{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-llava","slug":"llava","name":"LLaVA (7B, 13B, 34B)","type":"model","url":"https://ollama.com/library/llava","page_url":"https://unfragile.ai/llava","categories":["image-generation"],"tags":["ollama","open-source","vision","lmsys"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-llava__cap_0","uri":"capability://image.visual.visual.question.answering.with.clip.vision.encoder","name":"visual-question-answering-with-clip-vision-encoder","description":"Answers natural language questions about image content by processing images through a CLIP-based vision encoder that extracts visual features, then fuses those embeddings with text prompts through Vicuna's language model decoder. The model performs end-to-end training of both vision and language components, enabling it to ground language understanding in visual context and answer questions requiring spatial reasoning, object identification, and scene understanding.","intents":["I need to ask questions about what's in an image and get detailed answers","I want to build a chatbot that can understand and discuss images","I need to extract information from images by asking natural language questions"],"best_for":["developers building local vision-language applications without cloud dependencies","teams needing offline image understanding for privacy-sensitive use cases","researchers prototyping multimodal AI without API costs"],"limitations":["Context window of only 4K tokens for 13B/34B variants limits multi-turn conversations with large image descriptions","Maximum image resolution of 1344x1344 pixels may lose detail in high-resolution documents or distant objects","No quantitative benchmarks provided; qualitative claims of 'GPT-4-like' capabilities are unvalidated","Hallucination rates and failure modes on edge cases (unusual angles, low-light, abstract images) are undocumented"],"requires":["Ollama runtime installed and running locally or via Ollama Cloud","8-12GB VRAM minimum for 7B variant, 16GB+ for 13B, 40GB+ for 34B","Python 3.7+ with ollama library OR JavaScript/Node.js 14+ with ollama package OR HTTP client for REST API","Image file in JPEG/PNG format or base64-encoded string"],"input_types":["text (natural language question)","image (JPEG, PNG, base64-encoded, up to 1344x1344 pixels)"],"output_types":["text (natural language response)"],"categories":["image-visual","vision-language-understanding"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava__cap_1","uri":"capability://image.visual.image.captioning.and.description.generation","name":"image-captioning-and-description-generation","description":"Generates natural language descriptions and captions of images by encoding visual content through the CLIP vision encoder and decoding it into coherent text via the Vicuna language model. The model learns to summarize visual scenes, identify objects and their relationships, and produce human-readable descriptions without requiring explicit question prompts, making it suitable for batch image annotation and accessibility applications.","intents":["I need to automatically generate alt-text for images in bulk","I want to create captions for images in a dataset without manual labeling","I need to describe visual content for accessibility or documentation purposes"],"best_for":["content creators and publishers automating image metadata generation","accessibility teams generating alt-text at scale","data annotation teams reducing manual labeling effort for vision datasets"],"limitations":["Generated captions may be generic or miss fine-grained details in complex scenes","No control over caption length or style (e.g., short vs. detailed descriptions)","Batch processing performance is undocumented; inference speed per image unknown","No evaluation metrics provided for caption quality (BLEU, CIDEr, METEOR scores absent)"],"requires":["Ollama runtime with llava model loaded","8-12GB VRAM minimum for 7B variant","Image files in JPEG/PNG format","Python/JavaScript client library or HTTP API access"],"input_types":["image (JPEG, PNG, up to 1344x1344 pixels)"],"output_types":["text (natural language caption/description)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava__cap_10","uri":"capability://automation.workflow.offline.deployment.without.cloud.dependencies","name":"offline-deployment-without-cloud-dependencies","description":"Enables complete offline operation by running the entire vision-language model locally without requiring cloud API calls, internet connectivity, or external service dependencies. Once the model is downloaded and Ollama is running, inference can proceed indefinitely without network access, making it suitable for air-gapped environments, mobile deployments, or privacy-critical applications.","intents":["I need to deploy vision-language inference in an air-gapped or offline environment","I want to ensure image data never leaves my infrastructure","I need to run inference on edge devices or mobile platforms without cloud connectivity"],"best_for":["organizations in regulated industries (healthcare, finance, government) requiring data residency","teams deploying in air-gapped networks or remote locations without reliable internet","developers