{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-liquid-lfm-2-24b-a2b","slug":"liquid-lfm-2-24b-a2b","name":"LiquidAI: LFM2-24B-A2B","type":"model","url":"https://openrouter.ai/models/liquid~lfm-2-24b-a2b","page_url":"https://unfragile.ai/liquid-lfm-2-24b-a2b","categories":["deployment-infra"],"tags":["liquid","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$3.00e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-liquid-lfm-2-24b-a2b__cap_0","uri":"capability://text.generation.language.efficient.sparse.inference.with.mixture.of.experts","name":"efficient-sparse-inference-with-mixture-of-experts","description":"Executes inference using a Mixture-of-Experts (MoE) architecture where only 2B of 24B total parameters are active per forward pass, reducing computational cost and latency through sparse gating mechanisms. The model routes input tokens to specialized expert subnetworks based on learned routing weights, enabling efficient deployment on resource-constrained devices while maintaining quality comparable to dense models. This hybrid architecture balances model capacity with inference efficiency through selective expert activation rather than full parameter computation.","intents":["Deploy a capable language model on edge devices or low-power hardware without sacrificing quality","Reduce inference latency and token generation time for real-time conversational applications","Lower API costs by minimizing computational overhead per inference request","Run local inference without cloud dependencies while maintaining 24B-equivalent reasoning capability"],"best_for":["Teams building on-device AI applications for mobile, embedded, or IoT systems","Developers optimizing for latency-sensitive applications like real-time chat or voice assistants","Cost-conscious builders deploying high-volume inference workloads via API","Organizations with privacy requirements necessitating local model execution"],"limitations":["MoE routing adds ~5-15ms overhead per inference step compared to dense models due to gating computation","Expert load balancing may cause uneven token distribution, reducing effective parallelization on some hardware","Sparse activation patterns are less amenable to GPU optimization than dense matrix operations, potentially limiting speedup on certain accelerators","Fine-tuning MoE models requires careful handling of expert specialization to avoid collapse to single-expert solutions"],"requires":["API access via OpenRouter or compatible inference endpoint","Sufficient context window support (model-dependent, typically 4K-32K tokens)","For local deployment: hardware supporting quantized inference (e.g., ONNX Runtime, llama.cpp with MoE support)","Understanding of sparse model characteristics for prompt engineering and system design"],"input_types":["text (natural language prompts, code snippets, structured queries)","multi-turn conversation context"],"output_types":["text (natural language responses, code generation, structured text)","token logits for sampling or beam search"],"categories":["text-generation-language","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-liquid-lfm-2-24b-a2b__cap_1","uri":"capability://text.generation.language.multi.turn.conversational.reasoning","name":"multi-turn-conversational-reasoning","description":"Maintains coherent dialogue across multiple turns by processing conversation history as context, enabling the model to track entities, maintain conversational state, and reason about prior exchanges. The model uses standard transformer attention mechanisms to weight relevant historical context, allowing it to reference earlier statements, correct misunderstandings, and build on previous reasoning chains. This capability supports both stateless API calls (where full history is passed each turn) and stateful conversation management patterns.","intents":["Build conversational AI assistants that remember context and maintain coherent dialogue across multiple exchanges","Implement multi-turn debugging or code review workflows where the model references earlier code snippets and feedback","Create interactive tutoring systems where the model tracks student understanding and adapts explanations based on prior responses","Develop customer support chatbots that maintain context across conversation history without external state management"],"best_for":["Developers building chatbot applications with conversational UX expectations","Teams implementing interactive debugging or pair-programming assistants","Builders creating educational or customer-facing dialogue systems","Rapid prototypers who need conversation management without building custom state machines"],"limitations":["Context window is finite (typically 4K-32K tokens depending on deployment); long conversations require history truncation or summarization","Attention mechanism scales quadratically with context length, causing latency degradation as conversation history grows","Model may exhibit recency bias, over-weighting recent messages while forgetting earlier context","No built-in conversation state persistence; requires external storage for multi-session continuity across API calls"],"requires":["API endpoint supporting multi-turn message format (e.g., OpenRouter's chat completion API)","Client-side conversation history management or integration with conversation framework (e.g., LangChain, LlamaIndex)","Understanding of token counting to manage context window within model limits","Optional: external database or cache for persisting conversation state across sessions"],"input_types":["text (user messages, system prompts, conversation history)","structured conversation format (e.g., OpenAI messages API format with role/content pairs)"],"output_types":["text (assistant responses, reasoning traces)","structured metadata (e.g., confidence scores, cited sources if integrated with RAG)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-liquid-lfm-2-24b-a2b__cap_2","uri":"capability://code.generation.editing.code.generation.and.completion","name":"code-generation-and-completion","description":"Generates and completes code across multiple programming languages by predicting syntactically and semantically valid continuations of code snippets. The model uses transformer attention to understand code structure, variable scope, and API patterns from context, enabling both single-line completions and multi-function generation. Supports both inline completion (filling gaps in existing code) and full-function generation from docstrings or type signatures.","intents":["Auto-complete code in IDEs or editors to accelerate development velocity","Generate boilerplate or scaffolding code from function signatures or docstrings","Implement code-to-code translation or refactoring suggestions across languages","Create code review or linting suggestions by analyzing code patterns and best practices"],"best_for":["Software developers using API-based code completion in custom editors or IDE plugins","Teams building internal developer tools or code generation pipelines","Builders creating language-agnostic code transformation or migration tools","Developers working in less-common languages where IDE support is limited"],"limitations":["Code generation quality varies significantly by language; performance is strongest for Python, JavaScript, and Java, weaker for niche languages","Model may generate syntactically valid but semantically incorrect code (e.g., wrong algorithm, unsafe patterns); requires human review or testing","No built-in awareness of project-specific libraries, custom APIs, or internal code conventions without additional context injection","Sparse MoE routing may cause inconsistent code style or variable naming across generated segments if experts specialize differently"],"requires":["API access via OpenRouter or compatible endpoint","Code context (preceding lines, function signature, or docstring) to seed generation","Optional: syntax highlighting or AST parsing to validate generated code before execution","Understanding of model limitations to implement human-in-the-loop review workflows"],"input_types":["text (code snippets, docstrings, type signatures, comments)","structured code context (e.g., file path, language hint, surrounding function definitions)"],"output_types":["text (generated code, code completions, refactoring suggestions)","structured metadata (e.g., confidence scores, alternative suggestions)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-liquid-lfm-2-24b-a2b__cap_3","uri":"capability://planning.reasoning.instruction.following.and.task.decomposition","name":"instruction-following-and-task-decomposition","description":"Interprets natural language instructions and decomposes complex tasks into subtasks or step-by-step execution plans. The model uses attention mechanisms to identify task constraints, dependencies, and success criteria from instruction text, then generates structured plans or reasoning traces. Supports both implicit task decomposition (reasoning internally) and explicit plan generation (outputting step-by-step instructions for external execution).","intents":["Create AI agents that break down complex user requests into executable subtasks","Generate step-by-step instructions or workflows from high-level goal descriptions","Implement reasoning-based problem-solving where the model explains its approach before generating solutions","Build systems that validate instruction understanding by generating clarifying questions or constraint summaries"],"best_for":["Builders creating autonomous agents or task-planning systems","Teams implementing workflow automation or process mining tools","Developers building educational systems that explain problem-solving approaches","Rapid prototypers needing reasoning capabilities without custom planning algorithms"],"limitations":["Task decomposition quality depends on instruction clarity; ambiguous or underspecified tasks may produce incomplete or incorrect plans","Model may miss implicit constraints or domain-specific requirements not explicitly stated in instructions","No built-in execution or validation of generated plans; requires external systems to verify feasibility and track completion","Sparse MoE routing may cause inconsistent reasoning quality if task-planning experts are unevenly loaded across inference batches"],"requires":["Clear, well-structured instructions with explicit goals and constraints","Optional: domain-specific