{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-dolphin-mixtral","slug":"dolphin-mixtral","name":"Dolphin Mixtral (8x7B)","type":"model","url":"https://ollama.com/library/dolphin-mixtral","page_url":"https://unfragile.ai/dolphin-mixtral","categories":["text-writing"],"tags":["ollama","open-source","dolphin"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-dolphin-mixtral__cap_0","uri":"capability://text.generation.language.instruction.following.text.generation.with.mixture.of.experts.routing","name":"instruction-following text generation with mixture-of-experts routing","description":"Generates coherent text responses to natural language instructions using a Mixture of Experts (MoE) architecture where 8 expert sub-models (each 7B parameters) are dynamically routed based on input tokens, with Dolphin fine-tuning applied to enhance instruction adherence across diverse tasks. The routing mechanism learns to activate only relevant experts per token, reducing computational overhead compared to dense models while maintaining 32K-token context windows for extended conversations.","intents":["I need a local model that follows complex multi-step instructions without sending data to external APIs","I want to run a capable instruction-following model on consumer hardware without cloud dependencies","I need to fine-tune instruction-following behavior for domain-specific tasks while maintaining general capabilities"],"best_for":["solo developers building private LLM agents and chatbots","teams requiring on-premise inference for compliance or data sensitivity","researchers experimenting with mixture-of-experts architectures"],"limitations":["32K token context window is fixed and cannot be extended; documents longer than 32K tokens must be chunked or summarized before input","No benchmark scores published for instruction-following accuracy; claimed improvements over base Mixtral are not quantified","Inference speed not documented; MoE routing adds computational overhead compared to dense models of equivalent parameter count","Single-turn and multi-turn conversation quality depends entirely on Dolphin fine-tuning dataset composition, which is not fully disclosed"],"requires":["Ollama runtime (macOS, Windows, Linux, or Docker)","26GB disk space for 8x7b variant or 80GB for 8x22b variant","GPU with sufficient VRAM (exact requirements not documented; estimate 16GB+ for 8x7b, 48GB+ for 8x22b based on parameter count)","Python 3.8+ or Node.js 14+ for SDK usage (optional; CLI works without SDKs)"],"input_types":["text (natural language instructions, questions, prompts)","code snippets (for code-related instructions)"],"output_types":["text (streaming or buffered)","structured text (JSON, markdown, code blocks when instructed)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-dolphin-mixtral__cap_1","uri":"capability://code.generation.editing.code.generation.and.completion.with.coding.specific.fine.tuning","name":"code generation and completion with coding-specific fine-tuning","description":"Generates and completes code across multiple programming languages by leveraging Dolphin-Coder and MagiCoder datasets in its fine-tuning pipeline, enabling the model to understand code structure, syntax, and common patterns. The MoE architecture allows selective activation of experts optimized for code reasoning, reducing latency for code-heavy workloads compared to processing all parameters.","intents":["I need to generate boilerplate code or complete partial code snippets locally without cloud API calls","I want a code-generation model that can be integrated into my IDE or editor via Ollama's REST API","I need to understand and refactor existing code by asking the model to explain or improve it"],"best_for":["individual developers building local coding assistants (IDE plugins, terminal tools)","teams with proprietary code that cannot be sent to cloud APIs","educators teaching programming with a local, uncensored code-generation tool"],"limitations":["No specific coding benchmarks (e.g., HumanEval, MBPP scores) published; coding capability claims are not quantified","Code generation quality depends on prompt engineering; no built-in code validation or syntax checking","32K token context limits multi-file refactoring tasks; large codebases must be split across multiple requests","No language-specific optimizations documented; performance may vary significantly across Python, JavaScript, Rust, etc."],"requires":["Ollama runtime with dolphin-mixtral model loaded","26GB+ disk space and 16GB+ VRAM for 8x7b variant","Text editor or IDE with HTTP client capability (or custom integration layer)"],"input_types":["code snippets (partial or complete)","natural language code requests (e.g., 'write a function that validates email addresses')","code with comments asking for completion or refactoring"],"output_types":["code (in requested language)","code with explanatory comments","refactored code with reasoning"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-dolphin-mixtral__cap_10","uri":"capability://automation.workflow.model.variant.selection.with.performance.capability.trade.offs","name":"model variant selection with performance-capability trade-offs","description":"Offers two