{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"moondream","slug":"moondream","name":"Moondream","type":"model","url":"https://github.com/vikhyat/moondream","page_url":"https://unfragile.ai/moondream","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"moondream__cap_0","uri":"capability://image.visual.compact.vision.language.inference.with.sub.2b.parameter.models","name":"compact vision-language inference with sub-2b parameter models","description":"Executes multimodal inference using a lightweight vision-language architecture (2B or 0.5B parameters) that combines a vision encoder for image understanding with a text decoder for natural language generation. The MoondreamModel class orchestrates vision encoding, text processing, and spatial reasoning subsystems through a unified query() interface, enabling efficient inference on edge devices and resource-constrained hardware without cloud dependencies.","intents":["Run vision-language models locally on edge devices without cloud API calls","Deploy multimodal AI on devices with <4GB RAM or limited compute","Build offline-capable applications that process images and answer questions about them","Reduce latency and privacy concerns by keeping inference on-device"],"best_for":["Edge device developers (mobile, IoT, embedded systems)","Privacy-conscious teams avoiding cloud inference","Resource-constrained environments (Raspberry Pi, mobile phones, industrial devices)","Builders needing sub-100ms inference latency for real-time applications"],"limitations":["Model size (2B parameters) trades off accuracy vs. larger models (13B+); performance gaps on complex reasoning tasks","Limited context window compared to larger VLMs; struggles with very long documents or multi-page analysis","No built-in batching optimization; single-image inference only without custom batching logic","Quantization required for sub-1GB deployment; int8/int4 quantization reduces accuracy by 2-5%"],"requires":["Python 3.8+","PyTorch 1.13+ or compatible inference framework","2-4GB RAM minimum for 2B model, 1-2GB for 0.5B model","Hugging Face transformers library for model loading","Optional: ONNX Runtime or TensorRT for optimized inference"],"input_types":["image (PIL Image, numpy array, file path)","text (natural language query or prompt)"],"output_types":["text (natural language response)","structured data (coordinates, bounding boxes for object detection)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"moondream__cap_1","uri":"capability://image.visual.visual.question.answering.with.spatial.reasoning","name":"visual question answering with spatial reasoning","description":"Processes natural language questions about image content and generates contextually accurate answers by encoding the image through a vision encoder, fusing visual features with text embeddings, and decoding responses through transformer blocks. The system maintains spatial awareness through region encoding that maps pixel coordinates to semantic understanding, enabling answers about object locations, spatial relationships, and visual attributes without explicit bounding box annotations during inference.","intents":["Ask questions about what's in an image and get natural language answers","Understand spatial relationships between objects (e.g., 'Is the cat to the left of the dog?')","Extract information from visual content without manual annotation","Build interactive visual search or image understanding applications"],"best_for":["Developers building image Q&A interfaces or chatbots","Teams automating visual content analysis workflows","Applications requiring spatial reasoning without training custom detectors","Accessibility tools that describe images to users"],"limitations":["Accuracy degrades on complex multi-step reasoning (e.g., 'count all red objects and describe their arrangement'); single-hop reasoning is more reliable","Struggles with fine-grained visual distinctions (e.g., distinguishing similar dog breeds); trained primarily on general object categories","No explicit attention visualization; difficult to debug why specific answers were generated","Context limited to single image; cannot reason across multiple images or temporal sequences"],"requires":["Image input (PIL Image, numpy array, or file path)","Text query in natural language","Model weights loaded via Hugging Face (moondream2 or moondream-0.5b)"],"input_types":["image","text (natural language question)"],"output_types":["text (natural language answer)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"moondream__cap_10","uri":"capability://automation.workflow.command.line.interface.for.batch.inference.and.scripting","name":"command-line interface for batch inference and scripting","description":"Exposes model capabilities through a command-line interface (CLI) that accepts image paths, queries, and output format specifications, enabling batch processing and integration into shell scripts or automation pipelines. The CLI handles image loading, model inference, and result formatting without requiring Python code, making the model accessible to non-Python developers and enabling easy integration into existing workflows.","intents":["Process multiple images in batch mode from command line","Integrate model inference into shell scripts or CI/CD pipelines","Enable non-Python developers to use the model","Automate image analysis workflows