context or examples to improve task understanding","External task execution framework to implement generated plans","Monitoring or validation layer to catch hallucinated or infeasible subtasks"],"input_types":["text (natural language instructions, goal descriptions, constraint specifications)","structured task definitions (e.g., JSON with goal, constraints, available tools)"],"output_types":["text (step-by-step plans, reasoning traces, clarifying questions)","structured data (e.g., JSON task trees, dependency graphs)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-liquid-lfm-2-24b-a2b__cap_4","uri":"capability://memory.knowledge.knowledge.grounded.text.generation","name":"knowledge-grounded-text-generation","description":"Generates text informed by provided context or knowledge documents, using attention mechanisms to ground responses in supplied information rather than relying solely on training data. The model integrates context passages into the attention computation, allowing it to cite sources, synthesize information from multiple documents, and reduce hallucination by constraining generation to supported facts. This capability is commonly used in retrieval-augmented generation (RAG) pipelines where external knowledge is injected into the prompt.","intents":["Build question-answering systems that cite sources and ground answers in provided documents","Implement knowledge-grounded chatbots that synthesize information from multiple sources","Create summarization tools that extract and synthesize key points from long documents","Develop fact-checking or verification systems that validate claims against provided evidence"],"best_for":["Teams building RAG systems or knowledge-grounded AI applications","Developers creating customer support systems with access to knowledge bases","Builders implementing research or document analysis tools","Organizations requiring explainability through source citation"],"limitations":["Context window limits the amount of knowledge that can be grounded per request; large document sets require chunking or hierarchical retrieval","Model may still hallucinate or misinterpret context if knowledge documents are ambiguous or contradictory","Attention mechanism may over-weight irrelevant context if retrieval quality is poor, degrading response quality","No built-in mechanism to verify that generated text actually uses provided context; requires post-hoc citation validation"],"requires":["External retrieval system (e.g., vector database, BM25 search) to fetch relevant context documents","Knowledge documents or passages formatted for injection into prompts","Context window management to balance knowledge coverage with prompt length","Optional: citation tracking or source attribution mechanisms to validate grounding"],"input_types":["text (user queries, context documents, knowledge passages)","structured context (e.g., JSON with document metadata, relevance scores)"],"output_types":["text (grounded responses, summaries, answers with citations)","structured data (e.g., JSON with response and source references)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-liquid-lfm-2-24b-a2b__cap_5","uri":"capability://text.generation.language.api.based.inference.with.streaming","name":"api-based-inference-with-streaming","description":"Provides real-time text generation through streaming API endpoints, where tokens are emitted incrementally as they are generated rather than waiting for full response completion. The model uses token-by-token generation with streaming protocols (e.g., Server-Sent Events, WebSocket) to enable low-latency user feedback and progressive response rendering. Supports both buffered (full response at once) and streaming (incremental token) output modes.","intents":["Build responsive chat interfaces that show text appearing in real-time as the model generates it","Implement low-latency applications where users need immediate feedback (e.g., code completion, real-time translation)","Create streaming pipelines where downstream systems process tokens incrementally without waiting for full completion","Reduce perceived latency in interactive applications by showing partial results while generation continues"],"best_for":["Frontend developers building chat UIs or conversational interfaces","Teams implementing real-time applications with latency-sensitive UX","Builders creating streaming data pipelines or event-driven systems","Developers optimizing for user experience in interactive applications"],"limitations":["Streaming adds complexity to error handling; partial responses may be rendered before errors are detected","Token-by-token generation prevents some optimizations (e.g., batch decoding) that improve throughput, potentially increasing per-token latency","Network overhead from streaming protocol may exceed buffered response overhead for short responses (<100 tokens)","Client-side buffering or retry logic is required to handle network interruptions mid-stream"],"requires":["HTTP client supporting streaming responses (e.g., fetch with ReadableStream, axios with responseType: 'stream')","API endpoint supporting streaming output (OpenRouter supports streaming via standard OpenAI API format)","Frontend framework or