distinct model variants (8x7b with 32K context and 26GB size, 8x22b with 64K context and 80GB size) enabling users to select based on hardware constraints and performance requirements. The 8x22b variant provides 3x more parameters and 2x longer context but requires 3x more disk space and VRAM, creating explicit trade-offs between capability and resource consumption.","intents":["I need to choose between model size and capability based on my available hardware","I want to start with the smaller 8x7b model and upgrade to 8x22b if needed","I need to understand the performance and capability differences between variants"],"best_for":["developers with limited hardware (laptops, edge devices) who need the smaller 8x7b variant","teams with powerful servers who can leverage the larger 8x22b variant for better quality","organizations evaluating model size vs. quality trade-offs"],"limitations":["No published benchmarks comparing 8x7b and 8x22b performance; quality differences are not quantified","No automatic model selection logic; users must manually choose variant based on hardware estimates","Switching between variants requires re-downloading the model (26GB or 80GB transfer); no incremental updates","No guidance on which variant is suitable for specific tasks (coding, writing, reasoning); users must experiment","VRAM requirements not documented for either variant; users must estimate based on model size"],"requires":["Ollama runtime","26GB disk space for 8x7b or 80GB for 8x22b","Sufficient VRAM (estimate 16GB+ for 8x7b, 48GB+ for 8x22b; exact requirements unknown)"],"input_types":["model selection via `ollama run dolphin-mixtral:8x7b` or `ollama run dolphin-mixtral:8x22b`"],"output_types":["same as other capabilities (text generation, code, chat)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-dolphin-mixtral__cap_2","uri":"capability://text.generation.language.multi.turn.conversational.chat.with.stateless.message.api","name":"multi-turn conversational chat with stateless message api","description":"Maintains conversational context across multiple turns by accepting a message history array (with role and content fields) via Ollama's REST `/api/chat` endpoint, processing the entire conversation history to generate contextually-aware responses. The model does not maintain server-side session state; conversation history must be managed by the client application, enabling stateless deployment and horizontal scaling.","intents":["I want to build a chatbot that remembers previous messages in a conversation without managing external session storage","I need to integrate a local LLM into a web app or mobile app that sends chat history with each request","I want to experiment with conversation context length limits (up to 32K tokens) to optimize cost and latency"],"best_for":["developers building stateless chat applications (web, mobile, CLI)","teams prototyping conversational AI without managing complex session databases","applications requiring full conversation history control on the client side"],"limitations":["Stateless design requires client to manage and send full conversation history with each request; no server-side session persistence","32K token context window includes entire conversation history; long conversations will eventually exceed context and require truncation or summarization","No built-in conversation memory or retrieval; if a user returns after closing the app, previous conversations are lost unless explicitly saved","No rate limiting or conversation-level access control; all security must be implemented at the application layer"],"requires":["Ollama runtime with HTTP API enabled (default port 11434)","Client application capable of HTTP POST requests","Message history management logic in client code (no SDK-provided session manager)"],"input_types":["JSON array of message objects with 'role' (user/assistant) and 'content' (text) fields"],"output_types":["text (streaming via Server-Sent Events or buffered JSON response)","structured JSON response with model metadata"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-dolphin-mixtral__cap_3","uri":"capability://automation.workflow.local.inference.via.ollama.runtime.with.quantized.model.distribution","name":"local inference via ollama runtime with quantized model distribution","description":"Executes the Dolphin Mixtral model entirely on local hardware by distributing pre-quantized GGUF-format weights via Ollama's model library, eliminating network latency and external API dependencies. Ollama abstracts hardware-specific optimizations (GPU acceleration, memory management, quantization details) behind a unified CLI and REST API, enabling single-command deployment across macOS, Windows, Linux, and Docker.","intents":["I want to run a capable LLM on my laptop or server without cloud API costs or data transmission","I need to deploy an LLM in an air-gapped environment or behind a firewall for compliance reasons","I want to experiment with model inference without managing CUDA, quantization, or low-level optimization details"],"best_for":["individual developers prototyping LLM applications on personal hardware","enterprises with data residency or compliance requirements prohibiting cloud inference","researchers