{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-orca-mini","slug":"orca-mini","name":"Orca Mini (3B, 7B, 13B)","type":"model","url":"https://ollama.com/library/orca-mini","page_url":"https://unfragile.ai/orca-mini","categories":["text-writing"],"tags":["ollama","open-source","microsoft"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-orca-mini__cap_0","uri":"capability://text.generation.language.instruction.following.text.generation.via.transformer.architecture","name":"instruction-following text generation via transformer architecture","description":"Generates coherent text responses to natural language instructions using a fine-tuned transformer model trained on Orca-style datasets derived from GPT-4 explanation traces. The model processes input prompts through a standard decoder-only transformer stack and produces token-by-token output via autoregressive sampling, with context windows of 2K-4K tokens depending on variant size. Deployed as GGUF-quantized weights optimized for CPU and GPU inference via Ollama's runtime.","intents":["I need a lightweight model that can follow instructions and generate text without cloud dependencies","I want to run a capable instruction-following model on entry-level hardware like a laptop or Raspberry Pi","I need to integrate a text generation model into a local application with minimal latency overhead"],"best_for":["solo developers building local LLM applications on resource-constrained hardware","teams prototyping chatbots and assistants without cloud API costs","researchers experimenting with instruction-following models on commodity hardware"],"limitations":["Context window capped at 2K tokens (3B variant) or 4K tokens (7B/13B/70B variants), limiting multi-turn conversation depth and document processing","Model last updated 2 years ago — likely superseded by newer instruction-following models with better reasoning and factuality","No structured output support — cannot guarantee JSON, XML, or schema-compliant responses without post-processing","Hallucination tendency unknown — no documented evaluation against factuality benchmarks","Training data composition and cutoff date unknown — may produce outdated or biased responses"],"requires":["Ollama runtime (local installation or cloud-hosted)","RAM: 8GB minimum for 7B variant, 16GB for 13B, 64GB for 70B","Python 3.7+ or Node.js 14+ for SDK integration (optional)","API key for Ollama Cloud if using hosted deployment"],"input_types":["text"],"output_types":["text"],"categories":["text-generation-language","local-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-orca-mini__cap_1","uri":"capability://text.generation.language.multi.turn.conversational.chat.via.stateless.rest.api","name":"multi-turn conversational chat via stateless rest api","description":"Enables multi-turn conversations by accepting message arrays with role-based formatting (user/assistant) through Ollama's `/api/chat` endpoint, maintaining conversation context within a single request payload rather than server-side session state. Each request includes full conversation history up to the context window limit, allowing stateless scaling and integration into serverless or containerized environments. Responses stream token-by-token via HTTP chunked transfer encoding for real-time user feedback.","intents":["I want to build a chatbot interface that maintains conversation history without managing server-side session state","I need to integrate multi-turn dialogue into a web or mobile application with simple HTTP requests","I want to stream responses to users in real-time as tokens are generated"],"best_for":["web developers building chat UIs with React, Vue, or vanilla JavaScript","API-first teams integrating LLM capabilities into existing REST architectures","serverless/containerized deployments where session state management is undesirable"],"limitations":["Stateless design requires client to manage and send full conversation history with each request, increasing payload size and latency for long conversations","Context window limits (4K tokens max) mean conversations longer than ~1000 words will lose early context","No built-in conversation persistence — client must store message history separately","Streaming responses require HTTP/1.1 chunked encoding support; some proxies or load balancers may buffer entire response before forwarding"],"requires":["Ollama runtime with `/api/chat` endpoint exposed (default localhost:11434)","HTTP client supporting chunked transfer encoding (fetch API, axios, requests library, etc.)","Message format: JSON array with {role, content} objects"],"input_types":["text"],"output_types":["text (streamed)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-orca-mini__cap_2","uri":"capability://text.generation.language.single.turn.prompt.completion.with.configurable.sampling.parameters","name":"single-turn prompt completion with configurable sampling parameters","description":"Generates text completions for arbitrary prompts via Ollama's `/api/generate` endpoint, supporting configurable sampling strategies (temperature, top-p, top-k) and