{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-anthracite-org-magnum-v4-72b","slug":"anthracite-org-magnum-v4-72b","name":"Magnum v4 72B","type":"finetune","url":"https://openrouter.ai/models/anthracite-org~magnum-v4-72b","page_url":"https://unfragile.ai/anthracite-org-magnum-v4-72b","categories":["model-training","testing-quality"],"tags":["anthracite-org","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$3.00e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-anthracite-org-magnum-v4-72b__cap_0","uri":"capability://text.generation.language.claude.style.prose.generation.with.instruction.following","name":"claude-style prose generation with instruction-following","description":"Generates natural language responses mimicking Claude 3 Sonnet/Opus writing style through fine-tuning on Qwen2.5 72B base model. Uses instruction-tuned architecture to follow complex multi-step prompts while maintaining coherent, well-structured prose with appropriate tone and formality levels. The model learns stylistic patterns from Claude outputs during fine-tuning rather than using retrieval or prompt engineering alone.","intents":["I need a model that writes like Claude but runs on my own infrastructure or through a cheaper API","I want Claude-quality responses without vendor lock-in to Anthropic's API","I need to migrate from Claude to an open-weight alternative while maintaining output quality"],"best_for":["developers building chatbots who want Claude-quality prose without Anthropic pricing","teams evaluating open-weight alternatives to proprietary LLMs","builders needing inference flexibility across multiple providers (OpenRouter, local deployment)"],"limitations":["Fine-tuning approach means it approximates Claude style but may not match exact behavior on edge cases or specialized domains","72B parameter size requires significant VRAM (~45GB) for local deployment; inference speed slower than smaller models","Quality depends on fine-tuning dataset composition — no transparency on exact training data or techniques used","No native tool-use or function-calling capabilities documented; relies on prompt-based instruction following"],"requires":["OpenRouter API key for cloud access, or 48GB+ VRAM GPU for local inference","Compatible inference framework (vLLM, llama.cpp, or similar for local; OpenRouter handles cloud)","Input context length compatible with Qwen2.5 base (typically 128K tokens)"],"input_types":["text (natural language instructions, prompts, multi-turn conversations)"],"output_types":["text (prose, code snippets, structured responses, explanations)"],"categories":["text-generation-language","instruction-following"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthracite-org-magnum-v4-72b__cap_1","uri":"capability://text.generation.language.multi.turn.conversational.context.management","name":"multi-turn conversational context management","description":"Maintains coherent multi-turn dialogue through transformer-based attention mechanisms that track conversation history and speaker context. The instruction-tuned architecture processes entire conversation threads as input, allowing the model to reference previous exchanges, maintain consistent character/tone, and resolve pronouns and references across turns without explicit memory structures.","intents":["I need a model that remembers context across multiple conversation turns without losing coherence","I want to build a chatbot that can handle complex multi-step dialogues with proper context resolution","I need to maintain conversation state without implementing external memory systems"],"best_for":["chatbot developers building conversational AI without external state management","teams prototyping dialogue systems that need immediate context awareness","builders integrating into chat interfaces where conversation history is naturally available"],"limitations":["Context window is finite (~128K tokens for Qwen2.5 base); very long conversations require truncation or summarization","No explicit memory persistence — conversation state exists only during inference; requires external storage for session management","Attention mechanism scales quadratically with context length, causing latency increases on very long conversations","May hallucinate or lose track of details in conversations exceeding 50K tokens depending on fine-tuning"],"requires":["Full conversation history passed as input for each inference call","Conversation formatting compatible with instruction-tuned model expectations (typically role-based: user/assistant markers)","External session storage if persistence across API calls is needed"],"input_types":["text (multi-turn conversation history with speaker labels)"],"output_types":["text (next turn response maintaining conversation context)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthracite-org-magnum-v4-72b__cap_2","uri":"capability://code.generation.editing.code.generation.and.explanation.with.instruction.following","name":"code generation and explanation with instruction-following","description":"Generates code snippets and technical explanations by applying instruction-tuned patterns learned from fine-tuning on Claude outputs. The model understands code context from natural language descriptions, can generate multiple programming languages, and provides explanations alongside code. Implementation relies on transformer attention over code tokens and learned associations between natural language intent and code patterns.","intents":["I need a model to generate code from natural language descriptions without using Claude's API","I want to build a coding assistant that explains its generated code in Claude-like style","I need to generate code snippets in multiple languages with consistent quality"],"best_for":["developers building code generation tools who want Claude-quality output at lower cost","teams building IDE plugins or code completion tools","educators creating interactive coding tutorials with AI-generated explanations"],"limitations":["Code generation quality varies by language; likely stronger on popular languages (Python, JavaScript) than niche languages","No built-in code execution or validation — generated code may have syntax errors or logical bugs requiring human review","72B model size means slower inference than smaller code-specialized models (e.g., CodeLlama 34B)","Fine-tuning approach