{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hugging-face-spaces","slug":"hugging-face-spaces","name":"Hugging Face Spaces","type":"platform","url":"https://huggingface.co/spaces","page_url":"https://unfragile.ai/hugging-face-spaces","categories":["deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hugging-face-spaces__cap_0","uri":"capability://automation.workflow.gradio.app.containerization.and.deployment","name":"gradio app containerization and deployment","description":"Automatically packages Gradio Python applications into isolated Docker containers with automatic dependency detection from requirements.txt or pyproject.toml, then deploys them to Hugging Face's managed infrastructure with automatic HTTPS endpoints and public URLs. The platform detects Gradio imports and interface definitions, infers resource requirements, and handles container orchestration without requiring manual Dockerfile configuration.","intents":["Deploy a Gradio demo without writing Docker or managing servers","Share an interactive ML model interface with a public URL instantly","Iterate on a Gradio app and see changes reflected live without redeployment"],"best_for":["ML researchers sharing model demos","solo developers prototyping interactive interfaces","teams building quick proof-of-concepts without DevOps expertise"],"limitations":["Limited to Python-based Gradio apps; no native support for other frameworks","Cold start latency ~30-60 seconds on first request after deployment","Default timeout of 60 seconds per request; long-running inference requires async patterns","No built-in request queuing for concurrent users on free tier"],"requires":["Python 3.7+","Gradio library installed (any recent version)","Hugging Face account","requirements.txt or pyproject.toml for dependency specification"],"input_types":["Python source code","requirements.txt or pyproject.toml","Model files (local or remote URLs)"],"output_types":["Public HTTPS endpoint","Interactive web interface","Shareable Space URL"],"categories":["automation-workflow","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-spaces__cap_1","uri":"capability://automation.workflow.streamlit.app.deployment.with.persistent.state","name":"streamlit app deployment with persistent state","description":"Deploys Streamlit applications with automatic session state management and file-based persistence across reruns. The platform detects Streamlit imports, manages the rerun cycle, and provides a mounted filesystem for storing user uploads, cached models, and application state without requiring external databases. Streamlit's reactive programming model is preserved end-to-end.","intents":["Deploy a Streamlit dashboard that maintains state across user interactions","Build a data exploration tool where users can upload files and see results persist","Create a multi-page Streamlit app with navigation and session persistence"],"best_for":["data scientists building interactive dashboards","teams prototyping data apps with minimal backend code","developers familiar with Streamlit's reactive paradigm"],"limitations":["Streamlit reruns entire script on every interaction, causing latency for heavy computations","Session state is per-user and ephemeral; no cross-user state sharing without external DB","File uploads limited to 200MB per file on free tier","No native support for WebSocket-based real-time updates"],"requires":["Python 3.7+","Streamlit library installed","Hugging Face account","requirements.txt for dependencies"],"input_types":["Python source code","requirements.txt","User-uploaded files (CSV, JSON, images, etc.)"],"output_types":["Interactive web dashboard","Public HTTPS endpoint","Cached computation results"],"categories":["automation-workflow","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-spaces__cap_10","uri":"capability://data.processing.analysis.model.quantization.and.optimization.detection","name":"model quantization and optimization detection","description":"Automatically detects and applies model optimizations (quantization, pruning, distillation) when models are loaded from Hugging Face Hub. The platform identifies quantized variants of popular models (GGUF, AWQ, GPTQ) and suggests optimized versions that reduce memory footprint and inference latency. Integration with libraries like bitsandbytes and GPTQ enables transparent quantization without code changes.","intents":["Deploy large models on limited GPU memory by automatically using quantized variants","Reduce inference latency by using optimized model formats","Discover and use community-contributed quantized models without manual configuration"],"best_for":["teams deploying large language models on limited hardware","developers optimizing inference performance","researchers experimenting with model compression techniques"],"limitations":["Quantization quality varies; some models lose significant accuracy when quantized","Not all