{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-qwen2.5-coder","slug":"qwen2.5-coder","name":"Qwen 2.5 Coder (1.5B, 3B, 7B, 32B)","type":"model","url":"https://ollama.com/library/qwen2.5-coder","page_url":"https://unfragile.ai/qwen2.5-coder","categories":["code-editors"],"tags":["ollama","open-source","alibaba","code"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-qwen2.5-coder__cap_0","uri":"capability://code.generation.editing.code.generation.from.natural.language.prompts","name":"code-generation-from-natural-language-prompts","description":"Generates syntactically valid code from natural language descriptions using a transformer-based architecture trained on code-instruction pairs. The model processes user prompts through a 32K token context window and outputs complete code snippets, functions, or multi-file solutions. Generation is performed locally via Ollama's inference engine, eliminating cloud latency for code synthesis tasks.","intents":["I need to generate a Python function that validates email addresses","Write boilerplate code for a REST API endpoint in Node.js","Create a SQL query to join three tables and filter by date range","Generate a React component that displays a data table with pagination"],"best_for":["solo developers building prototypes and MVPs locally","teams requiring code generation without cloud API dependencies","developers working with proprietary codebases who cannot send code to external APIs"],"limitations":["No execution verification — generated code is not tested or validated before output","Context window of 32K tokens limits ability to generate code for very large files or multi-file refactoring tasks","No built-in awareness of project-specific conventions, dependencies, or architectural patterns unless explicitly provided in prompt","Code quality varies significantly across model sizes (0.5B produces lower-quality output than 32B)"],"requires":["Ollama runtime installed and running locally (or Ollama Cloud Pro/Max subscription)","Minimum 986MB disk space for 1.5B model, up to 20GB for 32B variant","Python 3.7+ or Node.js 14+ for SDK usage","GPU with sufficient VRAM for selected model size (exact requirements undocumented)"],"input_types":["text (natural language prompts)","text (code snippets for context)"],"output_types":["text (code in any programming language)","text (code with explanatory comments)"],"categories":["code-generation-editing","local-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-qwen2.5-coder__cap_1","uri":"capability://code.generation.editing.code.reasoning.and.explanation","name":"code-reasoning-and-explanation","description":"Analyzes existing code and produces natural language explanations of functionality, logic flow, and implementation details through instruction-tuned transformer inference. The model processes code snippets (up to 32K tokens) and generates human-readable descriptions of what code does, why it's structured that way, and how different components interact. This capability leverages the model's code-specialized training to understand programming semantics beyond simple pattern matching.","intents":["Explain what this legacy Python function does so I can refactor it","Help me understand how this JavaScript async/await pattern works","Describe the architecture of this microservice codebase","Generate documentation for this undocumented SQL stored procedure"],"best_for":["developers onboarding to unfamiliar codebases","teams documenting legacy code without access to original authors","educators explaining programming concepts through real code examples"],"limitations":["Explanations may be inaccurate for highly obfuscated or non-standard code patterns","Cannot reason about runtime behavior, performance characteristics, or security vulnerabilities without explicit code analysis","Limited to code understanding only — cannot generate architectural diagrams or visual representations","Smaller model variants (0.5B-3B) may produce shallow or incomplete explanations for complex algorithms"],"requires":["Ollama runtime with Qwen 2.5 Coder model loaded","Code snippet or file content as text input (max 32K tokens)","Sufficient context in prompt to specify explanation depth and target audience"],"input_types":["text (source code in any language)","text (code with comments or documentation)"],"output_types":["text (natural language explanation)","text (markdown-formatted documentation)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-qwen2.5-coder__cap_10","uri":"capability://code.generation.editing.offline.capable.code.generation.without.cloud.dependencies","name":"offline-capable-code-generation-without-cloud-dependencies","description":"Executes all code generation and analysis tasks entirely on local hardware without requiring cloud connectivity or external API calls. The model runs via Ollama's local inference engine, eliminating dependencies on OpenAI, Anthropic, or