{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-ollama--ollama","slug":"ollama--ollama","name":"ollama","type":"mcp","url":"https://ollama.com","page_url":"https://unfragile.ai/ollama--ollama","categories":["mcp-servers"],"tags":["deepseek","gemma","gemma3","glm","go","golang","gpt-oss","llama","llama3","llm","llms","minimax","mistral","ollama","qwen"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-ollama--ollama__cap_0","uri":"capability://text.generation.language.local.model.inference.with.hardware.acceleration","name":"local-model-inference-with-hardware-acceleration","description":"Executes large language models locally on consumer hardware by automatically detecting and routing inference through optimized backends (CUDA for NVIDIA, ROCm for AMD, Metal for Apple Silicon, Vulkan for cross-platform GPU support). Uses GGML backend with ML context management and KV cache system to minimize memory footprint while maintaining inference speed. The LlamaServer runner implementation handles request scheduling and memory allocation across detected hardware, enabling models to run without cloud dependencies.","intents":["Run LLMs locally without sending data to cloud APIs","Optimize inference performance across different GPU architectures","Deploy models on resource-constrained consumer hardware","Maintain privacy by keeping model execution on-device"],"best_for":["developers building privacy-first LLM applications","teams avoiding cloud inference costs and latency","researchers experimenting with model architectures locally","organizations with data residency requirements"],"limitations":["Inference speed depends on available VRAM; models larger than GPU memory require CPU offloading with significant latency penalty","KV cache grows linearly with sequence length, limiting context window on memory-constrained devices","No distributed inference across multiple machines — single-device execution only","MLX runner limited to Apple Silicon; other platforms require GGML or Vulkan backends"],"requires":["8GB+ RAM for 7B models, 16GB+ for 13B models","NVIDIA CUDA 11.8+ OR AMD ROCm 5.6+ OR Apple Silicon with Metal support OR Vulkan-capable GPU","macOS 11+, Linux (Ubuntu 20.04+), or Windows 10+","Disk space: 4GB for 7B quantized model, 13GB+ for unquantized variants"],"input_types":["text prompts","multimodal inputs (text + images for vision models)","structured chat conversation history"],"output_types":["text completions","streaming token sequences","structured JSON (via template system)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ollama--ollama__cap_1","uri":"capability://memory.knowledge.model.registry.and.layer.based.composition","name":"model-registry-and-layer-based-composition","description":"Manages models as composable layers stored in a content-addressed blob store, enabling efficient model distribution and customization through Modelfile syntax. Models are pulled from the Ollama library registry, decomposed into quantized weights, adapters, and system prompts as separate blobs, then reassembled on-device. The manifest system tracks layer dependencies and enables incremental updates — only changed layers are re-downloaded. Custom models can be created by layering base models with LoRA adapters, custom prompts, and parameters via Modelfile declarations.","intents":["Download and manage multiple LLM variants without duplicating base weights","Create custom model variants by composing base models with adapters and system prompts","Update models incrementally without re-downloading unchanged layers","Share model configurations reproducibly across teams"],"best_for":["teams managing multiple model variants for different tasks","developers building model-as-code workflows","organizations optimizing storage and bandwidth for model distribution","researchers experimenting with model composition and fine-tuning"],"limitations":["Modelfile syntax is Ollama-specific; no direct compatibility with Hugging Face model cards or ONNX manifests","Layer composition is linear — no support for complex DAG-based model architectures","Blob transfer requires authentication for private models; no built-in encryption for stored blobs","Model registry is centralized; no federation or private registry support out-of-box"],"requires":["Ollama CLI or API client","Network access to registry.ollama.ai (or custom registry endpoint)","Disk space for model blobs (4GB-100GB+ depending on model size and quantization)"],"input_types":["Modelfile (text configuration)","model name and tag (e.g., 'llama2:7b')","GGUF or SafeTensors model files for import"],"output_types":["model manifest (JSON with layer references)","quantized model weights (GGUF format)","model metadata (parameters, prompt template)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ollama--ollama__cap_10","uri":"capability://text.generation.language.streaming.response.generation.with.token.callbacks","name":"streaming-response-generation-with-token-callbacks","description":"Streams inference results token-by-token to clients via HTTP streaming (chunked transfer encoding), allowing real-time display of model output without waiting for full completion. Each token is sent as a separate JSON object in the response stream, with metadata (timestamp, token ID, logits if requested). The streaming implementation uses Go's http.Flusher to send tokens immediately after generation, not buffering. Clients receive tokens as they're generated, enabling responsive UIs and early stopping based on partial results.","intents":["Display model output in real-time as tokens are generated","Build responsive chat interfaces that show typing-like behavior","Implement early stopping when desired output is detected mid-generation","Monitor inference progress and token generation rate"],"best_for":["developers building interactive chat applications","teams creating real-time inference dashboards","organizations needing responsive user experiences","researchers analyzing token generation patterns"],"limitations":["Streaming adds latency to first token — buffering overhead for HTTP headers and initial flush","Client must handle partial JSON objects — streaming format is newline-delimited JSON, not standard JSON array","No built-in backpressure — fast clients can overwhelm slow networks with token requests","Streaming state is not resumable — if connection drops mid-stream, generation cannot be resumed from that point"],"requires":["HTTP client supporting streaming responses (most modern clients do)","Request with stream: true parameter","Handling of newline-delimited JSON format"],"input_types":["chat or generate request with stream: true"],"output_types":["newline-delimited JSON stream","each line contains: {model, created_at, message/response, done}","final line has done: true"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ollama--ollama__cap_11","uri":"capability://text.generation.language.cli.and.interactive.repl.for.model.interaction","name":"cli-and-interactive-repl-for-model-interaction","description":"Provides a command-line interface (CLI) for model management (pull, push, list, delete) and an interactive REPL for conversational inference. The interactive mode supports multi-line input, command history, and model switching without restarting. The REPL implements a stateful conversation context, maintaining chat history across turns and managing token limits. The CLI also exposes server control (start, stop, logs) and debugging tools (show model details, inspect layers).","intents":["Quickly test models without writing code","Manage model lifecycle from command line","Explore model behavior interactively","Debug model outputs and prompt formatting"],"best_for":["developers prototyping and testing models quickly","researchers exploring model behavior","operators managing Ollama deployments","teams without dedicated UI infrastructure"],"limitations":["REPL does not support streaming output — responses are buffered and displayed after completion","No built-in syntax highlighting or code formatting in REPL output","Command history is not persisted across sessions — history is lost when REPL exits","No support for batch processing — REPL is interactive only, not suitable for scripting"],"requires":["Ollama CLI installed (included with Ollama binary)","Terminal with ANSI color support (optional but recommended)"],"input_types":["CLI commands (pull, push, list, etc.)","natural language prompts in REPL","multi-line input in REPL"],"output_types":["model list and metadata","inference results in REPL","command status and logs"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ollama--ollama__cap_12","uri":"capability://planning.reasoning.thinking.models.and.extended.reasoning.support","name":"thinking-models-and-extended-reasoning-support","description":"Supports models with extended reasoning capabilities (e.g., OpenAI o1-style thinking models) that generate internal reasoning tokens before producing final output. The inference pipeline handles thinking tokens separately from output tokens, allowing models to 'think' through problems before responding. Thinking tokens are typically hidden from users but can be exposed for debugging. The KV cache system manages thinking token overhead, which can be 10-100x larger than output tokens for complex reasoning tasks.","intents":["Leverage models with extended reasoning for complex problem-solving","Debug model reasoning by inspecting thinking