{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-phi4","slug":"phi4","name":"Phi 4 (14B)","type":"model","url":"https://ollama.com/library/phi4","page_url":"https://unfragile.ai/phi4","categories":["text-writing"],"tags":["ollama","open-source","microsoft"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-phi4__cap_0","uri":"capability://text.generation.language.instruction.following.text.generation.with.supervised.fine.tuning","name":"instruction-following text generation with supervised fine-tuning","description":"Generates coherent, instruction-aligned text responses using a 14B-parameter transformer trained via supervised fine-tuning (SFT) on filtered synthetic and public domain datasets. The model processes English text input through a standard transformer decoder stack with 16K token context window, producing multi-turn conversational or task-specific outputs. Fine-tuning on curated instruction-response pairs ensures the model prioritizes explicit user directives over generic completions.","intents":["I need a small model that reliably follows my instructions without hallucinating off-topic content","I want to build a chatbot that understands task-specific prompts and responds accurately","I need text generation that works locally without sending data to external APIs"],"best_for":["solo developers building local LLM agents with strict data privacy requirements","teams deploying on memory-constrained infrastructure (edge devices, embedded systems)","researchers prototyping instruction-following behavior without large model overhead"],"limitations":["16K token context window limits multi-document reasoning and long conversation history","English-language primary training means degraded performance on non-English inputs","No explicit multi-modal support — text-only input/output","Instruction-following quality depends on prompt engineering; adversarial or out-of-distribution instructions may fail gracefully but unpredictably"],"requires":["Ollama runtime (any version supporting phi4:latest)","8GB+ system RAM for model loading (exact VRAM requirement undocumented)","Python 3.7+ or Node.js 14+ for SDK usage (optional; CLI works standalone)"],"input_types":["plain text","structured prompts with role/content format (chat API)","multi-turn conversation history"],"output_types":["plain text","structured JSON (via prompt engineering)","streaming text tokens"],"categories":["text-generation-language","instruction-following"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi4__cap_1","uri":"capability://planning.reasoning.reasoning.and.logic.task.execution","name":"reasoning and logic task execution","description":"Executes multi-step reasoning tasks by leveraging transformer attention mechanisms trained on synthetic reasoning datasets and academic Q&A materials. The model decomposes complex logical problems into intermediate steps, maintaining coherence across the 16K token context. This capability is optimized through fine-tuning on reasoning-heavy datasets, enabling chain-of-thought style outputs without explicit prompting.","intents":["I need to solve math problems, logic puzzles, or multi-step reasoning tasks locally","I want a model that can break down complex questions into intermediate reasoning steps","I need to verify logical consistency in generated responses without external verification tools"],"best_for":["educational technology platforms requiring local reasoning inference","research teams benchmarking small-model reasoning capabilities","developers building decision-support systems with offline-first requirements"],"limitations":["Reasoning performance not quantified in public benchmarks — claims 'state-of-the-art' but specific accuracy metrics on reasoning tasks (MATH, GSM8K, ARC) are undocumented","Context window of 16K tokens constrains multi-step reasoning chains; very complex problems may exceed available context","No explicit symbolic reasoning or formal logic support — relies on learned patterns rather than rule-based inference","Reasoning quality degrades on out-of-distribution problem types not seen during training"],"requires":["Ollama runtime with phi4 model loaded","Structured prompts that explicitly request step-by-step reasoning (e.g., 'Think step by step')","8GB+ system RAM"],"input_types":["natural language problem statements","mathematical expressions","logical puzzles in text form"],"output_types":["step-by-step reasoning traces","intermediate conclusions","final answers with justification"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi4__cap_10","uri":"capability://memory.knowledge.16k.token.context.window.with.fixed.size.attention","name":"16k token context window with fixed-size attention","description":"Processes input and generates output within a fixed 16,384-token context window using standard transformer attention mechanisms. The context window is a hard limit — inputs exceeding 16K tokens are truncated or rejected. Within this window, the model attends to all tokens with full attention, enabling coherent reasoning across the entire context but with quadratic memory complexity that limits window size.","intents":["I need to process documents or conversations up to ~12K tokens (accounting for output generation)","I want to understand the trade-off between context length and inference speed/memory","I need to implement context management strategies (summarization, retrieval) for longer documents"],"best_for":["applications processing single documents or short-to-medium conversations (up to 5-10 turns)","teams building RAG systems where context is pre-retrieved and limited to relevant chunks","developers optimizing for inference speed and memory efficiency over context length"],"limitations":["16K token