{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen-2.5-7b-instruct","slug":"qwen-qwen-2.5-7b-instruct","name":"Qwen: Qwen2.5 7B Instruct","type":"model","url":"https://openrouter.ai/models/qwen~qwen-2.5-7b-instruct","page_url":"https://unfragile.ai/qwen-qwen-2.5-7b-instruct","categories":["llm-apis"],"tags":["qwen","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$4.00e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen-2.5-7b-instruct__cap_0","uri":"capability://text.generation.language.instruction.following.conversational.generation","name":"instruction-following conversational generation","description":"Generates contextually appropriate responses to natural language instructions and multi-turn conversations using a transformer-based architecture trained on instruction-tuning datasets. The model processes input tokens through attention layers to maintain conversation coherence and follow explicit user directives, supporting both single-turn queries and extended dialogue contexts with implicit state management across turns.","intents":["I need a chatbot that understands complex instructions and follows them accurately across multiple conversation turns","I want to integrate a conversational AI that can handle nuanced requests without explicit context re-injection","I need an LLM that performs well on instruction-following benchmarks for production chatbot applications"],"best_for":["developers building conversational agents and chatbot applications","teams deploying customer support automation systems","builders creating multi-turn dialogue systems with instruction adherence requirements"],"limitations":["No persistent memory across separate conversation sessions — each new conversation starts without prior context","Maximum context window limits multi-turn conversations; exact window size not specified in artifact data","Instruction-following quality degrades with extremely long or ambiguous instructions requiring clarification","No built-in tool calling or function invocation — requires external orchestration for action execution"],"requires":["API access via OpenRouter or compatible inference endpoint","HTTP client library for API calls","Valid API credentials for authentication","Network connectivity to inference service"],"input_types":["text (natural language instructions)","text (multi-turn conversation history)","text (system prompts for behavior customization)"],"output_types":["text (natural language response)","text (streaming tokens for real-time output)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-7b-instruct__cap_1","uri":"capability://code.generation.editing.code.generation.and.completion","name":"code generation and completion","description":"Generates syntactically correct and semantically meaningful code snippets across multiple programming languages by leveraging transformer attention patterns trained on large code corpora. The model understands code structure, common patterns, and language-specific idioms, enabling both standalone function generation and in-context code completion within existing codebases when provided as context.","intents":["I need to generate boilerplate code or utility functions without writing them manually","I want code completion that understands the programming language and project context I'm working in","I need an LLM that can generate code in multiple languages (Python, JavaScript, Java, C++, etc.) with reasonable quality"],"best_for":["individual developers seeking code generation assistance for rapid prototyping","teams integrating code generation into IDE plugins or development workflows","builders creating code-focused applications where 7B parameter efficiency is critical"],"limitations":["No semantic understanding of project-specific libraries or custom frameworks — requires explicit context about dependencies","Generated code may contain logical errors or inefficiencies; human review is mandatory for production code","Limited ability to generate code for obscure or newly-released programming languages with minimal training data","No built-in code execution or validation — generated code must be tested separately","Context window limitations prevent analyzing very large codebases for context-aware generation"],"requires":["API access to Qwen2.5 7B via OpenRouter or compatible endpoint","Code context provided as text input (function signatures, imports, comments)","HTTP client for API communication","Valid authentication credentials"],"input_types":["text (code comments describing desired functionality)","text (partial code with gaps to fill)","text (function signatures and type hints)","text (natural language descriptions of algorithms)"],"output_types":["text (generated code in target language)","text (code with inline comments explaining logic)","text (multiple code variants for comparison)"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-7b-instruct__cap_2","uri":"capability://text.generation.language.knowledge.grounded.question.answering","name":"knowledge-grounded question answering","description":"Answers factual questions and provides information synthesis by retrieving relevant knowledge from its training data and combining multiple facts through transformer reasoning. The model performs implicit knowledge retrieval during inference by attending to learned representations of facts, enabling question answering without explicit external knowledge bases, though accuracy depends on training data recency and coverage.","intents":["I need an LLM that can answer factual questions about general knowledge, science, history, and technology","I want to build a Q&A system