tokens","Implement multi-step reasoning workflows locally","Understand model decision-making through reasoning traces"],"best_for":["developers building reasoning-heavy applications (math, logic, code generation)","researchers studying model reasoning and interpretability","teams needing explainable AI with reasoning traces","organizations solving complex problems requiring step-by-step reasoning"],"limitations":["Thinking models are significantly slower than standard models — 5-10x longer inference time due to reasoning overhead","Thinking token overhead consumes VRAM rapidly — a 7B thinking model may require 24GB+ VRAM for complex problems","Thinking tokens are model-specific — format and interpretation vary by model, no standard representation","Limited model availability — few open-source thinking models exist; most are proprietary (OpenAI o1)"],"requires":["Model with thinking capability (e.g., Qwen QwQ, DeepSeek-R1, or similar)","24GB+ VRAM for 7B thinking models, 48GB+ for larger variants","Patience — inference can take minutes for complex reasoning tasks"],"input_types":["complex problem or question requiring reasoning","optional: budget_tokens parameter to limit thinking length"],"output_types":["thinking tokens (hidden by default, exposed via debug flag)","final output after reasoning","reasoning trace for interpretability"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ollama--ollama__cap_13","uri":"capability://automation.workflow.docker.containerization.and.deployment","name":"docker-containerization-and-deployment","description":"Provides Docker images for containerized Ollama deployment, with built-in GPU support (NVIDIA CUDA, AMD ROCm) and multi-platform builds (Linux x86_64, ARM64). Docker images include the Ollama server, CLI, and all dependencies, enabling one-command deployment. GPU support is handled via docker run --gpus flag, automatically mounting GPU devices into the container. The Docker setup supports volume mounts for model persistence across container restarts.","intents":["Deploy Ollama in containerized environments (Kubernetes, Docker Compose)","Run Ollama with GPU support in containers without host driver installation","Distribute Ollama as a containerized service","Ensure reproducible deployments across different machines"],"best_for":["teams deploying Ollama in Kubernetes or Docker Compose","organizations containerizing ML inference pipelines","developers testing Ollama in isolated environments","DevOps teams managing multi-service deployments"],"limitations":["GPU support requires NVIDIA Docker runtime or AMD ROCm runtime — not all container orchestration platforms support this","Model storage in containers requires persistent volumes — models are not baked into images due to size","Network overhead — container networking adds latency vs host networking","No built-in health checks or auto-restart logic — requires external orchestration (Kubernetes, Docker Compose)"],"requires":["Docker 20.10+ with GPU support (nvidia-docker for NVIDIA, rocm-docker for AMD)","NVIDIA CUDA 11.8+ OR AMD ROCm 5.6+ (for GPU support)","Persistent volume for model storage (recommended)"],"input_types":["Docker image (ollama/ollama:latest)","environment variables (OLLAMA_HOST, OLLAMA_MODELS)","volume mounts for model persistence"],"output_types":["running Ollama server in container","exposed port 11434 for API access","logs via docker logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ollama--ollama__cap_2","uri":"capability://tool.use.integration.openai.and.anthropic.api.compatibility.layer","name":"openai-and-anthropic-api-compatibility-layer","description":"Provides drop-in compatibility with OpenAI and Anthropic API schemas, allowing existing client libraries and applications to redirect requests to local Ollama inference without code changes. The compatibility layer translates incoming OpenAI-format requests (e.g., /v1/chat/completions) to Ollama's native /api/chat endpoint, maps request parameters (temperature, max_tokens, stop sequences), and reformats responses to match expected OpenAI/Anthropic schemas. Streaming responses are converted to server-sent events (SSE) format matching OpenAI's stream protocol.","intents":["Migrate existing OpenAI/Anthropic integrations to local inference without refactoring","Use local models as drop-in replacements for cloud APIs in development/testing","Reduce cloud API costs by running compatible models locally","Maintain API compatibility while switching between cloud and local providers"],"best_for":["teams with existing OpenAI SDK integrations wanting to test locally","developers building cost-sensitive applications","organizations migrating from cloud to on-premise inference","QA teams