{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-wizardlm2","slug":"wizardlm2","name":"WizardLM 2 (7B, 8x22B)","type":"model","url":"https://ollama.com/library/wizardlm2","page_url":"https://unfragile.ai/wizardlm2","categories":["text-writing"],"tags":["ollama","open-source","wizardlm"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-wizardlm2__cap_0","uri":"capability://text.generation.language.multi.turn.conversational.chat.with.instruction.following","name":"multi-turn conversational chat with instruction-following","description":"Processes multi-turn chat interactions using a standard role/content message format (user/assistant/system roles) with transformer-based attention mechanisms optimized for instruction-following. Maintains conversation context across turns through full context window utilization (32K tokens for 7B, 64K for 8x22B variants), enabling coherent multi-step dialogues without explicit memory management. Implements instruction-tuning via supervised fine-tuning on complex reasoning tasks, allowing the model to follow nuanced user directives and adapt responses based on conversational context.","intents":["Build a chatbot that understands complex multi-step user requests and maintains coherent conversation state","Deploy an interactive assistant that can follow detailed instructions and adapt responses based on conversation history","Create a conversational interface for domain-specific applications requiring instruction-aware responses"],"best_for":["Solo developers building local chatbot prototypes without cloud dependencies","Teams deploying conversational AI on-premises with strict data residency requirements","Builders prototyping agentic systems that require instruction-following as a foundation"],"limitations":["Context window limits conversation length: 32K tokens (7B) or 64K tokens (8x22B) — approximately 8K-16K words before truncation","No explicit memory persistence across sessions — conversation history must be managed by the application layer","Instruction-following quality unverified against public benchmarks; claims based on internal Microsoft evaluation only","No built-in conversation branching, rollback, or alternative response generation"],"requires":["Ollama runtime (local) or Ollama Pro/Max subscription (cloud)","For 7B: 6-8GB VRAM (estimated for Q4 quantization)","For 8x22B: 48GB+ VRAM and high-end GPU (A100/H100 class)","Python 3.7+ or Node.js 14+ for SDK usage"],"input_types":["text (chat messages with role/content structure)"],"output_types":["text (streaming or buffered completion)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-wizardlm2__cap_1","uri":"capability://planning.reasoning.complex.reasoning.and.multi.step.problem.decomposition","name":"complex reasoning and multi-step problem decomposition","description":"Executes chain-of-thought reasoning patterns through transformer attention mechanisms trained on complex reasoning tasks, enabling step-by-step problem solving without explicit prompt engineering. The model decomposes multi-step problems by generating intermediate reasoning tokens that guide subsequent token generation, effectively implementing implicit planning through learned reasoning patterns. Supports both explicit reasoning traces (where the model outputs its reasoning steps) and implicit reasoning (where intermediate computations influence final answers), leveraging the instruction-tuned architecture to recognize when problems require decomposition.","intents":["Solve multi-step math problems, logic puzzles, or algorithmic challenges that require intermediate reasoning","Generate detailed explanations for complex concepts by having the model articulate reasoning steps","Build reasoning-intensive agents that can tackle problems requiring planning and backtracking"],"best_for":["Developers building educational tools or tutoring systems requiring step-by-step explanations","Researchers prototyping reasoning-focused LLM applications with local compute","Teams building autonomous agents that need to decompose complex tasks into subtasks"],"limitations":["Reasoning quality unverified against standard benchmarks (GSM8K, MATH, ARC) — only internal Microsoft evaluation cited","No explicit reasoning verification or constraint satisfaction — model can generate plausible-sounding but incorrect reasoning chains","Reasoning overhead increases token generation cost: complex problems may require 2-3x more tokens than direct answers","No built-in mechanism to validate or backtrack from incorrect reasoning branches"],"requires":["Ollama runtime with sufficient VRAM for model variant (7B: 6-8GB, 8x22B: 48GB+)","Prompts that explicitly request reasoning (e.g., 'Think step by step') for optimal results","Application-level handling of reasoning token overhead and latency"],"input_types":["text (problem statements, questions, or prompts requesting reasoning)"],"output_types":["text (reasoning traces with intermediate steps, final answers)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-wizardlm2__cap_10","uri":"capability://text.generation.language.open.source.model.distribution.with.community.transparency","name":"open-source model distribution with community transparency","description":"Distributes model weights as open-source artifacts through Ollama's package manager, enabling community inspection, fine-tuning, and redistribution. The model is available under an unspecified open-source license (license terms not documented), with 1.1M downloads on