limit is insufficient for full-document processing of books, long research papers, or extended conversations","No sliding window or sparse attention optimizations documented — full quadratic attention means context window cannot be extended without significant memory overhead","Token counting must be managed by the client application — no built-in token budgeting or automatic truncation","Older tokens in long contexts receive less attention due to position encoding decay, reducing recall of early context","No explicit support for hierarchical or multi-document reasoning within a single inference call"],"requires":["Token counting library to manage context window usage","Application-level logic to truncate or summarize inputs exceeding 16K tokens","Understanding of transformer attention mechanics and position encoding"],"input_types":["text inputs up to 16K tokens","conversation history (all messages combined must fit within 16K)"],"output_types":["generated text up to remaining context window (typically 4-8K tokens for output)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi4__cap_11","uri":"capability://text.generation.language.english.language.primary.optimization.with.limited.multilingual.support","name":"english-language primary optimization with limited multilingual support","description":"Phi 4 is trained primarily on English-language data (synthetic datasets, public domain English websites, English academic materials) and optimized for English instruction-following and reasoning. The model has not been explicitly fine-tuned for other languages, though it may produce limited output in other languages due to exposure during pre-training. Performance degrades significantly on non-English inputs.","intents":["I need a model optimized for English-language applications","I want to understand the language limitations before deploying Phi 4 in multilingual contexts","I need to decide whether to use Phi 4 or a multilingual model for my use case"],"best_for":["English-only applications (US, UK, Australian markets)","teams building English-language chatbots, content generation, or reasoning systems","developers who need to understand language limitations for compliance or user support"],"limitations":["Non-English language performance is undocumented — no benchmarks for French, Spanish, Chinese, etc.","Code-switching (mixing English and other languages) may confuse the model","Multilingual prompts (e.g., 'Respond in Spanish') may be ignored or produce English output","No explicit support for non-Latin scripts (Arabic, Chinese, Cyrillic) — tokenization may be inefficient","Translation quality is unknown — model may produce poor translations or refuse translation requests"],"requires":["English-language inputs for optimal performance","Acceptance that non-English use cases will have degraded quality"],"input_types":["English text (primary)","other languages (not recommended, performance not guaranteed)"],"output_types":["English text (primary)","other languages (limited, quality not guaranteed)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi4__cap_2","uri":"capability://automation.workflow.local.inference.with.streaming.token.output","name":"local inference with streaming token output","description":"Executes model inference entirely on local hardware via Ollama runtime, streaming generated tokens in real-time to the client without round-trip latency to remote servers. The model is loaded into system memory once and reused across multiple inference requests, with streaming implemented via chunked HTTP responses or SDK callbacks. This architecture keeps all data local and enables sub-100ms time-to-first-token on typical consumer hardware.","intents":["I need to run inference without sending data to external APIs or cloud services","I want real-time token streaming for responsive user interfaces","I need to deploy on air-gapped or offline systems without internet connectivity"],"best_for":["enterprises with strict data residency requirements (healthcare, finance, government)","developers building real-time chat interfaces requiring sub-second response latency","teams deploying on edge devices, laptops, or on-premises servers"],"limitations":["Inference speed depends entirely on local hardware — no GPU acceleration documented, CPU-only inference on typical laptops may produce 5-20 tokens/second","Model occupies 9.1GB disk space and requires 8GB+ RAM continuously while running","No built-in load balancing or multi-GPU support documented — single-instance deployment only","Streaming requires persistent connection; network interruptions during inference may truncate output"],"requires":["Ollama runtime installed and running (any recent version)","9.1GB free disk space for model download and storage","8GB+ system RAM available","Optional: NVIDIA/AMD GPU with CUDA/ROCm support for accelerated inference (not documented as requirement but improves performance)"],"input_types":["HTTP POST requests to /api/chat endpoint","CLI commands via `ollama run phi4`","SDK method calls (Python `ollama.chat()`, JavaScript `ollama.chat()`)"],"output_types":["streaming text tokens via chunked HTTP response","complete response object with token count metadata","SDK callbacks/promises for token-by-token processing"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi4__cap_3","uri":"capability://memory.knowledge.multi.turn.conversation.state.management","name":"multi-turn conversation state management","description":"Maintains conversation context across multiple turns by accepting message history in role/content format (user/assistant/system roles) and processing the full conversation history within the 16K token context window. The model uses standard transformer attention