building privacy-first applications where user data must never reach external servers"],"limitations":["Initial model download requires internet (4.7GB for 7B, 8.0GB for 13B, 20GB for 34B)","No automatic model updates; users must manually download new versions","Ollama Cloud features (managed hosting, concurrency scaling) unavailable in offline mode","No cloud-based monitoring, logging, or analytics; users responsible for local observability","Hardware constraints (GPU memory, CPU) cannot be scaled elastically as in cloud deployments"],"requires":["Ollama runtime installed locally","Sufficient disk space for model files (4.7GB-20GB depending on variant)","GPU with 8GB+ VRAM (7B), 16GB+ (13B), or 40GB+ (34B)","No internet connectivity required after initial model download"],"input_types":["text (prompts)","image (local files or base64-encoded)"],"output_types":["text (responses)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava__cap_11","uri":"capability://image.visual.multi.image.context.in.single.conversation","name":"multi-image-context-in-single-conversation","description":"Supports analyzing multiple images within a single conversation by passing different images in successive turns, enabling comparative analysis, sequential image understanding, or multi-image reasoning. The model maintains conversation history across turns, allowing users to reference previous images and ask questions that require understanding relationships between multiple images.","intents":["I want to compare two images and identify differences","I need to analyze a sequence of images (e.g., before/after, steps in a process)","I want to ask questions that reference multiple images in a conversation"],"best_for":["quality control teams comparing product images across batches","researchers analyzing image sequences or time-series visual data","developers building comparative analysis tools"],"limitations":["Context window limits the number of images that can be analyzed in a single conversation (4K tokens for 13B/34B means ~2-3 high-resolution images before context exhaustion)","No explicit multi-image reasoning capability documented; unclear if model can perform cross-image inference","Image descriptions consume tokens rapidly; each image reduces available context for conversation","No built-in image indexing or retrieval; all images must be provided in conversation history","Performance with many images (>5) is undocumented"],"requires":["Ollama runtime with llava model","7B variant recommended for longer multi-image conversations (32K context window)","HTTP API or SDK supporting message history with multiple images","Application-level conversation state management"],"input_types":["text (prompts and questions)","image (multiple JPEG/PNG files, up to 1344x1344 pixels each)"],"output_types":["text (comparative or sequential analysis responses)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava__cap_2","uri":"capability://image.visual.optical.character.recognition.and.text.extraction","name":"optical-character-recognition-and-text-extraction","description":"Extracts and recognizes text from images using improved visual reasoning capabilities introduced in v1.6, which increased input resolution to 4x more pixels and enhanced OCR-specific training. The CLIP vision encoder captures fine-grained visual details of text characters, and Vicuna decodes these into recognized text strings, enabling document digitization, form processing, and text-in-image extraction without specialized OCR libraries.","intents":["I need to extract text from scanned documents or photos of documents","I want to read text embedded in images (signs, screenshots, receipts)","I need to digitize handwritten or printed text from images"],"best_for":["document processing teams automating invoice/receipt digitization","researchers extracting text from historical documents or screenshots","accessibility teams converting image-based text to machine-readable format"],"limitations":["OCR accuracy on handwritten text is undocumented; likely inferior to specialized OCR engines (Tesseract, EasyOCR)","Maximum image resolution of 1344x1344 may be insufficient for high-DPI scans or small text","No structured output format (e.g., bounding boxes, confidence scores); returns only raw text","Performance on non-Latin scripts, rotated text, or degraded document quality is unknown","v1.6 improvements to OCR are mentioned but not quantified with accuracy benchmarks"],"requires":["Ollama runtime with llava:7b or llava:13b (34B variant may have text-only input limitation)","16GB+ VRAM recommended for reliable OCR performance","Image files in JPEG/PNG format with text at 1344x1344 resolution or smaller","Python/JavaScript client or HTTP API access"],"input_types":["image (JPEG, PNG, scanned documents, screenshots, up to 1344x1344 pixels)"],"output_types":["text (extracted/recognized text string)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava__cap_3","uri":"capability://image.visual.visual.reasoning.and.logical.inference","name":"visual-reasoning-and-logical-inference","description":"Performs logical inference and reasoning about visual content by combining CLIP's visual feature extraction with Vicuna's language reasoning capabilities. The