{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"qwen2-5-72b","slug":"qwen2-5-72b","name":"Qwen2.5 72B","type":"model","url":"https://qwenlm.github.io/blog/qwen2.5/","page_url":"https://unfragile.ai/qwen2-5-72b","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"qwen2-5-72b__cap_0","uri":"capability://text.generation.language.general.instruction.following.text.generation.with.128k.context.window","name":"general instruction-following text generation with 128k context window","description":"Dense transformer decoder generating coherent multi-turn text outputs up to 8K tokens per inference call, trained on 18 trillion tokens with improved instruction-following resilience compared to Qwen2. Processes full 128K token context window for long-document understanding, role-play scenarios, and system prompt diversity without degradation. Supports structured prompting patterns including JSON schema specification and conditional generation based on system instructions.","intents":["I need a model that can handle long documents (research papers, codebases, chat histories) without losing context","I want to build a chatbot that respects system prompts and role definitions across diverse user inputs","I need to generate structured outputs (JSON, YAML) from natural language instructions reliably","I want to run inference locally without API rate limits or data privacy concerns"],"best_for":["Teams building local-first LLM applications requiring unrestricted commercial deployment","Developers needing long-context understanding for document analysis and summarization","Builders creating multi-turn conversational agents with consistent system prompts"],"limitations":["Maximum generation per call is 8K tokens; longer outputs require multiple inference calls or streaming","Dense architecture (non-sparse, non-MoE) means inference latency scales linearly with 72B parameters—no efficiency gains from sparse routing","No built-in retrieval-augmented generation (RAG) integration; requires external vector database and retrieval pipeline for knowledge grounding","Training data composition unknown; potential biases or gaps in specific domains not documented"],"requires":["GPU with sufficient VRAM for 72B parameter model (typically 40GB+ for full precision, 20GB+ for quantized formats)","Hugging Face Transformers library or compatible inference framework (vLLM, Ollama, etc.)","Apache 2.0 license compliance for commercial deployment"],"input_types":["text prompts","system prompts with role definitions","structured data (tables, JSON schemas)","multi-turn conversation histories"],"output_types":["text generation (up to 8K tokens)","JSON/YAML structured output","code snippets","long-form content"],"categories":["text-generation-language","instruction-following"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwen2-5-72b__cap_1","uri":"capability://code.generation.editing.code.generation.and.completion.with.humaneval.85.performance","name":"code generation and completion with humaneval 85+ performance","description":"Transformer-based code generation achieving 85+ on HumanEval benchmark through dense pretraining on 18 trillion tokens. Supports code completion, function generation, and multi-file context understanding for Python, JavaScript, Java, C++, and other major languages. Generates syntactically valid code with proper error handling patterns and can reason about code structure across 128K token context for refactoring and bug-fixing tasks.","intents":["I need to generate production-ready code snippets from natural language descriptions","I want to complete partial code implementations with context-aware suggestions","I need to understand and refactor existing codebases by analyzing multi-file context","I want to generate test cases and error handling patterns alongside implementation code"],"best_for":["Solo developers and small teams building code generation tools or IDE plugins","Organizations seeking open-weight code models for on-premise deployment without vendor lock-in","Teams needing code generation with full codebase context (128K window enables entire small projects)"],"limitations":["HumanEval 85+ is strong but below specialized code models like CodeLlama 70B (90.5%) and GPT-4 (95+); complex algorithmic problems may require additional reasoning steps","No built-in integration with language-specific linters, type checkers, or AST-based validation; generated code requires external testing","Context window of 128K tokens is sufficient for ~30K lines of code, limiting applicability to very large monorepos without chunking strategies","No specialized code-specific post-training visible in documentation; general instruction-following may not capture domain-specific code patterns as effectively as Qwen2.5-Coder variants"],"requires":["GPU with 40GB+ VRAM for full precision inference (20GB+ with quantization)","Code-aware prompt engineering to specify