{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-meta-llama-llama-3-8b-instruct","slug":"meta-llama-llama-3-8b-instruct","name":"Meta: Llama 3 8B Instruct","type":"model","url":"https://openrouter.ai/models/meta-llama~llama-3-8b-instruct","page_url":"https://unfragile.ai/meta-llama-llama-3-8b-instruct","categories":["chatbots-assistants","testing-quality"],"tags":["meta-llama","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$3.00e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-meta-llama-llama-3-8b-instruct__cap_0","uri":"capability://text.generation.language.instruction.following.dialogue.generation","name":"instruction-following dialogue generation","description":"Generates contextually appropriate responses to user prompts using instruction-tuning on dialogue datasets. The model uses a transformer decoder architecture with 8 billion parameters, trained on supervised fine-tuning (SFT) data to follow explicit instructions and maintain conversational coherence across multi-turn exchanges. Responses are generated token-by-token via autoregressive sampling with temperature and top-p controls available through the OpenRouter API.","intents":["Build a conversational AI assistant that understands and follows user instructions accurately","Create a chatbot that maintains context across multiple dialogue turns without losing instruction adherence","Develop an interactive system where users can ask questions and receive detailed, instruction-aligned responses","Prototype a customer support agent that follows specific response guidelines and tone requirements"],"best_for":["Solo developers building lightweight chatbot prototypes without GPU infrastructure","Teams prototyping conversational AI features before committing to larger model deployments","Builders prioritizing inference latency and cost-efficiency over maximum reasoning capability","Non-technical founders testing chatbot MVPs with minimal infrastructure setup"],"limitations":["8B parameter size limits reasoning depth compared to 70B+ models — struggles with multi-step logical inference or complex mathematical problem-solving","Context window size not specified in artifact; likely 8K tokens or less, limiting ability to process long documents or maintain very long conversation histories","No native tool-use or function-calling capability — cannot directly invoke external APIs or execute code without wrapper integration","Instruction-tuning optimized for dialogue may reduce performance on non-conversational tasks like code generation or structured data extraction","Rate limiting and API quota constraints via OpenRouter may impact production-scale deployments with high concurrent users"],"requires":["OpenRouter API key (free tier available with limited usage, paid tier for production)","HTTP client library or SDK (curl, Python requests, JavaScript fetch, etc.)","Network connectivity to OpenRouter endpoints","Understanding of prompt engineering for instruction-following models"],"input_types":["text (natural language prompts)","multi-turn conversation history (as text sequences)"],"output_types":["text (natural language responses)","streaming text tokens (via server-sent events if supported by OpenRouter)"],"categories":["text-generation-language","chatbots-assistants"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-8b-instruct__cap_1","uri":"capability://text.generation.language.multi.turn.conversation.state.management","name":"multi-turn conversation state management","description":"Maintains coherent dialogue context across sequential user-assistant exchanges by processing the full conversation history as a single input sequence. The model uses positional embeddings and causal attention masking to understand prior turns, allowing it to reference earlier statements, correct misunderstandings, and adapt tone based on conversation flow. State is managed entirely client-side — the model itself is stateless and processes each request with full history prepended.","intents":["Build a chatbot that remembers context from earlier in the conversation and references it naturally","Create a multi-turn Q&A system where follow-up questions are understood in relation to previous answers","Develop an interactive debugging assistant that tracks the problem statement and solution attempts across turns","Implement a conversational onboarding flow where user preferences stated early are remembered and applied later"],"best_for":["Developers building stateless API-based chatbots where conversation history is managed by the client application","Teams implementing conversational UIs in web or mobile apps with client-side session management","Builders prototyping multi-turn dialogue systems without needing server-side conversation storage"],"limitations":["Context window limitations mean conversation history cannot grow indefinitely — older turns will be truncated or dropped when total tokens exceed model's context limit","No built-in conversation summarization — developers must implement their own summarization logic to compress long histories before hitting context limits","Client-side state management requires the application to maintain and pass full conversation history with each API request, increasing payload size and latency as conversations grow","No native support for multi-user or branching conversations — each conversation thread must be managed separately by the application"],"requires":["OpenRouter API key","Client application with