to weight recent messages more heavily than older ones, enabling coherent multi-turn dialogue without explicit state persistence. Conversation state is ephemeral — stored only in memory during the session.","intents":["I need to build a chatbot that remembers previous messages in a conversation","I want to maintain context across multiple user queries without re-sending the full history each time","I need to implement system prompts that guide the model's behavior across an entire conversation"],"best_for":["developers building conversational AI interfaces (chat UIs, voice assistants)","teams implementing customer support chatbots with multi-turn interactions","researchers studying dialogue coherence and context retention in smaller models"],"limitations":["16K token context window limits conversation length — typical conversations with 4K tokens of history leave only 12K for new input and output, constraining multi-turn depth","No explicit conversation persistence — state is lost when the Ollama process restarts; requires external database for durable conversation history","Token counting for conversation history must be managed by the client application — no built-in token budgeting or automatic history truncation","Older messages in long conversations receive less attention due to transformer position encoding, degrading recall of early context"],"requires":["Ollama runtime with phi4 model loaded","Client application to format messages as role/content objects","External storage (database, file system) if conversation persistence is required","Token counting library (e.g., tiktoken) to manage context window usage"],"input_types":["message array with role (user/assistant/system) and content fields","optional system prompt to set conversation tone/behavior"],"output_types":["assistant message response","metadata including token count for the full conversation"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi4__cap_4","uri":"capability://automation.workflow.cloud.hosted.inference.with.usage.based.pricing","name":"cloud-hosted inference with usage-based pricing","description":"Provides remote inference via Ollama Cloud, a managed service that hosts the Phi 4 model on Ollama's infrastructure with pay-as-you-go pricing. Requests are routed to geographically distributed servers (primarily US, with fallback to Europe and Singapore), and billing is based on tokens processed. Three pricing tiers offer different concurrency limits and usage quotas, enabling cost-scaling from hobby projects to production workloads.","intents":["I want to use Phi 4 without managing local infrastructure or GPU hardware","I need scalable inference that automatically handles traffic spikes","I want to prototype quickly without downloading and configuring Ollama locally"],"best_for":["startups and solo developers prototyping without upfront infrastructure investment","teams with variable inference load that don't justify dedicated GPU hardware","applications requiring geographic redundancy and automatic failover"],"limitations":["Free tier limited to 1 concurrent model and 'light usage' (exact token/day limit undocumented)","Pro tier ($20/month) provides 50x more usage than free tier but exact quota is undocumented — requires monitoring to avoid overage charges","Network latency to Ollama Cloud servers (100-300ms typical) adds to inference time compared to local execution","Data is transmitted to Ollama's infrastructure; not suitable for applications with strict data residency requirements","Pricing model is opaque — no published per-token rates, making cost prediction difficult for production workloads"],"requires":["Ollama Cloud account (free signup)","API key for authentication","Internet connectivity","Optional: Ollama CLI configured with cloud credentials, or direct HTTP API calls"],"input_types":["same message format as local inference (role/content chat messages)"],"output_types":["same streaming or complete response format as local inference"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi4__cap_5","uri":"capability://tool.use.integration.cross.platform.sdk.integration.python.and.javascript","name":"cross-platform sdk integration (python and javascript)","description":"Provides native SDK bindings for Python and JavaScript that abstract Ollama's REST API, enabling developers to integrate Phi 4 inference into applications without managing HTTP requests directly. The SDKs expose a unified `chat()` method that accepts message arrays and returns responses as objects or async iterables, with automatic serialization and error handling. Both SDKs support streaming responses via callbacks or async generators.","intents":["I want to integrate Phi 4 into my Python application without writing HTTP boilerplate","I need to build a Node.js/TypeScript application that calls Phi 4 with type safety","I want to stream responses in my application without managing chunked HTTP responses manually"],"best_for":["Python developers building data science pipelines, notebooks, or backend services","JavaScript/TypeScript developers building web applications or Node.js servers","teams using both Python and JavaScript who want consistent API patterns across codebases"],"limitations":["SDKs are thin wrappers around REST API — no built-in caching, retry logic, or circuit breakers","No type definitions for Python SDK (dynamic typing only); JavaScript SDK type safety depends on TypeScript version","Streaming implementation differs between Python (callbacks) and JavaScript (async iterables) — not fully consistent","SDKs require Ollama runtime to be running locally or accessible via network — no built-in fallback to cloud inference"],"requires":["Python 3.7+ (for Python SDK) or Node.js 14+ (for JavaScript