model can answer questions requiring multi-step reasoning about spatial relationships, object interactions, scene composition, and implicit visual knowledge, enabling it to go beyond simple object detection to understand complex visual scenarios and their implications.","intents":["I need to ask complex reasoning questions about images (e.g., 'Why is this person smiling?')","I want to understand relationships and interactions between objects in an image","I need to infer context or intent from visual scenes"],"best_for":["AI researchers studying visual reasoning in multimodal models","developers building intelligent image analysis systems requiring inference","teams automating quality control or anomaly detection in visual inspection"],"limitations":["Reasoning capability is claimed but not quantitatively benchmarked; no comparison to GPT-4V or other vision-language models","Failure modes on adversarial images, optical illusions, or ambiguous scenes are undocumented","Reasoning depth limited by 4K context window for 13B/34B variants; complex multi-step reasoning may be truncated","No explainability or reasoning trace provided; model returns final answer without showing reasoning steps"],"requires":["Ollama runtime with llava model","13B or 34B variant recommended for complex reasoning (7B may have reduced capability)","16GB+ VRAM for 13B, 40GB+ for 34B","Image files in JPEG/PNG format"],"input_types":["text (reasoning question)","image (JPEG, PNG, up to 1344x1344 pixels)"],"output_types":["text (reasoning response with inferred conclusions)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava__cap_4","uri":"capability://image.visual.multi.turn.visual.conversation","name":"multi-turn-visual-conversation","description":"Maintains conversational context across multiple turns of image-based questions and answers, enabling users to ask follow-up questions, request clarifications, and build on previous responses. The model uses Vicuna's language model to track conversation history and ground subsequent responses in both the image and prior dialogue, creating a stateful chat experience rather than isolated image-question pairs.","intents":["I want to have a back-and-forth conversation about an image","I need to ask follow-up questions that reference previous answers","I want to refine or clarify answers through iterative dialogue"],"best_for":["developers building interactive image analysis chatbots","teams creating user-facing applications requiring conversational image understanding","researchers studying dialogue grounding in multimodal contexts"],"limitations":["Context window severely limited: 4K tokens for 13B/34B variants means only 3-5 turns of conversation before context exhaustion","7B variant has 32K context window but may have reduced reasoning capability for complex multi-turn scenarios","No explicit conversation state management; context is implicitly managed through token counting","Long image descriptions consume tokens rapidly, leaving little room for conversation history","No built-in conversation memory or persistence; each session starts fresh"],"requires":["Ollama runtime with llava model","7B variant recommended for longer conversations (32K context window)","8-12GB VRAM for 7B, 16GB+ for 13B","Client library or HTTP API supporting message history (e.g., ollama.chat() with messages array)","Application-level conversation state management"],"input_types":["text (user messages in conversation)","image (JPEG, PNG, provided once at conversation start or per turn)"],"output_types":["text (conversational responses)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava__cap_5","uri":"capability://image.visual.local.inference.with.variable.model.sizes","name":"local-inference-with-variable-model-sizes","description":"Provides three model size variants (7B, 13B, 34B parameters) optimized for different hardware constraints, enabling deployment on consumer GPUs, enterprise servers, or edge devices. Each variant is distributed through Ollama's model library in a proprietary format (likely GGUF quantization) and can be run locally without cloud dependencies, with inference managed through Ollama's HTTP API, CLI, or language-specific SDKs (Python, JavaScript).","intents":["I need to run vision-language inference on my local machine without cloud costs","I want to choose a model size that fits my GPU memory constraints","I need to deploy vision-language models in air-gapped or privacy-sensitive environments"],"best_for":["developers building privacy-first applications requiring local inference","teams with GPU infrastructure seeking to avoid cloud API costs and latency","organizations in regulated industries (healthcare, finance) requiring data to stay on-premises"],"limitations":["Hardware requirements are inferred from model size; official specs not provided (8-12GB for 7B, 16GB+ for 13B, 40GB+ for 34B)","Inference speed benchmarks not provided; latency per image unknown","Quantization options and bit-depths not documented; unclear if int8, int4, or other formats available","No GPU acceleration details provided; unclear which NVIDIA/AMD/Intel GPUs are supported","Ollama Cloud concurrency limits (1 for Free, 3 for Pro $20/mo, 10 for Max $100/mo) may be insufficient