Ollama indicating community adoption. Open-source distribution enables researchers and developers to audit model behavior, implement custom quantizations, and fine-tune for domain-specific tasks without proprietary restrictions.","intents":["Audit model behavior and training approach for bias, safety, or alignment issues","Fine-tune the model for domain-specific tasks (e.g., medical, legal, technical domains)","Implement custom quantizations or optimizations for specific hardware"],"best_for":["Researchers studying LLM behavior, bias, and alignment","Teams fine-tuning models for domain-specific applications","Developers implementing custom optimizations or quantizations"],"limitations":["License terms not documented — unclear if commercial use is permitted, what attribution is required, or what derivative works are allowed","Training data composition unknown — cannot audit training data for bias, copyright issues, or problematic content","Fine-tuning guidance not provided — no documentation on fine-tuning procedures, data requirements, or hyperparameters","Community support informal — no official fine-tuning support or community guidelines documented"],"requires":["Understanding of LLM fine-tuning (for fine-tuning use cases)","Quantization tools like llama.cpp or GPTQ (for custom quantizations)","Sufficient compute for fine-tuning (varies by task and dataset size)"],"input_types":["model weights (GGUF format for local inference, or native weights for fine-tuning)"],"output_types":["fine-tuned model weights, custom quantizations, or audit reports"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-wizardlm2__cap_2","uri":"capability://tool.use.integration.tool.calling.and.function.invocation.for.agentic.workflows","name":"tool calling and function invocation for agentic workflows","description":"Supports structured function calling through schema-based tool definitions that the model can invoke to extend its capabilities beyond text generation. The model receives a schema describing available tools (functions, parameters, return types) and learns to recognize when a tool invocation is appropriate, generating structured function calls that applications can execute and feed results back into the conversation. This enables agentic workflows where the model acts as a reasoning engine that orchestrates external tools (APIs, databases, code execution) to solve problems iteratively.","intents":["Build autonomous agents that can call APIs, query databases, or execute code to gather information and solve problems","Create multi-step workflows where the model decides which tools to invoke based on task requirements","Implement retrieval-augmented generation (RAG) systems where the model decides when to search for external information"],"best_for":["Developers building agentic systems that require tool orchestration without external LLM APIs","Teams implementing local autonomous agents with strict data privacy requirements","Builders prototyping multi-step workflows combining reasoning and tool use"],"limitations":["Tool calling only supported on Ollama cloud models (Pro/Max tiers) — local inference via Ollama CLI does not support structured tool calling","No built-in tool execution or validation — application must implement tool invocation, error handling, and result formatting","Tool schema complexity limits: no documentation on maximum number of tools, parameter complexity, or nested schema support","No native support for parallel tool invocation or tool result aggregation — sequential tool calling only"],"requires":["Ollama Pro ($20/mo) or Max ($100/mo) subscription for cloud-based tool calling","Application-level tool registry and execution framework","Structured schema definitions for each tool (JSON Schema or equivalent)","Error handling and retry logic for failed tool invocations"],"input_types":["text (chat messages with tool schemas embedded in system prompts or via API parameters)"],"output_types":["text (tool invocation requests in structured format, typically JSON)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-wizardlm2__cap_3","uri":"capability://automation.workflow.local.inference.with.quantized.model.distribution","name":"local inference with quantized model distribution","description":"Distributes pre-quantized GGUF-format models through Ollama's package manager, enabling single-command local inference without manual quantization or compilation. Models are downloaded as compressed GGUF artifacts (4.1GB for 7B, 80GB for 8x22B) and loaded into memory for inference via Ollama's C++ runtime, which handles GPU acceleration (CUDA/Metal) and CPU fallback automatically. This approach eliminates cloud API dependencies and latency, enabling private inference with full model control and no data transmission to external servers.","intents":["Deploy LLM inference on-premises or in air-gapped environments without cloud connectivity","Run inference locally to avoid cloud API costs and latency for high-volume applications","Maintain full data privacy by keeping model and data on local hardware without external transmission"],"best_for":["Enterprise teams with strict data residency or compliance requirements (HIPAA, GDPR, etc.)","Developers building cost-sensitive applications with high inference volume","Researchers prototyping LLM applications without cloud infrastructure"],"limitations":["Quantization level not specified in documentation — unclear if Q4, Q5, or Q8 quantization used, affecting accuracy vs. VRAM