{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-llava-llama3","slug":"llava-llama3","name":"LLaVA Llama 3 (8B)","type":"model","url":"https://ollama.com/library/llava-llama3","page_url":"https://unfragile.ai/llava-llama3","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-llama3__cap_0","uri":"capability://image.visual.multimodal.vision.language.understanding.with.clip.vit.image.encoding","name":"multimodal vision-language understanding with clip-vit image encoding","description":"Processes images and text together by encoding images through CLIP-ViT-Large-patch14-336 vision encoder, projecting visual features into Llama 3's token space, then performing joint reasoning across both modalities. The architecture chains image embeddings directly into the LLM's attention mechanism, enabling the 8B Llama 3 Instruct backbone to perform visual question answering, image captioning, and cross-modal analysis in a single forward pass without separate vision-language fusion layers.","intents":["I need to ask questions about images and get detailed answers based on visual content","I want to generate natural language descriptions of images automatically","I need to analyze visual content and extract information from screenshots or diagrams","I want to run vision-language inference locally without cloud dependencies"],"best_for":["developers building local AI applications requiring offline vision-language capabilities","teams deploying edge AI systems with strict data privacy requirements","researchers experimenting with open-source multimodal models"],"limitations":["Fixed 8K token context window cannot be extended, limiting analysis of very long image sequences or detailed multi-image reasoning","CLIP-ViT-Large-patch14-336 vision encoder is frozen and cannot be fine-tuned, constraining adaptation to domain-specific visual patterns","No documented maximum image resolution or size constraints; inference latency for high-resolution images unknown","Model last updated 1 year ago; may lack knowledge of recent visual concepts or events","No built-in image generation capability despite artifact categorization; purely analytical"],"requires":["Ollama runtime (macOS, Windows, Linux, or Docker)","5.5GB disk space for GGUF quantized model","Minimum GPU VRAM requirement unknown (Ollama documentation does not specify for this model)","Image input in .png, .jpeg, .jpg, .svg, or .gif format"],"input_types":["image (PNG, JPEG, JPG, SVG, GIF)","text (natural language questions or prompts)"],"output_types":["text (streaming or buffered natural language responses)"],"categories":["image-visual","multimodal-understanding"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava-llama3__cap_1","uri":"capability://tool.use.integration.local.cli.and.rest.api.inference.with.streaming.responses","name":"local cli and rest api inference with streaming responses","description":"Exposes the vision-language model through three integration points: (1) Ollama CLI command `ollama run llava-llama3` for interactive chat, (2) HTTP REST API on localhost:11434 with `/api/chat` endpoint accepting multipart image + text payloads, and (3) language-specific SDKs (Python `ollama.chat()`, JavaScript) that abstract HTTP calls. All interfaces support streaming token-by-token responses, enabling real-time output rendering without waiting for full generation completion.","intents":["I want to test the model interactively from the command line without writing code","I need to integrate vision-language inference into my application via HTTP without language-specific bindings","I want to stream responses to users in real-time as tokens are generated","I need to build a Python or JavaScript application that calls the model programmatically"],"best_for":["developers prototyping multimodal features quickly without cloud infrastructure","teams building polyglot applications requiring language-agnostic API access","builders implementing real-time streaming UIs that render model output incrementally"],"limitations":["REST API is localhost-only by default; exposing to network requires manual configuration and introduces security considerations","No built-in authentication, rate limiting, or request queuing; production deployments require external API gateway","Streaming responses require client-side handling of chunked HTTP responses; no built-in retry logic or connection pooling","SDK documentation and examples are minimal; integration patterns must be inferred from Ollama core documentation"],"requires":["Ollama 0.1.0+ (specific version not documented)","HTTP client library for REST API calls (curl, requests, fetch, etc.)","Python 3.7+ for Python SDK or Node.js 14+ for JavaScript SDK"],"input_types":["text (CLI prompts or JSON request bodies)","image (multipart form data or base64-encoded in JSON)"],"output_types":["text (streaming or buffered JSON responses with token-level granularity)"],"categories":["tool-use-integration","api-design"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava-llama3__cap_2","uri":"capability://tool.use.integration.cloud.hosted.inference.with.tiered.concurrency.and.gpu.time.billing","name":"cloud-hosted inference with tiered concurrency and gpu-time billing","description":"Ollama Cloud provides managed hosting of the LLaVA Llama 3 model with three subscription tiers (Free, Pro $20/mo, Max $100/mo) that control concurrent model instances and total GPU compute time. Billing is metered by GPU seconds consumed during inference, not by token count, allowing variable-length requests to be priced fairly. Cloud deployment abstracts hardware provisioning and uses NVIDIA Blackwell/Vera Rubin GPU architectures for