SDK)","Ollama runtime running locally or accessible via network URL","SDK installation: `pip install ollama` or `npm install ollama`"],"input_types":["message objects with role and content fields","optional model parameter (defaults to 'phi4')"],"output_types":["response object with message content and metadata","streaming token iterables (async generators in JS, callbacks in Python)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi4__cap_6","uri":"capability://automation.workflow.cli.based.inference.without.sdk.dependencies","name":"cli-based inference without sdk dependencies","description":"Enables inference via command-line interface (`ollama run phi4`) without requiring any SDK installation or programming. The CLI accepts prompts as arguments or stdin, streams responses to stdout, and supports interactive multi-turn conversations in a REPL-like interface. This capability is implemented as a thin wrapper around the local inference engine, making it suitable for shell scripts, automation, and quick prototyping.","intents":["I want to test Phi 4 quickly without writing code","I need to integrate Phi 4 into shell scripts or Unix pipelines","I want to use Phi 4 in a terminal-based chat interface for interactive exploration"],"best_for":["researchers and data scientists prototyping ideas in notebooks or terminal environments","DevOps engineers integrating Phi 4 into CI/CD pipelines or automation scripts","non-technical users exploring LLM capabilities without programming knowledge"],"limitations":["CLI interface is stateless — each invocation is a separate inference request; no built-in conversation history across invocations","No structured output format (JSON, CSV) — responses are plain text only, requiring parsing for programmatic use","Interactive REPL mode doesn't support system prompts or fine-grained control over model parameters","Error handling is minimal — network or model errors produce cryptic messages"],"requires":["Ollama runtime installed and running","Bash, zsh, or other shell (any POSIX-compatible shell)","No additional dependencies beyond Ollama"],"input_types":["command-line arguments: `ollama run phi4 'your prompt here'`","stdin piping: `echo 'prompt' | ollama run phi4`","interactive REPL input (multi-line prompts)"],"output_types":["plain text streamed to stdout","exit code indicating success/failure"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi4__cap_7","uri":"capability://tool.use.integration.rest.api.inference.with.standard.http.semantics","name":"rest api inference with standard http semantics","description":"Exposes Phi 4 inference via a REST API endpoint (`POST /api/chat`) that accepts JSON-formatted message arrays and returns responses as JSON objects. The API supports both streaming (chunked HTTP responses) and non-streaming modes, with standard HTTP status codes and error responses. This capability enables integration with any HTTP client library or tool (curl, Postman, etc.) without language-specific SDKs.","intents":["I need to call Phi 4 from a language or environment not supported by the official SDKs","I want to integrate Phi 4 into a microservices architecture via standard HTTP","I need to debug inference requests using standard HTTP tools like curl or Postman"],"best_for":["polyglot teams using languages beyond Python/JavaScript (Go, Rust, Java, etc.)","microservices architectures where HTTP is the standard integration pattern","developers debugging or testing inference behavior with HTTP tools"],"limitations":["No built-in authentication or authorization — API is accessible to any client with network access to Ollama (requires firewall/network segmentation for security)","Streaming responses use chunked transfer encoding, which some HTTP clients or proxies may not handle correctly","No rate limiting or quota enforcement at the API level — requires external API gateway for production use","Request/response format is tightly coupled to Ollama's schema — no versioning or backward compatibility guarantees"],"requires":["Ollama runtime running with API server enabled (default: localhost:11434)","HTTP client library (curl, requests, fetch, etc.)","Network access to Ollama API endpoint"],"input_types":["JSON POST body with 'model' and 'messages' fields","optional 'stream' boolean to enable streaming responses"],"output_types":["JSON response object with 'message' field containing assistant response","streaming: newline-delimited JSON chunks (NDJSON format)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi4__cap_8","uri":"capability://planning.reasoning.synthetic.dataset.based.training.with.preference.optimization","name":"synthetic dataset-based training with preference optimization","description":"Phi 4 was trained using a blend of synthetic datasets (generated via automated processes), filtered public domain web data, and acquired academic materials, then fine-tuned with Direct Preference Optimization (DPO) to align outputs with human preferences. This training approach avoids reliance on large-scale human annotation while maintaining instruction-following quality. The synthetic data generation process is not publicly documented, but the resulting model exhibits strong performance on instruction-following and reasoning tasks.","intents":["I want to understand how small models can achieve instruction-following quality without massive human annotation","I need to evaluate whether Phi 4's training approach (synthetic + DPO) is suitable for my use case","I want to replicate Phi 4's training methodology for my own domain-specific model"],"best_for":["researchers studying data efficiency in language model training","teams building domain-specific models with limited human annotation budgets","organizations