{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-gemma2","slug":"gemma2","name":"Gemma 2 (2B, 9B, 27B)","type":"model","url":"https://ollama.com/library/gemma2","page_url":"https://unfragile.ai/gemma2","categories":["text-writing","testing-quality"],"tags":["ollama","open-source","google"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-gemma2__cap_0","uri":"capability://text.generation.language.instruction.following.text.generation.with.multi.size.model.selection","name":"instruction-following text generation with multi-size model selection","description":"Generates coherent, instruction-aligned text across three discrete parameter sizes (2B, 9B, 27B) using a transformer-based architecture optimized for efficiency-to-quality tradeoffs. Users select model size based on available hardware and latency requirements, with all variants sharing an 8K token context window. The model processes text input through a chat-based API (REST, Python, JavaScript) and streams or returns complete text responses, supporting creative writing, code generation, summarization, and conversational tasks.","intents":["Generate creative text (poems, scripts, marketing copy) without cloud API costs or latency","Run a lightweight chatbot locally that follows instructions reliably","Choose between speed (2B), balanced performance (9B), or maximum quality (27B) based on hardware constraints","Integrate instruction-following capabilities into applications via REST API or language-specific SDKs"],"best_for":["solo developers building local LLM agents with hardware constraints","teams deploying on-premise AI without cloud dependencies","researchers prototyping NLP tasks with open-source models","organizations requiring instruction-following without proprietary model lock-in"],"limitations":["8K token context window is insufficient for long-document summarization or multi-turn conversations exceeding ~4K tokens of history","No vision or multimodal capabilities — text-only input/output","Benchmark claims lack specificity (no named datasets or baseline comparisons provided); actual performance vs. competing 2B/9B/27B models unverified","Training data composition and alignment methodology undocumented — potential for unknown biases","No batch processing API documented; single-request inference only"],"requires":["Ollama runtime (ollama.com) installed locally or Ollama cloud account","Minimum VRAM: ~4-6GB (2B), ~8-12GB (9B), ~20-24GB (27B) — exact requirements undocumented","Python 3.7+ (for ollama Python library) OR Node.js 14+ (for JavaScript SDK) OR HTTP client for REST API","For cloud deployment: Ollama Pro ($20/mo, 3 concurrent models) or Max ($100/mo, 10 concurrent models)"],"input_types":["text (chat messages with role-based structure: user, assistant, system)"],"output_types":["text (streamed or complete response)"],"categories":["text-generation-language","instruction-following"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma2__cap_1","uri":"capability://tool.use.integration.local.rest.api.inference.with.streaming.support","name":"local rest api inference with streaming support","description":"Exposes Gemma 2 models via HTTP REST API on localhost:11434 with streaming and non-streaming response modes. The Ollama runtime manages model loading, GPU/CPU scheduling, and request queuing. Clients POST chat messages to `/api/chat` endpoint with optional parameters (temperature, top_p, num_predict) and receive responses as newline-delimited JSON (streaming) or complete JSON objects (non-streaming). Supports concurrent requests up to platform limits (1 free, 3 Pro, 10 Max).","intents":["Integrate Gemma 2 into web applications, microservices, or polyglot systems without language-specific SDKs","Stream responses to frontend clients for real-time chat UI updates","Build inference pipelines that orchestrate multiple models or tools alongside Gemma 2","Monitor and control inference via HTTP without vendor lock-in to proprietary APIs"],"best_for":["full-stack developers building web applications with local LLM backends","DevOps teams deploying Ollama in containerized environments (Docker, Kubernetes)","polyglot teams using multiple languages (Go, Rust, Java, etc.) that need HTTP-based model access","organizations requiring inference observability and custom request routing"],"limitations":["No built-in authentication or authorization — localhost:11434 is accessible to any process on the machine without credentials","Streaming responses require client-side handling of newline-delimited JSON; no built-in retry logic or backpressure handling","Request queuing and concurrency limits are opaque — no metrics API to monitor queue depth or model saturation","No request batching API — each inference request is processed independently, limiting throughput optimization","Ollama cloud deployment adds ~100-300ms latency vs. local inference due to network round-trip"],"requires":["Ollama runtime 0.1+ installed and running (ollama.com/download)","HTTP client library (curl, requests, fetch, etc.)","For streaming: client-side JSON parsing for newline-delimited format","For cloud: Ollama Pro/Max account with active session (resets every 5 hours)"],"input_types":["JSON object with 'model', 'messages' array, optional 'stream', 'temperature', 'top_p', 'num_predict'"],"output_types":["JSON (non-streaming): {\"model\": \"gemma2\", \"created_at\": \"...