means code style mimics Claude's preferences but may not match project-specific conventions without prompt engineering"],"requires":["Natural language description of desired code behavior","Optionally: code context or existing codebase snippets for reference","API access (OpenRouter) or local inference capability"],"input_types":["text (natural language code requests, code snippets for context, language specifications)"],"output_types":["text (code in requested language, explanations, comments)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthracite-org-magnum-v4-72b__cap_3","uri":"capability://planning.reasoning.reasoning.and.problem.decomposition.with.chain.of.thought.patterns","name":"reasoning and problem decomposition with chain-of-thought patterns","description":"Applies learned chain-of-thought reasoning patterns from Claude fine-tuning to break down complex problems into steps. The model generates intermediate reasoning steps before final answers, using transformer attention to track logical dependencies across reasoning chains. This is achieved through instruction-tuning on examples where Claude explicitly shows reasoning work.","intents":["I need a model that shows its reasoning steps like Claude does, not just final answers","I want to build an AI system that can tackle multi-step problems with transparent logic","I need better reasoning quality for math, logic, and analytical tasks without Claude's API"],"best_for":["developers building reasoning-heavy applications (tutoring, analysis, decision support)","teams needing interpretable AI outputs where reasoning transparency is important","builders creating educational tools that benefit from showing work"],"limitations":["Chain-of-thought reasoning adds latency (~2-3x longer inference time) due to generating intermediate steps","Reasoning quality degrades on very complex problems (>10 logical steps); model may lose track of dependencies","Fine-tuned patterns may not generalize to novel problem types outside training distribution","No formal verification of reasoning correctness — intermediate steps may contain logical errors"],"requires":["Prompts that explicitly request reasoning (e.g., 'Show your work' or 'Think step by step')","Sufficient context window to accommodate both reasoning steps and final answer","Tolerance for longer response times due to chain-of-thought generation"],"input_types":["text (problem statements, questions, requests for reasoning)"],"output_types":["text (intermediate reasoning steps, final answer, logical justification)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthracite-org-magnum-v4-72b__cap_4","uri":"capability://text.generation.language.content.summarization.and.abstraction","name":"content summarization and abstraction","description":"Condenses long-form text into summaries while preserving key information, using attention mechanisms to identify salient content and instruction-tuned patterns for summary formatting. The model learns from Claude's summarization style, which emphasizes clarity and hierarchical organization of information. Works by attending to important tokens and generating compressed representations.","intents":["I need to summarize long documents without using Claude's API","I want to extract key points from articles or reports in Claude's clear style","I need to create executive summaries that maintain technical accuracy"],"best_for":["document processing pipelines that need high-quality summarization","teams building research tools or knowledge management systems","builders creating content curation or news aggregation applications"],"limitations":["Summarization quality depends on input length; very long documents (>50K tokens) may lose important details","No control over summary length without prompt engineering; no built-in length constraints","May hallucinate details not present in source material, especially on unfamiliar topics","Struggles with highly technical or domain-specific content outside training distribution"],"requires":["Full source text to summarize (within context window limits)","Optional: length guidance or summary format specifications in prompt","API access or local inference capability"],"input_types":["text (articles, documents, reports, transcripts)"],"output_types":["text (summaries, key points, abstracts)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthracite-org-magnum-v4-72b__cap_5","uri":"capability://planning.reasoning.instruction.following.with.complex.multi.step.tasks","name":"instruction-following with complex multi-step tasks","description":"Executes complex, multi-part instructions by parsing task structure and maintaining execution context across steps. The instruction-tuned architecture learns to identify task boundaries, handle conditional logic (if-then patterns), and sequence operations correctly. Implementation relies on transformer attention to track task state and learned patterns from Claude's instruction-following training.","intents":["I need a model that reliably follows complex, multi-part instructions without getting confused","I want to automate workflows that require sequential task execution with conditional logic","I need to build systems where instruction clarity and compliance are critical"],"best_for":["developers building task automation systems or workflow engines","teams creating instruction-following agents for specific domains","builders needing reliable instruction compliance for safety-critical applications"],"limitations":["Instruction-following quality degrades with very complex nested logic (>5 conditional branches)","Model may misinterpret ambiguous instructions or skip steps if not explicitly numbered/formatted","No built-in error recovery; if a step fails, model doesn't automatically retry or report failure","Fine-tuned patterns may not generalize to instruction formats significantly different from training data"],"requires":["Clear, well-structured instructions (numbered steps, explicit conditionals, clear formatting)","Prompts that explicitly request step-by-step execution","Context for any domain-specific terminology or task requirements"],"input_types":["text (multi-step instructions, task descriptions, conditional logic)"],"output_types":["text (step-by-step execution results, task completion status, intermediate