that doesn't require maintaining a separate knowledge base or RAG pipeline","I need reliable answers to common questions without hallucination-prone retrieval systems"],"best_for":["developers building simple Q&A chatbots without complex knowledge management requirements","teams creating educational or informational applications with general knowledge needs","builders prototyping knowledge-based systems before investing in RAG infrastructure"],"limitations":["Knowledge cutoff date not specified — model may provide outdated information for recent events or rapidly-evolving fields","No explicit knowledge source attribution — cannot cite where information comes from or verify accuracy","Hallucination risk for obscure or specialized topics with limited training data coverage","No ability to update knowledge without retraining — cannot incorporate new information after deployment","Factual accuracy varies by domain; technical and scientific accuracy may be lower than specialized models"],"requires":["API access to Qwen2.5 7B inference endpoint","HTTP client for API requests","Valid authentication credentials","Network connectivity to inference service"],"input_types":["text (factual questions in natural language)","text (multi-part questions requiring synthesis)","text (questions with contextual background)"],"output_types":["text (factual answers with explanations)","text (synthesized information combining multiple facts)","text (confidence indicators or uncertainty expressions)"],"categories":["text-generation-language","knowledge-grounded-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-7b-instruct__cap_3","uri":"capability://text.generation.language.mathematical.reasoning.and.problem.solving","name":"mathematical reasoning and problem solving","description":"Solves mathematical problems and performs symbolic reasoning through learned patterns in mathematical notation and algorithmic approaches. The model processes mathematical expressions, equations, and problem descriptions to generate step-by-step solutions, leveraging transformer attention to track variable relationships and logical dependencies across solution steps.","intents":["I need an LLM that can solve algebra, calculus, and discrete math problems with shown work","I want to generate mathematical explanations and tutoring content for educational applications","I need a system that can parse mathematical notation and produce correct symbolic manipulations"],"best_for":["educators building AI-powered tutoring systems for mathematics","developers creating homework help or educational assessment tools","teams building scientific computing assistants that require mathematical reasoning"],"limitations":["Mathematical reasoning quality degrades significantly for advanced topics (abstract algebra, topology, advanced calculus)","No symbolic computation engine — cannot perform exact symbolic manipulation or verify solutions mathematically","Prone to arithmetic errors in multi-step calculations despite showing correct methodology","Limited ability to handle novel problem formulations not well-represented in training data","No integration with computer algebra systems (SymPy, Mathematica) for verification"],"requires":["API access to Qwen2.5 7B via OpenRouter","HTTP client for API communication","Valid authentication credentials","Optional: LaTeX or mathematical notation parser for input formatting"],"input_types":["text (mathematical problems in natural language)","text (equations and mathematical expressions)","text (step-by-step problem descriptions)"],"output_types":["text (step-by-step solutions with explanations)","text (mathematical expressions and equations)","text (reasoning chains showing problem-solving approach)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-7b-instruct__cap_4","uri":"capability://text.generation.language.multilingual.text.generation.and.translation","name":"multilingual text generation and translation","description":"Generates and translates text across multiple languages by leveraging multilingual token embeddings and cross-lingual attention patterns learned during training. The model maintains semantic consistency across language pairs and can perform zero-shot translation for language combinations not explicitly seen during training, using shared representation spaces across languages.","intents":["I need to translate content between multiple languages while preserving meaning and tone","I want to generate content in non-English languages for international applications","I need a system that can handle code-switching and multilingual conversations seamlessly"],"best_for":["teams building international applications requiring multi-language support","developers creating translation services without dedicated translation APIs","builders localizing content for global audiences across multiple languages"],"limitations":["Translation quality varies significantly by language pair; low-resource languages may produce lower-quality translations","No domain-specific terminology handling — technical or specialized vocabulary may be mistranslated","Cultural context and idioms may not translate correctly, requiring human review for sensitive content","No explicit language detection — requires specifying target language explicitly","Performance degrades for rare language combinations or languages with minimal training data"],"requires":["API access to Qwen2.5 7B inference endpoint","HTTP client for API requests","Valid authentication credentials","Language specification in prompts (source and target languages)"],"input_types":["text (content in source language)","text (natural