{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-deepseek-ai--deepseek-r1","slug":"deepseek-ai--deepseek-r1","name":"DeepSeek-R1","type":"model","url":"https://huggingface.co/deepseek-ai/DeepSeek-R1","page_url":"https://unfragile.ai/deepseek-ai--deepseek-r1","categories":["text-writing"],"tags":["transformers","safetensors","deepseek_v3","text-generation","conversational","custom_code","arxiv:2501.12948","license:mit","eval-results","text-generation-inference","endpoints_compatible","fp8","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-deepseek-ai--deepseek-r1__cap_0","uri":"capability://planning.reasoning.chain.of.thought.reasoning.with.reinforcement.learning.optimization","name":"chain-of-thought reasoning with reinforcement learning optimization","description":"DeepSeek-R1 implements a reasoning capability that explicitly generates intermediate thinking steps before producing final answers, trained via reinforcement learning to optimize for correctness rather than speed. The model learns to allocate computational budget dynamically—spending more tokens on harder problems and less on trivial ones—by training on a reward signal that incentivizes accurate reasoning traces. This differs from standard instruction-tuned models by making the reasoning process transparent and learnable rather than implicit in the weights.","intents":["I need a model that shows its work on complex math and logic problems so I can verify correctness","I want to reduce hallucinations by forcing explicit reasoning before answers","I need better performance on tasks requiring multi-step problem decomposition without prompt engineering"],"best_for":["researchers studying reasoning in language models","developers building verification systems for LLM outputs","teams solving STEM problems where interpretability matters"],"limitations":["Reasoning tokens increase latency by 2-10x compared to direct-answer models; unsuitable for real-time applications","Reasoning quality degrades on tasks outside training distribution (e.g., domain-specific jargon)","No fine-grained control over reasoning depth—model determines allocation automatically"],"requires":["Sufficient context window to accommodate reasoning traces (typically 4K-8K tokens for complex problems)","Inference infrastructure supporting long sequence generation","Understanding that reasoning traces are model-generated approximations, not formal proofs"],"input_types":["text prompts","mathematical problems","logic puzzles","code debugging tasks"],"output_types":["text with embedded reasoning traces","structured reasoning followed by final answer","step-by-step problem decomposition"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepseek-ai--deepseek-r1__cap_1","uri":"capability://text.generation.language.long.context.text.generation.with.efficient.attention.mechanisms","name":"long-context text generation with efficient attention mechanisms","description":"DeepSeek-R1 supports extended context windows (up to 128K tokens) through optimized attention implementations that reduce memory and computational overhead compared to standard dense attention. The model uses grouped-query attention (GQA) and other efficiency patterns to enable processing of long documents, codebases, or conversation histories without proportional increases in latency or memory consumption.","intents":["I need to process entire codebases or documentation files in a single prompt","I want to maintain conversation context across 50+ turns without degradation","I need to analyze long research papers or legal documents end-to-end"],"best_for":["developers working with large codebases requiring full-file context","researchers analyzing long documents or papers","teams building multi-turn conversational systems with deep history"],"limitations":["Context length advantage diminishes if input quality is poor—garbage in, garbage out applies to long contexts","Inference latency still scales with context length (O(n) rather than O(n²)), making 128K contexts slower than 4K","Attention patterns may not capture dependencies across very distant tokens as effectively as shorter contexts"],"requires":["GPU with sufficient VRAM (24GB+ for 128K context at batch size 1)","Inference framework supporting long-sequence inference (vLLM, text-generation-inference, or similar)","Awareness that longer contexts increase token consumption and cost proportionally"],"input_types":["text documents","source code files","conversation histories","concatenated documents"],"output_types":["text generation","code generation","summarization","question-answering over long context"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepseek-ai--deepseek-r1__cap_10","uri":"capability://automation.workflow.efficient.inference.with.quantization.and.optimization.support","name":"efficient inference with quantization and optimization support","description":"DeepSeek-R1 supports multiple quantization schemes (FP8, INT8) and is optimized for inference efficiency through techniques like grouped-query attention and flash attention. These optimizations reduce memory footprint and latency without significant quality degradation, enabling deployment on resource-constrained hardware.","intents":["I want to run the model on consumer GPUs with limited VRAM","I need to reduce inference latency for real-time applications","I want to minimize deployment costs through efficient inference"],"best_for":["developers deploying on edge devices or consumer GPUs","teams optimizing for inference cost and latency","organizations with limited computational resources"],"limitations":["Quantization introduces quality degradation (typically 1-5% on benchmarks); critical applications may require full precision","Optimization techniques may not be available in all inference frameworks","Memory savings come at the cost of slightly reduced reasoning quality"],"requires":["Inference framework supporting quantization (vLLM, text-generation-inference)","GPU with sufficient VRAM for quantized model (12GB+ for FP8)","Validation that quantization quality is acceptable for use case"],"input_types":["full-precision model weights","quantization parameters"],"output_types":["quantized model variants","optimized inference","performance metrics"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepseek-ai--deepseek-r1__cap_2","uri":"capability://text.generation.language.multi.language.text.generation.with.balanced.capability.across.languages","name":"multi-language text generation with balanced capability across languages","description":"DeepSeek-R1 is trained on a balanced multilingual corpus covering 30+ languages, enabling generation and reasoning in non-English languages without significant quality degradation. The model maintains reasoning capability across languages through unified tokenization and shared reasoning representations, rather than language-specific fine-tuning.","intents":["I need to generate code comments and documentation in multiple languages","I want reasoning and problem-solving in Chinese, Japanese, or other non-English languages","I need to build multilingual chatbots without separate models per language"],"best_for":["teams building products for non-English markets","researchers studying multilingual reasoning","developers supporting global user bases with single model"],"limitations":["Performance varies by language—English and Chinese are strongest, lower-resource languages may show 10-20% quality degradation","Tokenization efficiency differs across languages; CJK languages consume more tokens per semantic unit","Reasoning traces are most reliable in English and Chinese; other languages may have less coherent intermediate steps"],"requires":["UTF-8 encoding support in inference pipeline","Awareness of language-specific tokenization costs when budgeting context","Understanding that quality is not uniform across all 30+ supported languages"],"input_types":["text in any of 30+ supported languages","code with multilingual comments","mixed-language prompts"],"output_types":["text generation in target language","code with multilingual documentation","reasoning traces in target language"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepseek-ai--deepseek-r1__cap_3","uri":"capability://code.generation.editing.code.generation.and.debugging.with.language.agnostic.reasoning","name":"code generation and debugging with language-agnostic reasoning","description":"DeepSeek-R1 applies its reasoning capability to code generation tasks, explicitly decomposing algorithmic problems before writing code. The model generates intermediate reasoning about algorithm selection, edge cases, and implementation strategy, then produces code that reflects this reasoning. This approach reduces common code generation errors like off-by-one bugs and unhandled edge cases.","intents":["I need to generate correct algorithms for competitive programming or interview problems","I want code generation that explains the approach before implementation","I need to debug code by having the model reason through the logic step-by-step"],"best_for":["competitive programmers solving algorithm problems","teams conducting technical interviews with AI assistance","developers debugging complex algorithmic code"],"limitations":["Reasoning overhead makes code generation 3-5x slower than direct generation models; unsuitable for real-time code completion","Generated code may be correct but suboptimal in terms of performance or style compared to human-written code","Reasoning traces for code may not catch all edge cases despite explicit reasoning"],"requires":["Sufficient context window for reasoning traces (4K+ tokens for non-trivial problems)","Inference infrastructure supporting long sequence generation","Post-generation validation (testing, linting) to verify correctness"],"input_types":["algorithm problem descriptions","code snippets to debug","pseudocode to implement","natural language specifications"],"output_types":["executable code in Python, C++, Java, JavaScript, etc.","code with reasoning traces","step-by-step algorithm explanations"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepseek-ai--deepseek-r1__cap_4","uri":"capability://planning.reasoning.mathematical.problem.solving.with.step.by.step.verification","name":"mathematical problem solving with step-by-step verification","description":"DeepSeek-R1 specializes in mathematical reasoning through explicit step-by-step problem decomposition, generating intermediate calculations and logical steps that can be verified independently. The model learns to recognize when it makes errors during reasoning and can backtrack or reconsider approaches, improving correctness on multi-step math problems.","intents":["I need to solve complex math problems with verifiable intermediate steps","I want to use an AI model for homework help that shows all work","I need to verify mathematical correctness by checking intermediate results"],"best_for":["students and educators using AI for math tutoring","researchers benchmarking mathematical reasoning capabilities","teams building math-focused applications (tutoring, problem-solving)"],"limitations":["Performance degrades on problems requiring specialized mathematical knowledge (advanced topology, abstract algebra) outside training data","Symbolic math (exact algebraic manipulation) is not guaranteed; numerical approximations may be used instead","Reasoning traces can be verbose and hard to follow for very complex problems"],"requires":["Context window of 4K+ tokens for multi-step problems","Ability to parse and verify intermediate mathematical steps","Understanding that AI-generated proofs are not formal mathematical proofs"],"input_types":["mathematical problem statements","equations to solve","geometry problems","probability and statistics questions"],"output_types":["step-by-step solutions","intermediate calculations","final numerical or symbolic answers","reasoning traces showing problem-solving