{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-yi","slug":"yi","name":"Yi (6B, 9B, 34B)","type":"model","url":"https://ollama.com/library/yi","page_url":"https://unfragile.ai/yi","categories":["text-writing","testing-quality"],"tags":["ollama","open-source","01.ai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-yi__cap_0","uri":"capability://text.generation.language.multilingual.text.generation.with.english.chinese.bilingual.support","name":"multilingual text generation with english-chinese bilingual support","description":"Generates coherent, contextually relevant text in English and Chinese using a transformer-based architecture trained on 3 trillion tokens of high-quality bilingual corpus. The model processes input text through attention mechanisms and produces token-by-token output via standard language modeling, with support for both single-turn and multi-turn conversation patterns through message-based API interfaces.","intents":["Generate English or Chinese text responses for chatbot applications","Create multilingual content for applications serving English and Chinese-speaking users","Build bilingual conversational agents without separate model management","Process mixed-language prompts in applications with diverse user bases"],"best_for":["Developers building chatbots for English and Chinese markets","Teams deploying multilingual applications with resource constraints","Organizations requiring open-source alternatives to proprietary multilingual models"],"limitations":["4K token context window limits document processing to ~3,000 words per request","Bilingual only — no support for languages beyond English and Chinese","No documented performance metrics or benchmarks against competing multilingual models","Inference speed and throughput not publicly specified"],"requires":["Ollama runtime (any recent version supporting GGUF format)","3.5GB disk space minimum (6B variant) to 19GB (34B variant)","4-8GB VRAM for 6B variant, 6-12GB for 9B, 20-40GB for 34B (estimated)"],"input_types":["text"],"output_types":["text"],"categories":["text-generation-language","multilingual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-yi__cap_1","uri":"capability://tool.use.integration.local.inference.via.rest.api.with.message.based.chat.protocol","name":"local inference via rest api with message-based chat protocol","description":"Exposes a REST API endpoint (http://localhost:11434/api/chat) accepting JSON payloads with message arrays in OpenAI-compatible format, enabling stateless HTTP-based inference without SDK dependencies. Requests are processed through Ollama's inference engine which manages model loading, tokenization, and streaming response delivery back to clients.","intents":["Integrate Yi model into web applications without language-specific SDKs","Build polyglot applications that call the model from any HTTP-capable language","Stream responses to frontend applications in real-time","Run inference on local hardware without cloud API dependencies"],"best_for":["Web developers building JavaScript/TypeScript frontends with backend inference","Teams using heterogeneous tech stacks requiring language-agnostic API access","Organizations with strict data residency requirements"],"limitations":["Requires Ollama daemon running locally — adds operational complexity vs cloud APIs","No built-in authentication or rate limiting — requires external proxy for production","Streaming responses require client-side handling of chunked transfer encoding","Single-machine deployment limits horizontal scaling without additional orchestration"],"requires":["Ollama runtime installed and running on target machine","HTTP client library (curl, fetch, requests, etc.)","Network access to localhost:11434 (or configured Ollama bind address)"],"input_types":["JSON with message array"],"output_types":["JSON with streamed or complete text response"],"categories":["tool-use-integration","api-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-yi__cap_2","uri":"capability://text.generation.language.cli.based.interactive.chat.with.automatic.model.management","name":"cli-based interactive chat with automatic model management","description":"Provides `ollama run yi` command-line interface that automatically downloads, caches, and loads the specified model variant, then enters an interactive REPL-style chat loop where user input is tokenized, processed through the model, and streamed to stdout. Model lifecycle (loading, unloading, memory management) is handled transparently by Ollama.","intents":["Quickly test model capabilities without writing code","Prototype chatbot interactions locally for development and debugging","Run one-off inference tasks from shell scripts or automation","Evaluate model quality before integrating into applications"],"best_for":["Individual developers and researchers prototyping locally","DevOps engineers testing model behavior in CI/CD pipelines","Non-technical users wanting to interact with the model without coding"],"limitations":["No programmatic control over inference parameters (temperature, top-p, etc.) — hardcoded defaults only","Single-user interactive mode — not suitable for concurrent requests","No conversation history persistence — each session starts fresh","Limited to text input/output — no structured data extraction or tool calling"],"requires":["Ollama CLI installed and in system PATH","Terminal/shell environment","First run triggers automatic download of model (3.5GB-19GB depending on variant)"],"input_types":["text from