{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-meta-llama-llama-3.1-8b-instruct","slug":"meta-llama-llama-3.1-8b-instruct","name":"Meta: Llama 3.1 8B Instruct","type":"model","url":"https://openrouter.ai/models/meta-llama~llama-3.1-8b-instruct","page_url":"https://unfragile.ai/meta-llama-llama-3.1-8b-instruct","categories":["chatbots-assistants"],"tags":["meta-llama","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$2.00e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-meta-llama-llama-3.1-8b-instruct__cap_0","uri":"capability://text.generation.language.instruction.following.text.generation.with.context.awareness","name":"instruction-following text generation with context awareness","description":"Generates coherent, contextually-aware text responses to user prompts using transformer-based architecture with 8 billion parameters fine-tuned on instruction-following tasks. The model processes input tokens through multi-head attention layers and produces output via autoregressive decoding, maintaining semantic consistency across multi-turn conversations through attention mechanisms that weight relevant context tokens.","intents":["I need to generate natural language responses to user queries in a chatbot or assistant application","I want to build a conversational AI that understands nuanced instructions and follows them accurately","I need to integrate a lightweight language model that doesn't require massive computational resources"],"best_for":["developers building cost-conscious chatbot applications with moderate latency requirements","teams deploying edge or on-device inference where model size matters","builders prototyping multi-turn conversational agents with limited infrastructure budgets"],"limitations":["8B parameter count limits reasoning depth on complex multi-step problems compared to 70B+ models","context window size (typically 8K tokens) constrains ability to maintain coherence in very long conversations","instruction-tuning may introduce subtle biases toward common training patterns, reducing novelty in creative tasks","no built-in memory persistence across sessions — each conversation starts fresh without prior context"],"requires":["API access via OpenRouter or compatible inference endpoint","valid authentication credentials (API key)","HTTP client library or SDK for making requests","minimum 2-3 seconds latency tolerance for token generation"],"input_types":["text (natural language prompts)","structured prompts with system instructions","multi-turn conversation history as concatenated text"],"output_types":["text (natural language responses)","streaming token sequences","structured text (JSON, markdown, code blocks when prompted)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-8b-instruct__cap_1","uri":"capability://text.generation.language.multi.turn.conversation.state.management.via.api","name":"multi-turn conversation state management via api","description":"Maintains conversation context across sequential API calls by accepting conversation history as input (typically as a list of messages with roles like 'user' and 'assistant'), allowing the model to reference prior exchanges and maintain coherent dialogue flow. The API endpoint processes the full message history on each request, using attention mechanisms to weight recent and relevant prior messages when generating the next response.","intents":["I want to build a chatbot that remembers what was said earlier in the conversation","I need to implement a multi-turn dialogue system where context from previous exchanges influences responses","I want to maintain conversation state without managing a separate database or session store"],"best_for":["developers building stateless conversational APIs where clients manage history","teams implementing chat interfaces that need to replay conversation context on each request","builders prototyping dialogue systems without infrastructure for persistent session storage"],"limitations":["context window is finite (typically 8K tokens) — very long conversations will hit token limits and require truncation or summarization","each API call processes the entire conversation history, creating linear cost scaling with conversation length","no server-side session management — all state responsibility falls on the client application","attention mechanism may deprioritize very old messages in long conversations, causing early context loss"],"requires":["API endpoint accepting message array format (typically OpenAI-compatible format with 'role' and 'content' fields)","client-side logic to accumulate and pass conversation history","token counting mechanism to track cumulative message length against context window"],"input_types":["message array with role-based structure (user/assistant/system)","individual user messages","system prompts to guide conversation behavior"],"output_types":["assistant message text","streaming token sequences for real-time display","metadata (token counts, finish reasons)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-8b-instruct__cap_2","uri":"capability://text.generation.language.system.prompt.guided.behavior.steering","name":"system-prompt-guided behavior steering","description":"Accepts a 'system' message that sets behavioral constraints, tone, expertise level, and response format for the model before processing user queries. The system prompt is prepended to the conversation context and influences attention weights during generation, allowing fine-grained control over model personality, safety boundaries, and output structure without retraining or fine-tuning.","intents":["I want to create a specialized assistant with a specific persona or expertise (e.g., a Python expert, a medical advisor)","I need to enforce consistent tone and style across all responses from my chatbot","I want to guide the model toward structured outputs (JSON, markdown lists, code blocks) without explicit training"],"best_for":["developers building domain-specific chatbots with consistent personality","teams implementing safety guardrails through prompt engineering","builders prototyping specialized assistants for vertical use cases (customer support, technical