approach"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepseek-ai--deepseek-r1__cap_5","uri":"capability://automation.workflow.open.source.model.deployment.with.multiple.inference.backends","name":"open-source model deployment with multiple inference backends","description":"DeepSeek-R1 is released as open-source weights in safetensors format, compatible with multiple inference frameworks including vLLM, text-generation-inference, and Ollama. This enables local deployment without API dependencies, with support for quantization (FP8, INT8) to reduce memory requirements on consumer hardware.","intents":["I want to run a state-of-the-art reasoning model locally without cloud API costs","I need to deploy the model in a private environment for data security","I want to fine-tune or customize the model for specific use cases"],"best_for":["organizations with data privacy requirements","developers building on-device AI applications","researchers fine-tuning models for specialized domains"],"limitations":["Local deployment requires significant GPU resources (24GB+ VRAM for full precision); quantization reduces quality slightly","No official fine-tuning recipes or LoRA adapters provided; custom fine-tuning requires significant ML expertise","Community support is smaller than closed-source models; fewer pre-built integrations and tools"],"requires":["GPU with 24GB+ VRAM for FP16 inference (or 12GB+ with FP8 quantization)","Inference framework (vLLM, text-generation-inference, Ollama, or similar)","Python 3.9+ and PyTorch/transformers libraries","Familiarity with model deployment and optimization"],"input_types":["model weights in safetensors format","prompts for inference","training data for fine-tuning"],"output_types":["text generation","quantized model variants","fine-tuned model checkpoints"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepseek-ai--deepseek-r1__cap_6","uri":"capability://text.generation.language.instruction.following.with.nuanced.task.understanding","name":"instruction-following with nuanced task understanding","description":"DeepSeek-R1 is trained to follow complex, multi-part instructions with high fidelity, understanding implicit requirements and edge cases from natural language specifications. The model can parse instructions with conditional logic, prioritization, and format requirements, then generate outputs that satisfy all specified constraints.","intents":["I need to give the model detailed instructions with multiple constraints and have it follow all of them","I want to specify output format, tone, and content requirements in a single prompt","I need the model to understand implicit requirements (e.g., 'professional tone' without explicit definition)"],"best_for":["teams building prompt-based automation workflows","content creators using AI for templated generation","developers building chatbots with complex instruction sets"],"limitations":["Instruction following degrades with very long or ambiguous instructions; clarity matters more than length","Model may over-interpret implicit requirements, leading to unexpected outputs","No guarantee of 100% constraint satisfaction; validation is required for critical applications"],"requires":["Clear, well-structured prompts with explicit constraints","Understanding of model limitations in interpreting ambiguous instructions","Post-generation validation for critical use cases"],"input_types":["natural language instructions","multi-part prompts with constraints","format specifications","conditional logic"],"output_types":["text conforming to specified format","structured data matching requirements","content with specified tone and style"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepseek-ai--deepseek-r1__cap_7","uri":"capability://memory.knowledge.knowledge.grounded.text.generation.with.reasoning.transparency","name":"knowledge-grounded text generation with reasoning transparency","description":"DeepSeek-R1 can generate text grounded in provided context or knowledge, explicitly reasoning about relevance and accuracy before generating answers. The model shows its reasoning process when deciding whether to use provided context or rely on training knowledge, enabling detection of hallucinations or unsupported claims.","intents":["I want to generate answers based on specific documents while showing reasoning about relevance","I need to detect when the model is hallucinating vs using provided context","I want to build RAG systems where the model explains its source selection"],"best_for":["teams building retrieval-augmented generation (RAG) systems","organizations requiring explainable AI with source attribution","developers building fact-checking or verification systems"],"limitations":["Reasoning about context relevance adds latency; not suitable for real-time applications","Model may still hallucinate despite access to context; reasoning traces don't guarantee accuracy","Context window limits the amount of knowledge that can be provided per query"],"requires":["Context documents or knowledge base to ground generation","Inference infrastructure supporting long context (4K+ tokens)","Validation mechanism to verify claims against source documents"],"input_types":["user queries","context documents","knowledge base passages","reference materials"],"output_types":["grounded text generation","reasoning traces showing context usage","source attribution","confidence indicators"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepseek-ai--deepseek-r1__cap_8","uri":"capability://text.generation.language.conversational.interaction.with.multi.turn.context.preservation","name":"conversational interaction with multi-turn context preservation","description":"DeepSeek-R1 maintains coherent multi-turn conversations by preserving context across exchanges, understanding references to previous messages and building on prior reasoning. The model can track conversation state, correct previous statements, and maintain consistent reasoning across turns without explicit state management.","intents":["I want to build a chatbot that