needing deterministic local models for testing"],"limitations":["Not all OpenAI features are supported — vision models, function calling, and fine-tuning endpoints have limited or no compatibility","Parameter mapping is lossy — some OpenAI-specific parameters (e.g., logit_bias, top_logprobs) are silently ignored","Response format differences — Ollama's streaming format has slightly different token timing and metadata than OpenAI","No support for OpenAI's organization headers, usage tracking, or billing integration"],"requires":["Ollama server running on localhost:11434 (or custom OLLAMA_HOST)","OpenAI Python SDK 0.27+ OR Anthropic SDK 0.7+","Model compatible with chat format (most modern LLMs supported)"],"input_types":["OpenAI ChatCompletion request JSON","Anthropic Messages API request JSON","streaming or non-streaming request modes"],"output_types":["OpenAI ChatCompletion response JSON","Anthropic Messages response JSON","server-sent events (SSE) for streaming"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ollama--ollama__cap_3","uri":"capability://tool.use.integration.tool.calling.and.function.execution.with.schema.binding","name":"tool-calling-and-function-execution-with-schema-binding","description":"Enables models to declare and invoke external tools through a schema-based function registry. Models receive tool definitions as JSON schemas in their context, generate structured tool calls (name + arguments) in response, and Ollama routes those calls to registered handlers. The template system embeds tool schemas into the prompt, and the runner validates generated tool calls against declared schemas before execution. Supports both synchronous tool execution (blocking until result) and asynchronous patterns where tool results are fed back into the model for further reasoning.","intents":["Build agentic workflows where models can call APIs, databases, or custom functions","Implement tool-augmented reasoning without manual prompt engineering","Create multi-step workflows where model output triggers external actions","Enable models to interact with external systems while maintaining structured control flow"],"best_for":["developers building AI agents with external tool access","teams implementing retrieval-augmented generation (RAG) with tool calling","organizations automating workflows that require model reasoning + external actions","researchers exploring tool-augmented language models"],"limitations":["Tool schemas must be manually defined as JSON Schema — no automatic introspection from Python functions or OpenAPI specs","No built-in timeout or rate limiting for tool execution — runaway tool calls can block inference","Tool call validation is schema-only — no semantic validation or safety checks beyond type matching","Async tool execution requires manual state management — no built-in workflow persistence or retry logic"],"requires":["Model with tool-calling capability (e.g., Mistral, Llama 3.1+, Qwen)","Tool schemas defined as JSON Schema objects","Custom handler code to execute tools (Ollama provides routing, not execution)"],"input_types":["tool definitions (JSON Schema)","user query or task description","tool execution results (for multi-turn reasoning)"],"output_types":["structured tool calls (name + arguments JSON)","model reasoning text interspersed with tool invocations","final response after tool execution"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ollama--ollama__cap_4","uri":"capability://image.visual.multimodal.and.vision.model.inference","name":"multimodal-and-vision-model-inference","description":"Supports vision-language models that accept both text and image inputs, processing images through the model's vision encoder before feeding to the language decoder. Images are embedded as base64 or file paths in requests, automatically converted to the model's expected format (e.g., image tokens for LLaVA), and processed alongside text in a single inference pass. The template system handles image encoding and prompt formatting for different vision architectures (LLaVA, Qwen-VL, etc.), abstracting away model-specific image handling.","intents":["Analyze images with natural language queries locally","Extract text or structured data from images (OCR-like tasks)","Build vision-language applications without cloud vision APIs","Process document images, screenshots, or diagrams with reasoning"],"best_for":["developers building local image analysis tools","teams avoiding cloud vision API costs and latency","organizations with image data privacy requirements","researchers experimenting with vision-language models"],"limitations":["Vision models require significantly more VRAM than text-only models — 13B vision models need 16GB+ VRAM","Image