{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"yi-lightning","slug":"yi-lightning","name":"Yi-Lightning","type":"model","url":"https://www.01.ai","page_url":"https://unfragile.ai/yi-lightning","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"yi-lightning__cap_0","uri":"capability://text.generation.language.mixture.of.experts.inference.with.enterprise.optimization","name":"mixture-of-experts inference with enterprise optimization","description":"Yi-Lightning implements a Mixture-of-Experts (MoE) transformer architecture optimized for enterprise deployment across cloud and edge environments. The MoE design routes input tokens through sparse expert networks rather than dense layers, reducing computational overhead while maintaining reasoning quality. This architecture enables efficient inference on both high-end cloud GPUs and resource-constrained edge devices through selective expert activation patterns.","intents":["Deploy a high-performance LLM that runs efficiently on both cloud infrastructure and edge devices without model retraining","Reduce inference latency and computational cost compared to dense transformer models while maintaining reasoning capability","Build enterprise applications requiring multilingual reasoning across cloud and on-premise deployments"],"best_for":["Enterprise teams deploying LLMs across heterogeneous infrastructure (cloud + edge)","Organizations prioritizing inference efficiency and cost optimization","Builders requiring multilingual reasoning capabilities in production systems"],"limitations":["Specific expert count, routing mechanism, and sparsity patterns not documented — unable to assess computational overhead vs dense alternatives","No published inference latency benchmarks or throughput metrics for cloud vs edge deployment scenarios","MoE load balancing characteristics during high-concurrency inference unknown"],"requires":["Access to Yi-Lightning model weights (open-source availability confirmed but distribution method unclear)","Inference framework supporting MoE routing (VLLM, TensorRT, or custom implementation)","Hardware specifications for edge deployment not disclosed — prerequisites unknown"],"input_types":["text (natural language prompts)","structured prompts with reasoning chains"],"output_types":["text (natural language responses)","reasoning traces and intermediate steps"],"categories":["text-generation-language","model-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"yi-lightning__cap_1","uri":"capability://text.generation.language.multilingual.reasoning.and.generation","name":"multilingual reasoning and generation","description":"Yi-Lightning provides multilingual natural language understanding and generation capabilities, trained on diverse language data to support reasoning tasks across multiple languages. The model processes text input in various languages and generates coherent, contextually appropriate responses while maintaining reasoning quality across language boundaries. Integration with the WorldWise Enterprise LLM Platform enables language-aware routing and multi-agent coordination across linguistic contexts.","intents":["Build multilingual AI agents that reason and respond naturally in Chinese, English, and other supported languages","Deploy enterprise applications serving global users without separate language-specific models","Create cross-lingual reasoning chains where intermediate steps and final outputs maintain semantic consistency"],"best_for":["Global enterprises requiring multilingual AI capabilities without model fragmentation","Teams building international customer support or content generation systems","Developers creating multi-agent systems with cross-lingual coordination requirements"],"limitations":["Specific supported languages not enumerated — only Chinese and English confirmed from website content","No language-specific performance metrics or accuracy degradation data for non-English languages","Unknown whether multilingual training used balanced datasets or exhibits language-specific bias patterns"],"requires":["Text input in supported languages (minimum: Chinese, English)","No language detection or preprocessing mentioned — input language specification method unknown","Integration with WorldWise platform for enterprise deployment (optional for API access)"],"input_types":["text in multiple languages","mixed-language prompts"],"output_types":["text in requested language","multilingual reasoning traces"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"yi-lightning__cap_2","uri":"capability://planning.reasoning.benchmark.validated.reasoning.performance","name":"benchmark-validated reasoning performance","description":"Yi-Lightning claims top-tier performance on major LLM evaluation benchmarks, indicating strong capabilities in logical reasoning, mathematical problem-solving, and complex task decomposition. The model architecture and training methodology are optimized to achieve high scores on standardized evaluation suites, though specific benchmark names, datasets, and comparative scores are not disclosed in available documentation. Performance validation occurs through third-party benchmark evaluation frameworks.","intents":["Select an LLM foundation model with proven reasoning capability for enterprise applications requiring complex problem-solving","Evaluate whether Yi-Lightning meets performance requirements for specific reasoning-heavy use cases before deployment","Compare Yi-Lightning reasoning quality against competing foundation models using standardized benchmarks"],"best_for":["Enterprise procurement teams evaluating foundation models for reasoning-critical applications","Researchers benchmarking LLM performance across standardized evaluation suites","Teams building AI agents requiring strong chain-of-thought and task decomposition capabilities"],"limitations":["No