{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-qwen--qwen2.5-7b-instruct","slug":"qwen--qwen2.5-7b-instruct","name":"Qwen2.5-7B-Instruct","type":"model","url":"https://huggingface.co/Qwen/Qwen2.5-7B-Instruct","page_url":"https://unfragile.ai/qwen--qwen2.5-7b-instruct","categories":["chatbots-assistants"],"tags":["transformers","safetensors","qwen2","text-generation","chat","conversational","en","arxiv:2309.00071","arxiv:2407.10671","base_model:Qwen/Qwen2.5-7B","base_model:finetune:Qwen/Qwen2.5-7B","license:apache-2.0","text-generation-inference","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-qwen--qwen2.5-7b-instruct__cap_0","uri":"capability://text.generation.language.instruction.following.conversational.generation.with.multi.turn.context","name":"instruction-following conversational generation with multi-turn context","description":"Generates coherent, contextually-aware responses to user instructions using a transformer-based architecture fine-tuned on instruction-following datasets. The model maintains conversation history through standard transformer attention mechanisms, allowing it to track context across multiple turns without explicit memory management. Fine-tuning on instruction data (beyond base model pretraining) enables the model to follow complex directives, answer questions, and engage in multi-turn dialogue with reduced hallucination compared to base models.","intents":["Build a chatbot that understands user intent and responds appropriately across multiple conversation turns","Deploy a conversational AI assistant that can follow detailed instructions and maintain context","Create a question-answering system that understands nuanced queries and provides relevant answers","Develop an interactive agent that can engage in natural dialogue without losing conversation history"],"best_for":["Teams building open-source chatbot applications with full model control","Developers deploying on-premise or edge conversational AI without cloud dependencies","Researchers fine-tuning instruction-following models for domain-specific tasks","Organizations requiring Apache 2.0 licensed models for commercial applications"],"limitations":["Context window limited to ~32K tokens (standard transformer limitation), requiring conversation summarization for very long dialogues","No built-in memory persistence across sessions — requires external state management for multi-session continuity","Performance degrades with very long context (>16K tokens) due to quadratic attention complexity","Instruction-following quality depends on input format alignment with training data; poorly-formatted prompts yield inconsistent results","No native support for real-time streaming output without additional inference framework integration"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible inference framework (vLLM, Text Generation Inference, Ollama)","Minimum 16GB RAM for 7B model quantization, 32GB for full precision inference","CUDA 11.8+ for GPU acceleration (optional but recommended for <500ms latency)","Hugging Face transformers library 4.36+"],"input_types":["text (natural language instructions, questions, conversational prompts)","structured prompts (system messages + user messages in chat format)"],"output_types":["text (natural language responses)","streaming text tokens (when using compatible inference servers)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen2.5-7b-instruct__cap_1","uri":"capability://code.generation.editing.code.generation.and.explanation.with.syntax.awareness","name":"code generation and explanation with syntax awareness","description":"Generates executable code snippets and technical explanations by leveraging instruction-tuning on code-heavy datasets. The model understands programming syntax, common patterns, and library APIs across multiple languages, enabling it to produce contextually appropriate code that aligns with user intent. Code generation works through standard next-token prediction with implicit understanding of language-specific conventions (indentation, syntax rules, import statements) learned during training rather than explicit parsing.","intents":["Generate boilerplate code or function implementations from natural language descriptions","Explain existing code snippets and help debug syntax or logic errors","Provide code examples for specific libraries or frameworks in response to queries","Assist with code refactoring suggestions and optimization recommendations"],"best_for":["Solo developers prototyping features quickly without context-switching to documentation","Teams using open-source tooling without cloud-based code generation dependencies","Educational settings where students need code explanations alongside generation","Organizations with strict data governance requiring on-premise code generation"],"limitations":["No real-time syntax validation — generated code may contain subtle bugs or use deprecated APIs","Limited to code patterns seen in training data; novel or very recent library versions may generate incorrect usage","No built-in test generation or verification; developers must manually validate generated code","Context window constraints mean multi-file refactoring requires explicit file concatenation","Performance on very long code files (>2K lines) degrades due to attention complexity"],"requires":["Python 3.8+","PyTorch 2.0+ or inference framework supporting code generation","16GB+ RAM for quantized inference, 32GB+ for full precision","Optional: IDE integration layer (VS Code extension, Vim plugin) for seamless workflow"],"input_types":["text (natural language code requests, code snippets for explanation/refactoring)","structured prompts (system message specifying language/framework context)"],"output_types":["text (code snippets, explanations, refactoring suggestions)","structured code blocks (when using compatible