models have quantized variants available on Hugging Face Hub","Automatic quantization detection requires models to follow Hub naming conventions","No built-in benchmarking; users must manually evaluate accuracy trade-offs"],"requires":["Model hosted on Hugging Face Hub","Quantization-compatible libraries (bitsandbytes, GPTQ, etc.)","Python code using transformers library"],"input_types":["Model identifier (e.g., 'meta-llama/Llama-2-7b')","Quantization parameters"],"output_types":["Optimized model weights","Inference results","Performance metrics"],"categories":["data-processing-analysis","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-spaces__cap_11","uri":"capability://tool.use.integration.webhook.based.event.notifications.and.integrations","name":"webhook-based event notifications and integrations","description":"Provides webhook endpoints that trigger external services when Space events occur (deployment success/failure, user interactions, resource limits exceeded). Users configure webhooks to send notifications to Slack, Discord, or custom HTTP endpoints. The platform retries failed webhook deliveries with exponential backoff and provides a delivery log for debugging.","intents":["Get notified when a Space deployment fails or succeeds","Send Space metrics or user interactions to external monitoring systems","Trigger downstream workflows (e.g., update a dashboard) when a Space event occurs"],"best_for":["teams monitoring multiple Spaces in production","developers integrating Spaces with external tools","organizations building automated workflows"],"limitations":["Webhook delivery is not guaranteed; no built-in acknowledgment mechanism","Retry logic uses exponential backoff; failed deliveries may be delayed","No built-in filtering; all events are sent to the webhook endpoint","Webhook payloads are not signed; external services must validate authenticity"],"requires":["Hugging Face account with Space edit permissions","External webhook endpoint (HTTP POST)","Network connectivity between Hugging Face and webhook endpoint"],"input_types":["Webhook URL","Event types to subscribe to"],"output_types":["HTTP POST requests","Event payloads (JSON)","Delivery logs"],"categories":["tool-use-integration","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-spaces__cap_12","uri":"capability://memory.knowledge.hugging.face.hub.model.integration.and.auto.download","name":"hugging face hub model integration and auto-download","description":"Seamlessly integrates with Hugging Face Hub to automatically download and cache models, datasets, and tokenizers. The platform detects imports from the transformers library and automatically resolves model identifiers (e.g., 'meta-llama/Llama-2-7b') to Hub URLs, handling authentication for gated models via Hugging Face API tokens. Downloaded artifacts are cached in persistent storage to avoid repeated downloads.","intents":["Load a model from Hugging Face Hub without manually downloading files","Access gated models (requiring authentication) using Hugging Face API tokens","Cache large model files to avoid repeated downloads on Space restarts"],"best_for":["developers using Hugging Face models in Spaces","teams deploying models from the Hub","researchers accessing gated or private models"],"limitations":["Model downloads are not parallelized; large models may take minutes to download","No built-in bandwidth throttling; large downloads may consume significant resources","Gated model access requires Hugging Face API token; no support for other authentication methods","Model caching is LRU-based; old models are evicted when storage quota is exceeded"],"requires":["Hugging Face account","Hugging Face API token (for gated models)","transformers library or equivalent Hub client"],"input_types":["Model identifier (string)","Hugging Face API token"],"output_types":["Model weights","Tokenizer files","Configuration files"],"categories":["memory-knowledge","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-spaces__cap_2","uri":"capability://automation.workflow.gpu.accelerated.inference.with.automatic.hardware.allocation","name":"gpu-accelerated inference with automatic hardware allocation","description":"Allocates GPU resources (NVIDIA T4, A100, or A10G) to Spaces on-demand based on app requirements, with automatic driver installation and CUDA toolkit provisioning. The platform detects GPU-dependent libraries (PyTorch, TensorFlow, ONNX) and provisions appropriate hardware; users specify GPU tier in Space settings, and the platform handles resource scheduling and billing.","intents":["Run GPU-accelerated model inference without managing CUDA or driver installation","Deploy a large language model or vision model that requires GPU memory","Scale inference across multiple GPU types based on performance requirements"],"best_for":["researchers deploying large models (7B+ parameters)","teams building real-time computer vision