output constraints (max tokens, stop sequences). The model processes the raw prompt string without role-based formatting, suitable for completion tasks, code generation, and few-shot prompting. Supports both streaming and non-streaming modes with optional response formatting.","intents":["I need to generate text completions for prompts without managing conversation state","I want to control model behavior via temperature and sampling parameters for different use cases","I need to generate code snippets, summaries, or other structured text from templates"],"best_for":["developers building prompt-based applications (code generation, content creation, data extraction)","researchers experimenting with different sampling strategies and prompt engineering","applications requiring deterministic outputs via low temperature or constrained decoding"],"limitations":["No role-based formatting — unsuitable for multi-turn dialogue without manual prompt engineering","Sampling parameters (temperature, top-p) affect output quality unpredictably — no guidance on optimal values for different tasks","Stop sequences are string-based, not token-based — may not align with model tokenization boundaries","No built-in prompt validation or optimization — users must manually craft effective prompts"],"requires":["Ollama runtime with `/api/generate` endpoint","Prompt string (raw text, no role formatting required)","Optional: temperature (0.0-2.0), top_p (0.0-1.0), top_k (integer), num_predict (max tokens)"],"input_types":["text"],"output_types":["text (streaming or buffered)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-orca-mini__cap_3","uri":"capability://automation.workflow.local.cpu.and.gpu.inference.with.automatic.hardware.acceleration","name":"local cpu and gpu inference with automatic hardware acceleration","description":"Executes model inference on local hardware (CPU or GPU) via Ollama's runtime, which automatically detects available accelerators (NVIDIA CUDA, AMD ROCm) and offloads computation accordingly. GGUF quantization format enables efficient memory usage and inference speed on commodity hardware; the runtime manages memory allocation, KV-cache optimization, and batch processing without explicit user configuration. Supports fallback to CPU inference if GPU is unavailable or insufficient.","intents":["I want to run a capable language model on my laptop without cloud API costs or latency","I need to deploy a model on edge devices or servers with limited GPU resources","I want automatic hardware acceleration without manually configuring CUDA, ROCm, or other frameworks"],"best_for":["individual developers and researchers without access to cloud GPU resources","organizations with privacy requirements prohibiting cloud inference","edge deployment scenarios (local servers, IoT devices, offline-first applications)"],"limitations":["GPU acceleration requires NVIDIA CUDA 11.8+ or AMD ROCm 5.6+ — not all GPUs supported","Inference speed on CPU is significantly slower than GPU (10-100x slower depending on model size and hardware)","Memory requirements scale with model size: 3B requires ~2GB VRAM, 13B requires ~8GB VRAM, 70B requires ~40GB VRAM","No distributed inference across multiple GPUs — single-GPU or CPU-only","Quantization (GGUF format) trades inference speed for accuracy — exact quantization level unknown"],"requires":["Ollama runtime (macOS, Linux, Windows)","For GPU: NVIDIA GPU with CUDA 11.8+ or AMD GPU with ROCm 5.6+","RAM: 8GB minimum for 7B model, 16GB for 13B, 64GB for 70B","Disk space: 2GB (3B), 3.8GB (7B), 7.4GB (13B), 39GB (70B)"],"input_types":["text"],"output_types":["text"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-orca-mini__cap_4","uri":"capability://text.generation.language.command.line.interface.for.interactive.model.testing.and.deployment","name":"command-line interface for interactive model testing and deployment","description":"Provides a CLI tool (`ollama run orca-mini`) for interactive model testing, allowing developers to chat with the model directly in a terminal without writing code. The CLI manages model download, caching, and inference automatically; supports multi-line input, command history, and basic formatting. Useful for rapid prototyping, debugging prompts, and validating model behavior before integration into applications.","intents":["I want to quickly test a model's responses without writing code or setting up an application","I need to debug and refine prompts interactively before using them in production","I want to demonstrate model capabilities to non-technical stakeholders via a simple interface"],"best_for":["developers prototyping and debugging LLM applications","researchers experimenting with prompts and model behavior","non-technical users exploring model capabilities without programming"],"limitations":["No conversation persistence — each session starts fresh, no history saved between runs","Limited formatting options — plain text output only, no markdown or rich formatting","No parameter control via CLI — temperature, top-p, and other sampling parameters