language, style, and error handling expectations","External testing framework to validate generated code before deployment"],"input_types":["natural language function descriptions","partial code with TODO comments","code snippets with context","test specifications"],"output_types":["complete function implementations","multi-function modules","test cases","refactored code"],"categories":["code-generation-editing","code-completion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwen2-5-72b__cap_10","uri":"capability://automation.workflow.inference.optimization.through.quantization.and.framework.support.gguf.vllm.ollama","name":"inference optimization through quantization and framework support (gguf, vllm, ollama)","description":"Model weights available in multiple inference formats enabling optimization for diverse hardware and latency requirements. Supported through vLLM (paged attention for long-context), Ollama (simplified local deployment), Hugging Face Transformers (standard PyTorch), and community quantization formats (GGUF for CPU inference, AWQ/GPTQ for GPU quantization). Quantization reduces VRAM requirements by 50-75% with minimal quality loss, enabling deployment on consumer GPUs and edge devices.","intents":["I need to run 72B model inference on 24GB consumer GPU instead of 40GB enterprise GPU","I want to deploy model on CPU-only hardware (Raspberry Pi, servers without GPU)","I need to optimize inference latency for real-time applications using paged attention","I want to simplify local deployment without managing complex inference frameworks"],"best_for":["Teams optimizing cost-per-inference through quantization and hardware efficiency","Edge computing and IoT teams deploying on resource-constrained devices","Developers seeking simplified local deployment without infrastructure expertise","Organizations requiring CPU-only inference for security or compliance reasons"],"limitations":["Quantization formats (GGUF, AWQ, GPTQ) not officially documented; community-maintained formats may lag behind official releases or have compatibility issues","Quantized models have measurable quality degradation (typically 1-5% benchmark drop for 4-bit quantization); task-specific evaluation required","vLLM paged attention optimization requires specific GPU architecture (A100, H100, RTX 4090); older GPUs may not benefit from optimization","CPU inference (GGUF) is significantly slower than GPU inference; suitable for batch processing and non-real-time applications only"],"requires":["Inference framework: vLLM (GPU optimization), Ollama (simplified deployment), or Hugging Face Transformers (standard PyTorch)","Quantization tools: llama.cpp (GGUF), AutoGPTQ (GPTQ), AutoAWQ (AWQ) for format conversion","GPU with 8GB+ VRAM for quantized 72B model (4-bit), 16GB+ for 8-bit quantization"],"input_types":["full-precision model weights (safetensors, PyTorch)","quantization configuration (bits, group size, calibration data)"],"output_types":["quantized model weights (GGUF, GPTQ, AWQ formats)","optimized inference service with reduced latency/VRAM"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwen2-5-72b__cap_11","uri":"capability://text.generation.language.system.prompt.resilience.and.role.play.capability.with.improved.instruction.following","name":"system prompt resilience and role-play capability with improved instruction following","description":"Improved instruction-following (vs Qwen2) enables consistent role-play, system prompt adherence, and conditional behavior specification across diverse input patterns. Model resists prompt injection attempts and maintains defined system roles even with adversarial or off-topic user inputs. Supports complex multi-turn conversations with consistent character/persona definitions and context-aware response generation.","intents":["I need to build a chatbot with consistent personality and behavior that resists prompt injection","I want to create role-play scenarios (customer service agent, technical support, tutor) with reliable behavior","I need to enforce specific output formats and response constraints across diverse user inputs","I want to build multi-turn conversations where system context persists across turns without degradation"],"best_for":["Customer service and support teams building AI agents with consistent brand voice and behavior","Educational platforms creating role-specific tutors and teaching assistants","Security teams testing prompt injection resilience and adversarial robustness"],"limitations":["Improved instruction-following is relative to Qwen2; absolute resilience to sophisticated prompt injection not documented or guaranteed","System prompt resilience not quantified; no published benchmarks comparing to other models or baseline Qwen2","Complex role-play scenarios may still require careful prompt engineering and few-shot examples for reliable behavior","No built-in safeguards against generating harmful