without writing Python code"],"best_for":["DevOps engineers integrating model inference into pipelines","System administrators automating image processing workflows","Developers building shell-based automation tools","Teams requiring batch processing without Python development"],"limitations":["CLI interface is basic; limited to simple input/output formats (no streaming or real-time feedback)","No built-in progress reporting for batch jobs; difficult to monitor long-running processes","Error handling is minimal; failures in batch processing may not be clearly reported","No parallelization; batch processing is sequential, limiting throughput"],"requires":["Python 3.8+ with moondream installed","Image files accessible from command line (local paths or URLs)","Model weights downloaded (automatic via first run)"],"input_types":["command-line arguments (image path, query, output format)"],"output_types":["text (printed to stdout or written to file)","structured data (JSON output for programmatic parsing)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"moondream__cap_11","uri":"capability://image.visual.coordinate.based.region.pointing.and.gaze.detection","name":"coordinate-based region pointing and gaze detection","description":"Enables precise spatial pointing by outputting pixel coordinates or normalized region coordinates for detected objects or regions of interest, leveraging the region encoder subsystem that maps visual features to coordinate embeddings. The system supports gaze detection (pointing to specific image regions) and coordinate-based queries, enabling applications that require precise spatial references without explicit bounding box annotations during training.","intents":["Point to specific regions in images with pixel-accurate coordinates","Enable coordinate-based queries (e.g., 'What is at position (100, 200)?')","Build interactive image annotation tools with automatic region detection","Generate spatial metadata for image cropping or region-of-interest processing"],"best_for":["Developers building interactive image annotation or editing tools","Teams creating region-based image analysis applications","Applications requiring precise spatial references for downstream processing","Accessibility tools that describe specific image regions"],"limitations":["Coordinate precision depends on image resolution; low-resolution images produce coarse coordinates","No confidence scores for coordinate accuracy; difficult to assess reliability of spatial predictions","Gaze detection limited to single regions; cannot track multiple simultaneous points","Coordinate output format varies (normalized vs. absolute); requires consistent post-processing"],"requires":["Image input with clear, distinct regions","Region encoder enabled in model configuration","Optional: coordinate normalization logic for downstream processing"],"input_types":["image","text (optional: region description or query)"],"output_types":["structured data (coordinates as [x, y] or [x1, y1, x2, y2])","text (region description or analysis)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"moondream__cap_12","uri":"capability://image.visual.vision.encoder.with.overlap.cropping.for.high.resolution.image.handling","name":"vision encoder with overlap cropping for high-resolution image handling","description":"Processes variable-resolution images through a vision encoder that uses overlap_crop_image() strategy to handle high-resolution inputs without exceeding memory constraints. The encoder divides large images into overlapping patches, encodes each patch independently, and combines results through a spatial attention mechanism. This approach enables processing of high-resolution documents and charts that would otherwise exceed GPU memory limits. The encoder outputs a compact feature representation suitable for downstream text generation.","intents":["Process high-resolution images (4K, 8K) on memory-constrained devices","Analyze detailed document images without quality loss","Handle variable-resolution inputs without preprocessing","Maintain spatial coherence across image patches"],"best_for":["document processing teams handling high-resolution scans","edge device developers with strict memory budgets","applications requiring fine-grained visual understanding","systems processing diverse image resolutions"],"limitations":["Overlap cropping adds computational overhead (~20-30% slower than single-pass encoding)","Patch boundaries may cause artifacts in spatial reasoning tasks","Optimal patch size and overlap ratio require tuning per use case","Memory savings come at the cost of increased latency"],"requires":["Vision encoder module (part of Moondream)","Image input (any resolution)","GPU memory: 2GB+ for 2B model, 1GB+ for 0.5B model"],"input_types":["image (JPEG, PNG, WebP, PIL Image, torch.Tensor)","optional: patch size and overlap parameters"],"output_types":["encoded features (torch.Tensor, shape [seq_len, hidden_dim])","spatial metadata (patch positions, overlap regions)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"moondream__cap_13","uri":"capability://text.generation.language.text.encoder.and.decoder.with.transformer.based.generation","name":"text encoder and decoder with transformer-based generation","description":"Generates natural language outputs through a transformer-based text encoder/decoder architecture. The