require API calls","Single-threaded interaction — cannot run multiple concurrent conversations"],"requires":["Ollama CLI installed (macOS, Linux, Windows)","Terminal or command-line interface","Model downloaded locally (automatic on first run)"],"input_types":["text"],"output_types":["text"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-orca-mini__cap_5","uri":"capability://data.processing.analysis.model.quantization.and.gguf.format.optimization.for.memory.efficiency","name":"model quantization and gguf format optimization for memory efficiency","description":"Distributes Orca Mini models in GGUF (GPT-Generated Unified Format) quantization, which reduces model size and memory footprint through post-training quantization while maintaining inference quality. GGUF format enables efficient loading into memory, reduced VRAM requirements, and faster inference on CPU and GPU compared to full-precision weights. Ollama runtime handles quantization transparently — users select model variant and quantization is applied automatically.","intents":["I need to run a 13B parameter model on a machine with only 8GB RAM","I want faster inference speed without retraining the model","I need to reduce storage and download size for model distribution"],"best_for":["developers with limited hardware resources (laptops, edge devices, budget servers)","organizations optimizing inference latency and cost","researchers studying quantization trade-offs and model compression"],"limitations":["Quantization level unknown — documentation does not specify Q4, Q5, or other quantization schemes, making accuracy trade-offs unclear","Quantization is lossy — model outputs may differ from full-precision weights, but magnitude of difference unknown","No option to use full-precision weights — only quantized variants available","Quantization benefits vary by hardware — CPU inference may see larger speedups than GPU"],"requires":["Ollama runtime (handles quantization automatically)","No additional configuration required — quantization applied transparently"],"input_types":["text"],"output_types":["text"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-orca-mini__cap_6","uri":"capability://tool.use.integration.cloud.hosted.inference.via.ollama.cloud.with.api.key.authentication","name":"cloud-hosted inference via ollama cloud with api key authentication","description":"Offers cloud-hosted deployment of Orca Mini models via Ollama Cloud service, providing managed inference without local hardware requirements. Users authenticate with API keys and access models via the same REST API endpoints as local Ollama, enabling seamless migration between local and cloud deployments. Cloud service handles scaling, availability, and infrastructure management; pricing model unknown but implied to be pay-per-use or subscription-based.","intents":["I want to use Orca Mini without managing local hardware or infrastructure","I need scalable inference that automatically handles traffic spikes","I want to switch between local and cloud inference without changing application code"],"best_for":["teams without dedicated ML infrastructure or GPU resources","applications requiring high availability and automatic scaling","developers wanting to prototype locally and deploy to cloud with minimal changes"],"limitations":["Pricing model unknown — no documentation on cost structure, rate limits, or billing","Cloud deployment introduces network latency vs local inference — exact latency unknown","API key required for authentication — adds security management overhead vs local deployment","Vendor lock-in to Ollama Cloud — migrating to other cloud providers requires code changes","No documented SLA or uptime guarantees"],"requires":["Ollama Cloud account and API key","Internet connectivity","Same REST API client as local deployment (no code changes required)"],"input_types":["text"],"output_types":["text (streamed)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-orca-mini__cap_7","uri":"capability://tool.use.integration.language.sdk.integration.for.python.and.javascript.with.native.bindings","name":"language sdk integration for python and javascript with native bindings","description":"Provides official Python and JavaScript/TypeScript SDKs that wrap Ollama's REST API, enabling idiomatic language integration without manual HTTP client setup. SDKs handle connection pooling, error handling, and response streaming; support both chat and completion APIs with type hints (TypeScript) and docstrings (Python). Community integrations (40,000+ mentioned) extend support to additional languages and frameworks.","intents":["I want to integrate Orca Mini into a Python or Node.js application without writing HTTP client code","I need type-safe model interactions with IDE autocomplete and error checking","I want to use Orca Mini with existing frameworks like LangChain, LlamaIndex, or Hugging Face Transformers"],"best_for":["Python and JavaScript developers building LLM applications","teams using LangChain, LlamaIndex, or other LLM frameworks with Ollama support","developers wanting idiomatic language