conversation history storage (in-memory, database, or session storage)","Understanding of conversation formatting (typically system prompt + alternating user/assistant messages)","HTTP client capable of handling request payloads that grow with conversation length"],"input_types":["text (conversation history formatted as system prompt + user/assistant message pairs)"],"output_types":["text (assistant response to be appended to conversation history)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-8b-instruct__cap_2","uri":"capability://text.generation.language.zero.shot.task.adaptation.via.prompting","name":"zero-shot task adaptation via prompting","description":"Adapts to new tasks without fine-tuning by interpreting task descriptions in natural language prompts. The model leverages instruction-tuning to understand task specifications embedded in prompts (e.g., 'summarize this text', 'translate to Spanish', 'extract entities'), and applies learned patterns from training data to perform the requested task. This works through in-context learning where the model infers task intent from prompt structure and examples without updating its weights.","intents":["Use a single model for multiple tasks (summarization, translation, Q&A, classification) by changing the prompt without retraining","Quickly prototype task-specific behaviors by writing descriptive prompts rather than collecting training data","Build flexible AI features that adapt to user-defined instructions at runtime","Test whether a task is feasible with the model before investing in fine-tuning infrastructure"],"best_for":["Rapid prototypers and MVPs that need multi-task capability without fine-tuning infrastructure","Teams building general-purpose AI assistants that handle diverse user requests","Developers testing task feasibility before committing to specialized model training","Non-technical users who want to adapt model behavior through natural language instructions"],"limitations":["Zero-shot performance degrades on highly specialized or domain-specific tasks — tasks requiring deep domain knowledge or novel reasoning patterns perform better with few-shot examples or fine-tuning","No guarantee of consistent output format — the model may vary response structure even with identical prompts, requiring post-processing or output validation","Prompt engineering becomes critical; poorly written prompts lead to off-task or irrelevant responses, and optimization is often manual trial-and-error","Cannot learn from user feedback or corrections within a single conversation without explicit few-shot examples in the prompt","Performance on tasks requiring precise numerical computation, code execution, or formal logic is limited by the model's training data and architecture"],"requires":["OpenRouter API key","Skill in prompt engineering and task specification","Understanding of the model's training data and capabilities to set realistic expectations","Post-processing logic to validate and format outputs if consistency is critical"],"input_types":["text (task description + input data, formatted as natural language prompt)"],"output_types":["text (task-specific output: summaries, translations, classifications, extracted entities, etc.)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-8b-instruct__cap_3","uri":"capability://text.generation.language.few.shot.in.context.learning.with.examples","name":"few-shot in-context learning with examples","description":"Improves task performance by including a small number of input-output examples in the prompt before the actual task. The model uses these examples to infer task patterns and constraints, adapting its behavior without weight updates. This is implemented through prompt concatenation where examples are formatted consistently and placed before the target input, allowing the model's attention mechanism to learn task-specific patterns from the examples.","intents":["Improve accuracy on specific tasks by showing the model 2-5 examples of desired behavior before asking it to perform the task","Teach the model output format requirements (JSON structure, specific field names, tone) through example demonstration","Adapt the model to domain-specific terminology or conventions by including examples with those terms","Reduce hallucination or off-task responses by constraining the model's behavior through concrete examples"],"best_for":["Developers building task-specific AI features where a few examples significantly improve quality","Teams working with domain-specific data where standard prompts don't capture nuances","Builders who want to improve accuracy without fine-tuning infrastructure","Rapid prototypers validating whether few-shot learning is sufficient before investing in fine-tuning"],"limitations":["Few-shot learning effectiveness plateaus with 5-10 examples; adding more examples beyond this point shows diminishing returns and increases token usage","Example quality is critical — poor or inconsistent examples can degrade performance more than zero-shot; requires manual curation","Context window constraints limit the number of examples that can be included — with limited context, developers must choose between examples and input data","No guarantee that the model will follow example patterns consistently — edge cases or ambiguous inputs may still produce off-pattern responses","Requires manual prompt engineering to format examples correctly; inconsistent formatting reduces effectiveness"],"requires":["OpenRouter