{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-meta-llama-llama-3.2-3b-instruct","slug":"meta-llama-llama-3.2-3b-instruct","name":"Meta: Llama 3.2 3B Instruct","type":"model","url":"https://openrouter.ai/models/meta-llama~llama-3.2-3b-instruct","page_url":"https://unfragile.ai/meta-llama-llama-3.2-3b-instruct","categories":["llm-apis"],"tags":["meta-llama","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$5.10e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-meta-llama-llama-3.2-3b-instruct__cap_0","uri":"capability://text.generation.language.multilingual.instruction.following.dialogue.generation","name":"multilingual instruction-following dialogue generation","description":"Generates contextually appropriate responses to user prompts across 8+ languages using a transformer-based decoder architecture trained on instruction-tuning datasets. The model processes input tokens through multi-head attention layers (32 heads, 3B parameters distributed across 26 layers) and produces coherent, instruction-aligned text via autoregressive sampling with support for temperature, top-p, and top-k decoding strategies.","intents":["Build a chatbot that responds naturally in multiple languages without language-specific model switching","Create conversational AI that follows complex multi-step instructions reliably","Deploy a lightweight dialogue system that runs inference efficiently on edge devices or cost-constrained cloud infrastructure"],"best_for":["Teams building multilingual customer support chatbots with <100ms latency requirements","Developers prototyping conversational agents where model size and inference cost are primary constraints","Organizations needing instruction-following without fine-tuning on proprietary data"],"limitations":["3B parameter count limits reasoning depth on complex multi-hop problems compared to 70B+ models; struggles with advanced mathematics and code generation","Context window of 8,192 tokens constrains ability to maintain coherence across very long conversations or large document processing","No native tool-calling or function-calling capability — requires external orchestration layer to integrate with APIs or external tools","Multilingual support is balanced across languages rather than optimized for any single language, resulting in lower performance on specialized linguistic tasks vs monolingual models"],"requires":["API access via OpenRouter or Hugging Face Inference API (no local deployment without quantization)","Valid API key with sufficient rate limits for production workloads","HTTP/REST client library or SDK (Python requests, JavaScript fetch, etc.)","Input text encoded as UTF-8 with max 8,192 tokens per request"],"input_types":["plain text (user prompts, conversation history)","structured conversation format (system prompt + user/assistant message pairs)"],"output_types":["plain text (generated response)","token-level probability distributions (via logits output if supported by API)"],"categories":["text-generation-language","multilingual-nlp"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.2-3b-instruct__cap_1","uri":"capability://text.generation.language.reasoning.aware.text.summarization","name":"reasoning-aware text summarization","description":"Produces abstractive summaries of input text by applying chain-of-thought-like reasoning patterns learned during instruction tuning, allowing the model to identify key concepts and relationships before generating concise output. The model leverages its transformer attention mechanism to weight important tokens and generate summaries that preserve semantic meaning across variable input lengths up to 8,192 tokens.","intents":["Summarize long documents, articles, or conversation transcripts into key points without manual extraction","Generate executive summaries of technical documentation or meeting notes for quick review","Create abstractive summaries in multiple languages from multilingual source material"],"best_for":["Content teams processing high volumes of articles or reports needing quick summaries","Knowledge workers managing information overload across multiple languages","Developers building document processing pipelines where summarization is one step in a larger workflow"],"limitations":["Abstractive summaries may hallucinate details not present in source material, especially on specialized or technical content","Performance degrades on very long documents (>6,000 tokens) due to attention dilution across many tokens","No extractive summarization mode — cannot highlight specific source sentences, only generate new text","Summary length is not strictly controllable; model may produce longer or shorter output than requested"],"requires":["API access via OpenRouter or equivalent inference endpoint","Input text in UTF-8 format, max 8,192 tokens","Optional: system prompt specifying summary style, length preference, or target audience"],"input_types":["plain text (articles, documents, transcripts)","structured text with metadata (title + body, speaker + dialogue)"],"output_types":["plain text (generated summary)","structured summary (if prompted with specific format)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.2-3b-instruct__cap_2","uri":"capability://text.generation.language.cross.lingual.translation.with.instruction.following","name":"cross-lingual translation with instruction-following","description":"Translates text between 8+ supported languages by leveraging multilingual token embeddings and instruction-tuned prompting to specify source and target languages explicitly. The model processes source language tokens through shared transformer layers trained on parallel corpora, then generates target language output with awareness of linguistic nuances learned during instruction tuning (e.g., formal vs. informal register, domain-specific terminology).","intents":["Translate user-generated content or customer communications into multiple languages for global audiences","Build multilingual product interfaces by translating UI strings and help documentation","Create multilingual chatbots that respond in the user's preferred language without separate language-specific