library to handle incremental token rendering (e.g., React hooks, Vue watchers)","Error handling for incomplete streams and network failures"],"input_types":["text (prompts, conversation history)","streaming configuration (e.g., max_tokens, temperature)"],"output_types":["streaming text (tokens emitted incrementally via SSE or WebSocket)","structured streaming data (e.g., JSON objects with token metadata)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-liquid-lfm-2-24b-a2b__cap_6","uri":"capability://text.generation.language.structured.output.generation.with.format.control","name":"structured-output-generation-with-format-control","description":"Generates text constrained to specific formats or schemas (e.g., JSON, XML, CSV, function calls) by using prompt engineering, output validation, or constrained decoding techniques. The model learns to follow format specifications from examples or explicit instructions, enabling reliable extraction of structured data from unstructured prompts. Supports both soft constraints (instructions in prompt) and hard constraints (validation/filtering of generated tokens).","intents":["Extract structured data (JSON, CSV) from unstructured text or documents","Generate function calls or API requests in standardized formats for tool use","Create configuration files or code in specific formats (YAML, Terraform, SQL)","Implement data validation or schema-based generation for downstream systems"],"best_for":["Developers building data extraction or ETL pipelines","Teams implementing function-calling agents or tool-use systems","Builders creating code generation tools with format requirements","Organizations needing reliable structured output for downstream processing"],"limitations":["Soft constraints (prompt-based) are not guaranteed; model may occasionally violate format specifications despite instructions","Hard constraints (token filtering) reduce generation quality if they're too restrictive, potentially causing invalid or incomplete output","Complex schemas may exceed model's ability to follow consistently; requires validation and retry logic","Sparse MoE routing may cause inconsistent format adherence if format-handling experts are unevenly specialized"],"requires":["Clear format specification or examples in the prompt","Optional: JSON schema or grammar specification for validation","Output validation layer to check format compliance and retry on failure","Understanding of model's format-following limitations to set realistic expectations"],"input_types":["text (prompts with format specifications, examples, schema descriptions)","structured schemas (e.g., JSON schema, Pydantic models)"],"output_types":["structured text (JSON, XML, CSV, YAML, function calls)","validated structured data with error handling"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-liquid-lfm-2-24b-a2b__cap_7","uri":"capability://text.generation.language.cross.lingual.text.generation.and.translation","name":"cross-lingual-text-generation-and-translation","description":"Generates and translates text across multiple languages by routing language-specific tokens through specialized expert pathways in the MoE architecture. The model learns language-specific patterns and vocabulary during training, enabling both translation (source-to-target language conversion) and code-switching (mixing languages in single response). Supports both explicit translation prompts and implicit multilingual generation based on input language.","intents":["Build multilingual chatbots or assistants that respond in user's preferred language","Implement translation services for document or content localization","Create multilingual search or retrieval systems that understand queries in multiple languages","Develop code-switching applications for bilingual or multilingual communities"],"best_for":["Teams building global applications with multilingual user bases","Developers creating translation or localization tools","Builders implementing multilingual search or information retrieval systems","Organizations serving non-English-speaking markets"],"limitations":["Translation quality varies significantly by language pair; high-resource pairs (English-Spanish) are stronger than low-resource pairs (English-Amharic)","Model may struggle with domain-specific terminology or cultural context in translation","Sparse MoE routing may cause language-mixing or code-switching artifacts if language experts are not well-separated","No built-in awareness of language-specific idioms, cultural references, or regional dialects"],"requires":["Clear language specification in prompts (e.g., 'Translate to Spanish' or system prompt with target language)","Understanding of model's language coverage (typically 50+ languages with varying quality)","Optional: domain-specific glossaries or terminology databases for specialized translation","Quality assurance or human review for critical translations"],"input_types":["text (prompts in any supported language, source text for translation)","language hints or explicit translation instructions"],"output_types":["text (responses in target language, translations, code-switched