content within defined roles; requires external content filtering"],"requires":["GPU with 40GB+ VRAM for full precision (20GB+ with quantization)","Well-designed system prompts specifying role, constraints, and expected behavior","Optional: External content filtering for safety-critical applications"],"input_types":["system prompts defining role and constraints","multi-turn conversation histories","adversarial or off-topic user inputs"],"output_types":["role-consistent responses","structured outputs following system constraints","multi-turn conversation continuations"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwen2-5-72b__cap_12","uri":"capability://planning.reasoning.qwen2.5.math.specialized.mathematical.reasoning.with.cot.pot.tir.support","name":"qwen2.5-math specialized mathematical reasoning with cot/pot/tir support","description":"Specialized variant optimized for mathematical problem-solving with explicit support for multiple reasoning approaches: Chain-of-Thought (CoT) for step-by-step reasoning, Proof-of-Thought (PoT) for code-based mathematical computation, and Tool-Integrated Reasoning (TIR) for integration with external math tools. Available in 1.5B, 7B, and 72B sizes, enabling mathematical reasoning across different compute budgets.","intents":["Build math tutoring systems with transparent step-by-step reasoning","Create systems that generate executable code for mathematical computations","Develop math problem-solving tools that integrate with symbolic math libraries","Generate mathematical proofs and derivations with formal reasoning"],"best_for":["EdTech platforms requiring specialized mathematical reasoning","Research teams studying mathematical problem-solving in LLMs","Systems integrating LLMs with symbolic math engines (SymPy, Mathematica)"],"limitations":["PoT and TIR support not formally evaluated — no benchmark results for code-based reasoning","Integration with external math tools requires custom implementation","Smaller variants (1.5B, 7B) may have lower mathematical reasoning quality than 72B","No documented performance comparison with general Qwen2.5 on MATH/GSM8K benchmarks"],"requires":["Prompt engineering to elicit CoT, PoT, or TIR reasoning modes","Optional: Integration with symbolic math libraries (SymPy, Mathematica, Wolfram Alpha API)","Code execution environment for PoT (Python interpreter or similar)"],"input_types":["mathematical problems","equations and expressions","word problems","proof specifications"],"output_types":["step-by-step reasoning chains","executable mathematical code","final answers","mathematical proofs"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwen2-5-72b__cap_13","uri":"capability://automation.workflow.inference.framework.compatibility.and.deployment.flexibility","name":"inference framework compatibility and deployment flexibility","description":"Model weights distributed in formats compatible with multiple inference frameworks including vLLM, TensorRT-LLM, Ollama, and others, enabling flexible deployment across different hardware and software stacks. Supports both local deployment and cloud API access through Alibaba Cloud ModelStudio. Enables developers to choose deployment strategy based on latency, cost, and privacy requirements.","intents":["Deploy models locally for low-latency inference and data privacy","Use cloud APIs for on-demand scaling without infrastructure investment","Integrate models into existing inference pipelines and frameworks","Switch between local and cloud deployment based on workload requirements"],"best_for":["Teams with flexible deployment requirements across local and cloud","Organizations with data privacy requirements preventing cloud APIs","Developers building inference infrastructure and optimization tools","Enterprises with existing inference framework investments"],"limitations":["Specific inference framework support not documented — unclear which frameworks are officially supported","Quantization format availability not specified — unclear if GGUF, int8, int4 formats are available","Cloud API pricing and rate limits not documented in provided materials","Framework-specific optimization (e.g., vLLM paged attention) may not be available for all variants","No documented performance benchmarks across different inference frameworks"],"requires":["Inference framework installation (vLLM, TensorRT-LLM, Ollama, or alternative)","Model weights in compatible format (safetensors or PyTorch)","GPU hardware for local deployment, or API credentials for cloud deployment"],"input_types":["model weights","inference requests","configuration parameters"],"output_types":["inference outputs","performance metrics","deployment logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwen2-5-72b__cap_2","uri":"capability://text.generation.language.mathematical.reasoning.with.math.benchmark.80.and.structured.problem.solving","name":"mathematical reasoning with