tradeoff","VRAM requirements estimated but not officially specified: 7B requires ~6-8GB, 8x22B requires ~48GB+ (exact requirements unknown)","GPU acceleration limited to CUDA (NVIDIA) and Metal (Apple Silicon) — no official ROCm (AMD) or Intel Arc support documented","CPU-only inference extremely slow (minutes per token) — GPU required for practical use","No built-in model versioning or rollback — updating models overwrites previous versions"],"requires":["Ollama runtime (free, open-source) installed on target hardware","For GPU acceleration: NVIDIA GPU with CUDA 11.8+ or Apple Silicon Mac","Sufficient disk space: 4.1GB (7B) or 80GB (8x22B) plus OS/runtime overhead","For 7B: 6-8GB VRAM minimum; for 8x22B: 48GB+ VRAM (high-end GPU required)"],"input_types":["text (via REST API, Python SDK, JavaScript SDK, or CLI)"],"output_types":["text (streaming or buffered, JSON-formatted responses)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-wizardlm2__cap_4","uri":"capability://automation.workflow.multi.model.variant.selection.for.performance.cost.tradeoffs","name":"multi-model variant selection for performance-cost tradeoffs","description":"Offers three model size variants (7B, 8x22B MoE, 70B) enabling developers to select optimal performance-cost-VRAM tradeoffs for their deployment constraints. The 7B variant provides lightweight inference suitable for resource-constrained environments (laptops, edge devices), while the 8x22B Mixture-of-Experts variant uses sparse activation to achieve 176B effective parameters with lower VRAM than dense 70B models, and the 70B variant provides maximum reasoning capability for compute-rich environments. Developers can benchmark locally and switch variants by changing the model name in API calls (`ollama run wizardlm2:7b` vs. `ollama run wizardlm2:8x22b`).","intents":["Select appropriate model size for hardware constraints (e.g., 7B for laptops, 8x22B for servers)","Optimize inference cost by choosing smallest model that meets accuracy requirements","Scale inference capacity by switching between variants without code changes"],"best_for":["Teams managing heterogeneous hardware environments (laptops, servers, edge devices)","Developers optimizing for inference cost and latency tradeoffs","Builders prototyping with smaller models before scaling to larger variants"],"limitations":["70B variant marked 'coming soon' in documentation — availability and release date unknown","No published performance benchmarks comparing variants — quality/speed tradeoffs must be determined empirically","MoE (8x22B) variant requires careful VRAM management: sparse activation reduces peak VRAM but all expert parameters must be loaded","No automatic model selection or adaptive switching based on hardware — manual variant selection required"],"requires":["Ollama runtime supporting all three variants (7B, 8x22B, 70B)","For 7B: 6-8GB VRAM (laptops, consumer GPUs)","For 8x22B: 48GB+ VRAM (high-end GPUs like A100, H100)","For 70B: 48GB+ VRAM (similar to 8x22B due to dense architecture)"],"input_types":["text (same API across all variants)"],"output_types":["text (same output format across all variants)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-wizardlm2__cap_5","uri":"capability://text.generation.language.streaming.text.generation.with.low.time.to.first.token","name":"streaming text generation with low time-to-first-token","description":"Generates text incrementally via streaming API endpoints, returning tokens as they are generated rather than buffering the complete response. Ollama's streaming implementation prioritizes low time-to-first-token (TTFT) through optimized KV-cache management and batch processing, enabling responsive user interfaces that display text as it appears. Streaming is supported across all deployment modes (local REST API, Python SDK, JavaScript SDK, cloud API) via standard HTTP chunked transfer encoding or SDK-level streaming callbacks.","intents":["Build responsive chat interfaces that display model output in real-time as tokens are generated","Reduce perceived latency in conversational applications by showing partial results immediately","Implement streaming pipelines where downstream systems process tokens incrementally"],"best_for":["Frontend developers building interactive chat UIs requiring real-time token display","Teams building streaming pipelines or real-time data processing systems","Builders optimizing user experience in latency-sensitive applications"],"limitations":["TTFT metrics not published — 'low TTFT' is a generic claim without specific benchmarks (e.g., 50ms, 100ms, etc.)","Streaming adds complexity to error handling: partial responses may be sent before errors occur","No built-in token-level control: cannot pause, resume, or modify generation mid-stream","Streaming overhead: buffering and chunking adds ~5-10% latency vs. buffered responses (estimated)"],"requires":["HTTP client supporting chunked transfer encoding (for REST API streaming)","SDK support for streaming callbacks (Python: `stream=True` parameter, JavaScript: async iteration)","Application-level handling of partial responses and error recovery"],"input_types":["text (chat messages, same format as non-streaming)"],"output_types":["text (streamed tokens, typically JSON-formatted chunks)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-wizardlm2__cap_6","uri":"capability://tool.use.integration.rest.api.and.sdk.based.integration.with.multiple.language.support","name":"rest api and sdk-based