{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-google-gemma-3n-e4b-it","slug":"google-gemma-3n-e4b-it","name":"Google: Gemma 3n 4B","type":"model","url":"https://openrouter.ai/models/google~gemma-3n-e4b-it","page_url":"https://unfragile.ai/google-gemma-3n-e4b-it","categories":["chatbots-assistants"],"tags":["google","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$6.00e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-google-gemma-3n-e4b-it__cap_0","uri":"capability://text.generation.language.multimodal.text.image.audio.understanding.with.efficient.inference","name":"multimodal text-image-audio understanding with efficient inference","description":"Processes text, image, and audio inputs simultaneously through a unified transformer architecture optimized for mobile/edge deployment. Uses quantization and model compression techniques (likely INT8 or lower-bit precision) to reduce memory footprint while maintaining semantic understanding across modalities. Inference runs locally on device or via API without requiring cloud offloading for each request.","intents":["Build mobile apps that understand photos, voice notes, and text without sending data to cloud servers","Deploy multimodal understanding on low-resource devices like Raspberry Pi or older smartphones","Create offline-first assistants that process mixed-media inputs with sub-second latency","Reduce inference costs by running models locally instead of calling expensive cloud APIs"],"best_for":["Mobile app developers targeting iOS/Android with on-device ML","Edge computing teams building privacy-first applications","Developers in bandwidth-constrained regions needing offline capability","Teams optimizing inference cost per request at scale"],"limitations":["4B parameter size limits reasoning depth and context window compared to larger models (likely 8K-16K tokens max)","Quantization may reduce accuracy on nuanced language tasks by 2-5% vs full-precision variants","Audio processing likely requires preprocessing (e.g., WAV/MP3 conversion) — no raw streaming audio support","No fine-tuning API exposed; model weights are frozen for inference-only use"],"requires":["API key for OpenRouter or direct model access via Google's inference endpoints","For local deployment: 4GB+ RAM minimum, ARM64 or x86-64 processor","For API access: HTTP client library (curl, requests, fetch)","Image input: JPEG, PNG, WebP formats; audio: WAV, MP3, OGG"],"input_types":["text (UTF-8, up to context window limit)","image (JPEG, PNG, WebP, base64-encoded or URL)","audio (WAV, MP3, OGG, likely 16kHz mono or stereo)"],"output_types":["text (natural language response)","structured JSON (if prompted with schema)","token probability scores (for uncertainty estimation)"],"categories":["text-generation-language","image-visual","multimodal-understanding"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-3n-e4b-it__cap_1","uri":"capability://text.generation.language.instruction.following.chat.with.context.awareness","name":"instruction-following chat with context awareness","description":"Implements a chat interface that follows user instructions and maintains conversation context across multiple turns. Uses a transformer decoder with attention mechanisms to track prior messages and respond coherently. The 'it' suffix indicates instruction-tuning via RLHF or supervised fine-tuning, enabling the model to follow complex directives, refuse unsafe requests, and adapt tone/style per user preference.","intents":["Build conversational AI that remembers context across 10+ message exchanges","Create task-oriented assistants that follow multi-step instructions reliably","Deploy chatbots that refuse harmful requests and explain their reasoning","Develop interactive tutoring systems that adapt explanations based on user feedback"],"best_for":["Indie developers building lightweight chatbot MVPs","Teams needing HIPAA/GDPR-compliant on-device chat (no data sent to cloud)","Customer support teams using local inference to avoid third-party data processing","Educational platforms requiring offline-first conversational learning"],"limitations":["Context window likely 8K-16K tokens; long conversations require summarization or sliding-window truncation","Instruction-tuning may reduce creative/open-ended generation compared to base models","No multi-turn memory persistence — each session starts fresh unless explicitly managed by application","Quantization introduces occasional hallucinations or repetitive outputs on edge cases"],"requires":["API key for OpenRouter or local runtime (e.g., Ollama, MLX, TensorFlow Lite)","For API: HTTP POST with JSON payload containing message history","For local: 4GB+ RAM, compatible hardware (ARM64, x86-64, or GPU with CUDA/Metal support)","Message format: array of {role: 'user'|'assistant', content: string}"],"input_types":["text (natural language instructions, questions, multi-turn conversation history)"],"output_types":["text (natural language response, typically 100-2000 tokens per turn)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-3n-e4b-it__cap_2","uri":"capability://text.generation.language.efficient.token.generation.with.adaptive.sampling","name":"efficient token generation with adaptive sampling","description":"Generates text token-by-token using a quantized transformer decoder with optimized matrix multiplications for mobile hardware. Likely implements temperature scaling, top-k/top-p sampling, or beam search to control output diversity and quality. Inference is optimized for latency (sub-100ms per token on mobile) rather than throughput, enabling real-time interactive applications.","intents":["Generate