outputs)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthracite-org-magnum-v4-72b__cap_6","uri":"capability://text.generation.language.natural.language.question.answering.with.contextual.understanding","name":"natural language question answering with contextual understanding","description":"Answers questions by understanding context, identifying relevant information, and generating coherent responses. Uses transformer attention to locate answer-relevant tokens and instruction-tuned patterns to format responses appropriately. The model learns from Claude's question-answering style, which emphasizes accuracy, nuance, and acknowledgment of uncertainty.","intents":["I need a QA system that provides accurate, nuanced answers without Claude's API","I want to build a knowledge assistant that answers questions in Claude's careful style","I need to create FAQ systems or customer support bots with high-quality responses"],"best_for":["teams building customer support or FAQ automation systems","developers creating knowledge assistants or documentation chatbots","builders needing QA capabilities without external knowledge bases"],"limitations":["Answers are based on training data knowledge; no real-time information or web access","May confidently provide incorrect answers (hallucination) on unfamiliar topics","No built-in fact-checking or source attribution; answers lack citations","Knowledge cutoff limits applicability to recent events or rapidly changing information"],"requires":["Question in natural language format","Optional: context or background information to improve answer relevance","API access or local inference capability"],"input_types":["text (questions, queries, context)"],"output_types":["text (answers, explanations, clarifications)"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-anthracite-org-magnum-v4-72b__cap_7","uri":"capability://text.generation.language.creative.writing.and.content.generation","name":"creative writing and content generation","description":"Generates creative text including stories, essays, marketing copy, and other original content by learning stylistic patterns from Claude's creative outputs. The model uses transformer attention to maintain narrative coherence, character consistency, and thematic development across generated text. Fine-tuning captures Claude's approach to balancing creativity with clarity.","intents":["I need to generate creative content without using Claude's API","I want to build a writing assistant that produces Claude-quality prose","I need to automate content creation for marketing, storytelling, or creative projects"],"best_for":["content creators and marketers building AI-assisted writing tools","developers creating creative writing applications or story generators","teams automating content production for blogs, social media, or marketing"],"limitations":["Generated content may lack originality or repeat patterns from training data","Longer creative pieces (>2000 words) may lose coherence or repeat themes","Style consistency degrades if prompts don't clearly specify tone and voice","No built-in fact-checking; creative content may contain plausible-sounding but false details"],"requires":["Clear creative brief or prompt describing desired content, tone, and style","Optional: examples or reference material to guide generation","Tolerance for iterative refinement; first output may require editing"],"input_types":["text (creative prompts, style descriptions, content briefs)"],"output_types":["text (stories, essays, marketing copy, creative content)"],"categories":["text-generation-language","content-creation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"low","permissions":["OpenRouter API key for cloud access, or 48GB+ VRAM GPU for local inference","Compatible inference framework (vLLM, llama.cpp, or similar for local; OpenRouter handles cloud)","Input context length compatible with Qwen2.5 base (typically 128K tokens)","Full conversation history passed as input for each inference call","Conversation formatting compatible with instruction-tuned model expectations (typically role-based: user/assistant markers)","External session storage if persistence across API calls is needed","Natural language description of desired code behavior","Optionally: code context or existing codebase snippets for reference","API access (OpenRouter) or local inference capability","Prompts that explicitly request reasoning (e.g., 'Show your work' or 'Think step by step')"],"failure_modes":["Fine-tuning approach means it approximates Claude style but may not match exact behavior on edge cases or specialized domains","72B parameter size requires significant VRAM (~45GB) for local deployment; inference speed slower than smaller models","Quality depends on fine-tuning dataset composition — no transparency on exact training data or techniques used","No native tool-use or function-calling capabilities documented; relies on prompt-based instruction following","Context window is finite (~128K tokens for Qwen2.5 base); very long conversations require truncation or summarization","No explicit memory persistence — conversation state exists only during inference; requires external storage for session management","Attention mechanism scales quadratically with context length, causing latency increases on very long conversations","May hallucinate or lose track of details in conversations exceeding 50K tokens depending on fine-tuning","Code generation quality varies by language; likely stronger on popular languages (Python, JavaScript) than niche languages","No built-in code execution or validation — generated code may have syntax errors or logical bugs requiring human review","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.51,"ecosystem":0.34,"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":"2026-05-03T15:20:45.776Z","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=anthracite-org-magnum-v4-72b","compare_url":"https://unfragile.ai/compare?artifact=anthracite-org-magnum-v4-72b"}},"signature":"zs+gogi4uj1eXwLRj+efgvgIC/HYIUUNjss0pHBuoxHzy47AlwkAUJGclnD68wF9Ti/VEYY0CmecjYPRPcOQCw==","signedAt":"2026-06-20T07:01:54.914Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/anthracite-org-magnum-v4-72b","artifact":"https://unfragile.ai/anthracite-org-magnum-v4-72b","verify":"https://unfragile.ai/api/v1/verify?slug=anthracite-org-magnum-v4-72b","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"}}