\", \"message\": {\"role\": \"assistant\", \"content\": \"...\"}}","JSON Lines (streaming): newline-delimited JSON chunks with partial 'content' field"],"categories":["tool-use-integration","api-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma2__cap_10","uri":"capability://automation.workflow.model.discovery.and.automatic.version.management.via.ollama.registry","name":"model discovery and automatic version management via ollama registry","description":"Ollama maintains a public registry (ollama.com/library) of pre-quantized models including Gemma 2 variants. Users run `ollama pull gemma2` to download the latest version (9B by default) or `ollama pull gemma2:2b` / `gemma2:27b` for specific sizes. Ollama automatically manages model versioning, caching, and updates — re-running `ollama pull` fetches only changed layers (similar to Docker). The registry includes model metadata (size, context window, description) and tags for version pinning. Models are stored locally in `~/.ollama/models` and loaded on-demand into GPU/CPU memory.","intents":["Download and manage Gemma 2 models without manually handling quantization or format conversion","Pin specific model versions in applications to ensure reproducibility","Discover available model variants and their specifications via the Ollama registry","Update models to newer versions with a single command (`ollama pull`)"],"best_for":["developers new to LLMs who want simple model management without quantization knowledge","teams deploying models across multiple machines (Ollama handles versioning automatically)","organizations requiring reproducible model versions for compliance or testing","rapid prototyping where model switching should be frictionless"],"limitations":["Registry is centralized on ollama.com — no support for private model registries or air-gapped deployments","Model versioning is opaque — no changelog or release notes for Gemma 2 updates","No rollback mechanism — older model versions are not guaranteed to be available after updates","Registry does not expose quantization details (bit-width, method) — users cannot choose quantization format","Bandwidth-intensive for large models (27B is 16GB) — slow on limited internet connections"],"requires":["Ollama runtime installed (ollama.com/download)","Internet connectivity to download models from ollama.com registry","Disk space for model storage (~2GB for 2B, ~6GB for 9B, ~16GB for 27B)"],"input_types":["command-line: `ollama pull gemma2:tag`"],"output_types":["model files cached locally in ~/.ollama/models"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma2__cap_11","uri":"capability://text.generation.language.instruction.following.and.chat.based.interaction.pattern","name":"instruction-following and chat-based interaction pattern","description":"Gemma 2 is trained for instruction-following and multi-turn chat interactions using a role-based message format (user, assistant, system). The model expects messages in a specific structure: `[{\"role\": \"user\", \"content\": \"...\"}, {\"role\": \"assistant\", \"content\": \"...\"}]`. System messages can provide context or behavioral instructions. The model generates responses that continue the conversation naturally, maintaining context from previous turns. This pattern is enforced at the training level — Gemma 2 was fine-tuned on instruction-following data, not raw text prediction.","intents":["Build multi-turn chatbots that maintain conversation context across user messages","Use system prompts to guide model behavior (e.g., 'You are a helpful coding assistant')","Implement instruction-following tasks (summarization, translation, Q&A) via natural language prompts","Create conversational agents that respond appropriately to user intent"],"best_for":["developers building chatbot applications with multi-turn conversations","teams using prompt engineering to guide model behavior without fine-tuning","applications where natural language instructions are more intuitive than structured APIs","researchers studying instruction-following and prompt sensitivity"],"limitations":["Instruction-following quality is undocumented — no benchmarks comparing Gemma 2 to other models on instruction-following tasks","System prompts may be ignored or misinterpreted — no guarantee that behavioral instructions are followed","Multi-turn context is limited by 8K context window — long conversations require history