quantization support.","intents":["I want to use the model via API without managing local hardware or Ollama installation","I need to scale inference across multiple concurrent requests without provisioning infrastructure","I want predictable per-request costs based on actual GPU compute time rather than token counts","I need a managed service with automatic model updates and security patches"],"best_for":["teams without GPU infrastructure or DevOps capacity for local model deployment","applications with variable or bursty inference loads that don't justify dedicated hardware","startups prototyping multimodal features before committing to infrastructure investment"],"limitations":["Free tier limited to 1 concurrent model and light usage, making it unsuitable for production workloads","Pro tier (3 concurrent models) may be insufficient for high-traffic applications; Max tier ($100/mo) required for 10 concurrent models","GPU-time billing model is opaque; no published pricing per inference or per token, making cost estimation difficult","Cloud endpoint latency and time-to-first-token unknown; likely higher than local inference due to network round-trip","No documented SLA, uptime guarantees, or rate limit specifications"],"requires":["Ollama Cloud account (free tier available)","API key for authentication (provisioned via Ollama Cloud dashboard)","HTTP client or Ollama SDK configured with cloud endpoint URL"],"input_types":["image (PNG, JPEG, JPG, SVG, GIF via multipart or base64)","text (natural language prompts)"],"output_types":["text (streaming or buffered JSON responses)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava-llama3__cap_3","uri":"capability://text.generation.language.instruction.following.chat.with.llama.3.instruct.backbone","name":"instruction-following chat with llama 3 instruct backbone","description":"The model inherits Llama 3 Instruct's instruction-following capabilities, enabling it to follow complex multi-step prompts, maintain conversational context across turns, and adapt tone/style based on user directives. This is achieved through supervised fine-tuning on instruction-response pairs during Llama 3's training, combined with XTuner's vision-language fine-tuning that preserves instruction-following while adding visual understanding. The 8K token context window allows multi-turn conversations with image references.","intents":["I want to ask the model to perform specific tasks with images (e.g., 'extract text from this screenshot' or 'describe the mood of this photo')","I need the model to maintain context across multiple turns of conversation with image references","I want to customize the model's behavior with system prompts or role-play instructions","I need the model to follow complex, multi-step reasoning tasks that combine visual and textual analysis"],"best_for":["developers building conversational AI applications with visual context","teams creating interactive tools that require nuanced instruction interpretation","researchers studying instruction-following in multimodal models"],"limitations":["8K token context window limits conversation history; long multi-turn sessions will require context pruning or summarization","Instruction-following quality degrades with ambiguous or contradictory prompts; no documented robustness testing","Model may hallucinate or confabulate visual details not present in images; no built-in confidence scoring or uncertainty quantification","Instruction-following is optimized for English; behavior in other languages unknown"],"requires":["Ollama runtime with llava-llama3 model loaded","Understanding of prompt engineering best practices for instruction-following models"],"input_types":["text (natural language instructions and follow-up questions)","image (visual context for instructions)"],"output_types":["text (instruction-following responses with reasoning)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava-llama3__cap_4","uri":"capability://image.visual.image.captioning.and.visual.description.generation","name":"image captioning and visual description generation","description":"Generates natural language descriptions of images by encoding the image through CLIP-ViT, projecting visual features into Llama 3's embedding space, and using the language model to generate coherent captions. The model can produce captions of varying length and detail based on prompt engineering (e.g., 'describe this image in one sentence' vs. 'provide a detailed description'). This is a direct application of the vision-language architecture without requiring specialized captioning fine-tuning.","intents":["I want to automatically generate alt-text or captions for images in bulk","I need detailed descriptions of visual content for accessibility or documentation purposes","I want to generate image descriptions in different styles or levels of detail"],"best_for":["content creators and publishers needing accessibility compliance (alt-text generation)","teams building image search or discovery systems requiring semantic descriptions","accessibility-focused projects requiring high-quality image descriptions"],"limitations":["Caption quality depends heavily on prompt engineering; no built-in optimization for specific caption styles or domains","No evaluation against standard captioning benchmarks (COCO, Flickr30K); quality relative to specialized captioning models unknown","May produce verbose or redundant descriptions; no built-in summarization or length control beyond prompt-based hints","Hallucination risk: model may describe objects or text not actually present in the image"],"requires":["Ollama runtime with llava-llama3 model","Image in supported