encoder processes visual features and text prompts, while the decoder generates tokens autoregressively using standard transformer attention mechanisms. Supports configurable generation parameters (temperature, top-k, top-p sampling) for controlling output diversity and quality. The text processing subsystem integrates with the vision encoder through cross-attention, enabling grounded language generation that references visual content.","intents":["Generate natural language descriptions grounded in visual content","Control output diversity and quality through generation parameters","Implement custom decoding strategies (beam search, nucleus sampling)","Fine-tune text generation for domain-specific language patterns"],"best_for":["developers building conversational vision-language systems","teams requiring fine-grained control over generation quality","researchers studying vision-language grounding","applications with specific language style requirements"],"limitations":["Autoregressive generation is slow compared to non-autoregressive alternatives (~50-200ms per output)","No built-in beam search or advanced decoding strategies; requires custom implementation","Generation parameters (temperature, top-k) require manual tuning per use case","No native support for constrained generation (e.g., generating only specific object names)"],"requires":["Text encoder/decoder module (part of Moondream)","Visual features from vision encoder","Optional: generation parameters (temperature, max_tokens, top_k, top_p)"],"input_types":["visual features (torch.Tensor from vision encoder)","text prompt (optional, for VQA mode)","generation parameters (dict with temperature, top_k, etc.)"],"output_types":["generated text tokens (torch.Tensor)","decoded text string","generation metadata (tokens used, log probabilities)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"moondream__cap_2","uri":"capability://image.visual.object.detection.and.localization.with.coordinate.output","name":"object detection and localization with coordinate output","description":"Detects objects within images and outputs their spatial locations as pixel coordinates or normalized bounding boxes by leveraging the region encoder subsystem that transforms visual features into coordinate-aware embeddings. The system generates structured output (bounding box coordinates, confidence scores) through a specialized decoding path that interprets spatial tokens from the vision encoder, enabling precise object localization without requiring separate YOLO or Faster R-CNN models.","intents":["Detect and locate specific objects in images with bounding box coordinates","Build object detection pipelines without training custom detectors","Generate structured spatial data (coordinates, regions) from images for downstream processing","Implement visual search or region-based image analysis"],"best_for":["Developers needing lightweight object detection without model training","Teams building region-based image analysis tools","Applications requiring coordinate output for cropping or region-of-interest processing","Mobile/edge applications where YOLO or Faster R-CNN are too large"],"limitations":["Detection accuracy lower than specialized detectors (YOLO, Faster R-CNN); ~5-10% mAP gap on COCO benchmarks","Limited to ~10-20 objects per image before performance degrades; not optimized for dense object scenes","Coordinate precision varies with image resolution; normalized coordinates may lose sub-pixel accuracy","No confidence scores or class probabilities; binary detection only (object present/absent)"],"requires":["Image input with clear, distinct objects","Model variant with region encoder enabled (both 2B and 0.5B support this)","Optional: post-processing logic to filter or refine coordinates"],"input_types":["image","text (optional: object class or description to detect)"],"output_types":["structured data (bounding box coordinates as [x1, y1, x2, y2] or normalized format)","text (object labels or descriptions)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"moondream__cap_3","uri":"capability://image.visual.image.captioning.and.dense.visual.description","name":"image captioning and dense visual description","description":"Generates natural language descriptions of image content by encoding the full image through the vision encoder and decoding a sequence of text tokens via transformer blocks that attend to visual features. The system produces coherent, contextually relevant captions without explicit prompting, using the text decoder to generate descriptions that capture objects, actions, attributes, and spatial relationships present in the image.","intents":["Generate alt-text or captions for images automatically","Create natural language descriptions of visual content for accessibility","Build image-to-text pipelines for content management systems","Summarize image content in a single sentence or paragraph"],"best_for":["Content creators needing automated alt-text generation","Accessibility teams building image description tools","Digital asset management systems requiring auto-tagging","Developers building image-to-text pipelines for downstream NLP tasks"],"limitations":["Captions are generic and may miss fine-grained details; trained on broad image datasets (COCO, Flickr)","Hallucination risk: model may describe objects not present in image, especially for