resolution is limited by model architecture — most models support 336x336 or 768x768 max, limiting detail extraction","No built-in image preprocessing — images must be pre-resized and formatted; no automatic aspect ratio handling","Inference latency is 2-3x slower than text-only models due to vision encoder overhead"],"requires":["Vision-capable model (e.g., LLaVA, Qwen-VL, Llava-NeXT)","16GB+ VRAM for 13B vision models, 24GB+ for larger variants","Image input as base64 string or file path (JPEG, PNG, WebP supported)"],"input_types":["text query (string)","image (base64-encoded or file path)","combined text + image in single request"],"output_types":["text description or analysis","extracted structured data (JSON via template)","reasoning steps explaining image interpretation"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ollama--ollama__cap_5","uri":"capability://data.processing.analysis.embedding.generation.with.vector.output","name":"embedding-generation-with-vector-output","description":"Generates dense vector embeddings for text inputs using embedding-specific models (e.g., nomic-embed-text, mxbai-embed-large), producing fixed-dimensional vectors suitable for semantic search, clustering, or similarity comparison. The /api/embed endpoint accepts text strings and returns normalized embedding vectors. Embeddings can be stored in external vector databases (Pinecone, Weaviate, Milvus) or used directly for in-memory similarity search. The embedding models are optimized for low latency and small VRAM footprint compared to generative models.","intents":["Generate embeddings for semantic search without cloud APIs","Build RAG systems with local embedding models","Cluster or classify documents based on semantic similarity","Create vector databases for similarity-based retrieval"],"best_for":["teams building RAG applications with privacy requirements","developers avoiding embedding API costs (OpenAI, Cohere)","organizations with large document collections needing local indexing","researchers experimenting with embedding architectures"],"limitations":["Embedding quality varies significantly by model — open-source models often underperform OpenAI's text-embedding-3-large on benchmark tasks","No built-in vector database — embeddings must be stored externally or in memory","Batch embedding API is not optimized — processing 1000 documents requires 1000 sequential API calls (no batch endpoint)","Embedding dimensions vary by model (384-1024) — incompatible with vector databases expecting fixed dimensions"],"requires":["Embedding model (e.g., nomic-embed-text, mxbai-embed-large, all-minilm)","2GB+ VRAM for most embedding models","Text input (string or array of strings)"],"input_types":["text string","array of text strings","document chunks (typically 512 tokens max)"],"output_types":["embedding vector (float32 array)","array of embedding vectors","normalized vectors (L2 norm)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ollama--ollama__cap_6","uri":"capability://automation.workflow.request.scheduling.and.concurrent.model.execution","name":"request-scheduling-and-concurrent-model-execution","description":"Manages concurrent inference requests through a request scheduler that queues incoming requests and routes them to available runner instances. The scheduler implements fairness policies (FIFO, priority-based) and manages GPU memory allocation across concurrent requests. When multiple requests arrive, the scheduler decides whether to batch them together (if models support batching) or queue them sequentially. The KV cache system is shared across requests when possible, reducing memory overhead. The runner implementation (LlamaServer) handles context switching and memory cleanup between requests.","intents":["Handle multiple concurrent inference requests without out-of-memory errors","Optimize GPU utilization by batching compatible requests","Implement fair scheduling for multi-user inference scenarios","Manage memory pressure when requests exceed available VRAM"],"best_for":["teams running Ollama as a shared inference service","developers building multi-user LLM applications","organizations optimizing GPU utilization across concurrent workloads","researchers studying inference scheduling and batching strategies"],"limitations":["No dynamic batching — requests are processed sequentially or in fixed-size batches, not optimized per-request arrival pattern","Scheduling policy is not configurable — no way to set request priorities or implement custom fairness algorithms","Memory allocation is static per-request — no adaptive memory sharing or preemption when requests exceed VRAM","No request timeout or cancellation — long-running requests block subsequent requests