applications","developers prototyping GPU-intensive workloads before production deployment"],"limitations":["GPU allocation is not guaranteed on free tier; may be throttled during high demand","Paid GPU tiers have hourly costs ($0.50-$6.00/hour depending on GPU type)","No multi-GPU parallelism support; single GPU per Space instance","GPU memory is shared across concurrent requests; no built-in request queuing"],"requires":["Hugging Face account","GPU-compatible code (PyTorch, TensorFlow, ONNX, etc.)","Sufficient Hub storage quota for model files","Optional: paid subscription for guaranteed GPU access"],"input_types":["Python code with GPU-dependent libraries","Model weights (local or from Hugging Face Hub)","User input (text, images, audio)"],"output_types":["Inference results","Latency metrics","GPU utilization logs"],"categories":["automation-workflow","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-spaces__cap_3","uri":"capability://memory.knowledge.persistent.storage.with.automatic.model.caching","name":"persistent storage with automatic model caching","description":"Provides a mounted filesystem (typically 50GB on free tier) that persists across Space restarts and redeployments. The platform automatically caches downloaded models from Hugging Face Hub, PyPI, and other sources to avoid repeated downloads; implements LRU eviction when storage quota is exceeded. Users can store application state, user uploads, and cached artifacts without external storage services.","intents":["Cache large model files so they're not re-downloaded on every restart","Store user-uploaded files and intermediate computation results","Maintain application state (databases, logs, configuration) across deployments"],"best_for":["teams deploying models larger than available RAM","applications with frequent restarts or auto-scaling","developers building stateful applications without external databases"],"limitations":["Storage quota is 50GB on free tier; paid tiers offer up to 500GB","No built-in backup or versioning; data loss on Space deletion is permanent","Filesystem is not shared across multiple Space instances; no multi-instance coordination","I/O performance is slower than local NVMe; not suitable for high-throughput data pipelines"],"requires":["Hugging Face account","Python code with file I/O operations","Sufficient Hub storage quota"],"input_types":["Model files","User uploads","Configuration files"],"output_types":["Cached artifacts","Persistent state","Application logs"],"categories":["memory-knowledge","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-spaces__cap_4","uri":"capability://search.retrieval.public.sharing.and.community.discovery","name":"public sharing and community discovery","description":"Automatically generates a public, shareable URL for each Space with built-in SEO optimization, metadata extraction, and community discovery indexing. Spaces are discoverable via Hugging Face's search interface, trending lists, and social features (likes, comments, collections). The platform handles URL routing, CORS configuration, and embed code generation for sharing on external websites.","intents":["Share a demo with a public URL that doesn't require authentication","Make a Space discoverable to the ML community via search and trending lists","Embed a Space demo on a personal website or blog"],"best_for":["researchers sharing academic demos","open-source projects building community engagement","developers showcasing portfolio projects"],"limitations":["Public Spaces are indexed by search engines; no built-in privacy controls for sensitive data","Trending algorithm is opaque; visibility depends on community engagement metrics","Embed code uses iframes, which may be blocked by strict CSP policies","No built-in analytics; limited visibility into user traffic and engagement"],"requires":["Hugging Face account","Public Space (private Spaces are not discoverable)","Appropriate licensing and content policies"],"input_types":["Space metadata (name, description, tags)","Application code"],"output_types":["Public URL","Embed code","SEO metadata","Community engagement metrics"],"categories":["search-retrieval","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-spaces__cap_5","uri":"capability://automation.workflow.git.based.version.control.and.continuous.deployment","name":"git-based version control and continuous deployment","description":"Integrates with GitHub repositories to enable automatic redeployment on git push. Users link a Space to a GitHub repo, and the platform watches for commits to a specified branch (default: main), automatically pulling code changes and redeploying the application. Supports environment variables from Hugging Face Secrets for API keys and credentials without exposing them in git history.","intents":["Automatically redeploy a Space when code is pushed to GitHub","Manage Space code in version control without manual