ambiguous or low-quality images","No control over caption length or style; fixed decoding strategy produces variable-length outputs","Struggles with text-heavy images (documents, screenshots); vision encoder not optimized for OCR-like tasks"],"requires":["Image input (PIL Image, numpy array, or file path)","Model weights loaded (moondream2 or moondream-0.5b)","Optional: prompt engineering to guide caption style"],"input_types":["image"],"output_types":["text (natural language caption, typically 10-50 words)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"moondream__cap_4","uri":"capability://image.visual.document.and.chart.visual.understanding","name":"document and chart visual understanding","description":"Analyzes document images, charts, and diagrams by processing high-resolution visual content through the vision encoder with overlap_crop_image() preprocessing that tiles images into overlapping patches to preserve fine-grained details. The system answers questions about document structure, chart data, and visual information extraction through the VQA pipeline, enabling document understanding without OCR or specialized document parsing models.","intents":["Extract information from document images (forms, invoices, receipts)","Analyze charts and graphs to answer questions about data","Understand document layout and structure visually","Build document processing pipelines without OCR or template matching"],"best_for":["Teams automating document processing workflows","Developers building document Q&A systems","Applications requiring chart or graph analysis","Accessibility tools that describe document structure to users"],"limitations":["Accuracy on structured data extraction (tables, forms) is lower than specialized OCR+parsing pipelines; ~70-80% accuracy vs. 95%+ for dedicated tools","Struggles with small text or low-resolution documents; requires minimum ~100 DPI for reliable understanding","No native table extraction; cannot output structured table data without post-processing","Chart understanding limited to simple charts (bar, pie, line); complex multi-axis or 3D charts may confuse the model"],"requires":["Document or chart image (PDF pages converted to images, screenshots, etc.)","Minimum resolution ~100 DPI for readable text","Model weights loaded (moondream2 recommended over 0.5b for document tasks)"],"input_types":["image (document, chart, or diagram)","text (question about document content)"],"output_types":["text (extracted information, chart insights, document descriptions)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"moondream__cap_5","uri":"capability://image.visual.real.time.video.frame.analysis.and.redaction","name":"real-time video frame analysis and redaction","description":"Processes video frames sequentially through the vision encoder to perform frame-by-frame analysis, enabling real-time object detection, content filtering, or redaction by applying the VQA and detection capabilities to each frame. The system includes a video redaction application that detects sensitive content (faces, text, objects) and applies masking or blurring, leveraging the region encoder to output coordinates for redaction masks without requiring separate video processing frameworks.","intents":["Analyze video content frame-by-frame for object detection or scene understanding","Redact sensitive information (faces, license plates, text) from video streams","Build real-time video understanding applications on edge devices","Extract structured data (object locations, scene descriptions) from video"],"best_for":["Developers building privacy-preserving video processing tools","Teams automating video content analysis or moderation","Edge applications requiring real-time video understanding","Security or surveillance systems needing on-device processing"],"limitations":["Frame-by-frame processing has no temporal coherence; cannot track objects across frames or understand motion","Latency scales with video resolution and frame rate; 30 FPS at 1080p requires ~33ms per frame inference","No built-in video codec support; requires external libraries (OpenCV, ffmpeg) for video I/O","Redaction accuracy depends on detection accuracy; missed detections result in incomplete redaction"],"requires":["Video input (file path or stream) with external codec support (OpenCV, ffmpeg)","Frame extraction logic (not built-in; requires custom preprocessing)","Model weights loaded (moondream2 or 0.5b)","Optional: GPU acceleration for real-time performance at high frame rates"],"input_types":["image (video frame as PIL Image or numpy array)"],"output_types":["image (redacted frame with masks/blurs applied)","structured data (coordinates for redaction regions)","text (frame descriptions or analysis)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"moondream__cap_6","uri":"capability://memory.knowledge.model.weight.loading.and.variant.management","name":"model weight loading and variant management","description":"Manages model weight loading and variant selection through a configuration system (MoondreamConfig) that specifies architecture parameters, model size (2B vs. 0.5B), and quantization settings. The system integrates with Hugging Face Hub for automatic weight downloading and caching, supporting multiple model variants with different parameter counts and enabling dynamic model selection based on hardware constraints or