specific benchmark names, scores, or datasets provided — claims of 'top scores on major benchmarks' unsubstantiated","Unknown which benchmark suites were used (MMLU, HumanEval, GSM8K, etc.) or how Yi-Lightning ranks against GPT-4, Claude, or Llama","No breakdown of reasoning performance by task category (math, logic, code, knowledge) — aggregate claims only","Benchmark evaluation date unknown — performance claims may not reflect current state-of-art"],"requires":["Access to benchmark evaluation framework (OpenAI Evals, HELM, or custom harness)","Standardized test datasets (MMLU, HumanEval, GSM8K, etc.)","Computational resources for running full benchmark suite"],"input_types":["benchmark test prompts (multiple choice, code generation, math problems)","reasoning chain prompts"],"output_types":["structured answers matching benchmark format","reasoning traces and intermediate steps"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"yi-lightning__cap_3","uri":"capability://automation.workflow.cloud.and.edge.deployment.flexibility","name":"cloud and edge deployment flexibility","description":"Yi-Lightning is architected for deployment across both cloud infrastructure and edge devices through an efficient model design that reduces memory footprint and computational requirements. The MoE architecture enables selective computation, allowing the same model weights to run on high-capacity cloud GPUs or resource-constrained edge hardware (mobile, IoT, on-premise servers) with appropriate quantization and optimization. Integration with the WorldWise Enterprise LLM Platform provides orchestration and management across heterogeneous deployment targets.","intents":["Deploy a single LLM model across cloud and edge infrastructure without maintaining separate model variants","Build latency-sensitive applications where inference can execute locally on edge devices to avoid cloud round-trips","Create hybrid architectures where complex reasoning runs on cloud while simple inference executes on edge devices"],"best_for":["Enterprise teams with hybrid cloud-edge infrastructure requiring unified LLM deployment","IoT and mobile application developers needing on-device inference without cloud dependency","Organizations prioritizing data privacy through edge inference while maintaining cloud orchestration"],"limitations":["Specific hardware requirements for edge deployment not documented — GPU VRAM, CPU specifications, memory footprint all unknown","No quantization formats specified (GGUF, int8, int4, etc.) or performance impact of quantization on reasoning quality","Edge deployment latency and throughput benchmarks not provided — unable to assess suitability for real-time applications","Unknown whether edge deployment requires model pruning, distillation, or other modifications beyond quantization"],"requires":["Cloud infrastructure (AWS, Azure, GCP, or on-premise) for cloud deployment","Edge hardware specifications unknown — prerequisites cannot be determined","WorldWise Enterprise LLM Platform 2.5 or compatible inference framework","Model weights in quantized format (format not specified)"],"input_types":["text prompts","structured inference requests"],"output_types":["text responses","inference metadata (latency, token count)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"yi-lightning__cap_4","uri":"capability://planning.reasoning.enterprise.multi.agent.coordination","name":"enterprise multi-agent coordination","description":"Yi-Lightning integrates with the WorldWise Enterprise LLM Platform to enable multi-agent systems where multiple AI agents coordinate reasoning and task execution across complex workflows. The platform provides agent orchestration, state management, and inter-agent communication patterns that allow Yi-Lightning instances to collaborate on decomposed tasks. This capability supports enterprise automation scenarios where single-agent reasoning is insufficient and task parallelization or specialized agent roles are required.","intents":["Build enterprise AI agent systems where multiple specialized agents coordinate to solve complex problems","Implement task decomposition workflows where Yi-Lightning agents break down problems and delegate subtasks","Create multi-agent systems with role-based specialization (e.g., research agent, analysis agent, decision agent)"],"best_for":["Enterprise teams building complex automation workflows requiring multi-agent coordination","Organizations implementing 'Super Employee' style AI systems with multiple specialized agents","Teams requiring agent state management, inter-agent communication, and workflow orchestration"],"limitations":["Multi-agent coordination patterns and protocols not documented — implementation details unknown","No specification of agent communication format, state synchronization mechanism, or failure handling","Unknown whether agents share context or maintain isolated reasoning states","Latency impact of agent coordination and inter-agent communication not quantified","Requires WorldWise platform — cannot use Yi-Lightning directly for multi-agent scenarios without platform dependency"],"requires":["WorldWise Enterprise LLM Platform 2.5 or later","Enterprise license (pricing and terms not disclosed)","Integration with enterprise workflow systems or custom agent orchestration code","State management backend for agent coordination (technology not specified)"],"input_types":["complex task descriptions","structured agent coordination requests","agent role specifications"],"output_types":["coordinated agent responses","task decomposition results","multi-agent reasoning traces"],"categories":["planning-reasoning","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"yi-lightning__cap_5","uri":"capability://code.generation.editing.open.source.model.weights.and.community.deployment","name":"open-source model weights and community