parsing)"],"categories":["code-generation-editing","developer-assistance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen2.5-7b-instruct__cap_10","uri":"capability://text.generation.language.sentiment.analysis.and.opinion.mining","name":"sentiment analysis and opinion mining","description":"Analyzes sentiment, emotion, and opinion in text through learned patterns from instruction-tuning on sentiment analysis datasets. The model classifies text as positive/negative/neutral and can provide detailed explanations of sentiment drivers (which phrases or aspects contribute to overall sentiment). Sentiment analysis works through attention mechanisms that identify sentiment-bearing tokens and learned associations between linguistic patterns and emotional valence.","intents":["Classify customer feedback or reviews as positive, negative, or neutral","Identify specific aspects of products or services that drive customer sentiment","Monitor brand sentiment across social media or customer communications","Analyze emotional tone in text for mental health or well-being applications"],"best_for":["Customer experience teams analyzing feedback at scale","Social media monitoring platforms tracking brand sentiment","E-commerce platforms analyzing product reviews","Market research teams conducting opinion analysis"],"limitations":["Sentiment classification is coarse-grained (positive/negative/neutral); fine-grained emotions (joy, anger, fear) are less reliable","Sarcasm and irony are often misclassified; model struggles with sentiment inversion","Domain-specific sentiment may be misclassified if training data doesn't cover the domain","No confidence scoring; users cannot determine how confident the model is in its classification","Aspect-based sentiment analysis (identifying which aspects drive sentiment) is less reliable than overall sentiment"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible inference framework","16GB+ RAM for quantized inference","Optional: sentiment analysis evaluation library (TextBlob, VADER) for comparison"],"input_types":["text (reviews, feedback, social media posts, customer communications)","structured prompts (system message requesting sentiment analysis with specific aspects)"],"output_types":["text (sentiment classification + explanation of sentiment drivers)","structured data (when using compatible parsing for sentiment scores and aspects)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen2.5-7b-instruct__cap_11","uri":"capability://text.generation.language.language.understanding.and.semantic.similarity.assessment","name":"language understanding and semantic similarity assessment","description":"Understands semantic meaning in text and assesses similarity between phrases, sentences, or documents through learned representations in the transformer's embedding space. The model can determine if two texts convey similar meaning despite different wording, identify paraphrases, and assess semantic relatedness. This works through attention mechanisms that capture semantic relationships and learned patterns that associate similar meanings with similar token sequences.","intents":["Detect duplicate or near-duplicate content in document collections","Identify paraphrases or semantically similar text for plagiarism detection","Build semantic search systems that find conceptually related documents","Assess whether two statements convey the same meaning for fact-checking applications"],"best_for":["Content platforms detecting duplicate or plagiarized content","Search systems building semantic similarity matching","Academic integrity tools detecting paraphrased plagiarism","Question-answering systems finding semantically related documents"],"limitations":["Semantic similarity assessment is relative, not absolute; no standardized similarity scores","Struggles with domain-specific terminology; may miss semantic similarity in specialized fields","No explicit paraphrase detection; relies on learned patterns that may miss sophisticated paraphrases","Performance degrades for very short texts (<10 tokens) where context is insufficient","No built-in threshold for similarity classification; users must calibrate thresholds empirically"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible inference framework","16GB+ RAM for quantized inference","Optional: embedding extraction and similarity computation library (sentence-transformers, scikit-learn)"],"input_types":["text (pairs of texts to compare, documents to assess for similarity)","structured prompts (system message requesting similarity assessment)"],"output_types":["text (similarity assessment with explanation)","structured data (similarity scores when using compatible embedding extraction)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen2.5-7b-instruct__cap_12","uri":"capability://text.generation.language.conversational.context.management.and.turn.taking","name":"conversational context management and turn-taking","description":"Maintains conversation history and context across multiple turns, enabling coherent multi-turn dialogue without explicit memory management. The model uses standard transformer attention to process conversation history (previous user and assistant messages) and generate contextually appropriate responses that reference prior exchanges. Context management is implicit through token sequences rather than explicit state tracking.","intents":["Build chatbots that maintain conversation context across multiple exchanges","Create interactive assistants that remember user preferences and prior requests","Develop dialogue systems that can reference earlier parts of conversations","Build conversational agents that can clarify ambiguous requests based on context"],"best_for":["Teams building conversational AI applications with natural dialogue flow","Customer support systems that need to maintain context across multiple turns","Interactive tutoring systems that build on prior student responses","Personal assistant applications that remember user preferences"],"limitations":["Context window limited to ~32K tokens; very long conversations