accuracy requirements.","intents":["Load pre-trained model weights from Hugging Face Hub with automatic caching","Switch between model variants (2B, 0.5B) based on hardware constraints","Configure quantization settings for memory-constrained deployment","Manage model versioning and weight updates across applications"],"best_for":["Developers deploying models across heterogeneous hardware","Teams managing model versioning and updates","Applications requiring dynamic model selection based on device capabilities","Builders needing reproducible model loading across environments"],"limitations":["No built-in weight quantization; requires external tools (bitsandbytes, GPTQ) for int8/int4 compression","Limited variant support; only 2B and 0.5B models available (no fine-tuned variants pre-packaged)","No local weight caching control; Hugging Face cache directory must be writable","Model switching requires full reload; no in-memory variant swapping"],"requires":["Hugging Face transformers library (1.30+)","Internet connection for initial weight download","Disk space: ~4GB for 2B model, ~1GB for 0.5B model (unquantized)","Hugging Face API token (optional, for private model access)"],"input_types":["configuration (model variant, quantization settings)"],"output_types":["model object (MoondreamModel instance ready for inference)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"moondream__cap_7","uri":"capability://code.generation.editing.fine.tuning.and.model.adaptation.for.custom.tasks","name":"fine-tuning and model adaptation for custom tasks","description":"Supports fine-tuning of text encoder and region encoder components on custom datasets through a modular training system that freezes the vision encoder and adapts downstream components. The system includes dataset loaders for document VQA, chart QA, and custom tasks, enabling task-specific model adaptation without retraining the full vision encoder, reducing training time and data requirements while maintaining pre-trained visual understanding.","intents":["Adapt the model to domain-specific tasks (medical imaging, industrial inspection) with limited labeled data","Fine-tune text decoder for specific output formats or vocabularies","Improve performance on custom datasets without full model retraining","Build specialized models for niche applications (e.g., plant disease detection)"],"best_for":["Teams with domain-specific image understanding tasks","Developers building specialized models with limited training budgets","Applications requiring adaptation to proprietary or sensitive datasets","Researchers experimenting with vision-language model architectures"],"limitations":["Fine-tuning infrastructure requires PyTorch and GPU; no built-in distributed training support","Limited guidance on hyperparameter selection; requires experimentation for optimal results","No automatic data augmentation; custom augmentation pipelines must be implemented","Frozen vision encoder limits adaptation to visual features; cannot improve base image understanding"],"requires":["PyTorch 1.13+ with CUDA support (GPU recommended)","Custom dataset with image-text pairs or VQA annotations","Training script (reference implementations in sample.py and evaluation modules)","Computational resources: 8GB+ VRAM for batch size 8-16"],"input_types":["image-text pairs (for captioning fine-tuning)","image-question-answer triplets (for VQA fine-tuning)","image-region pairs (for region encoder fine-tuning)"],"output_types":["model weights (fine-tuned text encoder or region encoder)","evaluation metrics (loss, accuracy on validation set)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"moondream__cap_8","uri":"capability://data.processing.analysis.comprehensive.model.evaluation.and.benchmarking","name":"comprehensive model evaluation and benchmarking","description":"Provides evaluation infrastructure for assessing model performance across multiple benchmarks (VQA, document understanding, chart analysis, real-world QA) through scoring utilities and dataset loaders. The system includes evaluation scripts that compute metrics (accuracy, BLEU, CIDEr) on standard benchmarks, enabling quantitative comparison against baselines and tracking performance across model variants and fine-tuning iterations.","intents":["Benchmark model performance on standard VQA and document understanding datasets","Compare model variants (2B vs. 0.5B) on accuracy and speed trade-offs","Track performance improvements from fine-tuning on custom datasets","Generate evaluation reports for model validation and deployment decisions"],"best_for":["Researchers evaluating vision-language model performance","Teams validating model quality before production deployment","Developers comparing model variants for specific use cases","Organizations requiring quantitative performance metrics for compliance"],"limitations":["Evaluation limited to supported benchmarks (VQA, DocVQA, ChartQA); custom metrics require custom implementation","Benchmark datasets must be downloaded separately; no automatic dataset provisioning","Evaluation metrics (BLEU, CIDEr) may not correlate with human perception; requires manual validation","No built-in statistical significance testing; results may vary with random seeds"],"requires":["Benchmark datasets (VQA v2, DocVQA, ChartQA, etc.) downloaded and formatted","Evaluation scripts (provided in repository)","Model