math benchmark 80+ and structured problem-solving","description":"Achieves 80+ on MATH benchmark through transformer architecture trained on 18 trillion tokens, with capability to generate step-by-step mathematical reasoning and symbolic computation. Supports chain-of-thought (CoT) prompting for multi-step problem decomposition, program-of-thought (PoT) for code-based calculations, and tool-integrated reasoning (TIR) for external calculator/solver integration. Handles algebraic manipulation, calculus, geometry, and number theory problems with explicit intermediate steps.","intents":["I need to solve complex math problems with step-by-step reasoning that can be verified","I want to generate Python code that solves mathematical equations and validates solutions","I need to build a tutoring system that explains mathematical concepts with worked examples","I want to integrate external math solvers (SymPy, Wolfram Alpha) with LLM reasoning for hybrid problem-solving"],"best_for":["Educational technology companies building AI tutoring systems with explainable reasoning","Research teams needing open-weight models for mathematical problem-solving without proprietary API dependencies","Developers building hybrid systems combining LLM reasoning with symbolic math engines"],"limitations":["MATH benchmark 80+ is strong but below specialized models like GPT-4 (92%) and Qwen2.5-Math 72B (specialized variant); complex competition-level problems may require additional reasoning steps or external solvers","No built-in symbolic math engine; relies on code generation to invoke external tools (SymPy, Mathematica) rather than native symbolic reasoning","Chain-of-thought reasoning adds latency (multiple token generations per problem) and increases token consumption; streaming or batching required for production throughput","Training data composition for math problems unknown; potential gaps in specialized domains (abstract algebra, topology) not documented"],"requires":["GPU with 40GB+ VRAM for full precision (20GB+ with quantization)","Optional: External math libraries (SymPy, NumPy) for tool-integrated reasoning","Prompt engineering expertise to structure multi-step reasoning and tool calls"],"input_types":["mathematical problem statements (text or LaTeX)","equations and symbolic expressions","multi-step problem descriptions","tool specifications for external solvers"],"output_types":["step-by-step solutions with intermediate results","Python code for symbolic computation","structured reasoning traces","final numerical answers with confidence"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwen2-5-72b__cap_3","uri":"capability://text.generation.language.multilingual.text.generation.across.29.languages.with.language.specific.instruction.following","name":"multilingual text generation across 29+ languages with language-specific instruction following","description":"Supports generation in 29+ languages (Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and others) through unified transformer architecture trained on multilingual 18 trillion token corpus. Maintains instruction-following consistency across language boundaries and enables code-switching within single generation. Language-specific system prompts and role definitions work reliably without performance degradation.","intents":["I need to build a global chatbot that serves users in multiple languages with consistent behavior","I want to translate and localize content while preserving code, JSON, and structured data integrity","I need to generate multilingual documentation and support content from single model","I want to build language-agnostic applications that adapt to user language preferences at runtime"],"best_for":["Global SaaS companies building multilingual AI features without managing multiple language-specific models","Localization and translation teams seeking open-weight models for on-premise deployment","Developers building international applications requiring consistent AI behavior across 29+ languages"],"limitations":["Performance variance across languages not documented; non-listed languages (beyond 29 explicitly mentioned) may have degraded performance or require English fallback","No language detection or automatic routing; requires explicit language specification in system prompt or input","Multilingual training may dilute performance on individual languages compared to language-specific models; no benchmarks provided for non-English languages","Code-switching and mixed-language prompts behavior not documented; may require careful prompt engineering"],"requires":["GPU with 40GB+ VRAM for full precision (20GB+ with quantized formats)","UTF-8 encoding support for all target languages","Explicit language specification in system prompts or input formatting"],"input_types":["text in any of 29+ supported languages","mixed-language prompts (code-switching)","language-specific system