text)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-liquid-lfm-2-24b-a2b__cap_8","uri":"capability://text.generation.language.few.shot.learning.and.in.context.adaptation","name":"few-shot-learning-and-in-context-adaptation","description":"Adapts model behavior to new tasks or domains by providing examples (few-shot prompting) or task-specific instructions without retraining. The model uses attention mechanisms to learn patterns from provided examples, enabling rapid task adaptation for classification, extraction, summarization, or generation tasks. Supports both explicit examples (few-shot) and implicit adaptation through system prompts or role-playing instructions.","intents":["Quickly adapt the model to domain-specific tasks (e.g., legal document analysis, medical coding) using examples","Implement zero-shot or few-shot classification without fine-tuning","Create task-specific assistants by providing role descriptions and behavioral examples","Build flexible systems that handle diverse tasks with a single model by varying prompts"],"best_for":["Rapid prototypers building task-specific applications without fine-tuning infrastructure","Teams handling diverse tasks with a single model deployment","Developers building domain-specific assistants for specialized use cases","Organizations with limited training data or budget for fine-tuning"],"limitations":["Few-shot learning quality degrades with task complexity; simple classification works well, complex reasoning may require more examples","Example quality significantly impacts adaptation; poor examples can mislead the model","Context window limits the number of examples that can be provided; large example sets require selection or summarization","Sparse MoE routing may cause inconsistent few-shot learning if task-adaptation experts are not well-specialized"],"requires":["Well-crafted examples demonstrating the desired task behavior","Clear task instructions or role descriptions in the prompt","Understanding of in-context learning limitations and when fine-tuning is necessary","Evaluation framework to validate few-shot adaptation quality"],"input_types":["text (task instructions, examples with input/output pairs, user queries)","structured examples (e.g., JSON with example inputs and expected outputs)"],"output_types":["text (adapted responses following example patterns)","structured data (e.g., classifications, extractions following example format)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or compatible inference endpoint","Sufficient context window support (model-dependent, typically 4K-32K tokens)","For local deployment: hardware supporting quantized inference (e.g., ONNX Runtime, llama.cpp with MoE support)","Understanding of sparse model characteristics for prompt engineering and system design","API endpoint supporting multi-turn message format (e.g., OpenRouter's chat completion API)","Client-side conversation history management or integration with conversation framework (e.g., LangChain, LlamaIndex)","Understanding of token counting to manage context window within model limits","Optional: external database or cache for persisting conversation state across sessions","API access via OpenRouter or compatible endpoint","Code context (preceding lines, function signature, or docstring) to seed generation"],"failure_modes":["MoE routing adds ~5-15ms overhead per inference step compared to dense models due to gating computation","Expert load balancing may cause uneven token distribution, reducing effective parallelization on some hardware","Sparse activation patterns are less amenable to GPU optimization than dense matrix operations, potentially limiting speedup on certain accelerators","Fine-tuning MoE models requires careful handling of expert specialization to avoid collapse to single-expert solutions","Context window is finite (typically 4K-32K tokens depending on deployment); long conversations require history truncation or summarization","Attention mechanism scales quadratically with context length, causing latency degradation as conversation history grows","Model may exhibit recency bias, over-weighting recent messages while forgetting earlier context","No built-in conversation state persistence; requires external storage for multi-session continuity across API calls","Code generation quality varies significantly by language; performance is strongest for Python, JavaScript, and Java, weaker for niche languages","Model may generate syntactically valid but semantically incorrect code (e.g., wrong algorithm, unsafe patterns); requires human review or testing","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"ecosystem":0.24,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.484Z","last_scraped_at":"2026-05-03T15:20:45.776Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=liquid-lfm-2-24b-a2b","compare_url":"https://unfragile.ai/compare?artifact=liquid-lfm-2-24b-a2b"}},"signature":"wlRY/SNphwL/KWnGvg73reNobODsB7byfDFk4nRxVAlCizRTIPfl33PfxtID5ErMkAmntHw0erW/8Px9niqXCg==","signedAt":"2026-06-21T10:15:56.709Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/liquid-lfm-2-24b-a2b","artifact":"https://unfragile.ai/liquid-lfm-2-24b-a2b","verify":"https://unfragile.ai/api/v1/verify?slug=liquid-lfm-2-24b-a2b","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"}}