deployment","description":"Yi-Lightning is released as open-source, making model weights publicly available for download and local deployment without API dependencies. This enables developers to run the model on their own infrastructure, fine-tune for specific domains, and integrate into custom applications without vendor lock-in. Open-source availability supports community contributions, research use, and deployment scenarios where cloud APIs are infeasible (air-gapped networks, regulatory restrictions, cost optimization).","intents":["Download and deploy Yi-Lightning locally without relying on cloud APIs or 01.AI infrastructure","Fine-tune Yi-Lightning on proprietary datasets for domain-specific applications","Integrate Yi-Lightning into air-gapped or regulated environments where cloud API access is prohibited","Modify and extend Yi-Lightning architecture for research or specialized use cases"],"best_for":["Researchers and academics requiring model access for non-commercial research","Enterprise teams with regulatory requirements prohibiting cloud LLM usage","Developers building cost-optimized systems where inference volume justifies self-hosting","Organizations requiring model customization or fine-tuning on proprietary data"],"limitations":["Open-source license terms not accessible from provided material — commercial use restrictions unknown","Model weight distribution method not specified (HuggingFace, GitHub, direct download, etc.)","No guidance on fine-tuning methodology, training data requirements, or computational resources needed","Community support and documentation quality unknown — no evidence of active community or issue tracking","Quantization and optimization formats for local deployment not documented"],"requires":["GPU hardware with sufficient VRAM (requirements not specified — unknown if 24GB, 40GB, or 80GB+ needed)","Inference framework (VLLM, Ollama, LM Studio, or custom implementation)","Model weights download (size and format unknown)","Python 3.8+ or compatible runtime"],"input_types":["text prompts","fine-tuning datasets (format unknown)"],"output_types":["text responses","fine-tuned model weights"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"yi-lightning__cap_6","uri":"capability://tool.use.integration.commercial.licensing.and.enterprise.support","name":"commercial licensing and enterprise support","description":"Yi-Lightning offers commercial licensing options through 01.AI, enabling proprietary use, enterprise support, and custom deployment arrangements. A 'Commercial License' link is referenced on the company website, though specific license terms, pricing, support SLAs, and commercial use restrictions are not publicly documented. Commercial deployment likely includes access to WorldWise platform and enterprise infrastructure.","intents":["License Yi-Lightning for proprietary commercial applications","Obtain enterprise support, SLAs, and dedicated infrastructure","Negotiate custom deployment terms for large-scale enterprise use","Ensure legal compliance for commercial AI product deployment"],"best_for":["Enterprises deploying Yi-Lightning in production commercial applications","Companies requiring dedicated support and SLA guarantees","Organizations with specific compliance or data residency requirements"],"limitations":["Commercial license terms not published — specific restrictions and pricing unknown","Support SLA, response times, and service level guarantees not documented","Custom deployment options and negotiation process not specified","Compliance certifications (SOC 2, HIPAA, GDPR, etc.) not mentioned","Data privacy and retention policies for commercial deployments unknown","No published pricing or licensing model comparison"],"requires":["Direct contact with 01.AI sales team","Commercial license agreement (terms unknown)","Likely minimum commitment or volume requirements (unknown)"],"input_types":["commercial use case descriptions","deployment requirements and scale"],"output_types":["commercial license agreement","enterprise support contract","deployment infrastructure access"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"yi-lightning__headline","uri":"capability://model.training.high.performance.multilingual.large.language.model","name":"high-performance multilingual large language model","description":"Yi-Lightning is a high-performance multilingual large language model designed for both cloud and edge deployment, excelling in reasoning tasks and achieving top scores on major benchmarks.","intents":["best multilingual LLM","high-performance language model for reasoning tasks","top scoring LLM for cloud and edge","efficient multilingual model for deployment","best LLM for strong reasoning capabilities"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Access to Yi-Lightning model weights (open-source availability confirmed but distribution method unclear)","Inference framework supporting MoE routing (VLLM, TensorRT, or custom implementation)","Hardware specifications for edge deployment not disclosed — prerequisites unknown","Text input in supported languages (minimum: Chinese, English)","No language detection or preprocessing mentioned — input language specification method unknown","Integration with WorldWise platform for enterprise deployment (optional for API access)","Access to benchmark evaluation framework (OpenAI Evals, HELM, or custom harness)","Standardized test datasets (MMLU, HumanEval, GSM8K, 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Yi-Lightning ranks against GPT-4, Claude, or Llama","No breakdown of reasoning performance by task category (math, logic, code, knowledge) — aggregate claims only","Benchmark evaluation date unknown — performance claims may not reflect current state-of-art","builder identity is not verified yet","no observed match outcomes 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