integration with multiple language support","description":"Exposes inference capabilities through a standard REST API (POST /api/chat) and language-specific SDKs (Python, JavaScript) that abstract HTTP details and provide idiomatic interfaces. The REST API accepts JSON-formatted chat messages and returns responses in JSON, supporting both buffered and streaming modes via query parameters. SDKs provide type-safe interfaces (Python: `ollama.chat()`, JavaScript: `ollama.chat()`) that handle serialization, streaming callbacks, and error handling, enabling integration into existing Python/Node.js applications without manual HTTP management.","intents":["Integrate WizardLM 2 inference into Python applications (data science, backend services, automation)","Build Node.js/JavaScript applications (web backends, Electron apps, serverless functions) using native SDK","Expose inference via REST API for language-agnostic integration (Go, Rust, Java, etc.)"],"best_for":["Python developers building LLM applications with existing Python tooling (FastAPI, Django, etc.)","JavaScript/Node.js teams integrating inference into web backends or Electron apps","Polyglot teams using REST API for language-agnostic integration"],"limitations":["SDK support limited to Python and JavaScript — no official Go, Rust, Java, or C# SDKs","REST API documentation minimal — no OpenAPI/Swagger spec provided, requiring reverse-engineering from examples","No built-in authentication for local REST API — assumes trusted network (localhost or internal network only)","SDK error handling inconsistent: Python SDK may raise exceptions while JavaScript SDK returns error objects"],"requires":["Python 3.7+ (for Python SDK) or Node.js 14+ (for JavaScript SDK)","Ollama runtime running on localhost:11434 (default) or configured remote endpoint","For cloud API: Ollama Pro/Max subscription and API key"],"input_types":["text (JSON-formatted chat messages via REST or SDK)"],"output_types":["text (JSON-formatted responses, streaming or buffered)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-wizardlm2__cap_7","uri":"capability://automation.workflow.cloud.based.inference.with.usage.based.pricing.and.session.management","name":"cloud-based inference with usage-based pricing and session management","description":"Provides cloud-hosted inference via Ollama Pro ($20/mo) and Max ($100/mo) subscription tiers, where users pay for GPU time rather than tokens. Sessions reset every 5 hours (intra-session) and 7 days (weekly), with concurrency limits (3 concurrent models for Pro, 10 for Max). Cloud inference uses the same REST API and SDKs as local inference, enabling seamless switching between local and cloud deployments by changing the API endpoint and providing an API key. Cloud deployment handles GPU provisioning, scaling, and maintenance automatically.","intents":["Scale inference without managing GPU hardware or Ollama infrastructure","Use WizardLM 2 in production without on-premises GPU investment","Prototype with cloud inference before committing to local deployment"],"best_for":["Startups and small teams without GPU infrastructure or DevOps capacity","Developers prototyping LLM applications before scaling to production","Teams with variable inference load that benefits from pay-as-you-go pricing"],"limitations":["Usage model unclear: 'GPU time-based' pricing not quantified — no per-hour rates, per-token equivalents, or example costs provided","Session reset every 5 hours may interrupt long-running applications or batch jobs","Concurrency limits (3-10 models) may bottleneck high-throughput applications","Data location 'primarily in the United States; may route to Europe and Singapore' — no guarantee of data residency for compliance-sensitive applications","Data retention policy unknown — unclear if inference logs are retained and for how long"],"requires":["Ollama Pro ($20/mo) or Max ($100/mo) subscription","API key for authentication","Network connectivity to Ollama cloud endpoints","Application-level handling of session resets and concurrency limits"],"input_types":["text (same JSON format as local inference)"],"output_types":["text (same JSON format as local inference)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-wizardlm2__cap_8","uri":"capability://text.generation.language.multilingual.text.generation.with.unspecified.language.coverage","name":"multilingual text generation with unspecified language coverage","description":"Generates text in multiple languages through instruction-tuning on multilingual datasets, enabling the model to recognize language context from input and generate responses in the same language. The model supports language switching within conversations (e.g., user asks in Spanish, model responds in Spanish) without explicit language tags or configuration. Specific supported languages not documented — multilingual capability is claimed but language coverage, quality per language, and language-specific limitations are unknown.","intents":["Build chatbots serving multilingual user bases without separate language-specific models","Generate content in multiple languages from single model deployment","Support code-switching (mixing languages) in conversational contexts"],"best_for":["Teams building global applications serving multiple language communities","Developers prototyping multilingual chatbots without language-specific model management","Builders supporting code-switching in multilingual conversations"],"limitations":["Supported languages