API key","Curated examples of desired input-output behavior (typically 2-10 examples)","Understanding of prompt formatting and example consistency","Ability to measure and iterate on example quality"],"input_types":["text (prompt with formatted examples + target input)"],"output_types":["text (output following patterns demonstrated in examples)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-8b-instruct__cap_4","uri":"capability://safety.moderation.safety.aligned.response.generation","name":"safety-aligned response generation","description":"Generates responses that avoid harmful, illegal, or unethical content through safety training applied during instruction-tuning. The model uses constitutional AI principles and RLHF (reinforcement learning from human feedback) to learn safety boundaries, filtering harmful requests at generation time through learned safety patterns rather than post-hoc filtering. Safety constraints are embedded in the model's weights and attention patterns, allowing it to refuse harmful requests while maintaining helpfulness on legitimate tasks.","intents":["Deploy an AI assistant in production that refuses harmful requests without requiring external content filters","Build a system that maintains safety guardrails while remaining helpful for legitimate use cases","Create an AI feature that handles adversarial prompts gracefully without crashing or producing harmful content","Reduce moderation overhead by using a safety-trained model instead of implementing custom filtering logic"],"best_for":["Teams building public-facing AI applications that need built-in safety without custom moderation infrastructure","Developers deploying AI features in regulated industries (healthcare, finance, legal) where safety is non-negotiable","Builders prioritizing user trust and brand safety over maximum capability","Non-technical product managers who want safety guarantees without understanding content filtering details"],"limitations":["Safety training introduces capability tradeoffs — the model may refuse legitimate requests that resemble harmful patterns, reducing utility for edge cases","Safety boundaries are not perfectly consistent — adversarial prompts or jailbreak attempts may occasionally succeed, especially with creative rephrasing","No transparency into specific safety rules — developers cannot easily understand or customize which requests are refused, limiting fine-grained control","Safety training is based on human judgment which varies by culture, region, and context — responses may not align with all user expectations or local regulations","Cannot guarantee safety for all possible inputs — novel attack vectors or domain-specific harms may not be covered by training data"],"requires":["OpenRouter API key","Understanding that safety is probabilistic, not deterministic — additional application-level safeguards may be needed for high-risk use cases","Acceptance that some legitimate requests may be refused due to safety training"],"input_types":["text (user prompts, including potentially adversarial or harmful requests)"],"output_types":["text (safe responses or refusals for harmful requests)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-8b-instruct__cap_5","uri":"capability://text.generation.language.streaming.token.generation.with.real.time.output","name":"streaming token generation with real-time output","description":"Generates responses token-by-token and streams them to the client in real-time via server-sent events (SSE) or chunked HTTP responses. This allows users to see the model's response appearing incrementally rather than waiting for the full response to complete, improving perceived latency and enabling cancellation of long-running generations. The implementation uses OpenRouter's streaming API endpoint which yields tokens as they are generated by the model.","intents":["Build a chatbot UI that displays responses as they are generated, improving user experience and perceived speed","Implement a real-time code generation feature where users see code appearing line-by-line","Create an interactive writing assistant where suggestions appear incrementally as the user types","Enable users to cancel long-running generations mid-stream to save API costs"],"best_for":["Web and mobile developers building interactive AI UIs where real-time feedback is important","Teams building conversational interfaces where streaming improves user experience","Builders implementing long-form content generation (articles, code, documentation) where streaming reduces perceived latency","Cost-conscious teams who want to allow users to cancel generations early"],"limitations":["Streaming adds complexity to client-side code — requires handling partial tokens, buffering, and error recovery during streaming","Token-by-token streaming makes it harder to implement post-processing or validation of complete responses — validation must happen after streaming completes","Network latency and buffering can cause uneven token arrival rates, creating a choppy user experience if not handled with client-side smoothing","Some API clients and frameworks don't support streaming natively, requiring custom implementation or library selection","Streaming responses cannot be easily cached or reused since they are consumed as a stream rather than stored as complete responses"],"requires":["OpenRouter API key with streaming support enabled","HTTP client library that supports streaming (e.g., fetch with