prompts","structured data with multilingual content"],"output_types":["text generation in specified language","code and JSON with language-agnostic syntax","translated content preserving structure","multilingual conversation histories"],"categories":["text-generation-language","multilingual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwen2-5-72b__cap_4","uri":"capability://text.generation.language.structured.output.generation.with.json.schema.validation.and.conditional.formatting","name":"structured output generation with json schema validation and conditional formatting","description":"Generates valid JSON, YAML, and other structured formats through instruction-following training on 18 trillion tokens, with capability to follow explicit schema specifications in prompts. Supports conditional formatting based on input data types and can generate nested structures, arrays, and complex object hierarchies. Improved instruction-following (vs Qwen2) reduces malformed output and enables reliable schema adherence without external validation.","intents":["I need to extract structured data from unstructured text and guarantee valid JSON output","I want to generate API responses, configuration files, and data payloads from natural language","I need to build data pipelines that parse LLM outputs without custom parsing logic","I want to generate database records, CSV rows, and structured logs from text descriptions"],"best_for":["Data engineering teams building LLM-powered ETL pipelines requiring reliable structured output","API developers needing LLM-generated responses that conform to OpenAPI/JSON Schema specifications","Teams building no-code/low-code platforms that generate structured data from natural language"],"limitations":["No built-in JSON schema validation or correction; malformed output requires external parsing and retry logic","Complex nested schemas may require explicit examples in prompt (few-shot learning) to achieve high accuracy; no schema-aware tokenization or constrained decoding","Instruction-following improvements reduce but don't eliminate schema violations; production systems require fallback parsing and error handling","No support for custom validation rules or domain-specific constraints beyond JSON schema syntax"],"requires":["GPU with 40GB+ VRAM for full precision (20GB+ with quantized formats)","Explicit schema specification in system prompt or input (JSON Schema, YAML structure examples)","External JSON validation library for production error handling"],"input_types":["natural language descriptions of desired structure","JSON/YAML schema specifications","example outputs (few-shot prompting)","unstructured text for extraction"],"output_types":["valid JSON objects and arrays","YAML configuration files","CSV rows and tabular data","structured logs and event data"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwen2-5-72b__cap_5","uri":"capability://text.generation.language.long.context.document.understanding.and.summarization.with.128k.token.window","name":"long-context document understanding and summarization with 128k token window","description":"Processes full 128K token input context (approximately 30K-50K words or 100+ pages of text) through dense transformer architecture, enabling end-to-end document analysis without chunking or sliding windows. Supports summarization, question-answering, and information extraction across entire documents, research papers, codebases, and conversation histories. Maintains coherence and factual accuracy across long-range dependencies without context loss.","intents":["I need to summarize entire research papers, legal documents, or technical specifications in single inference call","I want to answer questions about specific sections of long documents without manual chunking","I need to analyze entire codebases (up to 30K lines) for refactoring, security, or performance issues","I want to extract key information from long conversation histories or meeting transcripts"],"best_for":["Legal and compliance teams analyzing contracts and regulatory documents","Research organizations processing academic papers and technical documentation","Code review and security teams analyzing large codebases for vulnerabilities","Customer support and analytics teams processing long conversation histories"],"limitations":["128K token context is sufficient for ~30K lines of code or ~50K words; very large documents (entire books, massive codebases) still require chunking or hierarchical summarization","Long-context inference latency increases with input length; 128K token processing is significantly slower than 4K token processing, requiring optimization for production throughput","Attention mechanism computational complexity is O(n²) in sequence length; 128K context requires substantial GPU memory (40GB+ VRAM) and inference time overhead","No built-in retrieval or indexing; all 128K tokens must be processed for