other cloud providers. Offline capability is achieved through local model weights and inference, enabling use in air-gapped environments or situations where cloud access is restricted.","intents":["Generate code in an air-gapped environment without internet connectivity","Avoid sending proprietary code to cloud services for compliance reasons","Use code generation in environments with restricted internet access","Maintain complete data privacy by keeping all inference local"],"best_for":["organizations with strict data privacy or compliance requirements","developers working in air-gapped or restricted network environments","teams wanting to avoid cloud vendor lock-in"],"limitations":["Local inference requires sufficient hardware (GPU or CPU) to run selected model size","No automatic updates to model weights — users must manually update via Ollama","Offline operation means no access to cloud-based features like model updates or A/B testing","Troubleshooting and support are limited compared to cloud services"],"requires":["Ollama runtime installed and running locally","Model weights downloaded and cached locally (398MB-20GB depending on size)","No internet connectivity required after model is downloaded"],"input_types":["text (code generation prompts)","text (code for analysis)"],"output_types":["text (generated code)","text (analysis results)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-qwen2.5-coder__cap_2","uri":"capability://code.generation.editing.code.fixing.and.bug.correction","name":"code-fixing-and-bug-correction","description":"Identifies and corrects bugs, syntax errors, and logic issues in provided code through instruction-tuned analysis and generation. The model processes buggy code as input and outputs corrected versions with explanations of what was wrong and how the fix addresses the issue. Correction is performed through a generate-and-compare approach where the model produces fixed code based on error patterns learned during training.","intents":["Fix this Python TypeError in my data processing pipeline","Correct the SQL syntax error in this query","Debug this JavaScript async function that's not awaiting promises correctly","Fix this off-by-one error in my loop logic"],"best_for":["developers debugging code locally without IDE plugins","teams without access to specialized linting or static analysis tools","learners understanding common programming mistakes and their corrections"],"limitations":["Cannot identify bugs that require runtime execution context or external state inspection","May produce incorrect fixes for ambiguous code where multiple valid solutions exist","No guarantee that fixed code is optimal or follows project-specific conventions","Smaller models (0.5B-3B) may miss subtle logic errors or produce incomplete fixes","Cannot verify that fixes don't introduce new bugs or break dependent code"],"requires":["Ollama runtime with Qwen 2.5 Coder model loaded","Buggy code snippet or file as text input (max 32K tokens)","Optional: error message, stack trace, or description of expected behavior"],"input_types":["text (buggy source code)","text (error messages or test failures)"],"output_types":["text (corrected code)","text (explanation of bug and fix)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-qwen2.5-coder__cap_3","uri":"capability://code.generation.editing.multi.language.code.generation.with.unified.interface","name":"multi-language-code-generation-with-unified-interface","description":"Generates syntactically correct code across multiple programming languages (Python, JavaScript, Java, C++, Go, Rust, SQL, etc.) through a single unified chat interface. The model's training on diverse code corpora enables it to switch between language contexts based on prompt specification, maintaining consistent code quality and style conventions across language families. Language selection is implicit in the prompt or explicit via instruction.","intents":["Generate equivalent functions in both Python and JavaScript for my polyglot project","Write a SQL query and its corresponding ORM code in TypeScript","Create a REST API endpoint in Go and its corresponding client in Python","Generate test cases in multiple languages for the same algorithm"],"best_for":["polyglot teams working across multiple programming languages","developers learning multiple languages by comparing generated implementations","projects requiring code generation in languages where IDE support is limited"],"limitations":["Language support matrix is undocumented — some languages may have lower quality output than others","No explicit language detection or validation — model may generate syntactically invalid code for less common languages","Cannot generate code for domain-specific languages (DSLs) unless explicitly trained on them","Cross-language consistency is not guaranteed — equivalent functions may have different quality across languages"],"requires":["Ollama