stdin"],"output_types":["text to stdout"],"categories":["text-generation-language","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-yi__cap_3","uri":"capability://text.generation.language.multi.variant.model.selection.with.size.performance.tradeoff","name":"multi-variant model selection with size-performance tradeoff","description":"Offers three pre-quantized model variants (6B, 9B, 34B parameters) distributed as separate GGUF artifacts, allowing users to select based on available hardware and latency requirements. Larger variants provide better quality/reasoning at cost of increased VRAM and inference latency; smaller variants enable deployment on resource-constrained devices. Selection is made via model tag (e.g., `ollama run yi:6b`).","intents":["Deploy on edge devices or laptops with limited VRAM using 6B variant","Balance quality and speed for production services using 9B variant","Maximize reasoning capability for complex tasks using 34B variant","Evaluate quality differences across model sizes before production deployment"],"best_for":["Teams with heterogeneous hardware (laptops, servers, edge devices)","Developers optimizing for specific latency/quality SLAs","Organizations with cost constraints requiring efficient model selection"],"limitations":["No intermediate sizes between 6B-9B or 9B-34B — limited granularity for tuning","Quality/capability differences between variants not documented — requires empirical testing","All variants share 4K context window — no size-based context scaling","Quantization level (Q4/Q5/Q8) not specified — actual VRAM usage may vary from estimates"],"requires":["Ollama runtime supporting GGUF format","Sufficient disk space for selected variant (3.5GB, 5GB, or 19GB)","VRAM matching variant requirements (4-8GB, 6-12GB, or 20-40GB estimated)"],"input_types":["text"],"output_types":["text"],"categories":["text-generation-language","model-selection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-yi__cap_4","uri":"capability://tool.use.integration.sdk.based.programmatic.inference.with.python.and.javascript","name":"sdk-based programmatic inference with python and javascript","description":"Provides official Python and JavaScript client libraries (`ollama` package) that wrap the REST API with language-native abstractions, handling JSON serialization, streaming response parsing, and error handling. Developers call `ollama.chat()` with message arrays, receiving structured responses without manual HTTP handling.","intents":["Build Python applications with native async/await support for model inference","Integrate Yi into Node.js/TypeScript backends with type safety","Simplify streaming response handling in application code","Reduce boilerplate for common inference patterns (single-turn, multi-turn chat)"],"best_for":["Python developers building data science or backend applications","TypeScript/Node.js teams building full-stack applications","Teams prioritizing developer experience and reduced integration code"],"limitations":["Limited to Python and JavaScript — no official Go, Rust, or Java SDKs","SDKs are thin wrappers over REST API — no performance optimization vs direct HTTP","No built-in retry logic, circuit breakers, or production-grade resilience patterns","Streaming responses require manual iteration — no high-level async generators in all versions"],"requires":["Python 3.7+ (for Python SDK) or Node.js 14+ (for JavaScript SDK)","ollama package installed via pip or npm","Ollama runtime running and accessible at configured endpoint (default localhost:11434)"],"input_types":["Python dict/list or JavaScript object with message structure"],"output_types":["Python dict or JavaScript object with response text"],"categories":["tool-use-integration","sdk"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-yi__cap_5","uri":"capability://tool.use.integration.cloud.deployment.via.ollama.pro.max.with.concurrent.model.limits","name":"cloud deployment via ollama pro/max with concurrent model limits","description":"Models are available through Ollama's cloud service (Ollama Pro/Max tiers) which provisions GPU infrastructure, manages model serving, and enforces concurrent model limits (1 for free, 3 for Pro, 10 for Max). Inference is billed on GPU compute time rather than tokens, with the same REST API and SDK interfaces as local deployment.","intents":["Deploy Yi without managing GPU hardware or Ollama infrastructure","Scale inference across multiple concurrent requests with cloud elasticity","Evaluate cloud deployment costs vs local hardware investment","Access Yi from applications without local model storage"],"best_for":["Teams without GPU infrastructure or DevOps expertise","Applications with variable load requiring elastic scaling","Organizations preferring managed services over self-hosted infrastructure"],"limitations":["Pricing model (GPU time) not publicly specified — cost comparison vs local deployment unclear","Concurrent model limits may constrain high-throughput applications (10 models max on Max tier)","Cloud deployment adds network latency vs local inference","Vendor lock-in to Ollama cloud platform — no multi-cloud option"],"requires":["Ollama Pro or Max subscription","API credentials for Ollama cloud service","Network connectivity to Ollama cloud endpoints"],"input_types":["text via REST API or SDK"],"output_types":["text"],"categories":["tool-use-integration","cloud-deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-yi__cap_6","uri":"capability://text.generation.language.4k.context.window.text.processing.with.token.level.awareness","name":"4k