indefinitely"],"requires":["Ollama server with sufficient VRAM for at least one full model inference","Multiple concurrent requests (via HTTP clients, SDKs, or load testing tools)"],"input_types":["HTTP requests (chat, generate, embed endpoints)","concurrent request streams"],"output_types":["queued request status","inference results when request reaches front of queue","error responses if memory exhausted"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ollama--ollama__cap_7","uri":"capability://data.processing.analysis.quantization.aware.model.loading.and.inference","name":"quantization-aware-model-loading-and-inference","description":"Loads quantized models (GGUF format with INT4, INT8, FP16 quantization levels) and executes inference without dequantizing to full precision, maintaining quantization throughout the inference pipeline. The GGML backend handles quantized matrix multiplications natively, reducing memory footprint and improving inference speed. Models are stored in quantized format on disk, and the loader automatically selects the appropriate quantization kernel based on hardware capabilities. Quantization is transparent to users — the same API works for quantized and full-precision models.","intents":["Run large models on consumer hardware with limited VRAM","Reduce model storage requirements and download bandwidth","Speed up inference through reduced memory bandwidth requirements","Deploy models on edge devices with minimal resources"],"best_for":["developers targeting resource-constrained devices (laptops, edge servers)","teams minimizing storage and bandwidth costs","organizations deploying models at scale with limited infrastructure","researchers studying quantization impact on model quality"],"limitations":["Quantization introduces accuracy loss — INT4 models typically lose 2-5% accuracy vs full precision, INT8 loses <1%","Not all quantization levels are equally supported — INT4 is well-optimized, but FP8 and other exotic formats have limited kernel support","Quantization is model-specific — a quantized model cannot be re-quantized to a different level without reloading from source","Some operations (attention, normalization) may still use full precision, limiting memory savings vs theoretical maximum"],"requires":["GGUF-format quantized model (4-bit, 5-bit, 8-bit variants available)","GGML backend with quantization kernel support (CPU, CUDA, Metal, ROCm)","Disk space: 4GB for 7B INT4 model, 7GB for 7B INT8 model"],"input_types":["GGUF quantized model file","quantization level specification (Q4_K_M, Q5_K_S, Q8_0, etc.)"],"output_types":["inference results (same as full-precision models)","memory usage metrics (reduced vs full precision)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ollama--ollama__cap_8","uri":"capability://text.generation.language.template.system.for.prompt.formatting.and.model.adaptation","name":"template-system-for-prompt-formatting-and-model-adaptation","description":"Provides a declarative template system that abstracts model-specific prompt formatting, system prompts, and parameter handling. Templates define how user messages, system prompts, and tool schemas are formatted into the exact token sequence each model expects. Different models have different prompt formats (Llama uses [INST], Mistral uses [TOOL_CALLS], etc.), and the template system handles these differences transparently. Templates are defined in Modelfiles and applied automatically during inference, eliminating manual prompt engineering per-model.","intents":["Switch between different models without rewriting prompt formatting logic","Customize system prompts and model behavior per-deployment","Ensure consistent prompt formatting across different client libraries","Adapt models to specific domains by injecting domain-specific system prompts"],"best_for":["teams managing multiple model variants with different prompt formats","developers building model-agnostic applications","organizations customizing models for specific use cases","researchers experimenting with prompt engineering at scale"],"limitations":["Template syntax is Ollama-specific — no compatibility with Hugging Face chat templates or Jinja2 standard","Templates are static — no dynamic template selection based on request parameters","Limited template functions — no conditional logic or complex string manipulation","Template errors are silent — malformed templates may produce incorrect prompts without warnings"],"requires":["Model with defined template in Modelfile or registry","Understanding of target model's prompt format"],"input_types":["Modelfile with template definition","user message (string)","system prompt (string)","tool schemas (JSON)"],"output_types":["formatted