evaluating whether synthetic data can replace human-labeled datasets"],"limitations":["Synthetic data generation methodology is proprietary and undocumented — cannot be replicated without access to Microsoft's data generation pipeline","No public benchmark comparing Phi 4's performance to models trained with pure human annotation — unclear if DPO + synthetic data matches human-annotated quality","DPO training requires preference pairs (better/worse responses), which are expensive to generate even synthetically — exact cost/effort not disclosed","Generalization to out-of-distribution tasks is unknown — synthetic training data may not cover edge cases or adversarial inputs"],"requires":["Understanding of DPO (Direct Preference Optimization) training methodology","Access to synthetic data generation tools (not provided by Microsoft)","Computational resources for fine-tuning (not specified)"],"input_types":["synthetic instruction-response pairs","preference pairs for DPO training"],"output_types":["fine-tuned model weights","evaluation metrics on instruction-following benchmarks"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-phi4__cap_9","uri":"capability://safety.moderation.safety.aligned.instruction.adherence.with.dpo.enforcement","name":"safety-aligned instruction adherence with dpo enforcement","description":"Implements safety constraints and instruction adherence through Direct Preference Optimization (DPO) fine-tuning, which explicitly trains the model to prefer safe, instruction-aligned responses over unsafe or off-topic ones. The DPO stage uses preference pairs where safe/aligned responses are marked as preferred, enabling the model to learn safety constraints without explicit rule-based filtering. This approach is integrated into the model weights rather than applied as post-hoc filtering.","intents":["I need a model that refuses harmful requests without explicit content filters","I want instruction-following that respects safety constraints learned during training","I need to deploy a model with built-in safety alignment rather than relying on external guardrails"],"best_for":["teams deploying models in high-risk domains (healthcare, finance, legal) where safety is critical","developers building consumer-facing applications requiring robust refusal behavior","researchers studying alignment and safety in smaller models"],"limitations":["Safety alignment methodology is not publicly detailed — no documentation of which harmful behaviors are covered or how preferences were defined","No published safety evaluation results — unclear how Phi 4's safety compares to larger models (GPT-4) or other small models (Llama 2)","DPO-based alignment can be brittle — adversarial prompts or jailbreak attempts may still succeed, especially on edge cases not in training data","Safety constraints are learned patterns, not hard rules — model may still generate unsafe content if prompted creatively","No transparency into failure modes — developers cannot audit which requests the model refuses or why"],"requires":["Understanding that safety is probabilistic, not guaranteed","External monitoring and evaluation of model outputs in production","Fallback mechanisms for handling refusals (e.g., escalation to human review)"],"input_types":["any text input (model will refuse unsafe requests)"],"output_types":["safe, instruction-aligned responses","refusal messages for harmful requests"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Ollama runtime (any version supporting phi4:latest)","8GB+ system RAM for model loading (exact VRAM requirement undocumented)","Python 3.7+ or Node.js 14+ for SDK usage (optional; CLI works standalone)","Ollama runtime with phi4 model loaded","Structured prompts that explicitly request step-by-step reasoning (e.g., 'Think step by step')","8GB+ system RAM","Token counting library to manage context window usage","Application-level logic to truncate or summarize inputs exceeding 16K tokens","Understanding of transformer attention mechanics and position encoding","English-language inputs for optimal performance"],"failure_modes":["16K token context window limits multi-document reasoning and long conversation history","English-language primary training means degraded performance on non-English inputs","No explicit multi-modal support — text-only input/output","Instruction-following quality depends on prompt engineering; adversarial or out-of-distribution instructions may fail gracefully but unpredictably","Reasoning performance not quantified in public benchmarks — claims 'state-of-the-art' but specific accuracy metrics on reasoning tasks (MATH, GSM8K, ARC) are undocumented","Context window of 16K tokens constrains multi-step reasoning chains; very complex problems may exceed available context","No explicit symbolic reasoning or formal logic support — relies on learned patterns rather than rule-based inference","Reasoning quality degrades on out-of-distribution problem types not seen during training","16K token limit is insufficient for full-document processing of books, long research papers, or extended conversations","No sliding window or sparse attention optimizations documented — full quadratic attention means context window cannot be extended without significant memory overhead","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=phi4","compare_url":"https://unfragile.ai/compare?artifact=phi4"}},"signature":"Zk+UjSxW2pxZkydpUlEEcNtJ8EmD7P8soy6WaKZrmfuEPG9h4Yb0YLJ2hDVuG0JJIVmYsM+4a3om3BU59ibABA==","signedAt":"2026-06-22T15:07:13.484Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/phi4","artifact":"https://unfragile.ai/phi4","verify":"https://unfragile.ai/api/v1/verify?slug=phi4","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"}}