context window text processing with token-level awareness","description":"Processes input text through tokenization (converting text to token IDs), then generates output within a hard 4,096 token context window that includes both input and output tokens. The model maintains positional embeddings and attention mechanisms across this window, enabling coherent multi-turn conversations up to the token limit.","intents":["Build multi-turn chatbots that maintain conversation history within 4K tokens","Process documents or articles up to ~3,000 words for summarization or Q&A","Implement retrieval-augmented generation (RAG) with context injection up to token limits","Handle conversation state management with explicit token counting"],"best_for":["Developers building conversational applications with moderate context needs","Teams implementing RAG systems with careful context budgeting","Applications where conversation length is naturally bounded (customer support, tutoring)"],"limitations":["4K token hard limit — cannot process documents longer than ~3,000 words without truncation","No sliding window or context compression — entire history must fit in window","Token counting required for reliable context management — no automatic overflow handling","Long conversations require explicit pruning or summarization to stay within limits"],"requires":["Tokenizer compatible with Yi model (provided by Ollama)","Application-level token counting logic for context budgeting","Understanding of token-to-word ratio (~1 token per 0.75 words for English)"],"input_types":["text"],"output_types":["text"],"categories":["text-generation-language","context-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-yi__cap_7","uri":"capability://automation.workflow.automatic.model.caching.and.lazy.loading.with.disk.based.storage","name":"automatic model caching and lazy loading with disk-based storage","description":"Ollama automatically downloads and caches model artifacts (GGUF files) on first use, storing them in a local directory (~/.ollama/models by default). Subsequent invocations load from cache without re-downloading. Model loading into VRAM is deferred until first inference request, enabling multiple models to coexist on disk with only active models consuming VRAM.","intents":["Manage multiple model variants without manual download orchestration","Reduce bandwidth usage by caching models across multiple runs","Optimize VRAM usage by loading models on-demand","Support model switching without restart or manual file management"],"best_for":["Developers prototyping with multiple models","Systems with limited VRAM but sufficient disk storage","Teams deploying models across multiple machines with shared storage"],"limitations":["Cache directory location is not easily configurable — hardcoded to ~/.ollama/models","No cache invalidation or version management — updates require manual deletion","Disk I/O latency on first load — model loading time not optimized for fast startup","No distributed cache support — each machine maintains separate copy"],"requires":["Disk space for model artifacts (3.5GB-19GB per variant)","Write permissions to ~/.ollama/models directory","Network access for initial download (can be cached after first run)"],"input_types":["model identifier (e.g., 'yi:6b')"],"output_types":["loaded model in VRAM"],"categories":["automation-workflow","resource-management"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Ollama runtime (any recent version supporting GGUF format)","3.5GB disk space minimum (6B variant) to 19GB (34B variant)","4-8GB VRAM for 6B variant, 6-12GB for 9B, 20-40GB for 34B (estimated)","Ollama runtime installed and running on target machine","HTTP client library (curl, fetch, requests, etc.)","Network access to localhost:11434 (or configured Ollama bind address)","Ollama CLI installed and in system PATH","Terminal/shell environment","First run triggers automatic download of model (3.5GB-19GB depending on variant)","Ollama runtime supporting GGUF format"],"failure_modes":["4K token context window limits document processing to ~3,000 words per request","Bilingual only — no support for languages beyond English and Chinese","No documented performance metrics or benchmarks against competing multilingual models","Inference speed and throughput not publicly specified","Requires Ollama daemon running locally — adds operational complexity vs cloud APIs","No built-in authentication or rate limiting — requires external proxy for production","Streaming responses require client-side handling of chunked transfer encoding","Single-machine deployment limits horizontal scaling without additional orchestration","No programmatic control over inference parameters (temperature, top-p, etc.) — hardcoded defaults only","Single-user interactive mode — not suitable for concurrent requests","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.49000000000000005,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.483Z","last_scraped_at":"2026-05-03T15:20:48.403Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=yi","compare_url":"https://unfragile.ai/compare?artifact=yi"}},"signature":"O+NTahdveQ3PfRzKQVo1shzxMSn1EvSNK/Zbie6IzM9es4f+BI3c/RRyaB9S+deOziibgu29auQk3brgArBNAQ==","signedAt":"2026-06-21T00:11:51.909Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/yi","artifact":"https://unfragile.ai/yi","verify":"https://unfragile.ai/api/v1/verify?slug=yi","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"}}