truncation","No explicit instruction format specification — unclear if Gemma 2 expects specific prompt templates","Instruction-following may degrade with adversarial or out-of-distribution prompts"],"requires":["Understanding of chat message format (role-based structure)","Prompt engineering skills to craft effective instructions","Context management for multi-turn conversations (history truncation, summarization)"],"input_types":["JSON array of messages with 'role' (user/assistant/system) and 'content' (string) fields"],"output_types":["text response following the instruction or continuing the conversation"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma2__cap_12","uri":"capability://automation.workflow.local.model.execution.without.cloud.api.dependencies.or.data.transmission","name":"local model execution without cloud api dependencies or data transmission","description":"Gemma 2 runs entirely on local hardware (GPU, CPU, or Apple Silicon) via Ollama, with no data transmission to external servers. All inference, including prompt processing and response generation, occurs on the user's machine or local network. This eliminates cloud API latency, data privacy concerns, and per-token billing. Local execution requires sufficient VRAM (4-6GB for 2B, 8-12GB for 9B, 20-24GB for 27B) and supports GPU acceleration via CUDA (NVIDIA), Metal (Apple), or ROCm (AMD). CPU-only inference is supported but significantly slower.","intents":["Run Gemma 2 inference without sending data to cloud providers (privacy-critical applications)","Eliminate cloud API latency and per-token costs for high-volume inference","Deploy Gemma 2 in air-gapped or offline environments without internet connectivity","Avoid vendor lock-in to proprietary cloud LLM APIs"],"best_for":["organizations with strict data privacy requirements (healthcare, finance, legal)","teams building high-volume applications where cloud API costs are prohibitive","developers without reliable internet connectivity or in regions with poor cloud coverage","applications requiring sub-100ms latency (local inference is faster than cloud)"],"limitations":["Requires local GPU hardware (NVIDIA, Apple, AMD) for acceptable inference speed — CPU-only inference is 10-100x slower","VRAM requirements are substantial (8-24GB depending on model size) — not feasible on laptops or edge devices without 27B model","No automatic scaling — single machine has fixed concurrency limits (1-3 models depending on VRAM)","Maintenance burden — users must manage Ollama updates, GPU drivers, and model updates","No built-in monitoring or observability — users must implement their own logging and metrics"],"requires":["Local GPU (NVIDIA with CUDA 11.8+, Apple Silicon with Metal, AMD with ROCm) OR CPU (slow)","Sufficient VRAM: 4-6GB (2B), 8-12GB (9B), 20-24GB (27B)","Ollama runtime installed and running","For GPU acceleration: appropriate drivers (NVIDIA CUDA, Apple Metal, AMD ROCm)"],"input_types":["text (same as cloud inference)"],"output_types":["text (same as cloud inference)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma2__cap_2","uri":"capability://tool.use.integration.language.specific.sdk.bindings.python.javascript.with.chat.api","name":"language-specific sdk bindings (python, javascript) with chat api","description":"Provides native Python (`ollama` package) and JavaScript/Node.js (`ollama` npm package) libraries that wrap the REST API with idiomatic language patterns. Python SDK uses synchronous and async methods; JavaScript SDK supports promises and async/await. Both SDKs handle JSON serialization, streaming response parsing, and error handling, exposing a simple `chat()` function that accepts model name and message list. SDKs automatically discover local Ollama instance or connect to cloud endpoint.","intents":["Build Python scripts or notebooks that call Gemma 2 without HTTP boilerplate","Integrate Gemma 2 into Node.js/TypeScript applications with type-safe chat interfaces","Use async/await patterns for non-blocking inference in concurrent applications","Prototype LLM agents and chains with minimal setup overhead"],"best_for":["Python developers building data science notebooks, CLI tools, or backend services","JavaScript/TypeScript developers building Node.js backends or Electron desktop apps","teams using LangChain or LlamaIndex (both support Ollama via these SDKs)","rapid prototyping and MVP development where setup time matters"],"limitations":["Python SDK does not support streaming in synchronous mode — must use async context for streaming responses","JavaScript SDK lacks TypeScript type definitions for response objects (types are inferred from runtime)","No built-in retry logic, exponential backoff, or circuit breaker patterns — applications must implement their own resilience","SDKs do not expose model-specific parameters (temperature, top_p, num_predict) in all methods — requires