prompt string ready for tokenization","prompt with embedded tool schemas","prompt with custom system message"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ollama--ollama__cap_9","uri":"capability://data.processing.analysis.model.import.and.conversion.from.external.formats","name":"model-import-and-conversion-from-external-formats","description":"Imports models from external sources (Hugging Face, local GGUF files, SafeTensors, PyTorch checkpoints) and converts them to Ollama's internal format (GGUF with manifest). The import pipeline handles format detection, quantization (if needed), and layer decomposition into the blob store. Users can import models via CLI (ollama import) or by providing a Modelfile with a FROM statement pointing to an external model source. The conversion process is transparent — users don't need to manually run quantization tools.","intents":["Use Hugging Face models with Ollama without manual GGUF conversion","Import custom fine-tuned models into Ollama's registry","Convert between model formats (PyTorch to GGUF) automatically","Build custom model variants from external base models"],"best_for":["researchers importing custom-trained models","teams using Hugging Face models wanting Ollama integration","developers building model pipelines with external sources","organizations migrating from other inference frameworks"],"limitations":["Import is one-way — models imported to Ollama cannot be easily exported back to original format","Quantization during import is automatic but not configurable — no way to specify INT4 vs INT8 preference","Large model imports are slow — converting a 70B model to GGUF can take 30+ minutes on CPU","Some model architectures are not supported — custom architectures or very new models may fail import"],"requires":["Source model in supported format (GGUF, SafeTensors, PyTorch, Hugging Face Hub)","Disk space for both source and converted model (2x model size temporarily)","Network access to Hugging Face Hub if importing from there"],"input_types":["Hugging Face model ID (e.g., 'meta-llama/Llama-2-7b')","local GGUF file path","SafeTensors or PyTorch checkpoint path","Modelfile with FROM statement"],"output_types":["GGUF model in Ollama blob store","model manifest with layer references","quantized model ready for inference"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["8GB+ RAM for 7B models, 16GB+ for 13B models","NVIDIA CUDA 11.8+ OR AMD ROCm 5.6+ OR Apple Silicon with Metal support OR Vulkan-capable GPU","macOS 11+, Linux (Ubuntu 20.04+), or Windows 10+","Disk space: 4GB for 7B quantized model, 13GB+ for unquantized variants","Ollama CLI or API client","Network access to registry.ollama.ai (or custom registry endpoint)","Disk space for model blobs (4GB-100GB+ depending on model size and quantization)","HTTP client supporting streaming responses (most modern clients do)","Request with stream: true parameter","Handling of newline-delimited JSON format"],"failure_modes":["Inference speed depends on available VRAM; models larger than GPU memory require CPU offloading with significant latency penalty","KV cache grows linearly with sequence length, limiting context window on memory-constrained devices","No distributed inference across multiple machines — single-device execution only","MLX runner limited to Apple Silicon; other platforms require GGML or Vulkan backends","Modelfile syntax is Ollama-specific; no direct compatibility with Hugging Face model cards or ONNX manifests","Layer composition is linear — no support for complex DAG-based model architectures","Blob transfer requires authentication for private models; no built-in encryption for stored blobs","Model registry is centralized; no federation or private registry support out-of-box","Streaming adds latency to first token — buffering overhead for HTTP headers and initial flush","Client must handle partial JSON objects — streaming format is newline-delimited JSON, not standard JSON array","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.9515527371519872,"quality":0.35,"ecosystem":0.65,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:57:19.180Z","last_commit":"2026-05-03T03:46:36Z"},"community":{"stars":170611,"forks":15951,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ollama--ollama","compare_url":"https://unfragile.ai/compare?artifact=ollama--ollama"}},"signature":"AXRmYBBmDKWCwNWrwQ5Y7uM5K53u5miRqTZW6xGOlv4yl6J+eazRI51gni15wwftK8ksOqw4lOh7Y2e3Ne+zAg==","signedAt":"2026-06-21T03:12:54.885Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ollama--ollama","artifact":"https://unfragile.ai/ollama--ollama","verify":"https://unfragile.ai/api/v1/verify?slug=ollama--ollama","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"}}