benchmarking local vs. cloud inference trade-offs"],"limitations":["Inference speed not documented; MoE routing and quantization overhead may result in slower token generation than cloud APIs for latency-sensitive applications","VRAM requirements not specified; users must estimate based on model size and quantization (26GB model size suggests 16GB+ VRAM needed, but exact requirements unknown)","Quantization format and bit-depth not disclosed; Ollama abstracts this detail, making it difficult to optimize for specific hardware","No auto-scaling or load balancing; single Ollama instance handles one request at a time (concurrent requests queue)"],"requires":["Ollama runtime (free, open-source) installed on macOS, Windows, Linux, or Docker","26GB disk space for 8x7b model or 80GB for 8x22b model","GPU with sufficient VRAM (estimate 16GB+ for 8x7b, 48GB+ for 8x22b; CPU-only inference possible but very slow)","Network connectivity for initial model download (1.7M downloads available, ~26GB transfer)"],"input_types":["CLI commands (e.g., `ollama run dolphin-mixtral`)","HTTP POST requests to Ollama REST API","Python or JavaScript SDK calls"],"output_types":["text (CLI output or API response)","streaming text (via Server-Sent Events in API mode)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-dolphin-mixtral__cap_4","uri":"capability://text.generation.language.uncensored.instruction.following.without.safety.guardrails","name":"uncensored instruction-following without safety guardrails","description":"Generates responses to instructions without built-in content filtering, safety checks, or alignment constraints that are typical in commercial LLMs. The model is fine-tuned on datasets (Synthia, OpenHermes, PureDove) that emphasize instruction-following over safety, enabling it to respond to requests that commercial models would refuse. No technical definition of 'uncensored' is provided; safety behavior is entirely dependent on fine-tuning dataset composition.","intents":["I need a model that will attempt to answer any instruction without refusing based on content policy","I want to research model behavior on adversarial or sensitive prompts without hitting safety guardrails","I need a model for creative writing, roleplay, or other applications where safety filters are undesirable"],"best_for":["researchers studying LLM safety, alignment, and refusal behavior","developers building applications where safety filtering is explicitly undesirable (creative writing, adversarial testing)","teams with custom safety policies who want to implement their own guardrails"],"limitations":["No definition of 'uncensored' provided; unclear which specific safety behaviors are disabled or reduced","No documentation of failure modes, harmful outputs, or bias characteristics; users must discover limitations through testing","Lack of safety guardrails increases risk of generating harmful, illegal, or unethical content; not suitable for public-facing applications without additional filtering","Fine-tuning datasets (Synthia, OpenHermes, PureDove) composition not fully disclosed; impossible to audit what behaviors were explicitly trained or removed","No alignment or RLHF (Reinforcement Learning from Human Feedback) mentioned; model may exhibit unpredictable behavior on edge cases"],"requires":["Ollama runtime with dolphin-mixtral model loaded","Awareness of legal and ethical implications of deploying an uncensored model","Custom safety layer or content filtering if deploying to end users"],"input_types":["any text instruction (no input filtering)"],"output_types":["text (potentially harmful, illegal, or unethical content without filtering)"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-dolphin-mixtral__cap_5","uri":"capability://text.generation.language.extended.context.processing.with.32k.64k.token.windows","name":"extended context processing with 32k-64k token windows","description":"Processes input sequences up to 32K tokens (8x7b variant) or 64K tokens (8x22b variant) in a single forward pass, enabling analysis of long documents, multi-file code reviews, or extended conversations without chunking. The context window is a hard architectural limit inherited from the base Mixtral model; longer inputs must be truncated or summarized before processing.","intents":["I need to analyze a long document (research paper, legal contract, codebase) in a single request without splitting it","I want to maintain conversation history across many turns without losing early context","I need to perform code review on multiple files simultaneously by concatenating them within the context window"],"best_for":["developers analyzing long documents or multi-file codebases","applications requiring extended conversation history (customer support, tutoring)","researchers processing long-form text (papers, books, transcripts)"],"limitations":["32K token limit (8x7b) or 64K token limit (8x22b) is a hard architectural constraint; inputs exceeding this are truncated without warning","Token counting is approximate; actual token count depends on tokenizer behavior and may vary by 5-10% from estimates","Longer context windows increase inference latency and VRAM usage; 64K token requests may be impractical