every query, preventing efficient multi-query workflows on same document"],"requires":["GPU with 40GB+ VRAM for full precision inference with 128K context (80GB+ recommended for optimal throughput)","Inference framework supporting long-context processing (vLLM with paged attention, Ollama with optimizations, or similar)","Document preprocessing to fit within 128K token limit (tokenizer required for accurate counting)"],"input_types":["full documents (text, code, markdown)","research papers and technical specifications","conversation histories and transcripts","multiple related documents concatenated"],"output_types":["summaries at various granularities","extracted key information and facts","answers to specific questions with source citations","structured analysis (tables, outlines)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwen2-5-72b__cap_6","uri":"capability://text.generation.language.apache.2.0.licensed.open.weight.model.for.unrestricted.commercial.deployment","name":"apache 2.0 licensed open-weight model for unrestricted commercial deployment","description":"Distributed under Apache 2.0 license enabling unrestricted commercial use, modification, and redistribution without royalty payments or usage restrictions. Full model weights available on Hugging Face, ModelScope, and GitHub for local deployment, fine-tuning, and integration into proprietary products. No API rate limits, data logging, or vendor lock-in; complete control over inference infrastructure and data privacy.","intents":["I need to deploy an LLM in production without API costs, rate limits, or data privacy concerns","I want to fine-tune a model on proprietary data without sharing it with third parties","I need to build a commercial product using LLM technology without licensing fees or usage restrictions","I want to maintain full control over model updates, versioning, and deployment infrastructure"],"best_for":["Enterprises with data privacy requirements or regulatory constraints (HIPAA, GDPR, SOC 2)","Startups and scale-ups seeking to avoid per-token API costs and vendor lock-in","Organizations building proprietary products requiring model customization and fine-tuning","Teams deploying in air-gapped or on-premise environments without internet access"],"limitations":["Apache 2.0 license requires attribution in documentation; commercial products must include license notice and copyright attribution","Exception: 3B and 72B variants have different licensing terms (specific restrictions unknown); requires verification before deployment","Qwen2.5-Coder and Qwen2.5-Math variant licenses not explicitly documented; may differ from base model","Open-weight models require infrastructure investment (GPU procurement, deployment, monitoring) vs. API-based alternatives; total cost of ownership may exceed API costs for small-scale deployments"],"requires":["GPU infrastructure for local deployment (40GB+ VRAM for 72B model)","Inference framework (vLLM, Ollama, Hugging Face Transformers, or similar)","License compliance documentation for commercial products","Operational expertise for model deployment, monitoring, and updates"],"input_types":["model weights (safetensors, PyTorch, or other formats)","fine-tuning datasets","custom system prompts and configurations"],"output_types":["locally-deployed inference service","fine-tuned model variants","integrated LLM features in proprietary products"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwen2-5-72b__cap_7","uri":"capability://text.generation.language.multi.size.model.family.scaling.from.0.5b.to.72b.parameters.for.deployment.flexibility","name":"multi-size model family scaling from 0.5b to 72b parameters for deployment flexibility","description":"Qwen2.5 family spans seven parameter sizes (0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B) enabling deployment across diverse hardware constraints and latency requirements. Unified architecture and training approach ensures consistent instruction-following and capability scaling across sizes. Smaller variants (0.5B-7B) suitable for edge devices and real-time applications; larger variants (32B-72B) for complex reasoning and long-context tasks.","intents":["I need to deploy LLM features on mobile devices and edge hardware with minimal latency","I want to scale from prototyping (small model) to production (large model) without retraining","I need to optimize cost-latency tradeoffs by selecting appropriate model size for each task","I want to run inference on consumer GPUs (8GB-24GB VRAM) without expensive enterprise hardware"],"best_for":["Mobile and edge computing teams requiring sub-100ms latency and minimal resource consumption","Startups optimizing cost-per-inference across diverse deployment scenarios","Teams building tiered AI features (basic on 7B, advanced on 72B) with unified codebase","Organizations with heterogeneous hardware (from Raspberry Pi to A100 clusters)"],"limitations":["Smaller