runtime with Qwen 2.5 Coder model loaded","Explicit language specification in prompt (e.g., 'Write this in Rust')","Knowledge of target language syntax to validate generated code"],"input_types":["text (natural language description)","text (code in source language for translation)"],"output_types":["text (code in target language)","text (multiple language implementations)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-qwen2.5-coder__cap_4","uri":"capability://code.generation.editing.context.aware.code.completion.with.32k.token.window","name":"context-aware-code-completion-with-32k-token-window","description":"Completes code based on surrounding context using a 32K token context window that captures file history, imports, function signatures, and architectural patterns. The model processes partial code and generates continuations that respect existing code style, naming conventions, and project structure. Context awareness is achieved through the transformer's attention mechanism operating over the full 32K window, enabling multi-file understanding when context is provided.","intents":["Auto-complete a function body based on its signature and docstring","Continue a partially written class with methods that follow existing patterns","Generate the next lines of a complex algorithm based on previous implementation","Complete a SQL query based on table schema and previous query patterns in the file"],"best_for":["developers using Ollama locally as a code completion engine","teams building custom IDE integrations with local inference","developers who want completion without sending code to cloud services"],"limitations":["32K token context window is fixed and cannot be extended, limiting multi-file awareness for large codebases","Completion quality degrades if context is not properly formatted or includes irrelevant code","No incremental/streaming completion — full completion is generated before output begins","Smaller model variants produce lower-quality completions for complex code patterns","No integration with IDE-specific completion ranking or filtering"],"requires":["Ollama runtime with Qwen 2.5 Coder model loaded","Code context provided as text (up to 32K tokens total)","Cursor position or explicit marker indicating where completion should begin"],"input_types":["text (partial code with context)","text (file content with cursor position)"],"output_types":["text (code completion)","text (multiple completion suggestions)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-qwen2.5-coder__cap_5","uri":"capability://text.generation.language.instruction.tuned.chat.interface.for.code.tasks","name":"instruction-tuned-chat-interface-for-code-tasks","description":"Provides a conversational interface for code-related tasks through instruction-tuned chat interactions where users can ask questions, request modifications, and iterate on code through multi-turn dialogue. The model maintains conversation context across turns and responds to follow-up instructions like 'add error handling', 'optimize for performance', or 'add unit tests'. Chat is implemented via standard message format (role/content) compatible with Ollama's REST API and SDKs.","intents":["Ask the model to generate code, then request modifications in follow-up messages","Have a conversation about code design decisions and architectural trade-offs","Request iterative improvements like 'add type hints', 'make it more efficient', 'add comments'","Ask questions about generated code and get explanations in follow-up turns"],"best_for":["developers who prefer conversational interaction over single-shot code generation","teams building chatbot interfaces for code assistance","learners who benefit from iterative dialogue and explanation"],"limitations":["Conversation context is not persisted across sessions — each new chat starts fresh","No built-in memory of previous conversations or project-specific context","Model may lose track of earlier conversation context if dialogue extends beyond context window","No explicit conversation state management — relies on user to maintain context in messages"],"requires":["Ollama runtime with Qwen 2.5 Coder model loaded","REST API endpoint at localhost:11434/api/chat or SDK client (Python/JavaScript)","Message format with 'role' (user/assistant) and 'content' fields"],"input_types":["text (natural language questions)","text (code snippets for context)","text (modification requests)"],"output_types":["text (code)","text (explanations)","text (conversation responses)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-qwen2.5-coder__cap_6","uri":"capability://code.generation.editing.local.inference.with.variable.model.sizes.0.5b.to.32b","name":"local-inference-with-variable-model-sizes-0-5b-to-32b","description":"Executes code generation and understanding tasks locally on user hardware with six model size options (0.5B, 1.5B, 3B, 7B, 14B, 32B) enabling trade-offs between