weights loaded (moondream2 or 0.5b)","Optional: GPU for faster evaluation on large datasets"],"input_types":["image-question-answer triplets (VQA format)","image-text pairs (captioning format)"],"output_types":["structured data (evaluation metrics: accuracy, BLEU, CIDEr scores)","text (evaluation reports, performance summaries)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"moondream__cap_9","uri":"capability://tool.use.integration.gradio.web.interface.and.interactive.demos","name":"gradio web interface and interactive demos","description":"Provides interactive web-based interfaces for model testing and demonstration through Gradio applications that expose image upload, text input, and result visualization. The system includes pre-built Gradio demos for image captioning, VQA, and object detection, enabling non-technical users to interact with the model through a browser without writing code, while developers can extend or customize the interface for specific applications.","intents":["Create interactive demos for model evaluation and stakeholder feedback","Build web interfaces for image understanding tasks without frontend engineering","Enable non-technical users to test model capabilities","Prototype applications quickly with minimal UI development"],"best_for":["Researchers sharing model demos with collaborators","Teams building quick prototypes for stakeholder feedback","Developers creating simple web interfaces for image analysis","Educational applications demonstrating vision-language models"],"limitations":["Gradio interfaces are basic; limited customization for production UIs","No built-in authentication or rate limiting; not suitable for public-facing applications","Single-user inference; no concurrent request handling or queuing","Deployment requires Gradio server; no static HTML export for simple hosting"],"requires":["Gradio library (0.40+)","Model weights loaded (moondream2 or 0.5b)","Python environment with dependencies installed","Optional: Gradio Share for public URL generation"],"input_types":["image (uploaded via web interface)","text (typed into web form)"],"output_types":["text (model response displayed in web interface)","image (annotated images with bounding boxes or redactions)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"moondream__headline","uri":"capability://image.visual.compact.vision.language.model.for.edge.devices","name":"compact vision-language model for edge devices","description":"Moondream is an ultra-compact vision-language model designed to efficiently process images and text, enabling tasks like image captioning, visual question answering, and object detection on resource-constrained environments.","intents":["best vision-language model","vision-language model for edge devices","efficient image processing model","image captioning tool","visual question answering system","object detection model for low-resource environments"],"best_for":["edge devices","resource-constrained environments"],"limitations":["limited to visual and textual tasks"],"requires":["compatible hardware"],"input_types":["images","text prompts"],"output_types":["text descriptions","answers to questions"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.13+ or compatible inference framework","2-4GB RAM minimum for 2B model, 1-2GB for 0.5B model","Hugging Face transformers library for model loading","Optional: ONNX Runtime or TensorRT for optimized inference","Image input (PIL Image, numpy array, or file path)","Text query in natural language","Model weights loaded via Hugging Face (moondream2 or moondream-0.5b)","Python 3.8+ with moondream installed","Image files accessible from command line (local paths or URLs)"],"failure_modes":["Model size (2B parameters) trades off accuracy vs. larger models (13B+); performance gaps on complex reasoning tasks","Limited context window compared to larger VLMs; struggles with very long documents or multi-page analysis","No built-in batching optimization; single-image inference only without custom batching logic","Quantization required for sub-1GB deployment; int8/int4 quantization reduces accuracy by 2-5%","Accuracy degrades on complex multi-step reasoning (e.g., 'count all red objects and describe their arrangement'); single-hop reasoning is more reliable","Struggles with fine-grained visual distinctions (e.g., distinguishing similar dog breeds); trained primarily on general object categories","No explicit attention visualization; difficult to debug why specific answers were generated","Context limited to single image; cannot reason across multiple images or temporal sequences","CLI interface is basic; limited to simple input/output formats (no streaming or real-time feedback)","No built-in progress reporting for batch jobs; difficult to monitor long-running processes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:04.693Z","last_scraped_at":null,"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=moondream","compare_url":"https://unfragile.ai/compare?artifact=moondream"}},"signature":"lfPe+WdsjXpO07Vcv5cjYBfwmI2c5ByX1Vqp4aA55XojsUhf+IcFeFmiXLAx0MF11SbJEjuW+Sf1s5RmSwsSDA==","signedAt":"2026-06-21T03:41:39.977Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/moondream","artifact":"https://unfragile.ai/moondream","verify":"https://unfragile.ai/api/v1/verify?slug=moondream","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"}}