require summarization or truncation","Model may lose track of context after 10+ turns; coherence degrades with conversation length","No explicit memory of user preferences across sessions; requires external state management for persistence","Attention distribution may favor recent messages over earlier context; important early context may be forgotten","No built-in conversation summarization; users must manually manage context for long conversations"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible inference framework","16GB+ RAM for quantized inference","Optional: conversation management library (LangChain, LlamaIndex) for context orchestration","Optional: external state store (database, cache) for multi-session persistence"],"input_types":["text (conversation history as sequence of user/assistant messages)","structured prompts (system message + conversation history + current user message)"],"output_types":["text (contextually appropriate assistant response)","streaming text tokens (when using compatible inference servers)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen2.5-7b-instruct__cap_2","uri":"capability://text.generation.language.mathematical.reasoning.and.step.by.step.problem.solving","name":"mathematical reasoning and step-by-step problem solving","description":"Solves mathematical problems and provides step-by-step reasoning through instruction-tuning on mathematical datasets and chain-of-thought examples. The model learns to decompose complex problems into intermediate steps, show work, and arrive at correct answers by training on examples where reasoning is explicitly annotated. This capability relies on learned patterns rather than symbolic computation, making it effective for algebra, calculus, and logic problems within the model's training distribution.","intents":["Solve math problems with detailed step-by-step explanations for educational purposes","Verify mathematical reasoning and identify errors in student work","Generate practice problems with solutions for tutoring applications","Assist with technical calculations in engineering or scientific contexts"],"best_for":["Educational platforms building AI tutoring systems with open-source models","Researchers studying mathematical reasoning in language models","Organizations building homework assistance tools with on-premise deployment","STEM educators creating supplementary learning materials"],"limitations":["Accuracy degrades on problems requiring more than 5-7 reasoning steps; very complex proofs often fail","No symbolic computation capability — cannot guarantee mathematical correctness for novel problems outside training distribution","Struggles with problems requiring precise numerical computation (very large numbers, high-precision decimals)","Cannot perform calculations beyond what was seen in training; may hallucinate intermediate steps","No built-in verification mechanism; users must independently validate mathematical correctness"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible inference framework","16GB+ RAM for quantized inference","Optional: symbolic math library (SymPy) for verification layer"],"input_types":["text (mathematical problems in natural language or LaTeX notation)","structured prompts (system message requesting step-by-step reasoning)"],"output_types":["text (step-by-step solutions with intermediate reasoning)","structured mathematical notation (when using compatible parsing)"],"categories":["text-generation-language","reasoning-assistance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen2.5-7b-instruct__cap_3","uri":"capability://text.generation.language.multilingual.text.generation.and.translation","name":"multilingual text generation and translation","description":"Generates coherent text and translates between languages using a multilingual tokenizer and training data spanning 29+ languages. The model maintains language-specific conventions and cultural context through exposure to diverse linguistic patterns during pretraining and instruction-tuning. Translation and generation work through the same transformer mechanism, with language identity implicitly encoded in token embeddings and attention patterns learned during training.","intents":["Translate text between major languages (English, Chinese, Spanish, French, German, Japanese, Korean, etc.) with context preservation","Generate content in non-English languages for international applications","Build multilingual chatbots that respond in the user's preferred language","Create localization pipelines for software and content without external translation services"],"best_for":["Global teams building multilingual applications with open-source requirements","Organizations reducing translation costs by using on-premise models","Developers building language-agnostic conversational AI for international markets","Content creators needing quick translations without cloud API dependencies"],"limitations":["Translation quality varies significantly by language pair; English↔Chinese is strong, but English↔low-resource languages (Swahili, Tagalog) is weaker","No explicit language detection; users must specify target language or rely on implicit inference from context","Cultural nuances and idioms may not translate correctly; model produces literal translations in edge cases","Performance degrades for very long documents (>4K tokens) due to context window constraints","No domain-specific terminology handling; technical or specialized vocabulary may be mistranslated"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible inference framework","16GB+ RAM for quantized inference","Transformers library 4.36+ with multilingual tokenizer support"],"input_types":["text (natural language in any supported language)","structured prompts (system message specifying source/target language)"],"output_types":["text (translated or generated text in target language)","streaming text tokens (when using compatible inference