gains","Specific latency benchmarks ('ultra-low,' 'blazing fast') not quantified — actual inference speed vs. competitors (Llama 2, Mistral) unknown","Quantization support and available model variants (FP32, FP16, 8-bit, 4-bit) not documented — unclear which formats optimize for which hardware","VRAM and CPU requirements for different deployment targets not specified — developers must estimate or empirically test","No published inference cost comparison between Azure MaaS and local deployment — cost-benefit analysis requires manual calculation","Edge deployment optimization techniques (distillation, pruning, quantization) not disclosed — unclear if model is pre-optimized or requires post-training","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-4","compare_url":"https://unfragile.ai/compare?artifact=phi-4"}},"signature":"l29ujoNei1BpnQLqAzJIaUBGe0WlUnrttIBqx9/k6kSYUKyMF1onYVNolMH7c0mZml8FA7waX3+A/aRQ6y9xAg==","signedAt":"2026-06-21T18:41:23.959Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/phi-4","artifact":"https://unfragile.ai/phi-4","verify":"https://unfragile.ai/api/v1/verify?slug=phi-4","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"}}