for production workloads"],"requires":["Ollama runtime (free, open-source) installed on Linux, macOS, or Windows","GPU with 8GB+ VRAM (7B), 16GB+ (13B), or 40GB+ (34B)","Python 3.7+ with ollama package OR Node.js 14+ with ollama package OR HTTP client","Internet connection for initial model download (4.7GB for 7B, 8.0GB for 13B, 20GB for 34B)"],"input_types":["text (prompts)","image (JPEG, PNG, base64-encoded)"],"output_types":["text (responses)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava__cap_6","uri":"capability://tool.use.integration.ollama.http.api.integration","name":"ollama-http-api-integration","description":"Exposes vision-language inference through Ollama's HTTP REST API endpoints (/api/generate, /api/chat), enabling integration with any HTTP client, web framework, or orchestration tool. The API supports streaming responses, message history for multi-turn conversations, and base64-encoded image input, providing a language-agnostic interface to the vision-language model without requiring language-specific SDKs.","intents":["I need to integrate vision-language inference into a web application or microservice","I want to call the model from a language without an official SDK (Go, Rust, Java)","I need to stream responses for real-time UI updates"],"best_for":["backend developers building REST APIs that incorporate vision-language capabilities","teams using polyglot architectures requiring language-agnostic model access","developers building web applications with streaming image analysis"],"limitations":["API documentation is minimal; endpoint schemas, error codes, and response formats not fully specified in provided materials","No built-in authentication or rate limiting; requires external API gateway for production security","Streaming response format not documented; unclear if Server-Sent Events (SSE), chunked transfer encoding, or other format used","No request validation or input sanitization documented; client responsible for validating image size and format","Base64 encoding overhead increases request size by ~33% compared to binary image transmission"],"requires":["Ollama runtime running and listening on localhost:11434 (or configured remote address)","HTTP client library (curl, requests, fetch, etc.)","Knowledge of Ollama API endpoint paths (/api/generate, /api/chat)","Base64 encoding capability for image input"],"input_types":["JSON request body with text prompt and base64-encoded image"],"output_types":["JSON response with text output (streaming or non-streaming)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava__cap_7","uri":"capability://tool.use.integration.python.and.javascript.sdk.integration","name":"python-and-javascript-sdk-integration","description":"Provides native Python (ollama package) and JavaScript/Node.js (ollama package) SDKs that wrap Ollama's HTTP API, offering idiomatic interfaces for model interaction. The SDKs handle base64 encoding of images, message history management, and streaming response parsing, reducing boilerplate code and enabling developers to integrate vision-language inference with minimal setup.","intents":["I want to call LLaVA from Python without managing HTTP requests manually","I need to build a Node.js application that analyzes images with LLaVA","I want to use LLaVA in a Jupyter notebook for interactive image analysis"],"best_for":["Python developers and data scientists using Jupyter notebooks or scripts","JavaScript/Node.js developers building web backends or CLI tools","teams preferring language-specific APIs over raw HTTP calls"],"limitations":["SDK documentation is minimal; API surface, error handling, and streaming behavior not fully documented","No async/await support documented for Python SDK; unclear if concurrent requests are supported","JavaScript SDK may not support all Ollama features; feature parity with HTTP API unknown","No built-in retry logic, timeout handling, or connection pooling documented","SDK versions and compatibility with Ollama runtime versions not specified"],"requires":["Python 3.7+ with 'pip install ollama' OR Node.js 14+ with 'npm install ollama'","Ollama runtime running locally or on accessible network address","Basic knowledge of Python async/await or JavaScript promises (if using streaming)"],"input_types":["text (prompts)","image (file paths or PIL Image objects for Python; file paths or Buffer for JavaScript)"],"output_types":["text (responses as strings or streaming iterables)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava__cap_8","uri":"capability://image.visual.high.resolution.image.processing.with.dynamic.aspect.ratios","name":"high-resolution-image-processing-with-dynamic-aspect-ratios","description":"Processes images at up to 1344x1344 pixels with support for dynamic aspect ratios (672x672, 336x1344, 1344x336) introduced in v1.6, enabling fine-grained visual analysis without image resizing or cropping. The vision encoder adapts to different aspect ratios, preserving visual information in wide, tall, or square images while maintaining computational efficiency through resolution-aware processing.","intents":["I need to analyze high-resolution images without losing detail","I want to process wide or tall images (e.g., panoramas, screenshots) without