format (.png, .jpeg, .jpg, .svg, .gif)"],"input_types":["image (PNG, JPEG, JPG, SVG, GIF)","text (optional caption style prompt, e.g., 'one sentence' or 'detailed')"],"output_types":["text (natural language caption or description)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava-llama3__cap_5","uri":"capability://image.visual.visual.question.answering.with.image.grounded.reasoning","name":"visual question answering with image-grounded reasoning","description":"Answers natural language questions about image content by encoding the image and question together, then using Llama 3's reasoning capabilities to ground answers in visual features. The model performs single-image VQA without requiring separate question-image alignment modules; the CLIP-ViT encoder and Llama 3 attention mechanism jointly attend to relevant image regions and question tokens. Supports open-ended questions (e.g., 'what is happening?') and factual queries (e.g., 'how many objects are in the image?').","intents":["I want to ask questions about specific images and get accurate answers based on visual content","I need to extract factual information from images (counts, text, object identification)","I want to analyze visual content for reasoning tasks (e.g., 'why is this happening?')","I need to build a chatbot that can discuss images with users"],"best_for":["developers building image search or discovery systems with natural language queries","teams creating accessibility tools that answer questions about visual content","researchers studying visual reasoning in language models"],"limitations":["No documented VQA benchmark performance (e.g., VQA v2, OK-VQA scores); accuracy relative to specialized VQA models unknown","Struggles with counting tasks, spatial reasoning, and fine-grained visual details; no ablation studies documenting failure modes","May confabulate answers when visual information is ambiguous or insufficient; no confidence scoring or uncertainty quantification","Single-image VQA only; cannot reason across multiple images or temporal sequences","Context window (8K tokens) limits multi-turn VQA conversations with detailed follow-ups"],"requires":["Ollama runtime with llava-llama3 model","Image in supported format","Natural language question as text input"],"input_types":["image (PNG, JPEG, JPG, SVG, GIF)","text (natural language question)"],"output_types":["text (natural language answer)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava-llama3__cap_6","uri":"capability://image.visual.document.and.screenshot.analysis.with.ocr.adjacent.text.understanding","name":"document and screenshot analysis with ocr-adjacent text understanding","description":"Analyzes documents, screenshots, and diagrams by encoding visual content and using Llama 3 to extract and reason about text and layout information. While not a dedicated OCR system, the model can read text from images, understand document structure, and answer questions about content. This works through CLIP-ViT's ability to encode text-heavy images and Llama 3's language understanding, enabling tasks like form field extraction, code snippet analysis from screenshots, and document summarization.","intents":["I want to extract text and information from screenshots or scanned documents","I need to analyze code snippets or technical diagrams from images","I want to understand the structure and content of forms or documents visually","I need to summarize or answer questions about document content from images"],"best_for":["teams automating document processing workflows without dedicated OCR infrastructure","developers building tools that analyze screenshots or code images","accessibility projects requiring document content extraction for screen readers"],"limitations":["Not a dedicated OCR system; accuracy on small text, handwriting, or complex layouts unknown and likely lower than specialized OCR (Tesseract, AWS Textract)","No documented performance on document benchmarks (DocVQA, InfographicVQA); relative accuracy unknown","May struggle with rotated text, multi-column layouts, or dense information; no layout-aware processing","No confidence scores for extracted text; hallucination risk for ambiguous or low-quality images","Maximum image resolution/size constraints unknown; inference latency for high-resolution documents not documented"],"requires":["Ollama runtime with llava-llama3 model","Document or screenshot image in supported format","Clear, legible text in the image (handwriting and very small fonts may fail)"],"input_types":["image (PNG, JPEG, JPG, SVG, GIF of documents, screenshots, or diagrams)","text (optional questions or extraction instructions)"],"output_types":["text (extracted text, answers, or summaries)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava-llama3__cap_7","uri":"capability://automation.workflow.batch.inference.via.cli.or.api.with.streaming.output","name":"batch inference via cli or api with streaming output","description":"Processes multiple images and prompts sequentially through the Ollama CLI or REST API, with streaming responses enabling real-time output collection. The model maintains state between requests (GPU memory is not released between calls), allowing efficient batch processing without repeated model loading. Streaming is implemented via chunked HTTP responses or line-delimited JSON, enabling applications to render output incrementally without waiting for full generation.","intents":["I want to process a batch of images with the same question or task","I need to integrate image analysis into a data pipeline or ETL workflow","I want to stream model output to users in real-time as it's generated","I need to