deployment steps","Use GitHub Actions to run tests before deploying to a Space"],"best_for":["teams using GitHub for source control","developers building CI/CD pipelines for ML applications","open-source projects with frequent updates"],"limitations":["Redeployment latency is ~30-60 seconds after git push","No built-in rollback mechanism; failed deployments require manual intervention","GitHub Actions integration is limited; no native support for complex CI/CD workflows","Secrets are stored in Hugging Face, not GitHub; requires separate secret management"],"requires":["GitHub account with repository access","Hugging Face account with Space creation permissions","Git knowledge (clone, push, commit)"],"input_types":["GitHub repository URL","Branch name","Environment variables"],"output_types":["Deployed application","Deployment logs","Git commit history"],"categories":["automation-workflow","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-spaces__cap_6","uri":"capability://safety.moderation.environment.variable.and.secrets.management","name":"environment variable and secrets management","description":"Provides a secure secrets store for API keys, database credentials, and other sensitive configuration. Secrets are encrypted at rest, injected as environment variables at runtime, and never exposed in logs or git history. The platform supports both Space-level secrets (shared across all instances) and user-level secrets (reusable across multiple Spaces).","intents":["Store API keys for external services (OpenAI, Anthropic, etc.) without committing to git","Configure database credentials for applications that need persistent data","Manage environment-specific configuration (dev, staging, production)"],"best_for":["applications integrating with external APIs","teams managing multiple Spaces with shared credentials","developers following security best practices"],"limitations":["Secrets are not versioned; no audit trail of secret changes","No built-in secret rotation; manual updates required","Secrets are visible to all collaborators on a Space; no fine-grained access control","No integration with external secret managers (AWS Secrets Manager, HashiCorp Vault)"],"requires":["Hugging Face account","Space with edit permissions","API keys or credentials to store"],"input_types":["Secret name (string)","Secret value (string)"],"output_types":["Environment variables","Encrypted storage"],"categories":["safety-moderation","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-spaces__cap_7","uri":"capability://automation.workflow.custom.domain.and.https.configuration","name":"custom domain and https configuration","description":"Allows users to configure custom domains (e.g., demo.example.com) for Spaces with automatic HTTPS provisioning via Let's Encrypt. The platform handles DNS validation, certificate renewal, and HTTPS enforcement without requiring manual certificate management. Supports both root domains and subdomains with automatic redirects.","intents":["Use a branded domain for a Space instead of the default huggingface.co subdomain","Configure HTTPS for a custom domain without managing SSL certificates","Set up automatic redirects from old domains to new ones"],"best_for":["organizations deploying customer-facing demos","teams building branded applications","developers managing multiple Spaces with custom branding"],"limitations":["Custom domains require paid Space tier; not available on free tier","DNS propagation delays may cause temporary unavailability during setup","No built-in CDN or edge caching; latency depends on Hugging Face's infrastructure","Certificate renewal is automatic but requires DNS records to remain valid"],"requires":["Paid Hugging Face Space subscription","Domain ownership (ability to modify DNS records)","DNS provider that supports CNAME or A records"],"input_types":["Domain name","DNS configuration (CNAME or A record)"],"output_types":["HTTPS endpoint","SSL certificate","DNS validation records"],"categories":["automation-workflow","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-spaces__cap_8","uri":"capability://safety.moderation.private.space.access.control.with.team.collaboration","name":"private space access control with team collaboration","description":"Enables fine-grained access control for Spaces with support for private visibility, invite-based access, and team collaboration. Users can restrict Space visibility to specific Hugging Face users or teams, manage collaborator permissions (view, edit, admin), and track who has accessed the Space. Private Spaces are not indexed by search engines or community discovery.","intents":["Share a Space with a specific team without making it public","Collaborate on a Space with multiple developers with different permission levels","Restrict access to a Space containing sensitive data or proprietary models"],"best_for":["teams building internal tools and demos","organizations