cropping","I need to extract text or details from images with varying aspect ratios"],"best_for":["document processing teams handling scanned pages and forms with varying dimensions","teams analyzing screenshots, panoramic photos, or other non-square images","researchers studying the impact of resolution and aspect ratio on vision-language model performance"],"limitations":["Maximum resolution of 1344x1344 may be insufficient for very high-DPI scans or distant objects in large images","Aspect ratio support (336x1344, 1344x336, 672x672) is fixed; custom aspect ratios not supported","Memory and compute requirements scale with resolution; 1344x1344 images require more VRAM than 672x672","Performance impact of different aspect ratios not documented; unclear if 336x1344 is faster/slower than 1344x1344","v1.6 improvements to resolution are mentioned but not quantified with accuracy benchmarks"],"requires":["Ollama runtime with llava:7b or llava:13b (v1.6 or later)","16GB+ VRAM recommended for consistent 1344x1344 processing","Image files in JPEG/PNG format with dimensions up to 1344x1344 pixels"],"input_types":["image (JPEG, PNG, up to 1344x1344 pixels, aspect ratios: 1:1, 1:4, 4:1, or other)"],"output_types":["text (analysis results)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava__cap_9","uri":"capability://text.generation.language.streaming.response.generation","name":"streaming-response-generation","description":"Generates responses token-by-token with streaming output, enabling real-time display of model output as it is generated rather than waiting for the complete response. Streaming is supported through both Ollama's HTTP API (/api/generate with stream=true) and language-specific SDKs, allowing developers to build responsive UIs that show partial results immediately.","intents":["I want to show image analysis results to users as they are generated, not after completion","I need to build a real-time chatbot UI that displays responses incrementally","I want to reduce perceived latency by streaming partial results"],"best_for":["web developers building interactive image analysis interfaces","teams building chatbot UIs requiring real-time response display","applications with long-running inference where streaming improves user experience"],"limitations":["Streaming format and protocol not documented; unclear if Server-Sent Events, chunked transfer encoding, or newline-delimited JSON used","No guidance on handling streaming errors or connection interruptions","Streaming overhead (per-token HTTP overhead) may increase total latency compared to batch responses","Client-side streaming parsing required; no built-in UI components provided","Streaming behavior with multi-turn conversations not documented"],"requires":["Ollama runtime with streaming support enabled","HTTP client with streaming support (fetch API with ReadableStream, requests with stream=True, etc.)","Client-side code to parse and display streamed tokens","UI framework capable of incremental DOM updates (React, Vue, etc.)"],"input_types":["text (prompts)","image (base64-encoded)"],"output_types":["text (streamed token-by-token)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Ollama runtime installed and running locally or via Ollama Cloud","8-12GB VRAM minimum for 7B variant, 16GB+ for 13B, 40GB+ for 34B","Python 3.7+ with ollama library OR JavaScript/Node.js 14+ with ollama package OR HTTP client for REST API","Image file in JPEG/PNG format or base64-encoded string","Ollama runtime with llava model loaded","8-12GB VRAM minimum for 7B variant","Image files in JPEG/PNG format","Python/JavaScript client library or HTTP API access","Ollama runtime installed locally","Sufficient disk space for model files (4.7GB-20GB depending on variant)"],"failure_modes":["Context window of only 4K tokens for 13B/34B variants limits multi-turn conversations with large image descriptions","Maximum image resolution of 1344x1344 pixels may lose detail in high-resolution documents or distant objects","No quantitative benchmarks provided; qualitative claims of 'GPT-4-like' capabilities are unvalidated","Hallucination rates and failure modes on edge cases (unusual angles, low-light, abstract images) are undocumented","Generated captions may be generic or miss fine-grained details in complex scenes","No control over caption length or style (e.g., short vs. detailed descriptions)","Batch processing performance is undocumented; inference speed per image unknown","No evaluation metrics provided for caption quality (BLEU, CIDEr, METEOR scores absent)","Initial model download requires internet (4.7GB for 7B, 8.0GB for 13B, 20GB for 34B)","No automatic model updates; users must manually download new versions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.42,"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=llava","compare_url":"https://unfragile.ai/compare?artifact=llava"}},"signature":"1Lq8hAUNsaAmmEs4wrQy5PQH+ORF27JkLTtAsaSzklEc6ZWlWmuMJ7SzOpkk/Jom+fWUQ8I02pkQc51jnzA7Ag==","signedAt":"2026-06-21T18:18:51.364Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llava","artifact":"https://unfragile.ai/llava","verify":"https://unfragile.ai/api/v1/verify?slug=llava","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"}}