passing raw kwargs","No caching or request deduplication — identical requests are re-executed"],"requires":["Python 3.7+ with `pip install ollama` (or `poetry add ollama`)","Node.js 14+ with `npm install ollama` (or `yarn add ollama`)","Ollama runtime running locally (default localhost:11434) or OLLAMA_HOST environment variable set to cloud endpoint","For async Python: asyncio event loop (Python 3.7+)"],"input_types":["Python: list of dicts with 'role' (str) and 'content' (str) keys","JavaScript: array of objects with role and content properties"],"output_types":["Python: dict with 'model', 'created_at', 'message' (dict with 'role', 'content'), 'done' (bool)","JavaScript: object with same structure; streaming returns async generator of partial response objects"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma2__cap_3","uri":"capability://text.generation.language.multi.size.model.variant.selection.with.performance.quality.tradeoff","name":"multi-size model variant selection with performance-quality tradeoff","description":"Gemma 2 is released in three parameter sizes (2B, 9B, 27B) with identical API surface and 8K context window, allowing developers to select based on hardware availability and latency requirements. The 2B variant (~1.6GB disk, ~4-6GB VRAM) prioritizes speed and edge deployment; 9B (~5.4GB disk, ~8-12GB VRAM) balances quality and latency; 27B (~16GB disk, ~20-24GB VRAM) targets maximum output quality. Google claims 27B outperforms models 50B+ parameters, though specific benchmarks are undocumented. Model selection is a single parameter change (`ollama run gemma2:2b` vs. `gemma2:27b`).","intents":["Deploy Gemma 2 on resource-constrained hardware (laptops, edge devices) using the 2B variant","Scale inference quality by upgrading to 9B or 27B on better hardware without code changes","Benchmark quality vs. latency tradeoffs for a specific use case across all three sizes","Right-size model selection for cost-sensitive cloud deployments (Ollama Pro/Max billing)"],"best_for":["developers targeting heterogeneous hardware (laptops, servers, edge devices)","teams optimizing for latency-sensitive applications (chat, real-time code completion)","researchers comparing instruction-following quality across parameter scales","cost-conscious organizations using Ollama cloud with per-GPU-minute billing"],"limitations":["No documented inference latency or throughput benchmarks for any variant — actual speed differences unknown","8K context window is shared across all sizes; larger models do not offer extended context","No guidance on when to use 2B vs. 9B vs. 27B for specific tasks — requires empirical testing","VRAM requirements are estimated from model size; exact overhead (KV cache, activations) undocumented","No quantization variants (e.g., 4-bit, 8-bit) documented — all variants appear to be full precision or Ollama's default quantization"],"requires":["Ollama runtime with sufficient VRAM for selected variant","Model tags: `gemma2:2b`, `gemma2:9b`, `gemma2:27b` (or `gemma2:latest` for 9B default)","For cloud: Ollama Pro/Max account with sufficient concurrent model slots"],"input_types":["text (identical chat API across all variants)"],"output_types":["text (identical output format across all variants)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma2__cap_4","uri":"capability://tool.use.integration.framework.integration.via.langchain.and.llamaindex.adapters","name":"framework integration via langchain and llamaindex adapters","description":"Gemma 2 integrates with LangChain (via `langchain_community.llms.Ollama` class) and LlamaIndex (via `OllamaLLM` class) through standardized LLM provider interfaces. These frameworks abstract the Ollama REST API and SDK calls, enabling Gemma 2 to be used interchangeably with other LLMs in chains, agents, and RAG pipelines. LangChain integration supports streaming, callbacks, and tool-calling abstractions; LlamaIndex integration supports embedding models and document indexing workflows. Both frameworks handle prompt templating, message formatting, and response parsing.","intents":["Build LangChain chains and agents that use Gemma 2 as the reasoning engine without writing Ollama API code","Create RAG pipelines with LlamaIndex that retrieve documents and use Gemma 2 for synthesis","Swap Gemma 2 for other LLMs (GPT-4, Claude, Llama) by changing a single configuration parameter","Leverage framework-level features (memory management, tool calling, streaming callbacks) with Gemma 2"],"best_for":["developers already using LangChain or LlamaIndex who want local inference","teams building complex agentic workflows that benefit from framework abstractions","organizations migrating from cloud LLMs to local models without rewriting application logic","researchers prototyping multi-step reasoning tasks (chains, agents, RAG)"],"limitations":["LangChain Ollama integration does not expose