variants (0.5B-3B) have significantly lower benchmark performance (MMLU, HumanEval) than 72B; task-specific evaluation required before deployment","No documented performance scaling curves; unclear which tasks benefit from larger models vs. which plateau at smaller sizes","Unified architecture means all variants share same context window (128K) and generation limits (8K), but inference latency scales with parameter count","Fine-tuning on small variants may not transfer to large variants; separate fine-tuning required for each size"],"requires":["Model selection based on hardware constraints: 0.5B-3B for edge (2GB-8GB VRAM), 7B-14B for consumer GPU (8GB-24GB), 32B-72B for enterprise GPU (40GB+)","Inference framework supporting model size variations (vLLM, Ollama, Hugging Face Transformers)","Quantization tools (GGUF, bitsandbytes, AWQ) for smaller VRAM deployments"],"input_types":["model weights for any size variant","unified prompts and system instructions","fine-tuning datasets"],"output_types":["text generation at various latency/quality tradeoffs","inference service with size-specific optimization"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwen2-5-72b__cap_8","uri":"capability://code.generation.editing.specialized.code.generation.variant.qwen2.5.coder.trained.on.5.5.trillion.code.tokens","name":"specialized code generation variant (qwen2.5-coder) trained on 5.5 trillion code tokens","description":"Qwen2.5-Coder family (1.5B, 7B, 32B sizes) trained on 5.5 trillion tokens of code-related data, providing deeper code understanding and generation than general-purpose base model. Optimized for HumanEval, code completion, and multi-language code generation through specialized post-training. Maintains 128K context window and instruction-following consistency while focusing on code-specific patterns and syntax.","intents":["I need a specialized code model that outperforms general-purpose models on programming tasks","I want to deploy code generation on resource-constrained hardware (7B variant on consumer GPU)","I need to fine-tune a code model on proprietary codebases without starting from general-purpose base","I want to build IDE plugins and code completion tools with specialized code understanding"],"best_for":["Development teams building code-specific AI features (IDE plugins, code review tools, documentation generators)","Organizations with code-heavy workloads seeking specialized models without general-purpose overhead","Teams deploying code generation on edge devices and consumer hardware (7B variant)"],"limitations":["Specialized training on code may reduce general-purpose text generation quality; not suitable for mixed code-text tasks without careful prompt engineering","Qwen2.5-Coder license status not documented; may differ from base model Apache 2.0 licensing","No published benchmarks comparing Qwen2.5-Coder to base model or other specialized code models; performance improvement over 72B base model unclear","Smaller sizes (1.5B, 7B) may have significantly lower code generation quality than 32B variant; size selection requires task-specific evaluation"],"requires":["GPU with 8GB+ VRAM for 7B variant, 20GB+ for 32B variant","Code-specific prompt engineering and examples","External testing and validation framework"],"input_types":["code snippets and partial implementations","natural language code descriptions","test specifications","multi-file code context"],"output_types":["complete code implementations","code completions and suggestions","refactored code","test cases"],"categories":["code-generation-editing","code-completion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwen2-5-72b__cap_9","uri":"capability://text.generation.language.specialized.mathematical.reasoning.variant.qwen2.5.math.with.cot.pot.tir.training","name":"specialized mathematical reasoning variant (qwen2.5-math) with cot/pot/tir training","description":"Qwen2.5-Math family (1.5B, 7B, 72B sizes) trained with chain-of-thought (CoT) for symbolic reasoning, program-of-thought (PoT) for code-based computation, and tool-integrated reasoning (TIR) for external solver integration. Achieves 80+ on MATH benchmark through specialized post-training on mathematical problem-solving patterns. Maintains 128K context and instruction-following while optimizing for step-by-step mathematical reasoning.","intents":["I need a model specialized for mathematical problem-solving with explicit reasoning steps","I want to build tutoring systems that explain math concepts with worked examples","I need to integrate external math solvers (SymPy, Wolfram Alpha) with LLM reasoning","I want to generate code-based solutions to mathematical problems with symbolic computation"],"best_for":["Educational technology companies building math tutoring and homework help systems","Research teams needing specialized math reasoning for scientific computing and data analysis","Developers