inference speed and output quality. Smaller models (0.5B-3B) run on CPU or modest GPUs for fast iteration, while larger models (7B-32B) require more VRAM but produce higher-quality code. Model selection is made at runtime via Ollama's `ollama run` command or API.","intents":["Run code generation on a laptop with limited GPU memory using the 1.5B model","Deploy the 7B model on a server for team-wide code assistance","Use the 32B model for critical code generation tasks requiring highest quality","Switch between model sizes based on task complexity and available hardware"],"best_for":["developers with varying hardware capabilities (laptops, desktops, servers)","teams wanting to optimize cost/quality trade-offs through model selection","organizations requiring local inference for compliance or privacy reasons"],"limitations":["Exact GPU VRAM requirements for each model size are undocumented","Inference speed benchmarks are not provided — users must test on their hardware","Smaller models (0.5B-3B) produce significantly lower-quality code than larger variants","No automatic model selection based on hardware — users must manually choose appropriate size","Context window is fixed at 32K tokens across all sizes, not scaled to model capacity"],"requires":["Ollama runtime installed and running","Disk space: 398MB (0.5B) to 20GB (32B) depending on model choice","GPU with sufficient VRAM (exact requirements undocumented) or CPU fallback","Python 3.7+ or Node.js 14+ for SDK usage"],"input_types":["text (code generation prompts)","text (code for analysis)"],"output_types":["text (generated code)","text (analysis results)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-qwen2.5-coder__cap_7","uri":"capability://automation.workflow.ollama.cloud.deployment.with.gpu.time.billing","name":"ollama-cloud-deployment-with-gpu-time-billing","description":"Enables remote execution of Qwen 2.5 Coder models on Ollama Cloud infrastructure with GPU time-based billing and concurrency limits. Users can run models on cloud GPUs without local hardware investment, with usage tracked via session limits (reset every 5 hours) and weekly limits (reset every 7 days). Cloud deployment is accessed via Ollama Pro ($20/mo, 3 concurrent models) or Max ($100/mo, 10 concurrent models) tiers.","intents":["Run the 32B model on cloud GPUs without purchasing expensive hardware","Deploy code generation as a service for a team using Ollama Cloud","Scale code generation workloads across multiple concurrent model instances","Avoid local GPU setup complexity by using managed cloud infrastructure"],"best_for":["teams without local GPU infrastructure","developers wanting to test larger models (14B, 32B) without hardware investment","organizations requiring managed infrastructure and automatic scaling"],"limitations":["Cloud inference adds network latency compared to local execution","Concurrency limits (3 for Pro, 10 for Max) may be insufficient for high-traffic applications","Session limits (5-hour reset) and weekly limits may interrupt long-running workloads","Billing is based on GPU time, not tokens — cost is unpredictable without usage patterns","No SLA or uptime guarantees documented for cloud service"],"requires":["Ollama Cloud account with Pro ($20/mo) or Max ($100/mo) subscription","Internet connectivity for cloud API access","Ollama CLI or SDK configured with cloud credentials"],"input_types":["text (code generation prompts)","text (code for analysis)"],"output_types":["text (generated code)","text (analysis results)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-qwen2.5-coder__cap_8","uri":"capability://tool.use.integration.rest.api.and.sdk.integration.with.40000.community.integrations","name":"rest-api-and-sdk-integration-with-40000-community-integrations","description":"Exposes Qwen 2.5 Coder through standardized REST API (localhost:11434/api/chat) and official SDKs (Python, JavaScript) compatible with 40,000+ community integrations. The REST API accepts JSON payloads with message history and returns streaming or non-streaming responses. SDKs provide language-native bindings for chat, completion, and embedding operations, enabling integration into existing applications without custom HTTP handling.","intents":["Integrate Qwen 2.5 Coder into a custom IDE plugin via REST API","Build a code generation service in Python using the official SDK","Connect Qwen 2.5 Coder to existing tools and workflows via community integrations","Create a web application that calls the model via REST API"],"best_for":["developers building custom integrations with Ollama models","teams leveraging existing community integrations for rapid deployment","applications requiring programmatic access to code generation"],"limitations":["REST API documentation is minimal — users must infer behavior from Ollama's generic API docs","Community integrations (40,000+) are not enumerated or curated — quality and maintenance