all Ollama parameters (e.g., top_k, repeat_penalty) — limited to temperature, top_p, num_predict","LlamaIndex OllamaLLM does not support streaming in all contexts (e.g., agent loops) — may require custom callbacks","Framework abstractions add ~50-100ms latency per call due to serialization and deserialization overhead","Tool-calling support in LangChain requires Gemma 2 to follow specific JSON schema formats — not guaranteed for all prompts","No built-in prompt optimization for Gemma 2 — frameworks use generic templates that may not match Gemma 2's training format"],"requires":["LangChain 0.0.200+ with `langchain_community` package (`pip install langchain langchain_community`)","LlamaIndex 0.9.0+ (`pip install llama-index`)","Ollama runtime running locally or accessible via OLLAMA_HOST environment variable","For LangChain agents: additional dependencies (e.g., `langchain_core` for base classes)"],"input_types":["LangChain: BaseMessage objects (HumanMessage, AIMessage, SystemMessage) or strings","LlamaIndex: ChatMessage objects or strings"],"output_types":["LangChain: AIMessage with content field; streaming returns LLMResult objects","LlamaIndex: ChatResponse object with message field"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma2__cap_5","uri":"capability://automation.workflow.cloud.hosted.inference.with.usage.based.billing.and.session.management","name":"cloud-hosted inference with usage-based billing and session management","description":"Ollama Pro and Max tiers provide cloud-hosted Gemma 2 inference with automatic GPU scheduling and usage-based billing. Pro ($20/mo) allows 3 concurrent models with 50x free tier quota; Max ($100/mo) allows 10 concurrent models with 5x Pro quota. Usage is metered in GPU minutes (not tokens), with sessions resetting every 5 hours and weekly limits resetting every 7 days. Cloud deployment routes requests to NVIDIA-optimized infrastructure (Blackwell/Vera Rubin architectures) with potential acceleration via NVFP4 quantization. Users connect via same REST API and SDKs as local Ollama by setting OLLAMA_HOST environment variable.","intents":["Run Gemma 2 inference without local GPU hardware or VRAM constraints","Scale inference across multiple concurrent models without managing infrastructure","Pay only for GPU time used, avoiding fixed monthly costs for underutilized deployments","Prototype and test Gemma 2 without committing to local hardware investment"],"best_for":["developers without local GPU hardware (MacBook Air, cloud VMs without GPUs)","teams with variable inference load that benefits from pay-as-you-go pricing","organizations evaluating Gemma 2 before committing to local deployment","applications requiring high concurrency (3+ simultaneous models) without managing Kubernetes"],"limitations":["Cloud inference adds ~100-300ms latency vs. local inference due to network round-trip and request queuing","Session limits reset every 5 hours — long-running applications must handle reconnection and state management","Weekly usage limits may cause request failures if quota is exceeded — no automatic queuing or backpressure","Billing is opaque — GPU minutes are not directly comparable to token counts, making cost prediction difficult","No SLA or uptime guarantees documented — cloud infrastructure availability is not specified","Concurrent model limits (3 Pro, 10 Max) may be insufficient for high-throughput applications"],"requires":["Ollama Pro ($20/mo) or Max ($100/mo) account with active subscription","OLLAMA_HOST environment variable set to cloud endpoint (provided by Ollama)","Internet connectivity (cloud inference requires network access)","Same REST API and SDK code as local deployment (no code changes required)"],"input_types":["text (identical to local inference)"],"output_types":["text (identical to local inference)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma2__cap_6","uri":"capability://text.generation.language.8k.token.context.window.with.fixed.sequence.length.across.all.variants","name":"8k token context window with fixed sequence length across all variants","description":"All Gemma 2 variants (2B, 9B, 27B) share a fixed 8K token context window, limiting the maximum input + output length to approximately 8,000 tokens. This constraint is enforced at the model architecture level and cannot be extended via context window extension techniques (e.g., RoPE scaling, ALiBi). The context window includes both user input and model output; a 4K input prompt leaves ~4K tokens for generation. Ollama's API does not provide explicit context window validation — requests exceeding 8K tokens are truncated or rejected at inference time.","intents":["Understand maximum input size for single-turn conversations and document summarization tasks","Design multi-turn conversation systems that manage context history within 8K limit","Estimate token budgets for prompt engineering and