models"],"best_for":["Global SaaS platforms needing cost-effective translation for user-facing content","Teams building multilingual chatbots or customer support systems","Content creators publishing in multiple languages without dedicated translation teams"],"limitations":["Translation quality varies significantly by language pair; high-resource pairs (English-Spanish) perform better than low-resource pairs (English-Amharic)","No domain-specific terminology handling without fine-tuning; may mistranslate technical jargon or proper nouns","Context window of 8,192 tokens limits ability to maintain consistency across very long documents","Instruction-following approach requires explicit source/target language specification in prompt, adding complexity vs. dedicated translation APIs"],"requires":["API access via OpenRouter or Hugging Face Inference API","Input text in UTF-8 format with explicit source and target language specification in prompt","Knowledge of supported language codes (e.g., 'English', 'Spanish', 'Mandarin Chinese')"],"input_types":["plain text in any supported language","structured text with language metadata"],"output_types":["plain text in target language","structured translation with confidence scores (if API supports logits output)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.2-3b-instruct__cap_3","uri":"capability://text.generation.language.few.shot.in.context.learning.for.task.adaptation","name":"few-shot in-context learning for task adaptation","description":"Adapts to new tasks by learning from examples provided in the prompt (few-shot learning) without requiring model fine-tuning. The model processes example input-output pairs through its transformer attention mechanism, learns task-specific patterns from the examples, and applies those patterns to new inputs. This works through in-context learning — the model's ability to recognize patterns in the prompt and generalize them, enabled by instruction tuning that teaches the model to follow implicit task specifications.","intents":["Adapt the model to domain-specific tasks (e.g., sentiment analysis, entity extraction, classification) by providing 2-5 examples in the prompt","Quickly prototype new NLP tasks without collecting training data or fine-tuning","Build flexible systems that can handle multiple tasks with a single model by switching prompts"],"best_for":["Rapid prototyping teams that need to test new NLP tasks quickly without infrastructure for fine-tuning","Developers building multi-task systems where task switching via prompts is simpler than maintaining multiple models","Organizations with limited ML expertise that need to adapt models to new domains without training"],"limitations":["Few-shot learning performance plateaus with 5-10 examples; adding more examples doesn't consistently improve accuracy and may degrade performance due to context dilution","Requires high-quality, representative examples; poor example selection leads to poor task performance","No persistent learning — each request requires examples to be included in the prompt, increasing token usage and latency","Performance on complex reasoning tasks (multi-step logic, mathematical reasoning) remains limited compared to larger models even with examples"],"requires":["API access via OpenRouter or Hugging Face Inference API","Well-crafted prompt with clear task specification and 2-10 representative examples","Input text in UTF-8 format, with total prompt+input staying within 8,192 token limit"],"input_types":["plain text prompts with embedded examples","structured prompt templates with example slots"],"output_types":["plain text (task-specific output based on examples)","structured output (if examples demonstrate structured format)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.2-3b-instruct__cap_4","uri":"capability://data.processing.analysis.structured.data.extraction.via.prompt.based.schema.specification","name":"structured data extraction via prompt-based schema specification","description":"Extracts structured information (entities, relationships, attributes) from unstructured text by specifying an output schema in natural language or JSON format within the prompt. The model processes the input text and schema specification through its transformer, then generates output in the specified format (JSON, CSV, key-value pairs) by learning the format from the prompt specification. This relies on instruction tuning to teach the model to follow format specifications and the model's ability to generate valid structured output.","intents":["Extract key information from documents, emails, or user input into structured formats for downstream processing","Parse semi-structured text (e.g., resumes, invoices, product descriptions) into consistent JSON or database records","Build data pipelines that convert unstructured content into structured data without custom parsing logic"],"best_for":["Data teams building ETL pipelines that need to extract structured data from documents or text","Developers building form-filling or data entry automation systems","Organizations processing high volumes of unstructured text (customer feedback, support tickets, contracts) that need to be structured for analysis"],"limitations":["Output format compliance is not guaranteed; model may generate invalid JSON or miss required fields, requiring post-processing validation","Accuracy degrades on complex schemas with many fields (>20 fields) or nested structures; model may omit fields or hallucinate values","No native validation or error handling; requires external schema validation and retry logic for production use","Performance on domain-specific extraction (e.g., legal contracts, medical records) is lower than fine-tuned models without domain-specific examples in the prompt"],"requires":["API access via OpenRouter or Hugging Face Inference API","Clear schema specification in the prompt (JSON schema, natural language description, or example output format)","Input text in UTF-8 format, max 8,192 tokens","Post-processing logic to validate and handle malformed output"],"input_types":["plain text (documents, emails, descriptions)","structured text with schema