process images from a queue or message broker asynchronously"],"best_for":["teams building image processing pipelines or batch jobs","developers implementing real-time streaming UIs with incremental output rendering","applications processing images from queues (SQS, RabbitMQ, Kafka)"],"limitations":["No built-in batching optimization; requests are processed sequentially, not in parallel batches","Streaming requires client-side handling of chunked responses; no built-in retry logic or connection pooling","No request queuing or priority scheduling; high-load scenarios may cause request timeouts","GPU memory is held between requests; long-running batch jobs may exhaust memory if not monitored","No built-in progress tracking or job status reporting for batch operations"],"requires":["Ollama runtime with llava-llama3 model loaded","HTTP client or SDK supporting streaming responses","Batch input (list of images and prompts) in application memory or file system"],"input_types":["image (PNG, JPEG, JPG, SVG, GIF)","text (prompts or questions)"],"output_types":["text (streaming or buffered responses)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-llava-llama3__cap_8","uri":"capability://safety.moderation.offline.inference.with.no.cloud.dependencies.or.api.keys","name":"offline inference with no cloud dependencies or api keys","description":"Runs the entire vision-language model locally on user hardware without requiring cloud API calls, internet connectivity, or API keys. The GGUF quantized format (5.5GB) is downloaded once and cached locally; all inference happens on-device using Ollama's optimized inference runtime. This enables privacy-preserving analysis where images and prompts never leave the user's machine, and eliminates API rate limits, latency, and per-request costs.","intents":["I want to analyze sensitive images without sending them to cloud services","I need to run inference without internet connectivity or API key management","I want to eliminate per-request API costs and rate limiting for high-volume inference","I need to deploy the model in air-gapped or regulated environments"],"best_for":["teams handling sensitive or regulated data (healthcare, finance, government)","developers building privacy-first applications","organizations in regions with restricted cloud access or data residency requirements","projects requiring offline-first functionality"],"limitations":["Requires local GPU or CPU with sufficient VRAM; minimum requirements unknown (Ollama documentation does not specify)","Model inference latency is hardware-dependent; slower than cloud GPUs for users with consumer hardware","No automatic model updates; users must manually pull new versions from Ollama library","Debugging and support is limited to Ollama community; no vendor SLA or guaranteed uptime","Scaling to multiple concurrent requests requires manual load balancing or multiple model instances"],"requires":["Ollama runtime installed (macOS, Windows, Linux, or Docker)","5.5GB disk space for GGUF model","GPU with unknown minimum VRAM (Ollama documentation does not specify; likely 4-8GB for 8B model)","No internet connectivity required after initial model download"],"input_types":["image (PNG, JPEG, JPG, SVG, GIF)","text (prompts)"],"output_types":["text (responses)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Ollama runtime (macOS, Windows, Linux, or Docker)","5.5GB disk space for GGUF quantized model","Minimum GPU VRAM requirement unknown (Ollama documentation does not specify for this model)","Image input in .png, .jpeg, .jpg, .svg, or .gif format","Ollama 0.1.0+ (specific version not documented)","HTTP client library for REST API calls (curl, requests, fetch, etc.)","Python 3.7+ for Python SDK or Node.js 14+ for JavaScript SDK","Ollama Cloud account (free tier available)","API key for authentication (provisioned via Ollama Cloud dashboard)","HTTP client or Ollama SDK configured with cloud endpoint URL"],"failure_modes":["Fixed 8K token context window cannot be extended, limiting analysis of very long image sequences or detailed multi-image reasoning","CLIP-ViT-Large-patch14-336 vision encoder is frozen and cannot be fine-tuned, constraining adaptation to domain-specific visual patterns","No documented maximum image resolution or size constraints; inference latency for high-resolution images unknown","Model last updated 1 year ago; may lack knowledge of recent visual concepts or events","No built-in image generation capability despite artifact categorization; purely analytical","REST API is localhost-only by default; exposing to network requires manual configuration and introduces security considerations","No built-in authentication, rate limiting, or request queuing; production deployments require external API gateway","Streaming responses require client-side handling of chunked HTTP responses; no built-in retry logic or connection pooling","SDK documentation and examples are minimal; integration patterns must be inferred from Ollama core documentation","Free tier limited to 1 concurrent model and light usage, making it unsuitable for production workloads","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"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-llama3","compare_url":"https://unfragile.ai/compare?artifact=llava-llama3"}},"signature":"MtOWhYWi2TUff+uCO4KE1hIyPRnp3F/0Zgq/+fbi3KT+t7YqgRDSrKTbY3sXycevf0NDuMr0h5xGMGhqjEviCQ==","signedAt":"2026-06-22T13:22:06.050Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llava-llama3","artifact":"https://unfragile.ai/llava-llama3","verify":"https://unfragile.ai/api/v1/verify?slug=llava-llama3","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"}}