few-shot examples","Identify use cases where Gemma 2 is insufficient (long-document analysis, book summarization)"],"best_for":["developers building chatbots with multi-turn conversation history management","teams summarizing documents up to ~6K tokens (leaving 2K for output)","applications with short, focused user queries (customer support, Q&A)","researchers studying instruction-following on bounded-context tasks"],"limitations":["8K context is insufficient for long-document analysis (research papers, books, code repositories > 6K tokens)","Multi-turn conversations require explicit history management — no automatic context window sliding or summarization","No context window extension techniques documented — cannot use RoPE scaling or other methods to extend context","Behavior on context overflow is undocumented — unclear if truncation, rejection, or degraded output occurs","No API to query remaining context budget — applications must manually track token counts"],"requires":["Token counter for target language (e.g., `tiktoken` for Python, `js-tiktoken` for JavaScript)","Understanding of Gemma 2's tokenization (likely BPE-based, but exact tokenizer undocumented)","Application-level context management for multi-turn conversations"],"input_types":["text (up to ~8K tokens including output)"],"output_types":["text (up to ~8K tokens total, minus input tokens)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma2__cap_7","uri":"capability://text.generation.language.text.only.input.output.modality.without.vision.or.audio.support","name":"text-only input/output modality without vision or audio support","description":"Gemma 2 processes text-only input and produces text-only output. The model does not support image inputs (no vision capability), audio inputs, or multimodal outputs. Chat API accepts only text messages in the 'content' field; image or binary data is not supported. This constraint is architectural — Gemma 2 was not trained on multimodal data and lacks the vision encoder/decoder components required for image understanding.","intents":["Understand that Gemma 2 cannot analyze images, PDFs, or other visual content","Design applications that extract text from images separately (OCR) before passing to Gemma 2","Identify when multimodal models (GPT-4V, Claude 3 Vision, LLaVA) are required instead","Build text-only pipelines that do not require vision capabilities"],"best_for":["text-focused applications (chatbots, summarization, code generation, translation)","teams with separate OCR/vision pipelines that extract text before Gemma 2 processing","organizations prioritizing inference speed and cost over multimodal capabilities","applications where vision is not required (customer support, content generation, Q&A)"],"limitations":["Cannot process images, PDFs, screenshots, or other visual content directly","No audio input or output — cannot transcribe speech or generate speech","No video understanding — cannot analyze video frames or temporal sequences","Requires external OCR/vision tools for document analysis workflows","Cannot generate images, diagrams, or other non-text outputs"],"requires":["Text-only input data (no images, audio, or binary formats)","External OCR tool (e.g., Tesseract, AWS Textract) if document processing is required","Separate vision model (e.g., GPT-4V, Claude 3 Vision) if image understanding is needed"],"input_types":["text (strings only, no binary data)"],"output_types":["text (strings only, no images or audio)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma2__cap_8","uri":"capability://text.generation.language.streaming.response.generation.with.newline.delimited.json.format","name":"streaming response generation with newline-delimited json format","description":"Ollama's streaming API returns Gemma 2 responses as newline-delimited JSON chunks, with each chunk containing a partial 'content' field representing tokens generated since the last chunk. Clients enable streaming by setting `stream: true` in the REST API request or using async streaming methods in Python/JavaScript SDKs. Streaming begins immediately after the first token is generated (low time-to-first-token), enabling real-time UI updates in chat applications. The final chunk includes `done: true` flag signaling completion.","intents":["Display Gemma 2 responses in real-time as tokens are generated (chat UI, streaming output)","Reduce perceived latency by showing partial responses while generation is in progress","Build interactive applications that respond to user input before full response is complete","Implement token-level callbacks for monitoring, filtering, or post-processing generation"],"best_for":["web applications with chat interfaces (React, Vue, Svelte frontends)","CLI tools that display streaming output (Python, Node.js scripts)","real-time applications where user experience depends on immediate feedback","applications