specification in prompt"],"output_types":["JSON (structured data)","CSV (tabular data)","key-value pairs (simple structured data)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.2-3b-instruct__cap_5","uri":"capability://text.generation.language.conversational.context.management.with.multi.turn.dialogue","name":"conversational context management with multi-turn dialogue","description":"Maintains coherent multi-turn conversations by processing conversation history (system prompt + alternating user/assistant messages) as a single input sequence through the transformer. The model uses attention mechanisms to weight relevant prior messages and generates responses that are contextually appropriate to the full conversation history. Context is managed entirely within the prompt — the model does not maintain persistent state between API calls, requiring the client to manage conversation history and pass it with each request.","intents":["Build chatbots that maintain conversation context across multiple user turns without losing coherence","Create conversational agents that reference earlier messages and build on previous responses","Implement multi-turn dialogue systems where user intent depends on conversation history"],"best_for":["Teams building customer support chatbots that need to handle multi-turn conversations","Developers creating conversational AI assistants for specific domains (e.g., technical support, sales)","Organizations building dialogue systems where conversation history is critical to response quality"],"limitations":["Context window of 8,192 tokens limits conversation length; long conversations require truncation or summarization of older messages","No persistent memory between sessions — each conversation starts fresh; requires external database to maintain conversation history across sessions","Attention mechanism may lose track of important context in very long conversations (>50 turns) due to token dilution","Client is responsible for managing conversation history and formatting; no built-in conversation state management or session handling"],"requires":["API access via OpenRouter or Hugging Face Inference API","Client-side conversation history management (list of user/assistant messages)","Proper message formatting (system prompt + user/assistant message pairs in standard format)","Logic to handle context window limits (truncation, summarization, or rolling window of recent messages)"],"input_types":["conversation history as formatted message list (system + user/assistant pairs)","plain text user input appended to conversation history"],"output_types":["plain text (assistant response)","structured response with metadata (if prompted)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.2-3b-instruct__cap_6","uri":"capability://text.generation.language.zero.shot.task.generalization.via.instruction.following","name":"zero-shot task generalization via instruction following","description":"Performs new tasks without examples by following natural language instructions in the prompt, leveraging instruction tuning that teaches the model to interpret task specifications and apply them to novel inputs. The model processes the instruction and input through its transformer, learns the task implicitly from the instruction text, and generates appropriate output. This works because instruction tuning exposes the model to diverse task descriptions during training, enabling it to generalize to unseen tasks at inference time.","intents":["Perform ad-hoc NLP tasks (classification, extraction, generation, analysis) without providing examples or fine-tuning","Build flexible systems that can handle diverse tasks with a single model by changing the instruction prompt","Quickly prototype new capabilities by writing natural language task descriptions"],"best_for":["Developers building general-purpose NLP systems that need to handle diverse tasks","Rapid prototyping teams that need to test new task ideas without collecting training data","Organizations with limited ML expertise that need flexible NLP capabilities without model training"],"limitations":["Zero-shot performance is significantly lower than few-shot (with examples) or fine-tuned models on complex tasks","Instruction clarity is critical; ambiguous or poorly-written instructions lead to poor task performance","Performance on specialized domains (legal, medical, scientific) is lower without domain-specific examples or fine-tuning","No way to improve performance without adding examples (few-shot) or fine-tuning; instruction optimization has diminishing returns"],"requires":["API access via OpenRouter or Hugging Face Inference API","Clear, well-written natural language instruction describing the task","Input text in UTF-8 format, max 8,192 tokens"],"input_types":["plain text with natural language task instruction","structured prompt with task description and input"],"output_types":["plain text (task-specific output)","structured output (if instruction specifies format)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.2-3b-instruct__cap_7","uri":"capability://text.generation.language.api.based.inference.with.streaming.response.generation","name":"api-based inference with streaming response generation","description":"Provides real-time text generation through HTTP API endpoints (OpenRouter, Hugging Face Inference API) with support for streaming responses via server-sent events (SSE) or chunked transfer encoding. The model generates tokens sequentially and streams them to the client as they are produced, enabling real-time display of generated text without waiting for the full response. This reduces perceived latency and allows clients to process partial results before generation completes.","intents":["Build responsive chatbot interfaces that display text as it's generated, improving user experience","Create real-time text generation pipelines that process partial results incrementally","Implement long-running text generation tasks that need to show progress to users"],"best_for":["Web and mobile application developers building conversational UIs","Teams building real-time dashboards or monitoring systems that display