servers)"],"categories":["text-generation-language","translation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen2.5-7b-instruct__cap_4","uri":"capability://text.generation.language.knowledge.grounded.question.answering.with.context.retrieval","name":"knowledge-grounded question answering with context retrieval","description":"Answers questions by leveraging knowledge learned during pretraining and instruction-tuning, with the ability to incorporate external context through prompt engineering. The model uses standard transformer attention to process provided context (documents, passages, or knowledge bases) and generate answers grounded in that context. This is not true retrieval-augmented generation (RAG) but rather context-aware generation where external knowledge must be explicitly provided in the prompt.","intents":["Build question-answering systems that answer based on provided documents or knowledge bases","Create FAQ assistants that reference specific documentation or knowledge articles","Develop search result summarization tools that synthesize answers from multiple sources","Build customer support chatbots that answer questions based on company documentation"],"best_for":["Teams building domain-specific QA systems with controlled knowledge sources","Organizations implementing document-based customer support without external APIs","Developers prototyping RAG systems before committing to specialized retrieval infrastructure","Educational platforms building tutoring systems with curated knowledge bases"],"limitations":["No built-in retrieval mechanism — requires external vector database or search system to identify relevant context","Performance degrades when context exceeds 8K tokens; attention becomes diffuse and answer quality drops","May hallucinate answers not present in provided context if training data is relevant","No explicit confidence scoring; users cannot determine if answer is grounded in provided context vs. model knowledge","Requires careful prompt engineering to ensure model prioritizes provided context over training knowledge"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible inference framework","16GB+ RAM for quantized inference","External retrieval system (vector database, BM25 search, or similar) for context identification","Optional: RAG framework (LangChain, LlamaIndex) for orchestration"],"input_types":["text (question + context documents/passages)","structured prompts (system message + user question + retrieved context)"],"output_types":["text (answer grounded in provided context)","structured data (when using compatible parsing for answer extraction)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen2.5-7b-instruct__cap_5","uri":"capability://text.generation.language.instruction.following.with.system.prompt.customization","name":"instruction-following with system prompt customization","description":"Follows complex, multi-part instructions and adapts behavior based on system prompts that define roles, constraints, and output formats. The model learns during instruction-tuning to parse system messages and apply them consistently throughout generation, enabling persona-based responses, format constraints (JSON, markdown, etc.), and task-specific behavior modification. This works through attention mechanisms that weight system tokens higher and learned patterns that associate system directives with output modifications.","intents":["Create specialized AI assistants with distinct personas (technical expert, creative writer, data analyst, etc.)","Build systems that enforce output format constraints (JSON, CSV, markdown) without post-processing","Develop task-specific agents that follow detailed procedural instructions","Create content generation pipelines with style and tone customization"],"best_for":["Developers building multi-purpose AI assistants with role-based customization","Teams creating structured output generation without external parsing/validation","Organizations building AI agents with explicit behavioral constraints","Content creators needing style-consistent generation across multiple pieces"],"limitations":["System prompt adherence degrades with very long or conflicting instructions (>500 tokens)","No hard constraints — model may violate format requirements or ignore constraints if training data conflicts","Instruction injection vulnerabilities exist; adversarial system prompts can override intended behavior","Performance varies based on instruction clarity; ambiguous or complex directives yield inconsistent results","No built-in validation that output matches specified format; requires external verification"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible inference framework","16GB+ RAM for quantized inference","Understanding of prompt engineering best practices for consistent behavior"],"input_types":["text (system prompt defining behavior + user instruction)","structured prompts (system message + user message in chat format)"],"output_types":["text (response following system prompt constraints)","structured data (JSON, CSV, markdown when format is specified in system prompt)"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen2.5-7b-instruct__cap_6","uri":"capability://text.generation.language.summarization.and.content.condensation","name":"summarization and content condensation","description":"Condenses long documents, articles, or conversations into concise summaries while preserving key information. The model learns summarization patterns through instruction-tuning on datasets where documents are paired with human-written summaries, enabling it to identify salient information and generate coherent abstracts. Summarization works through standard sequence-to-sequence generation with learned patterns for information selection and compression.","intents":["Generate executive summaries of long documents or research papers","Create condensed versions of articles for quick consumption","Summarize meeting transcripts or conversation logs","Build