monitoring token generation for safety, filtering, or analytics"],"limitations":["Streaming responses require client-side JSON parsing for newline-delimited format — not all HTTP clients handle this natively","No built-in backpressure handling — fast clients may overwhelm slow consumers, requiring manual buffering","Streaming disables response caching — each request generates new tokens even for identical inputs","Error handling is complex — errors may occur mid-stream, after partial content has been sent","No server-side streaming timeout — long-running generations may exhaust client connection limits"],"requires":["HTTP client supporting streaming (e.g., `requests.get(..., stream=True)` in Python, `fetch()` with ReadableStream in JavaScript)","JSON parsing for newline-delimited format (manual parsing or library like `ndjson`)","For web: server-side streaming proxy (e.g., Express middleware) or direct client-to-Ollama connection"],"input_types":["JSON with `stream: true` flag"],"output_types":["newline-delimited JSON: {\"model\": \"gemma2\", \"created_at\": \"...\", \"message\": {\"content\": \"partial token\"}, \"done\": false} (repeated), then {\"done\": true} on final chunk"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma2__cap_9","uri":"capability://text.generation.language.temperature.and.sampling.parameter.control.for.output.diversity","name":"temperature and sampling parameter control for output diversity","description":"Ollama's API exposes temperature, top_p (nucleus sampling), and num_predict (max output tokens) parameters for controlling Gemma 2's generation behavior. Temperature (0.0-2.0) controls randomness — lower values (0.0-0.5) produce deterministic, focused outputs; higher values (1.0+) increase diversity and creativity. Top_p (0.0-1.0) implements nucleus sampling, truncating the probability distribution to the smallest set of tokens accounting for top_p cumulative probability. num_predict limits output length in tokens. These parameters are passed in REST API requests or SDK method calls and affect generation without reloading the model.","intents":["Reduce hallucination and randomness in factual tasks (Q&A, summarization) by lowering temperature","Increase creativity and diversity in generative tasks (creative writing, brainstorming) by raising temperature","Control output length to fit token budgets or UI constraints via num_predict","Fine-tune generation behavior per-request without model retraining or fine-tuning"],"best_for":["applications requiring task-specific generation behavior (deterministic for facts, creative for writing)","teams A/B testing different temperature settings to optimize user experience","developers building configurable AI features where users control creativity/accuracy tradeoff","researchers studying the effect of sampling parameters on model behavior"],"limitations":["No guidance on optimal temperature/top_p values for Gemma 2 — requires empirical tuning","Temperature and top_p interact in complex ways; documentation does not explain their combined effect","num_predict is a hard limit that may truncate mid-sentence if set too low","No parameter validation — invalid values (e.g., temperature > 2.0) may cause undefined behavior","Parameters are per-request only — no way to set defaults or profiles for different use cases"],"requires":["Understanding of temperature and sampling concepts (or willingness to experiment)","REST API or SDK support for parameter passing (all Ollama clients support this)"],"input_types":["JSON with optional 'temperature' (float, default 0.7), 'top_p' (float, default 0.9), 'num_predict' (int, default -1 for unlimited)"],"output_types":["text (generation behavior varies based on parameters)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["Ollama runtime (ollama.com) installed locally or Ollama cloud account","Minimum VRAM: ~4-6GB (2B), ~8-12GB (9B), ~20-24GB (27B) — exact requirements undocumented","Python 3.7+ (for ollama Python library) OR Node.js 14+ (for JavaScript SDK) OR HTTP client for REST API","For cloud deployment: Ollama Pro ($20/mo, 3 concurrent models) or Max ($100/mo, 10 concurrent models)","Ollama runtime 0.1+ installed and running (ollama.com/download)","HTTP client library (curl, requests, fetch, etc.)","For streaming: client-side JSON parsing for newline-delimited format","For cloud: Ollama Pro/Max account with active session (resets every 5 hours)","Ollama runtime installed (ollama.com/download)","Internet connectivity to download models from ollama.com registry"],"failure_modes":["8K token context window is insufficient for long-document summarization or multi-turn conversations exceeding ~4K tokens of history","No vision or multimodal capabilities — text-only input/output","Benchmark claims lack specificity (no named datasets or baseline comparisons provided); 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