building hybrid systems combining LLM reasoning with symbolic math engines"],"limitations":["Qwen2.5-Math license status not documented; may differ from base model Apache 2.0 licensing","No published benchmarks comparing Qwen2.5-Math to base model; performance improvement over 72B base model on general math tasks unclear","Specialized training on math may reduce general-purpose text generation quality; not suitable for mixed math-text tasks without careful prompt engineering","Tool-integrated reasoning (TIR) requires external math libraries and tool definitions; no built-in integration with specific solvers"],"requires":["GPU with 8GB+ VRAM for 7B variant, 20GB+ for 32B, 40GB+ for 72B","Optional: External math libraries (SymPy, NumPy, Mathematica) for tool-integrated reasoning","Prompt engineering expertise for CoT/PoT/TIR patterns"],"input_types":["mathematical problem statements","equations and symbolic expressions","multi-step problem descriptions","tool specifications for external solvers"],"output_types":["step-by-step solutions with reasoning","Python code for symbolic computation","structured reasoning traces","final answers with confidence"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"qwen2-5-72b__headline","uri":"capability://text.generation.language.large.scale.open.ai.language.model","name":"large-scale open ai language model","description":"Qwen2.5 72B is a powerful open AI language model with 72 billion parameters, capable of advanced text and code generation, and supports a 128K context window for extensive input processing.","intents":["best AI language model","AI model for code generation","open-source model for text generation","high-performance model for multi-language support","AI model for structured output generation"],"best_for":["developers needing high-performance AI models","businesses seeking open-source solutions"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["GPU with sufficient VRAM for 72B parameter model (typically 40GB+ for full precision, 20GB+ for quantized formats)","Hugging Face Transformers library or compatible inference framework (vLLM, Ollama, etc.)","Apache 2.0 license compliance for commercial deployment","GPU with 40GB+ VRAM for full precision inference (20GB+ with quantization)","Code-aware prompt engineering to specify language, style, and error handling expectations","External testing framework to validate generated code before deployment","Inference framework: vLLM (GPU optimization), Ollama (simplified deployment), or Hugging Face Transformers (standard PyTorch)","Quantization tools: llama.cpp (GGUF), AutoGPTQ (GPTQ), AutoAWQ (AWQ) for format conversion","GPU with 8GB+ VRAM for quantized 72B model (4-bit), 16GB+ for 8-bit quantization","GPU with 40GB+ VRAM for full precision (20GB+ with quantization)"],"failure_modes":["Maximum generation per call is 8K tokens; longer outputs require multiple inference calls or streaming","Dense architecture (non-sparse, non-MoE) means inference latency scales linearly with 72B parameters—no efficiency gains from sparse routing","No built-in retrieval-augmented generation (RAG) integration; requires external vector database and retrieval pipeline for knowledge grounding","Training data composition unknown; potential biases or gaps in specific domains not documented","HumanEval 85+ is strong but below specialized code models like CodeLlama 70B (90.5%) and GPT-4 (95+); complex algorithmic problems may require additional reasoning steps","No built-in integration with language-specific linters, type checkers, or AST-based validation; generated code requires external testing","Context window of 128K tokens is sufficient for ~30K lines of code, limiting applicability to very large monorepos without chunking strategies","No specialized code-specific post-training visible in documentation; general instruction-following may not capture domain-specific code patterns as effectively as Qwen2.5-Coder variants","Quantization formats (GGUF, AWQ, GPTQ) not officially documented; community-maintained formats may lag behind official releases or have compatibility issues","Quantized models have measurable quality degradation (typically 1-5% benchmark drop for 4-bit quantization); task-specific evaluation required","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"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":"2026-05-24T12:16:25.061Z","last_scraped_at":null,"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=qwen2-5-72b","compare_url":"https://unfragile.ai/compare?artifact=qwen2-5-72b"}},"signature":"XkVsbKIylpzevvvtateUKRrvZ029q/3faBCRTAw1kfz/aN9GS+5CFmR7qppru2BfV/iHmrr1dZ8CRHDaL96XCA==","signedAt":"2026-06-22T08:27:55.449Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen2-5-72b","artifact":"https://unfragile.ai/qwen2-5-72b","verify":"https://unfragile.ai/api/v1/verify?slug=qwen2-5-72b","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"}}