status unknown","No official integrations with major IDEs (VS Code, JetBrains) documented","SDK support is limited to Python and JavaScript — no official Go, Rust, or Java SDKs","API rate limiting and concurrency handling are not documented"],"requires":["Ollama runtime running with HTTP API enabled (default: localhost:11434)","Python 3.7+ for Python SDK or Node.js 14+ for JavaScript SDK","Knowledge of REST API or SDK syntax for chosen language"],"input_types":["JSON (REST API payloads)","Python/JavaScript objects (SDK calls)"],"output_types":["JSON (REST API responses)","Python/JavaScript objects (SDK responses)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-qwen2.5-coder__cap_9","uri":"capability://code.generation.editing.code.specialized.training.with.benchmark.competitive.performance","name":"code-specialized-training-with-benchmark-competitive-performance","description":"Achieves competitive performance on code-specific benchmarks (EvalPlus, LiveCodeBench, BigCodeBench) through instruction-tuning on code-focused datasets. The 32B variant claims performance comparable to GPT-4o on these benchmarks, and the 7B variant is marked as 'latest' with strong performance across model sizes. Training methodology and specific benchmark scores are not documented, but the model is explicitly optimized for code tasks rather than general language understanding.","intents":["Evaluate whether Qwen 2.5 Coder is suitable for production code generation tasks","Compare code generation quality across different model sizes","Assess whether the model meets quality standards for specific programming languages"],"best_for":["teams evaluating open-source code generation models","organizations benchmarking against proprietary models like GPT-4o","developers choosing between Qwen 2.5 Coder and other open-source alternatives"],"limitations":["Benchmark scores are claimed but not independently verified","Specific benchmark results (exact scores, pass rates) are not provided in documentation","Benchmarks may not reflect performance on real-world code generation tasks","No breakdown of performance by programming language or task type","Smaller models (0.5B-7B) performance on benchmarks is not documented separately"],"requires":["Access to benchmark datasets (EvalPlus, LiveCodeBench, BigCodeBench) for independent verification","Understanding of benchmark methodology to interpret results"],"input_types":["benchmark test cases"],"output_types":["benchmark scores","pass/fail results"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Ollama runtime installed and running locally (or Ollama Cloud Pro/Max subscription)","Minimum 986MB disk space for 1.5B model, up to 20GB for 32B variant","Python 3.7+ or Node.js 14+ for SDK usage","GPU with sufficient VRAM for selected model size (exact requirements undocumented)","Ollama runtime with Qwen 2.5 Coder model loaded","Code snippet or file content as text input (max 32K tokens)","Sufficient context in prompt to specify explanation depth and target audience","Ollama runtime installed and running locally","Model weights downloaded and cached locally (398MB-20GB depending on size)","No internet connectivity required after model is downloaded"],"failure_modes":["No execution verification — generated code is not tested or validated before output","Context window of 32K tokens limits ability to generate code for very large files or multi-file refactoring tasks","No built-in awareness of project-specific conventions, dependencies, or architectural patterns unless explicitly provided in prompt","Code quality varies significantly across model sizes (0.5B produces lower-quality output than 32B)","Explanations may be inaccurate for highly obfuscated or non-standard code patterns","Cannot reason about runtime behavior, performance characteristics, or security vulnerabilities without explicit code analysis","Limited to code understanding only — cannot generate architectural diagrams or visual representations","Smaller model variants (0.5B-3B) may produce shallow or incomplete explanations for complex algorithms","Local inference requires sufficient hardware (GPU or CPU) to run selected model size","No automatic updates to model weights — users must manually update via Ollama","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.42,"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":"2026-05-24T12:16:24.483Z","last_scraped_at":"2026-05-03T15:20:48.403Z","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-coder","compare_url":"https://unfragile.ai/compare?artifact=qwen2.5-coder"}},"signature":"r3SL6YYtcJ89U2pugeUhEZTCD504zv8gMYdUPjaYSFtNPGFz5DsIe8/ATEJVhF96JEr9AUrEzIWM7wCp241OCg==","signedAt":"2026-06-22T15:10:59.910Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen2.5-coder","artifact":"https://unfragile.ai/qwen2.5-coder","verify":"https://unfragile.ai/api/v1/verify?slug=qwen2.5-coder","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"}}