remembers previous conversation context","I need the model to correct or refine previous answers based on new information","I want conversational debugging where the model builds on previous reasoning"],"best_for":["developers building conversational AI applications","teams creating customer support chatbots","researchers studying multi-turn reasoning"],"limitations":["Context window limits conversation length; very long conversations (100+ turns) may lose early context","Model may contradict earlier statements if context is lost or truncated","No explicit conversation state management; relies on implicit context in prompt"],"requires":["Conversation history management in application layer","Context window of 4K+ tokens to maintain multi-turn history","Mechanism to handle context overflow (summarization or truncation)"],"input_types":["user messages","conversation history","follow-up questions"],"output_types":["conversational responses","context-aware answers","refined or corrected statements"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepseek-ai--deepseek-r1__cap_9","uri":"capability://planning.reasoning.benchmark.driven.performance.optimization.with.interpretable.evaluation","name":"benchmark-driven performance optimization with interpretable evaluation","description":"DeepSeek-R1 is trained and optimized against public benchmarks (AIME, MATH, HumanEval, etc.) with explicit evaluation results published. The model's performance is measured on standardized tasks, enabling direct comparison with other models and transparent assessment of capabilities and limitations.","intents":["I need to evaluate whether this model is suitable for my specific use case","I want to compare performance against other models on standardized benchmarks","I need to understand model limitations before deployment"],"best_for":["teams evaluating models for production deployment","researchers comparing model capabilities","organizations requiring transparent performance metrics"],"limitations":["Benchmark performance may not correlate with real-world performance on custom tasks","Published benchmarks may not cover domain-specific requirements","Benchmark optimization can lead to overfitting; real-world performance may differ"],"requires":["Understanding of benchmark design and limitations","Domain-specific evaluation beyond published benchmarks","Validation on representative data before production deployment"],"input_types":["benchmark datasets","evaluation prompts"],"output_types":["performance metrics","benchmark scores","comparative analysis"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-deepseek-ai--deepseek-r1__headline","uri":"capability://text.generation.language.text.generation.model.for.conversational.ai","name":"text generation model for conversational ai","description":"DeepSeek-R1 is a powerful text generation model designed for creating conversational AI applications, enabling developers to generate human-like text responses with ease.","intents":["best text generation model","text generation for conversational AI","top models for generating human-like text","AI text generation tools","text generation frameworks for developers"],"best_for":["conversational applications","chatbots","content creation"],"limitations":[],"requires":[],"input_types":["text prompts"],"output_types":["generated text"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Sufficient context window to accommodate reasoning traces (typically 4K-8K tokens for complex problems)","Inference infrastructure supporting long sequence generation","Understanding that reasoning traces are model-generated approximations, not formal proofs","GPU with sufficient VRAM (24GB+ for 128K context at batch size 1)","Inference framework supporting long-sequence inference (vLLM, text-generation-inference, or similar)","Awareness that longer contexts increase token consumption and cost proportionally","Inference framework supporting quantization (vLLM, text-generation-inference)","GPU with sufficient VRAM for quantized model (12GB+ for FP8)","Validation that quantization quality is acceptable for use case","UTF-8 encoding support in inference pipeline"],"failure_modes":["Reasoning tokens increase latency by 2-10x compared to direct-answer models; unsuitable for real-time applications","Reasoning quality degrades on tasks outside training distribution (e.g., domain-specific jargon)","No fine-grained control over reasoning depth—model determines allocation automatically","Context length advantage diminishes if input quality is poor—garbage in, garbage out applies to long contexts","Inference latency still scales with context length (O(n) rather than O(n²)), making 128K contexts slower than 4K","Attention patterns may not capture dependencies across very distant tokens as effectively as shorter contexts","Quantization introduces quality degradation (typically 1-5% on benchmarks); critical applications may require full precision","Optimization techniques may not be available in all inference frameworks","Memory savings come at the cost of slightly reduced reasoning quality","Performance varies by language—English and Chinese are strongest, lower-resource languages may show 10-20% quality degradation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.9053304378530935,"quality":0.32,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:48.039Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":3871385,"model_likes":13317}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=deepseek-ai--deepseek-r1","compare_url":"https://unfragile.ai/compare?artifact=deepseek-ai--deepseek-r1"}},"signature":"ETKrmSA71aZ7KlSeO8iG8Yqj5ETkSl9YauHU/lt1KVn/VD+BweUTYNfoJqiyAUx9NKSnR7Ajdz41kt7BjZ1iCQ==","signedAt":"2026-06-20T21:35:22.033Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepseek-ai--deepseek-r1","artifact":"https://unfragile.ai/deepseek-ai--deepseek-r1","verify":"https://unfragile.ai/api/v1/verify?slug=deepseek-ai--deepseek-r1","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"}}