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The model uses standard transformer attention mechanisms with optimized token context windows, trained on curated instruction-following and reasoning datasets to improve logical consistency and factual grounding in back-and-forth exchanges.","intents":["Build a chatbot that maintains conversation context across 10+ turns without losing coherence","Create an AI assistant that reasons through multi-step problems while referencing earlier conversation points","Deploy a conversational agent that can handle complex follow-up questions with contextual awareness"],"best_for":["Teams building production chatbots requiring sustained context windows","Developers creating reasoning-heavy conversational agents","Organizations needing open-weight alternatives to closed-model APIs"],"limitations":["Context window limited to model's native 8K tokens; longer conversations require external memory management","Fine-tuning approach optimized for instruction-following may reduce creative/open-ended generation vs base Llama 3.1","No built-in retrieval augmentation — factual accuracy depends on training data and cannot be updated post-deployment without retraining","Inference latency scales linearly with context length; 8K token contexts incur ~2-3x latency vs 2K token contexts"],"requires":["OpenRouter API key for access","HTTP client capable of streaming token responses","Minimum 16GB VRAM if self-hosting; recommended 40GB+ for optimal throughput"],"input_types":["text (natural language queries)","structured prompts with system instructions","conversation history as concatenated text"],"output_types":["text (streaming or batch completion)","structured JSON via prompt engineering"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-sao10k-l3.1-70b-hanami-x1__cap_1","uri":"capability://text.generation.language.instruction.following.with.system.prompt.customization","name":"instruction-following with system prompt customization","description":"The model accepts system prompts and user instructions to adapt behavior for specific use cases, using standard transformer prompt engineering patterns where system context is prepended to user input and processed through the full attention mechanism. Fine-tuning on diverse instruction datasets enables the model to follow complex, multi-part directives and role-play scenarios with reasonable consistency.","intents":["Configure the model to act as a specific persona (e.g., code reviewer, technical writer, domain expert)","Enforce output format constraints (JSON, markdown, code blocks) through system instructions","Adapt the model's tone and style (formal, casual, technical) for different audiences"],"best_for":["Developers building specialized AI agents with fixed behavioral profiles","Teams needing consistent output formatting across multiple API calls","Organizations deploying domain-specific assistants (legal, medical, technical support)"],"limitations":["System prompt injection attacks possible if user input is not sanitized; no built-in prompt defense mechanisms","Instruction-following quality degrades with extremely long or contradictory system prompts (>2K tokens)","Fine-tuning optimized for English; non-English instruction-following may be less reliable","No explicit instruction hierarchy — conflicting system and user instructions may produce unpredictable behavior"],"requires":["OpenRouter API key","Understanding of prompt engineering best practices","Validation layer for user inputs to prevent prompt injection"],"input_types":["text system prompts","text user instructions","structured prompt templates"],"output_types":["text in specified format","code blocks","structured data (via prompt engineering)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-sao10k-l3.1-70b-hanami-x1__cap_2","uri":"capability://code.generation.editing.code.generation.and.technical.explanation","name":"code generation and technical explanation","description":"The model generates code snippets and technical explanations by leveraging transformer-based pattern matching on code-heavy training data, producing syntactically valid code across multiple programming languages. The fine-tuning process includes code-specific datasets, enabling the model to understand context from comments, function signatures, and error messages to generate contextually appropriate code solutions.","intents":["Generate boilerplate code or function implementations from natural language descriptions","Explain existing code snippets and technical concepts in plain language","Suggest bug fixes or optimizations based on error messages and code context"],"best_for":["Developers using AI as a coding assistant for rapid prototyping","Technical documentation teams automating code example generation","Teams building internal code generation tools or linters"],"limitations":["Code generation quality varies by language; Python and JavaScript are well-supported, but niche languages may produce incorrect syntax","No real-time compilation or execution validation — generated code requires manual testing","Context window limits prevent analyzing very large codebases (>8K tokens); multi-file refactoring requires external orchestration","Security vulnerabilities in generated code are not detected; model may produce code with SQL injection, XSS, or other flaws if not explicitly instructed otherwise"],"requires":["OpenRouter API key","Code editor or IDE for testing generated code","Manual code review process for production use"],"input_types":["natural language code requests","existing code snippets","error messages and stack traces","function signatures and docstrings"],"output_types":["code in multiple languages","technical explanations","refactored code","bug fix suggestions"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-sao10k-l3.1-70b-hanami-x1__cap_3","uri":"capability://text.generation.language.knowledge.synthesis.and.summarization","name":"knowledge synthesis and summarization","description":"The model synthesizes information from long text passages and generates summaries by using transformer attention mechanisms to identify salient information and compress it into coherent summaries. Fine-tuning on summarization and information extraction tasks enables the model to preserve key facts while reducing verbosity, supporting both abstractive and extractive summarization patterns.","intents":["Summarize long documents, articles, or research papers into key takeaways","Extract structured information (facts, dates, entities) from unstructured text","Generate executive summaries or abstracts for business documents"],"best_for":["Content teams automating document summarization workflows","Researchers processing large volumes of papers or reports","Business intelligence teams extracting insights from unstructured data"],"limitations":["Summarization quality degrades with highly technical or domain-specific jargon not well-represented in training data","Abstractive summaries may hallucinate facts not present in source material; extractive