not documented — unclear which languages are covered (e.g., only major languages like Spanish/French/German, or 100+ languages)","Language quality not benchmarked — no per-language evaluation metrics (BLEU, METEOR, etc.) provided","No language-specific optimizations documented — unclear if model performs equally across all supported languages","Language detection implicit — no explicit language tag in API, relying on model to infer language from context","No language-specific safety or content filtering — moderation policies may vary by language"],"requires":["Input text in supported language (specific languages unknown)","No explicit language configuration — model infers language from input"],"input_types":["text (in any supported language, language not specified in API)"],"output_types":["text (in same language as input, or specified language if explicitly requested)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-wizardlm2__cap_9","uri":"capability://memory.knowledge.context.aware.response.generation.within.token.limits","name":"context-aware response generation within token limits","description":"Generates responses that incorporate full conversation history up to the context window limit (32K tokens for 7B, 64K for 8x22B), enabling the model to reference previous messages, maintain character consistency, and avoid repeating information. The model processes the entire conversation history as input tokens, using transformer attention to weight recent messages more heavily while still considering earlier context. When conversation history exceeds the context window, the application must implement truncation strategies (e.g., sliding window, summarization) to fit within limits.","intents":["Build chatbots that remember and reference earlier conversation turns without explicit memory management","Maintain character consistency and personality across long conversations","Avoid repetition by having the model track what has already been discussed"],"best_for":["Developers building conversational AI with natural context awareness","Teams implementing customer support chatbots requiring conversation history","Builders creating interactive storytelling or roleplay applications"],"limitations":["Context window limits conversation length: 32K tokens (7B) ≈ 8K words, 64K tokens (8x22B) ≈ 16K words before truncation required","No built-in context management — application must implement truncation, summarization, or sliding window strategies","Attention mechanism may not weight distant context appropriately — very early messages may be forgotten in long conversations","Context window size fixed at model load time — cannot dynamically adjust based on conversation length"],"requires":["Application-level conversation history management","Truncation or summarization strategy for conversations exceeding context window","Token counting to estimate conversation length before API calls"],"input_types":["text (full conversation history as concatenated messages)"],"output_types":["text (response incorporating full context)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Ollama runtime (local) or Ollama Pro/Max subscription (cloud)","For 7B: 6-8GB VRAM (estimated for Q4 quantization)","For 8x22B: 48GB+ VRAM and high-end GPU (A100/H100 class)","Python 3.7+ or Node.js 14+ for SDK usage","Ollama runtime with sufficient VRAM for model variant (7B: 6-8GB, 8x22B: 48GB+)","Prompts that explicitly request reasoning (e.g., 'Think step by step') for optimal results","Application-level handling of reasoning token overhead and latency","Understanding of LLM fine-tuning (for fine-tuning use cases)","Quantization tools like llama.cpp or GPTQ (for custom quantizations)","Sufficient compute for fine-tuning (varies by task and dataset size)"],"failure_modes":["Context window limits conversation length: 32K tokens (7B) or 64K tokens (8x22B) — approximately 8K-16K words before truncation","No explicit memory persistence across sessions — conversation history must be managed by the application layer","Instruction-following quality unverified against public benchmarks; claims based on internal Microsoft evaluation only","No built-in conversation branching, rollback, or alternative response generation","Reasoning quality unverified against standard benchmarks (GSM8K, MATH, ARC) — only internal Microsoft evaluation cited","No explicit reasoning verification or constraint satisfaction — model can generate plausible-sounding but incorrect reasoning chains","Reasoning overhead increases token generation cost: complex problems may require 2-3x more tokens than direct answers","No built-in mechanism to validate or backtrack from incorrect reasoning branches","License terms not documented — unclear if commercial use is permitted, what attribution is required, or what derivative works are allowed","Training data composition unknown — cannot audit training data for bias, copyright issues, or problematic content","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"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=wizardlm2","compare_url":"https://unfragile.ai/compare?artifact=wizardlm2"}},"signature":"QR4vE/7WcjzpQpoi5ZMjfve71g5Eb01/ZL+ajojYOmOP1OfPk65qZ+WE6kh0oqs+lPmGAZFWiNBP8irWJNfjCQ==","signedAt":"2026-06-21T02:30:08.117Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/wizardlm2","artifact":"https://unfragile.ai/wizardlm2","verify":"https://unfragile.ai/api/v1/verify?slug=wizardlm2","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"}}