language translation requests)","text (multilingual conversation context)"],"output_types":["text (translated content in target language)","text (multilingual responses maintaining language consistency)","text (code-switched output for bilingual contexts)"],"categories":["text-generation-language","multilingual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-7b-instruct__cap_5","uri":"capability://text.generation.language.content.summarization.and.abstraction","name":"content summarization and abstraction","description":"Condenses long-form text into concise summaries by identifying key information and abstracting away redundancy through transformer attention mechanisms that weight important tokens. The model performs both extractive summarization (selecting key sentences) and abstractive summarization (generating new sentences capturing main ideas), with configurable summary length and detail level through prompt engineering.","intents":["I need to automatically summarize long documents, articles, or reports into key points","I want to generate executive summaries or abstracts for content management systems","I need to condense meeting notes, research papers, or technical documentation into digestible formats"],"best_for":["teams building document management or content curation platforms","developers creating research tools or literature review assistants","builders automating content summarization for news aggregation or knowledge management"],"limitations":["Summary quality depends on input text clarity and structure; poorly-written source material produces poor summaries","No guarantee that summaries capture all critical information — important details may be omitted","Context window limitations prevent summarizing very long documents (>10,000 tokens) in single pass","No ability to preserve specific formatting, citations, or references from original text","Abstractive summaries may introduce subtle inaccuracies or misrepresentations of nuanced content"],"requires":["API access to Qwen2.5 7B via OpenRouter","HTTP client for API communication","Valid authentication credentials","Source text provided as input (up to context window limit)"],"input_types":["text (long-form articles, documents, or reports)","text (meeting transcripts or conversation logs)","text (research papers or technical documentation)"],"output_types":["text (concise summaries of specified length)","text (bullet-point key takeaways)","text (abstractive summaries with new phrasing)"],"categories":["text-generation-language","content-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-7b-instruct__cap_6","uri":"capability://text.generation.language.creative.writing.and.content.generation","name":"creative writing and content generation","description":"Generates original creative content including stories, poetry, dialogue, and marketing copy by sampling from learned distributions of language patterns and narrative structures. The model maintains narrative coherence across multiple paragraphs, adapts tone and style to prompts, and generates diverse outputs through temperature-based sampling, enabling both deterministic and creative generation modes.","intents":["I need to generate creative writing samples, story outlines, or narrative content","I want to create marketing copy, product descriptions, or promotional content at scale","I need to generate dialogue, character descriptions, or creative prompts for games or interactive fiction"],"best_for":["content creators and writers seeking AI-assisted creative ideation","marketing teams automating product description and copy generation","game developers creating procedural narrative content or dialogue systems"],"limitations":["Generated content may lack originality or contain clichéd phrases from training data","Tone and style consistency degrades over very long outputs (>2000 tokens)","No understanding of brand voice or specific creative guidelines without extensive prompt engineering","Generated content requires human review and editing for publication quality","May inadvertently reproduce copyrighted material or training data verbatim in some cases"],"requires":["API access to Qwen2.5 7B inference endpoint","HTTP client for API requests","Valid authentication credentials","Detailed prompts specifying tone, style, and content requirements"],"input_types":["text (creative writing prompts and story outlines)","text (style and tone specifications)","text (character descriptions or world-building context)"],"output_types":["text (original creative writing and stories)","text (marketing copy and product descriptions)","text (dialogue and character interactions)"],"categories":["text-generation-language","creative-content"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-7b-instruct__cap_7","uri":"capability://data.processing.analysis.structured.data.extraction.and.parsing","name":"structured data extraction and parsing","description":"Extracts structured information from unstructured text by identifying entities, relationships, and patterns, then formatting results as JSON, tables, or other structured formats. The model uses contextual understanding to disambiguate entities and relationships, performing information extraction through attention mechanisms that identify relevant text spans and their semantic roles.","intents":["I need to extract key information (names, dates, amounts) from documents or text automatically","I want to convert unstructured text into structured JSON or database records","I need to identify entities and relationships in documents for knowledge graph construction"],"best_for":["teams building document