ReadableStream, axios with responseType: 'stream', etc.)","Client-side code to handle partial tokens, buffer them, and render incrementally","Understanding of SSE or chunked transfer encoding"],"input_types":["text (prompt, same as non-streaming)"],"output_types":["streaming text tokens (via SSE or chunked HTTP response)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-8b-instruct__cap_6","uri":"capability://text.generation.language.temperature.and.sampling.parameter.control","name":"temperature and sampling parameter control","description":"Allows fine-grained control over response randomness and diversity through temperature, top-p (nucleus sampling), and top-k parameters exposed via the OpenRouter API. Temperature scales the logit distribution before sampling (lower = more deterministic, higher = more random), top-p limits sampling to the smallest set of tokens with cumulative probability ≥ p, and top-k limits to the k most likely tokens. These parameters are passed in the API request and affect the model's sampling behavior without retraining.","intents":["Generate deterministic, consistent responses for tasks requiring reliability (customer support, data extraction) by setting low temperature","Generate creative, diverse responses for tasks requiring novelty (brainstorming, content creation) by setting high temperature","Fine-tune response diversity to match specific use cases (e.g., temperature 0.7 for balanced dialogue)","Reduce hallucination in factual tasks by using low temperature and top-p constraints"],"best_for":["Developers building task-specific AI features where response consistency is critical","Teams experimenting with different temperature settings to optimize quality for their use case","Builders implementing multiple AI features with different randomness requirements (deterministic extraction vs. creative writing)","Rapid prototypers tuning model behavior without fine-tuning"],"limitations":["Temperature tuning is empirical — optimal values vary by task and require manual testing; no principled way to select temperature a priori","Low temperature (< 0.3) can produce repetitive or stilted responses even for tasks that benefit from some randomness","High temperature (> 1.5) increases hallucination and off-topic responses, especially on factual or constrained tasks","Temperature effects interact with prompt quality — a poorly written prompt may produce poor results regardless of temperature","No built-in mechanism to measure or optimize temperature automatically; requires external evaluation or user feedback"],"requires":["OpenRouter API key","Understanding of temperature, top-p, and top-k parameters and their effects","Ability to test and measure output quality for different parameter values","HTTP client that supports passing these parameters in API requests"],"input_types":["text (prompt)"],"output_types":["text (response with randomness controlled by temperature/sampling parameters)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-8b-instruct__cap_7","uri":"capability://tool.use.integration.api.based.inference.without.local.deployment","name":"api-based inference without local deployment","description":"Provides access to Llama 3 8B through OpenRouter's managed API, eliminating the need for local GPU infrastructure, model downloading, or deployment complexity. Requests are sent via HTTP to OpenRouter's endpoints, which handle model loading, inference, and response streaming. This is a fully managed service where the user only needs an API key and HTTP client — no infrastructure setup, scaling, or maintenance required.","intents":["Access a capable 8B model without owning or managing GPU hardware","Prototype and deploy AI features quickly without infrastructure setup","Scale inference automatically without managing load balancing or auto-scaling","Reduce operational overhead by outsourcing model serving to a managed provider"],"best_for":["Solo developers and small teams without GPU infrastructure or DevOps expertise","Startups and MVPs that need to minimize infrastructure costs and complexity","Teams building AI features that don't require sub-100ms latency or on-premises deployment","Developers prototyping before committing to self-hosted infrastructure"],"limitations":["API latency is higher than local inference — expect 100-500ms per request depending on network and OpenRouter load, vs. 10-50ms for local GPU inference","Ongoing API costs scale with usage — high-volume applications may be more cost-effective with self-hosted infrastructure","Vendor lock-in — switching providers requires changing API endpoints and potentially rewriting integration code","Data privacy concerns — prompts and responses are sent to OpenRouter's servers, which may not be acceptable for sensitive applications","Rate limiting and quota constraints may impact high-concurrency applications; requires careful request batching and queue management","No guarantee of uptime or SLA unless using a paid tier with explicit guarantees"],"requires":["OpenRouter API key (free tier available with limited usage, paid tier for production)","Network connectivity to OpenRouter endpoints","HTTP client library (curl, requests, fetch, etc.)","Acceptance of data being sent to third-party servers"],"input_types":["text (prompts)"],"output_types":["text (responses)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3-8b-instruct__cap_8","uri":"capability://text.generation.language.cost.optimized.inference.for.budget.constrained.applications","name":"cost-optimized