help, tutoring)"],"limitations":["system prompt effectiveness depends on model training — instruction-tuned models follow system prompts better than base models, but compliance is not guaranteed","adversarial or jailbreak prompts can override system instructions, requiring additional safety layers","system prompt tokens consume part of the context window, reducing space for conversation history","no formal specification for system prompt behavior — results may vary across different model versions or inference engines"],"requires":["API support for 'system' role in message array","understanding of prompt engineering principles to craft effective system prompts","testing and iteration to validate that system prompts achieve desired behavior"],"input_types":["system message (text string with behavioral instructions)","user messages (processed in context of system prompt)"],"output_types":["text responses adhering to system prompt constraints","structured outputs (JSON, code, markdown) when system prompt specifies format"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-8b-instruct__cap_3","uri":"capability://text.generation.language.streaming.token.generation.for.real.time.response.display","name":"streaming token generation for real-time response display","description":"Outputs response tokens sequentially via server-sent events (SSE) or chunked HTTP responses, allowing client applications to display text as it's generated rather than waiting for the complete response. The model generates tokens autoregressively (one at a time), and the API streams each token immediately upon generation, enabling perceived responsiveness and lower time-to-first-token latency.","intents":["I want to build a chat UI that shows responses appearing in real-time as the model generates them","I need to reduce perceived latency in conversational interfaces by displaying tokens as they arrive","I want to allow users to cancel long-running generations mid-stream without waiting for completion"],"best_for":["frontend developers building interactive chat interfaces with real-time response display","teams implementing web-based conversational UIs where perceived latency matters","builders creating streaming-first applications (Discord bots, Slack integrations, web chat)"],"limitations":["streaming adds complexity to error handling — errors mid-stream require client-side recovery logic","token-by-token streaming increases HTTP overhead compared to single-request batching","client must implement buffering and display logic to handle variable token arrival rates","streaming responses cannot be easily cached or reused without reconstructing the full response"],"requires":["HTTP client supporting streaming responses (fetch with ReadableStream, axios with responseType: 'stream', etc.)","server-sent events (SSE) or chunked transfer encoding support","frontend logic to parse and display streamed tokens in real-time","handling of stream termination and error states"],"input_types":["standard message array (same as non-streaming requests)","optional stream configuration parameters"],"output_types":["streamed text tokens (one per chunk)","metadata events (finish reason, token counts) at stream end"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-8b-instruct__cap_4","uri":"capability://code.generation.editing.code.generation.and.explanation.with.instruction.tuned.context","name":"code generation and explanation with instruction-tuned context","description":"Generates syntactically valid code snippets and full programs in multiple languages (Python, JavaScript, Java, C++, SQL, etc.) based on natural language descriptions, leveraging instruction-tuning to understand code-specific requests and produce contextually appropriate implementations. The model uses attention over code tokens to maintain consistency within generated code blocks and can explain generated code or refactor existing code when prompted.","intents":["I want to generate boilerplate code or function implementations from natural language descriptions","I need to explain or document existing code snippets","I want to refactor or optimize code with guidance on best practices"],"best_for":["developers using AI to accelerate routine coding tasks and boilerplate generation","teams using AI-assisted code review and documentation","educators using AI to explain code concepts to students"],"limitations":["8B parameter model may struggle with complex algorithms or architectural patterns requiring deep reasoning","generated code requires human review — model may produce syntactically valid but logically incorrect code","no real-time compilation or testing — generated code is not validated before output","context window limits ability to work with very large codebases or complex multi-file refactoring","no built-in knowledge of proprietary libraries or internal APIs unless provided in context"],"requires":["API access to Llama 3.1 8B Instruct","code review process to validate generated code before deployment","testing infrastructure to verify generated code correctness"],"input_types":["natural language code requests (e.g., 'write a Python function that validates email addresses')","existing code snippets for refactoring or explanation","code with inline comments or docstrings for context"],"output_types":["code snippets in requested language","full program implementations","code explanations and documentation","refactored code with improvement suggestions"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-8b-instruct__cap_5","uri":"capability://text.generation.language.structured.output.generation.with.format.constraints","name":"structured output generation with format constraints","description":"Generates responses in specified structured formats (JSON, YAML, XML, CSV, markdown tables) by including format instructions in the system prompt or user message, leveraging the model's instruction-following capability to produce parseable structured data. The model uses attention over structural tokens to maintain valid syntax and can be guided toward specific schema compliance through careful prompt engineering.","intents":["I want to extract structured data from unstructured text (e.g., convert a paragraph