generated text","Developers creating streaming data pipelines that need incremental text processing"],"limitations":["Streaming adds complexity to client implementation; requires handling partial tokens, buffering, and connection management","Network latency and API response time add overhead; streaming may not improve end-to-end latency if API is slow to generate first token","No built-in error recovery; connection drops require client-side retry logic","Token-level streaming may produce incomplete words or formatting artifacts; requires client-side buffering and post-processing"],"requires":["API access via OpenRouter or Hugging Face Inference API with streaming support","HTTP client library with streaming support (e.g., Python requests with stream=True, JavaScript fetch with ReadableStream)","Client-side logic to handle SSE or chunked transfer encoding","Proper error handling and connection management for production use"],"input_types":["plain text prompts via HTTP POST","structured JSON payloads with model parameters"],"output_types":["streaming text tokens via SSE or chunked transfer encoding","partial text updates in real-time"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.2-3b-instruct__cap_8","uri":"capability://text.generation.language.temperature.and.sampling.parameter.control.for.output.diversity","name":"temperature and sampling parameter control for output diversity","description":"Controls the randomness and diversity of generated text through temperature and sampling parameters (temperature, top-p, top-k) passed to the API. Lower temperature (0.0-0.5) produces more deterministic, focused output; higher temperature (0.7-1.5) produces more diverse, creative output. Top-p (nucleus sampling) and top-k limit the vocabulary considered at each step, reducing hallucination while maintaining diversity. These parameters control the probability distribution over the next token without modifying the model itself.","intents":["Generate deterministic, consistent responses for tasks like summarization or extraction by using low temperature","Generate diverse, creative responses for tasks like brainstorming or content creation by using high temperature","Balance between consistency and diversity by tuning temperature and sampling parameters for specific use cases"],"best_for":["Developers building systems that need to tune output diversity for specific tasks","Teams creating content generation systems that need to balance creativity and consistency","Organizations building chatbots that need different response styles for different contexts"],"limitations":["Parameter tuning is empirical and task-specific; optimal values vary by task and domain","High temperature increases hallucination risk; may generate plausible-sounding but false information","Low temperature may produce repetitive or generic output; reduces model's ability to generate creative responses","No built-in guidance on parameter selection; requires experimentation and domain knowledge"],"requires":["API access via OpenRouter or Hugging Face Inference API that supports temperature and sampling parameters","Understanding of temperature and sampling parameter semantics","Experimentation and testing to find optimal values for specific tasks"],"input_types":["API request with temperature, top-p, top-k parameters"],"output_types":["text output with controlled diversity based on parameter settings"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or Hugging Face Inference API (no local deployment without quantization)","Valid API key with sufficient rate limits for production workloads","HTTP/REST client library or SDK (Python requests, JavaScript fetch, etc.)","Input text encoded as UTF-8 with max 8,192 tokens per request","API access via OpenRouter or equivalent inference endpoint","Input text in UTF-8 format, max 8,192 tokens","Optional: system prompt specifying summary style, length preference, or target audience","API access via OpenRouter or Hugging Face Inference API","Input text in UTF-8 format with explicit source and target language specification in prompt","Knowledge of supported language codes (e.g., 'English', 'Spanish', 'Mandarin Chinese')"],"failure_modes":["3B parameter count limits reasoning depth on complex multi-hop problems compared to 70B+ models; struggles with advanced mathematics and code generation","Context window of 8,192 tokens constrains ability to maintain coherence across very long conversations or large document processing","No native tool-calling or function-calling capability — requires external orchestration layer to integrate with APIs or external tools","Multilingual support is balanced across languages rather than optimized for any single language, resulting in lower performance on specialized linguistic tasks vs monolingual models","Abstractive summaries may hallucinate details not present in source material, especially on specialized or technical content","Performance degrades on very long documents (>6,000 tokens) due to attention dilution across many tokens","No extractive summarization mode — cannot highlight specific source sentences, only generate new text","Summary length is not strictly controllable; model may produce longer or shorter output than requested","Translation quality varies significantly by language pair; high-resource pairs (English-Spanish) perform better than low-resource pairs (English-Amharic)","No domain-specific terminology handling without fine-tuning; may mistranslate technical jargon or proper nouns","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"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=meta-llama-llama-3.2-3b-instruct","compare_url":"https://unfragile.ai/compare?artifact=meta-llama-llama-3.2-3b-instruct"}},"signature":"z9qpHSburl+7soe2K0Z6u/x16tIeKX2K5mXvXPhRoQrM95idKvnhTDRDUF9HkKJ8DWYCns2ZIgjEUD87cyPQAw==","signedAt":"2026-06-22T06:56:40.824Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/meta-llama-llama-3.2-3b-instruct","artifact":"https://unfragile.ai/meta-llama-llama-3.2-3b-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=meta-llama-llama-3.2-3b-instruct","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"}}