content aggregation systems that synthesize information from multiple sources"],"best_for":["Content platforms building automated summarization features","Enterprise teams processing large volumes of documents for quick review","News aggregation services condensing articles for readers","Research organizations summarizing academic papers for literature reviews"],"limitations":["Summarization quality degrades for documents >8K tokens; model loses track of overall structure","May omit important details if they appear late in the document due to attention distribution","No control over summary length without explicit instruction; requires prompt engineering for length constraints","Abstractive summarization may introduce subtle inaccuracies or hallucinated details not in source","No built-in fact-checking; summaries may contain errors if source document is misleading"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible inference framework","16GB+ RAM for quantized inference","Input documents in text format (may require OCR for PDFs or images)"],"input_types":["text (long documents, articles, transcripts)","structured prompts (system message requesting summary with specific length or style)"],"output_types":["text (concise summary)","structured data (when using compatible parsing for bullet-point extraction)"],"categories":["text-generation-language","content-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen2.5-7b-instruct__cap_7","uri":"capability://text.generation.language.creative.writing.and.content.generation.with.style.control","name":"creative writing and content generation with style control","description":"Generates creative content (stories, poetry, marketing copy, dialogue) with style and tone customization through instruction-tuning on diverse writing datasets. The model learns to adapt writing style based on explicit instructions (formal/casual, technical/accessible, humorous/serious) and can generate coherent narratives spanning multiple paragraphs. Creative generation works through learned patterns of narrative structure, character development, and stylistic conventions from training data.","intents":["Generate creative stories, poetry, or dialogue for entertainment or educational content","Create marketing copy and product descriptions with brand voice customization","Develop character backgrounds and dialogue for games or interactive fiction","Generate creative prompts or writing exercises for writers and educators"],"best_for":["Content creators and writers using AI as a brainstorming and drafting tool","Marketing teams generating product descriptions and promotional content","Game developers creating narrative content and character dialogue","Educational platforms building creative writing assistance tools"],"limitations":["Generated content may be derivative of training data; originality is limited to recombination of learned patterns","Longer narratives (>2K tokens) often lose coherence and consistency; character motivations may become inconsistent","Style control is imprecise; subtle style differences (e.g., 'noir detective' vs 'hardboiled detective') may not be reliably distinguished","No built-in copyright awareness; model may generate content similar to copyrighted works in training data","Requires human review for quality; generated content often needs editing for grammar, consistency, and originality"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible inference framework","16GB+ RAM for quantized inference","Understanding of prompt engineering for style and tone specification"],"input_types":["text (creative prompts, story premises, style descriptions)","structured prompts (system message defining writing style + user prompt)"],"output_types":["text (creative content: stories, poetry, dialogue, marketing copy)","streaming text tokens (when using compatible inference servers)"],"categories":["text-generation-language","creative-content"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen2.5-7b-instruct__cap_8","uri":"capability://text.generation.language.logical.reasoning.and.argument.analysis","name":"logical reasoning and argument analysis","description":"Analyzes logical arguments, identifies fallacies, and constructs sound reasoning through instruction-tuning on logic and reasoning datasets. The model learns to evaluate premises, trace logical implications, and identify contradictions by training on examples where reasoning is explicitly annotated. This capability enables the model to engage in debate, critique arguments, and construct logical proofs within the scope of its training distribution.","intents":["Analyze arguments for logical fallacies and validity","Construct logical proofs or formal arguments for academic purposes","Evaluate the strength of reasoning in essays or debates","Build systems that can engage in structured logical debate or argumentation"],"best_for":["Educational platforms teaching logic and critical thinking","Academic writing assistants that evaluate argument quality","Debate platforms that need argument analysis and critique","Research tools that evaluate the logical structure of academic arguments"],"limitations":["Reasoning quality degrades on complex arguments with >5 premises; model loses track of logical chains","No symbolic logic capability — cannot guarantee logical correctness; relies on learned patterns","Struggles with novel logical structures not well-represented in training data","May miss subtle logical fallacies or accept invalid arguments if they appear plausible","No built-in formal verification; users must independently validate logical correctness"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible inference framework","16GB+ RAM for quantized inference","Optional: formal logic library (Sympy, Z3) for verification"],"input_types":["text (arguments, premises, logical statements in natural language)","structured prompts (system message requesting logical analysis)"],"output_types":["text (logical