real-time text responses in mobile apps without noticeable lag","Control output randomness/creativity via temperature and sampling parameters","Stream responses token-by-token for progressive UI updates","Implement deterministic outputs for reproducible testing or logging"],"best_for":["Mobile app developers needing <200ms time-to-first-token latency","Teams building real-time chat interfaces on consumer hardware","Developers optimizing for battery life and thermal efficiency","Applications requiring reproducible outputs (e.g., testing, compliance logging)"],"limitations":["Quantization introduces non-determinism; same prompt may produce slightly different outputs due to rounding errors","Beam search disabled or limited to beam_size=1 on mobile to reduce memory; greedy/sampling-only decoding","No speculative decoding or KV-cache optimization exposed via API — latency depends on hardware","Streaming responses may have variable token arrival times (100-300ms variance) due to device thermal throttling"],"requires":["API key for OpenRouter with streaming support (Server-Sent Events or WebSocket)","For local: inference runtime with quantized model support (Ollama, MLX, TensorFlow Lite)","Client-side: event listener for streaming tokens or polling mechanism","Sampling parameters: temperature (0.0-2.0), top_k (1-100), top_p (0.0-1.0), max_tokens (1-8192)"],"input_types":["text prompt (UTF-8, up to context window)","sampling configuration (temperature, top_k, top_p, seed for reproducibility)"],"output_types":["text stream (tokens emitted one-by-one via SSE or callback)","token probability scores (if logprobs requested)","stop reason (e.g., 'stop_token', 'max_tokens', 'length')"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-3n-e4b-it__cap_3","uri":"capability://tool.use.integration.api.based.inference.with.rate.limiting.and.quota.management","name":"api-based inference with rate limiting and quota management","description":"Exposes Gemma 3n via OpenRouter's REST API with HTTP POST endpoints for text generation and multimodal understanding. Requests are routed through OpenRouter's load balancer, which handles rate limiting, quota enforcement, and billing. Responses include usage metadata (prompt tokens, completion tokens, total cost) for cost tracking and optimization.","intents":["Integrate Gemma 3n into web apps or backend services without managing model infrastructure","Monitor token usage and costs in real-time for billing and optimization","Scale inference across multiple requests without worrying about GPU/CPU allocation","Switch between Gemma 3n and other models (Llama, Mistral, GPT) via a unified API"],"best_for":["Startups and indie developers avoiding infrastructure overhead","Teams needing multi-model support without vendor lock-in","Applications with variable load that benefit from managed scaling","Cost-conscious builders optimizing per-request inference expenses"],"limitations":["API latency adds 50-200ms overhead vs local inference due to network round-trip and load balancer routing","Rate limits enforced per API key (likely 100-1000 requests/minute depending on tier); burst traffic may queue","No fine-tuning or custom model weights; inference-only access to base/instruction-tuned model","Requests logged by OpenRouter for abuse prevention; not suitable for highly sensitive data without encryption","Streaming responses may have variable latency due to shared infrastructure and other users' load"],"requires":["OpenRouter API key (free tier available with limited quota, paid tiers for production)","HTTP client library (curl, requests, fetch, axios)","JSON payload with model ID 'google/gemma-3n-e4b-it', messages array, and optional parameters","Network connectivity and HTTPS support"],"input_types":["text (JSON-encoded in request body)","image (base64-encoded or URL in multimodal requests)","audio (base64-encoded or URL in multimodal requests)"],"output_types":["JSON response with 'choices' array containing generated text, usage metadata, and finish_reason","Server-Sent Events stream (if streaming=true) with token-by-token updates"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-3n-e4b-it__cap_4","uri":"capability://code.generation.editing.mobile.optimized.model.compression.with.quantization","name":"mobile-optimized model compression with quantization","description":"Gemma 3n applies post-training quantization (likely INT8 or INT4) and architectural pruning to reduce model size from ~12GB (full precision) to ~1-2GB (quantized), enabling deployment on devices with 4GB+ RAM. Quantization uses symmetric or asymmetric schemes with per-channel or per-token scaling to minimize accuracy loss. Inference kernels are optimized for ARM NEON (mobile) and x86 VNNI (laptop) instruction sets.","intents":["Deploy multimodal AI on phones and tablets without requiring cloud connectivity","Reduce model download size from 12GB to <2GB for faster app installation","Enable offline-first applications that work in airplane mode or low-bandwidth regions","Minimize battery drain by using efficient quantized operations instead of full-precision math"],"best_for":["Mobile app developers targeting iOS/Android with on-device inference","Teams in emerging markets with limited bandwidth and older devices","Privacy-focused applications that cannot send data to cloud servers","Edge AI teams building embedded systems with <8GB RAM"],"limitations":["Quantization introduces 1-5% accuracy loss on language understanding tasks compared to full-precision baseline","INT4 quantization may cause occasional token repetition or incoherent outputs on edge cases","Model weights are frozen; no fine-tuning or