inference for budget-constrained applications","description":"Llama 3 8B offers a favorable cost-to-capability ratio compared to larger models, making it suitable for applications with tight budget constraints. At 8B parameters, it requires less compute than 70B+ models, resulting in lower per-token API costs while maintaining reasonable quality for many tasks. This enables developers to build AI features at scale without prohibitive inference costs, or to allocate budgets across multiple AI features rather than concentrating on a single large model.","intents":["Build AI features with limited budget by using a smaller, cheaper model instead of GPT-4 or 70B models","Scale AI applications to more users without proportional cost increases","Allocate inference budget across multiple AI features (chat, summarization, classification) instead of concentrating on one","Prototype AI features cheaply before investing in larger models or fine-tuning"],"best_for":["Startups and bootstrapped teams with limited budgets for AI infrastructure","Developers building high-volume applications where per-token costs are critical","Teams building multiple AI features that need to fit within a fixed budget","Non-technical founders prototyping AI products with minimal funding"],"limitations":["Lower cost comes with capability tradeoffs — 8B models struggle with complex reasoning, long-context understanding, and specialized tasks compared to 70B+ models","Cost savings may be offset by lower quality requiring more prompt engineering, few-shot examples, or post-processing to achieve acceptable results","High-volume applications may hit rate limits or quota constraints on cheaper tiers, requiring upgrades that reduce cost advantages","Switching to larger models later requires rewriting prompts and potentially retraining fine-tuned models, creating technical debt","Cost comparison is only valid if quality is acceptable for the use case — if the model is too weak, total cost of ownership (including human review/correction) may be higher"],"requires":["OpenRouter API key with pricing transparency","Understanding of model capabilities and limitations to assess whether 8B is sufficient for your use case","Ability to measure quality and cost tradeoffs for your specific application"],"input_types":["text (prompts)"],"output_types":["text (responses)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["OpenRouter API key (free tier available with limited usage, paid tier for production)","HTTP client library or SDK (curl, Python requests, JavaScript fetch, etc.)","Network connectivity to OpenRouter endpoints","Understanding of prompt engineering for instruction-following models","OpenRouter API key","Client application with conversation history storage (in-memory, database, or session storage)","Understanding of conversation formatting (typically system prompt + alternating user/assistant messages)","HTTP client capable of handling request payloads that grow with conversation length","Skill in prompt engineering and task specification","Understanding of the model's training data and capabilities to set realistic expectations"],"failure_modes":["8B parameter size limits reasoning depth compared to 70B+ models — struggles with multi-step logical inference or complex mathematical problem-solving","Context window size not specified in artifact; likely 8K tokens or less, limiting ability to process long documents or maintain very long conversation histories","No native tool-use or function-calling capability — cannot directly invoke external APIs or execute code without wrapper integration","Instruction-tuning optimized for dialogue may reduce performance on non-conversational tasks like code generation or structured data extraction","Rate limiting and API quota constraints via OpenRouter may impact production-scale deployments with high concurrent users","Context window limitations mean conversation history cannot grow indefinitely — older turns will be truncated or dropped when total tokens exceed model's context limit","No built-in conversation summarization — developers must implement their own summarization logic to compress long histories before hitting context limits","Client-side state management requires the application to maintain and pass full conversation history with each API request, increasing payload size and latency as conversations grow","No native support for multi-user or branching conversations — each conversation thread must be managed separately by the application","Zero-shot performance degrades on highly specialized or domain-specific tasks — tasks requiring deep domain knowledge or novel reasoning patterns perform better with few-shot examples or fine-tuning","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"ecosystem":0.34,"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":"2026-05-03T15:20:45.777Z","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-3-8b-instruct","compare_url":"https://unfragile.ai/compare?artifact=meta-llama-llama-3-8b-instruct"}},"signature":"6K0X2aDjsFSTH+EQiC+ap6H2Eu/S7wMCpeiP1dRZtntSUcF7CU+/fWYahj+SPhNkFehhln4Li60ClNp1dsmyBQ==","signedAt":"2026-06-21T18:31:24.036Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/meta-llama-llama-3-8b-instruct","artifact":"https://unfragile.ai/meta-llama-llama-3-8b-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=meta-llama-llama-3-8b-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"}}