bindings vs raw HTTP APIs"],"limitations":["Official SDKs limited to Python and JavaScript — other languages require community integrations or manual HTTP clients","Community integrations (40,000+) are not enumerated or officially supported — quality and maintenance unknown","SDK documentation and examples unknown — may lack comprehensive guides","No async/await support mentioned for Python SDK — may block on I/O","Type hints in TypeScript SDK may not cover all API parameters or response variations"],"requires":["Python 3.7+ (Python SDK) or Node.js 14+ (JavaScript SDK)","Ollama runtime running locally or accessible via network","SDK installation: `pip install ollama` (Python) or `npm install ollama` (JavaScript)"],"input_types":["text"],"output_types":["text"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-orca-mini__cap_8","uri":"capability://automation.workflow.model.variant.selection.across.parameter.sizes.3b.7b.13b.70b","name":"model variant selection across parameter sizes (3b, 7b, 13b, 70b)","description":"Offers four model variants with different parameter counts (3B, 7B, 13B, 70B) enabling trade-offs between inference speed, memory usage, and reasoning capability. Users select variant via model name (e.g., `ollama run orca-mini:7b`) and Ollama automatically downloads and caches the appropriate weights. Smaller variants (3B) run on entry-level hardware; larger variants (13B, 70B) provide improved reasoning but require more resources.","intents":["I want to choose a model size that fits my hardware constraints and performance requirements","I need to compare reasoning quality across different model sizes for my use case","I want to start with a small model and scale up as requirements grow"],"best_for":["developers optimizing for specific hardware (laptops, edge devices, servers)","teams experimenting with model size vs quality trade-offs","organizations with heterogeneous hardware wanting a single model family"],"limitations":["Context window varies by variant: 2K tokens (3B) vs 4K tokens (7B/13B/70B) — smaller models have reduced context","Reasoning capability likely degrades with model size (3B < 7B < 13B < 70B) but no benchmarks provided","No guidance on which variant to choose for specific tasks — users must experiment","Larger variants (70B) require 64GB+ RAM — not accessible to most individual developers","No fine-tuning or distillation options — users cannot customize models for specific domains"],"requires":["Ollama runtime","RAM: 8GB (7B), 16GB (13B), 64GB (70B), unknown for 3B","Disk space: 2GB (3B), 3.8GB (7B), 7.4GB (13B), 39GB (70B)"],"input_types":["text"],"output_types":["text"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"low","permissions":["Ollama runtime (local installation or cloud-hosted)","RAM: 8GB minimum for 7B variant, 16GB for 13B, 64GB for 70B","Python 3.7+ or Node.js 14+ for SDK integration (optional)","API key for Ollama Cloud if using hosted deployment","Ollama runtime with `/api/chat` endpoint exposed (default localhost:11434)","HTTP client supporting chunked transfer encoding (fetch API, axios, requests library, etc.)","Message format: JSON array with {role, content} objects","Ollama runtime with `/api/generate` endpoint","Prompt string (raw text, no role formatting required)","Optional: temperature (0.0-2.0), top_p (0.0-1.0), top_k (integer), num_predict (max tokens)"],"failure_modes":["Context window capped at 2K tokens (3B variant) or 4K tokens (7B/13B/70B variants), limiting multi-turn conversation depth and document processing","Model last updated 2 years ago — likely superseded by newer instruction-following models with better reasoning and factuality","No structured output support — cannot guarantee JSON, XML, or schema-compliant responses without post-processing","Hallucination tendency unknown — no documented evaluation against factuality benchmarks","Training data composition and cutoff date unknown — may produce outdated or biased responses","Stateless design requires client to manage and send full conversation history with each request, increasing payload size and latency for long conversations","Context window limits (4K tokens max) mean conversations longer than ~1000 words will lose early context","No built-in conversation persistence — client must store message history separately","Streaming responses require HTTP/1.1 chunked encoding support; some proxies or load balancers may buffer entire response before forwarding","No role-based formatting — unsuitable for multi-turn dialogue without manual prompt engineering","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"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=orca-mini","compare_url":"https://unfragile.ai/compare?artifact=orca-mini"}},"signature":"x/dtvaUSsk/PQmwW1dscN6GzQZJB9wpXzvDJGBHN/A24RJCyv/Xnl8l0Oq9y2T3TVGfaME4o9w+TkGZLgoOLBQ==","signedAt":"2026-06-21T16:55:07.757Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/orca-mini","artifact":"https://unfragile.ai/orca-mini","verify":"https://unfragile.ai/api/v1/verify?slug=orca-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"}}