deploying confidential models","developers collaborating on private projects"],"limitations":["Private Spaces are not discoverable; sharing requires explicit invites","No built-in audit logging; limited visibility into access history","Permissions are binary (view/edit/admin); no granular permission control","Private Spaces may have reduced resource allocation on free tier"],"requires":["Hugging Face account","Space creation permissions","Hugging Face usernames or team IDs for collaborators"],"input_types":["Collaborator usernames or team IDs","Permission level (view, edit, admin)"],"output_types":["Access control list","Invite links","Collaboration history"],"categories":["safety-moderation","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-spaces__cap_9","uri":"capability://automation.workflow.automatic.resource.scaling.and.load.balancing","name":"automatic resource scaling and load balancing","description":"Automatically scales Space instances based on incoming traffic, with load balancing across multiple replicas. The platform monitors request latency and queue depth, spinning up additional instances when demand exceeds capacity and scaling down during low-traffic periods. Scaling is transparent to users; requests are routed to available instances via a load balancer.","intents":["Handle traffic spikes without manual intervention or downtime","Optimize resource costs by scaling down during low-traffic periods","Ensure consistent response times across varying load conditions"],"best_for":["popular Spaces with unpredictable traffic patterns","applications serving diverse user bases","teams building production-grade demos"],"limitations":["Scaling latency is ~30-60 seconds; sudden traffic spikes may cause temporary slowdowns","Scaling is not guaranteed on free tier; paid tiers have priority","No built-in request queuing; excess requests are rejected if capacity is exceeded","Scaling behavior is opaque; no visibility into scaling metrics or thresholds"],"requires":["Hugging Face account","Space with sufficient resource allocation","Stateless application design (no in-memory state shared across instances)"],"input_types":["Application code","Traffic patterns"],"output_types":["Scaled instances","Load balancing decisions","Resource utilization metrics"],"categories":["automation-workflow","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hugging-face-spaces__headline","uri":"capability://deployment.infra.free.hosting.platform.for.machine.learning.demo.applications","name":"free hosting platform for machine learning demo applications","description":"Hugging Face Spaces is a free platform that allows users to deploy machine learning demo applications using Gradio and Streamlit, featuring GPU support and community sharing of open-source AI demos.","intents":["best free ML demo hosting platform","ML demo hosting for Gradio apps","how to deploy Streamlit apps with GPU support","open-source AI demo platforms","community sharing for ML applications"],"best_for":["developers looking to showcase ML projects","educators demonstrating AI concepts"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","Gradio library installed (any recent version)","Hugging Face account","requirements.txt or pyproject.toml for dependency specification","Streamlit library installed","requirements.txt for dependencies","Model hosted on Hugging Face Hub","Quantization-compatible libraries (bitsandbytes, GPTQ, etc.)","Python code using transformers library","Hugging Face account with Space edit permissions"],"failure_modes":["Limited to Python-based Gradio apps; no native support for other frameworks","Cold start latency ~30-60 seconds on first request after deployment","Default timeout of 60 seconds per request; long-running inference requires async patterns","No built-in request queuing for concurrent users on free tier","Streamlit reruns entire script on every interaction, causing latency for heavy computations","Session state is per-user and ephemeral; no cross-user state sharing without external DB","File uploads limited to 200MB per file on free tier","No native support for WebSocket-based real-time updates","Quantization quality varies; some models lose significant accuracy when quantized","Not all models have quantized variants available on Hugging Face Hub","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.327Z","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=hugging-face-spaces","compare_url":"https://unfragile.ai/compare?artifact=hugging-face-spaces"}},"signature":"ZX2aA0uWhwIGTXOLNxKUroPxcL8/NMIaDZzKQOOW0LW0dRB/6wJtz38GmaFO2ZA/6bCytPf4rJIYZpDJaYZbCg==","signedAt":"2026-06-21T08:51:22.337Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hugging-face-spaces","artifact":"https://unfragile.ai/hugging-face-spaces","verify":"https://unfragile.ai/api/v1/verify?slug=hugging-face-spaces","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"}}