into JSON fields)","I need the model to output data in a specific format that my downstream system can parse","I want to generate configuration files, API responses, or database records in a structured format"],"best_for":["developers building data extraction pipelines that need structured outputs","teams integrating LLM outputs with downstream systems expecting specific formats","builders creating configuration generators or template systems"],"limitations":["no formal schema validation — model may produce syntactically valid but semantically incorrect JSON","complex nested structures may exceed model's ability to maintain consistency across deep hierarchies","format compliance is probabilistic — some outputs may deviate from specified format, requiring post-processing validation","instruction-following quality degrades with very complex or unusual format specifications"],"requires":["clear format specification in system prompt or user message","JSON schema or format examples in context to guide model behavior","post-processing validation to ensure output matches expected structure","fallback parsing logic to handle malformed outputs"],"input_types":["natural language text to be structured","format specification (JSON schema, example output, format description)","system prompt with format constraints"],"output_types":["JSON objects or arrays","YAML, XML, or CSV formatted text","markdown tables or structured text","configuration files in various formats"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-8b-instruct__cap_6","uri":"capability://text.generation.language.multi.language.understanding.and.response.generation","name":"multi-language understanding and response generation","description":"Processes input text in multiple languages (English, Spanish, French, German, Chinese, Japanese, etc.) and generates coherent responses in the requested language, using multilingual token embeddings and cross-lingual attention mechanisms trained on diverse language pairs. The model can translate between languages, answer questions in non-English languages, and maintain context across language switches within a conversation.","intents":["I want to build a chatbot that serves users in multiple languages without separate model deployments","I need to translate or explain content across language boundaries","I want to support international users with native-language responses"],"best_for":["teams building globally-distributed applications with multilingual user bases","developers creating translation or localization tools","builders serving non-English-speaking markets without language-specific model variants"],"limitations":["multilingual capability comes at cost of reduced performance in any single language compared to language-specific models","some low-resource languages may have weaker performance due to less training data","code-switching (mixing languages) may confuse the model in some contexts","translation quality varies by language pair — some combinations are more reliable than others"],"requires":["API access to Llama 3.1 8B Instruct","language specification in system prompt or user message when non-English response is desired","understanding that multilingual models trade single-language performance for cross-language capability"],"input_types":["text in any supported language","language specification or code-switching within messages","translation requests"],"output_types":["responses in requested language","translations between language pairs","multilingual explanations or summaries"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-8b-instruct__cap_7","uri":"capability://planning.reasoning.reasoning.and.step.by.step.problem.decomposition","name":"reasoning and step-by-step problem decomposition","description":"Generates multi-step reasoning chains and problem decompositions when prompted with complex questions, using attention mechanisms to maintain logical consistency across reasoning steps. The model can be guided toward explicit reasoning via prompts like 'think step by step' or 'explain your reasoning', leveraging instruction-tuning to produce coherent intermediate reasoning before arriving at final answers.","intents":["I want the model to show its reasoning for complex problems rather than jumping to conclusions","I need to break down complex tasks into smaller steps for better accuracy","I want to verify model reasoning by examining intermediate steps"],"best_for":["developers building educational or tutoring systems where reasoning transparency matters","teams implementing verification systems that need to audit model decision-making","builders tackling complex reasoning tasks where step-by-step decomposition improves accuracy"],"limitations":["8B parameter model has limited reasoning depth compared to 70B+ models — complex multi-step problems may exceed its capability","reasoning chains are generated autoregressively and may contain logical errors or circular reasoning","no formal verification of reasoning correctness — intermediate steps may be plausible but incorrect","step-by-step reasoning increases token consumption and latency compared to direct answers"],"requires":["prompts that explicitly request reasoning (e.g., 'think step by step', 'explain your reasoning')","understanding that reasoning quality is probabilistic and may require multiple attempts","validation logic to verify correctness of final answers"],"input_types":["complex questions or problems","prompts requesting step-by-step reasoning","context or constraints for the problem"],"output_types":["multi-step reasoning chains","intermediate reasoning steps","final answers with supporting reasoning","problem decompositions"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-3.1-8b-instruct__cap_8","uri":"capability://memory.knowledge.knowledge.grounded.response.generation.with.context.injection","name":"knowledge-grounded response generation with context injection","description":"Generates responses that incorporate and reference provided context documents or knowledge snippets, using attention mechanisms to weight relevant context passages when generating answers. 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