adaptation to domain-specific data","Inference speed varies significantly by hardware (2x-10x variance between iPhone 12 and iPhone 15)","No GPU acceleration on older mobile devices; CPU-only inference may be slow for long sequences"],"requires":["Mobile device with ARM64 processor (iOS 12+, Android 8+) or x86-64 laptop","4GB+ RAM available for model loading and inference","Inference runtime: TensorFlow Lite, Core ML (iOS), ONNX Runtime, or Ollama","Storage: 2-4GB free space for model weights"],"input_types":["text (UTF-8, up to context window)","image (JPEG, PNG, WebP)","audio (WAV, MP3)"],"output_types":["text (natural language response)","token logits (if requested for uncertainty estimation)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-3n-e4b-it__cap_5","uri":"capability://text.generation.language.context.aware.response.generation.with.instruction.adherence","name":"context-aware response generation with instruction adherence","description":"Generates responses that follow explicit user instructions (e.g., 'respond in JSON', 'use a formal tone', 'explain like I'm 5') by leveraging instruction-tuning via RLHF. The model learns to parse instruction tokens and adjust generation strategy accordingly. Attention mechanisms track both conversation history and instruction context to produce coherent, on-brand outputs.","intents":["Generate structured outputs (JSON, YAML, CSV) by instructing the model in natural language","Adapt response tone/style (formal, casual, technical, beginner-friendly) per user preference","Implement multi-step reasoning by instructing the model to 'think step-by-step' or 'show your work'","Create domain-specific assistants that follow custom guidelines (e.g., medical disclaimers, legal caveats)"],"best_for":["Developers building task-oriented chatbots with specific output requirements","Teams creating brand-consistent AI assistants with tone/style guidelines","Educational platforms needing adaptive explanations for different learner levels","Applications requiring structured data extraction from unstructured text"],"limitations":["Instruction-following reliability decreases with complex or ambiguous instructions; may require prompt engineering","No guaranteed schema compliance; JSON output may be malformed without additional validation","Instruction-tuning may reduce creative generation compared to base models","Conflicting instructions (e.g., 'be concise' + 'explain in detail') may produce inconsistent results"],"requires":["Clear, unambiguous instructions in the user message or system prompt","For structured output: schema specification or examples in the prompt","Post-processing validation to ensure output matches expected format (JSON schema validation, regex matching)"],"input_types":["text prompt with explicit instructions (e.g., 'Respond in JSON with keys: name, age, email')","conversation history with mixed user/assistant messages","optional system prompt defining model behavior"],"output_types":["text in requested format (JSON, YAML, CSV, markdown, plain text)","structured data (if prompt specifies schema)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["API key for OpenRouter or direct model access via Google's inference endpoints","For local deployment: 4GB+ RAM minimum, ARM64 or x86-64 processor","For API access: HTTP client library (curl, requests, fetch)","Image input: JPEG, PNG, WebP formats; audio: WAV, MP3, OGG","API key for OpenRouter or local runtime (e.g., Ollama, MLX, TensorFlow Lite)","For API: HTTP POST with JSON payload containing message history","For local: 4GB+ RAM, compatible hardware (ARM64, x86-64, or GPU with CUDA/Metal support)","Message format: array of {role: 'user'|'assistant', content: string}","API key for OpenRouter with streaming support (Server-Sent Events or WebSocket)","For local: inference runtime with quantized model support (Ollama, MLX, TensorFlow Lite)"],"failure_modes":["4B parameter size limits reasoning depth and context window compared to larger models (likely 8K-16K tokens max)","Quantization may reduce accuracy on nuanced language tasks by 2-5% vs full-precision variants","Audio processing likely requires preprocessing (e.g., WAV/MP3 conversion) — no raw streaming audio support","No fine-tuning API exposed; model weights are frozen for inference-only use","Context window likely 8K-16K tokens; long conversations require summarization or sliding-window truncation","Instruction-tuning may reduce creative/open-ended generation compared to base models","No multi-turn memory persistence — each session starts fresh unless explicitly managed by application","Quantization introduces occasional hallucinations or repetitive outputs on edge cases","Quantization introduces non-determinism; same prompt may produce slightly different outputs due to rounding errors","Beam search disabled or limited to beam_size=1 on mobile to reduce memory; greedy/sampling-only decoding","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.24,"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":"2026-05-24T12:16:24.484Z","last_scraped_at":"2026-05-03T15:20:45.776Z","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=google-gemma-3n-e4b-it","compare_url":"https://unfragile.ai/compare?artifact=google-gemma-3n-e4b-it"}},"signature":"DSynqUgTXwBd/j/97NAG7kXOK2kYijoyHU0XGVm73hRBMuoz4yEd7ch//a1MELTaSFc75XO0dMZa/l3+avhjBg==","signedAt":"2026-06-21T22:16:14.608Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-gemma-3n-e4b-it","artifact":"https://unfragile.ai/google-gemma-3n-e4b-it","verify":"https://unfragile.ai/api/v1/verify?slug=google-gemma-3n-e4b-it","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"}}