summaries are more reliable but less concise","Context window limits prevent summarizing documents >8K tokens without chunking and external orchestration","No built-in fact-checking; summaries should be validated against source material for accuracy-critical applications"],"requires":["OpenRouter API key","Text preprocessing for documents exceeding 8K tokens","Validation layer to verify summary accuracy for high-stakes use cases"],"input_types":["long-form text (articles, papers, reports)","structured documents (emails, meeting notes)","multi-paragraph passages"],"output_types":["abstractive summaries (natural language)","extractive summaries (key sentences)","structured data (facts, entities, dates)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-sao10k-l3.1-70b-hanami-x1__cap_4","uri":"capability://text.generation.language.creative.writing.and.content.generation","name":"creative writing and content generation","description":"The model generates creative text including stories, poetry, marketing copy, and other narrative content by leveraging transformer-based language modeling trained on diverse creative writing datasets. Fine-tuning balances instruction-following with creative flexibility, enabling the model to generate coherent narratives while respecting stylistic constraints and tone specifications from system prompts.","intents":["Generate creative story ideas, plot outlines, or full narrative passages","Create marketing copy, social media content, or advertising headlines","Write poetry, song lyrics, or other creative text in specified styles"],"best_for":["Content creators and marketing teams automating copy generation","Game developers generating narrative content or dialogue","Writers using AI as a brainstorming and ideation tool"],"limitations":["Creative output quality is subjective and varies by prompt; no objective metrics for creativity or originality","Model may produce clichéd or derivative content if not given specific stylistic constraints","No built-in plagiarism detection; generated content should be checked against existing works for originality","Tone and voice consistency degrades over very long outputs (>2K tokens); shorter passages are more coherent"],"requires":["OpenRouter API key","Clear stylistic guidelines and tone specifications in system prompts","Human editorial review for publication-quality content"],"input_types":["creative prompts and story ideas","style and tone specifications","genre and format constraints"],"output_types":["narrative text","marketing copy","poetry and creative writing","dialogue and character descriptions"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-sao10k-l3.1-70b-hanami-x1__cap_5","uri":"capability://text.generation.language.question.answering.with.contextual.reasoning","name":"question answering with contextual reasoning","description":"The model answers questions by processing query text through transformer attention mechanisms and generating responses based on patterns learned during training, with fine-tuning on question-answering datasets enabling improved reasoning over multiple facts and logical inference. The model can answer factual questions, perform calculations, and reason through multi-step problems without external knowledge retrieval.","intents":["Answer factual questions about general knowledge topics","Perform logical reasoning and multi-step problem solving","Provide explanations and justifications for answers"],"best_for":["Teams building FAQ systems or customer support chatbots","Educational platforms providing tutoring and explanation","Developers creating reasoning-based agents without external knowledge bases"],"limitations":["Factual accuracy limited to training data cutoff (knowledge cutoff date unknown for Hanami variant); cannot answer questions about recent events","No built-in fact-checking or confidence scoring; model may confidently provide incorrect information (hallucination)","Reasoning quality degrades on highly specialized or niche topics not well-represented in training data","No access to external knowledge bases or real-time information; all answers are generated from learned patterns"],"requires":["OpenRouter API key","Fact-checking layer for accuracy-critical applications","Awareness of model's knowledge cutoff date"],"input_types":["natural language questions","multi-part questions with context","questions with follow-up clarifications"],"output_types":["natural language answers","step-by-step explanations","reasoning chains"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":21,"verified":false,"data_access_risk":"high","permissions":["OpenRouter API key for access","HTTP client capable of streaming token responses","Minimum 16GB VRAM if self-hosting; recommended 40GB+ for optimal throughput","OpenRouter API key","Understanding of prompt engineering best practices","Validation layer for user inputs to prevent prompt injection","Code editor or IDE for testing generated code","Manual code review process for production use","Text preprocessing for documents exceeding 8K tokens","Validation layer to verify summary accuracy for high-stakes use cases"],"failure_modes":["Context window limited to model's native 8K tokens; longer conversations require external memory management","Fine-tuning approach optimized for instruction-following may reduce creative/open-ended generation vs base Llama 3.1","No built-in retrieval augmentation — factual accuracy depends on training data and cannot be updated post-deployment without retraining","Inference latency scales linearly with context length; 8K token contexts incur ~2-3x latency vs 2K token contexts","System prompt injection attacks possible if user input is not sanitized; no built-in prompt defense mechanisms","Instruction-following quality degrades with extremely long or contradictory system prompts (>2K tokens)","Fine-tuning optimized for English; non-English instruction-following may be less reliable","No explicit instruction hierarchy — conflicting system and user instructions may produce unpredictable behavior","Code generation quality varies by language; Python and JavaScript are well-supported, but niche languages may produce incorrect syntax","No real-time compilation or execution validation — generated code requires manual testing","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.24,"match_graph":0.25,"freshness":0.9,"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":"2026-05-24T12:16:24.485Z","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=sao10k-l3.1-70b-hanami-x1","compare_url":"https://unfragile.ai/compare?artifact=sao10k-l3.1-70b-hanami-x1"}},"signature":"zV6a5qvhjFqQ6D3AZTaHPsW+Hv8izFAkD/ysGmt4K2WWIK8OJhkl/v1jkcR5bXBSQ2Dw1SuGJP5lBFvFZTrSBw==","signedAt":"2026-06-15T22:26:00.897Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sao10k-l3.1-70b-hanami-x1","artifact":"https://unfragile.ai/sao10k-l3.1-70b-hanami-x1","verify":"https://unfragile.ai/api/v1/verify?slug=sao10k-l3.1-70b-hanami-x1","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"}}