analysis, fallacy identification, argument critique)","structured data (when using compatible parsing for formal logic extraction)"],"categories":["text-generation-language","reasoning-assistance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen2.5-7b-instruct__cap_9","uri":"capability://data.processing.analysis.information.extraction.and.structured.data.generation","name":"information extraction and structured data generation","description":"Extracts structured information from unstructured text and generates structured outputs (JSON, tables, lists) based on user specifications. The model learns to identify relevant entities, relationships, and attributes through instruction-tuning on information extraction datasets, then formats output according to specified schemas. This works through learned patterns that associate natural language descriptions with structured representations, without explicit schema validation.","intents":["Extract key information (names, dates, amounts) from documents or text","Convert unstructured text into structured formats (JSON, CSV, tables)","Build data pipelines that parse documents and populate databases","Create knowledge graphs from unstructured text by extracting entities and relationships"],"best_for":["Data engineering teams building ETL pipelines with open-source models","Organizations processing documents without external NLP services","Developers building knowledge extraction systems for domain-specific applications","Teams migrating from rule-based extraction to neural approaches"],"limitations":["No schema validation — generated JSON may be malformed or incomplete; requires post-processing validation","Extraction accuracy varies by information type; common entities (names, dates) are reliable, but domain-specific information is less accurate","No explicit entity linking; model cannot disambiguate between entities with similar names","Performance degrades on documents with complex structure or multiple information types","Requires explicit schema specification in prompt; implicit schema inference is unreliable"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible inference framework","16GB+ RAM for quantized inference","JSON schema library (jsonschema) for output validation","Optional: structured output parsing library (Pydantic, Marvin) for schema enforcement"],"input_types":["text (unstructured documents, articles, transcripts)","structured prompts (system message with schema specification + user text)"],"output_types":["structured data (JSON, CSV, tables, lists)","text (when using compatible parsing for structured output extraction)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-qwen--qwen2.5-7b-instruct__headline","uri":"capability://text.generation.language.text.generation.model.for.chatbots.and.conversational.ai","name":"text generation model for chatbots and conversational ai","description":"Qwen2.5-7B-Instruct is a powerful text-generation model designed specifically for creating chatbots and enhancing conversational AI applications, enabling developers to build more interactive and responsive systems.","intents":["best text generation model for chatbots","text generation for conversational AI","top models for chatbot development","AI models for generating conversational text","best tools for building chat assistants"],"best_for":["chatbot development","conversational applications"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 2.0+ or compatible inference framework (vLLM, Text Generation Inference, Ollama)","Minimum 16GB RAM for 7B model quantization, 32GB for full precision inference","CUDA 11.8+ for GPU acceleration (optional but recommended for <500ms latency)","Hugging Face transformers library 4.36+","PyTorch 2.0+ or inference framework supporting code generation","16GB+ RAM for quantized inference, 32GB+ for full precision","Optional: IDE integration layer (VS Code extension, Vim plugin) for seamless workflow","PyTorch 2.0+ or compatible inference framework","16GB+ RAM for quantized inference"],"failure_modes":["Context window limited to ~32K tokens (standard transformer limitation), requiring conversation summarization for very long dialogues","No built-in memory persistence across sessions — requires external state management for multi-session continuity","Performance degrades with very long context (>16K tokens) due to quadratic attention complexity","Instruction-following quality depends on input format alignment with training data; poorly-formatted prompts yield inconsistent results","No native support for real-time streaming output without additional inference framework integration","No real-time syntax validation — generated code may contain subtle bugs or use deprecated APIs","Limited to code patterns seen in training data; novel or very recent library versions may generate incorrect usage","No built-in test generation or verification; developers must manually validate generated code","Context window constraints mean multi-file refactoring requires explicit file concatenation","Performance on very long code files (>2K lines) degrades due to attention complexity","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.9199490533329067,"quality":0.35,"ecosystem":0.5000000000000001,"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":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:48.039Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":13784608,"model_likes":1253}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=qwen--qwen2.5-7b-instruct","compare_url":"https://unfragile.ai/compare?artifact=qwen--qwen2.5-7b-instruct"}},"signature":"Z5B/Iau5d/p+MvT2//nqS3rUqJUy0Gl1kxuN9OkTNvmUkcFWL9r8IRV63sl6WwlhE0x9/lc5CIqKCBsoQMK4AQ==","signedAt":"2026-06-22T19:50:18.858Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen--qwen2.5-7b-instruct","artifact":"https://unfragile.ai/qwen--qwen2.5-7b-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=qwen--qwen2.5-7b-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"}}