processing or data extraction pipelines","developers automating data entry from unstructured sources","builders creating knowledge extraction systems for information management"],"limitations":["Extraction accuracy depends on text clarity and entity prominence; ambiguous or implicit information may be missed","No schema validation — extracted data may not conform to specified formats without post-processing","Limited ability to handle domain-specific entities or relationships not well-represented in training data","No built-in deduplication or entity linking — duplicate or coreferent entities may not be recognized","Hallucination risk for missing information — model may invent plausible but incorrect data to fill gaps"],"requires":["API access to Qwen2.5 7B via OpenRouter","HTTP client for API communication","Valid authentication credentials","Schema or format specification in prompts (JSON structure, field names, etc.)"],"input_types":["text (unstructured documents or passages)","text (entity and relationship type specifications)","text (output format requirements)"],"output_types":["text (JSON-formatted structured data)","text (CSV or table-formatted extraction results)","text (entity lists with attributes)"],"categories":["data-processing-analysis","information-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-7b-instruct__cap_8","uri":"capability://text.generation.language.prompt.based.behavior.customization","name":"prompt-based behavior customization","description":"Adapts model behavior and output style through system prompts and few-shot examples that establish context and expected behavior patterns. The model uses prompt-based instruction following to adopt different personas, writing styles, technical levels, and response formats without fine-tuning, leveraging in-context learning to apply behavioral patterns from examples.","intents":["I need to customize the model's tone, style, and behavior for different use cases without retraining","I want to create specialized assistants (technical, casual, formal) using the same base model","I need to provide few-shot examples to teach the model domain-specific behavior or output formats"],"best_for":["developers building multi-purpose AI assistants with customizable behavior","teams creating specialized chatbots for different user segments or domains","builders prototyping different AI personalities or interaction styles"],"limitations":["Behavior customization quality depends on prompt clarity and example quality; vague prompts produce inconsistent behavior","Few-shot learning limited by context window — cannot provide unlimited examples","No persistent behavior learning — customization applies only to current conversation","Complex behavioral requirements may require multiple prompt iterations to achieve desired results","Behavioral consistency may degrade over long conversations as model drifts from initial instructions"],"requires":["API access to Qwen2.5 7B inference endpoint","HTTP client for API requests","Valid authentication credentials","Well-crafted system prompts and few-shot examples"],"input_types":["text (system prompts defining behavior and constraints)","text (few-shot examples demonstrating desired output format)","text (user queries to be processed with customized behavior)"],"output_types":["text (responses following customized behavior patterns)","text (output in specified formats or styles)"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or compatible inference endpoint","HTTP client library for API calls","Valid API credentials for authentication","Network connectivity to inference service","API access to Qwen2.5 7B via OpenRouter or compatible endpoint","Code context provided as text input (function signatures, imports, comments)","HTTP client for API communication","Valid authentication credentials","API access to Qwen2.5 7B inference endpoint","HTTP client for API requests"],"failure_modes":["No persistent memory across separate conversation sessions — each new conversation starts without prior context","Maximum context window limits multi-turn conversations; exact window size not specified in artifact data","Instruction-following quality degrades with extremely long or ambiguous instructions requiring clarification","No built-in tool calling or function invocation — requires external orchestration for action execution","No semantic understanding of project-specific libraries or custom frameworks — requires explicit context about dependencies","Generated code may contain logical errors or inefficiencies; human review is mandatory for production code","Limited ability to generate code for obscure or newly-released programming languages with minimal training data","No built-in code execution or validation — generated code must be tested separately","Context window limitations prevent analyzing very large codebases for context-aware generation","Knowledge cutoff date not specified — model may provide outdated information for recent events or rapidly-evolving fields","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":"2026-05-24T12:16:24.485Z","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=qwen-qwen-2.5-7b-instruct","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen-2.5-7b-instruct"}},"signature":"25UlGDMG9mYLrsjuhhKFjzWOHThIRv2MWcMVrWwH6MrzWvO9pZ9h+uloaos9/LJXAC3oslpb/sDT8OVD1a6tAQ==","signedAt":"2026-06-19T21:53:08.047Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen-2.5-7b-instruct","artifact":"https://unfragile.ai/qwen-qwen-2.5-7b-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen-2.5-7b-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"}}