on consumer hardware","Model quality may degrade at extreme context lengths (e.g., 30K+ tokens); no benchmarks provided for long-context performance","No sliding-window or streaming context management; entire context must fit in VRAM simultaneously"],"requires":["Ollama runtime with sufficient VRAM to hold model weights plus context (estimate 16GB+ for 8x7b with 32K context, 48GB+ for 8x22b with 64K context)","Client-side token counting logic to avoid exceeding context limits","Document preprocessing (chunking, summarization) for inputs longer than context window"],"input_types":["long text documents (up to 32K or 64K tokens)","concatenated code files","conversation history with many turns"],"output_types":["text analysis, summary, or response based on full context"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-dolphin-mixtral__cap_6","uri":"capability://tool.use.integration.rest.api.and.sdk.integration.with.multiple.language.bindings","name":"rest api and sdk integration with multiple language bindings","description":"Exposes inference capabilities via Ollama's standardized HTTP REST API (default port 11434) with official SDKs for Python and JavaScript, enabling integration into web applications, backend services, and scripts without direct model loading. The API supports both streaming (Server-Sent Events) and buffered responses, with standard chat completion message format compatible with OpenAI-style integrations.","intents":["I want to integrate a local LLM into my web app or backend service via HTTP without managing model loading","I need to use the same code to switch between local Ollama and cloud APIs (OpenAI, Anthropic) by changing endpoints","I want to build a Python or JavaScript application that calls the model without learning Ollama's internals"],"best_for":["full-stack developers building web applications with local LLM backends","teams building API-agnostic applications that can swap inference providers","developers familiar with OpenAI API who want to use local models with minimal code changes"],"limitations":["No built-in authentication or authorization; all security must be implemented at the application or network layer (firewall, reverse proxy)","HTTP overhead adds latency compared to in-process inference; each request incurs network serialization and deserialization","No request queuing or load balancing; concurrent requests queue sequentially on a single Ollama instance","SDK documentation is minimal; developers must refer to HTTP API docs and reverse-engineer SDK behavior","No built-in request logging, monitoring, or observability; users must implement custom logging"],"requires":["Ollama runtime running with HTTP API enabled (default: localhost:11434)","Python 3.8+ (for Python SDK) or Node.js 14+ (for JavaScript SDK)","HTTP client library (requests, fetch, axios, etc.) if using raw HTTP instead of SDKs","Network connectivity between client and Ollama instance (localhost for single-machine, network for distributed)"],"input_types":["JSON request bodies with message arrays (chat API)","HTTP POST requests with standard chat completion format"],"output_types":["JSON response with generated text","Server-Sent Events stream for real-time streaming responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-dolphin-mixtral__cap_7","uri":"capability://automation.workflow.cross.platform.deployment.with.docker.containerization","name":"cross-platform deployment with docker containerization","description":"Packages Ollama runtime and Dolphin Mixtral model as Docker containers, enabling consistent deployment across macOS, Windows, Linux, and cloud platforms (AWS, GCP, Azure) without manual dependency installation. Docker abstraction handles GPU driver compatibility, CUDA version management, and OS-specific optimizations, reducing deployment friction.","intents":["I want to deploy a local LLM to a cloud VM or Kubernetes cluster without managing CUDA and driver versions","I need to ensure consistent model behavior across development, staging, and production environments","I want to containerize my LLM application with Ollama as a sidecar service"],"best_for":["DevOps engineers deploying LLM services to cloud infrastructure","teams using Kubernetes or Docker Compose for application orchestration","developers building reproducible LLM applications with Docker"],"limitations":["Docker adds ~5-10% overhead compared to native execution due to containerization layers","GPU passthrough in Docker requires nvidia-docker or similar; not all cloud providers support GPU containers","Container image size is large (26GB+ for model weights); pulling and pushing images is slow and bandwidth-intensive","No built-in health checks or auto-restart logic; orchestration platform must implement liveness probes","Persistent storage for model weights must be managed via Docker volumes; ephemeral containers lose model cache"],"requires":["Docker runtime (Docker Desktop on macOS/Windows, Docker Engine on Linux)","nvidia-docker or similar for GPU support (optional; CPU-only inference possible)","Sufficient disk space on container host for model weights (26GB+ for 8x7b)","Docker Compose or Kubernetes for multi-container orchestration (optional)"],"input_types":["HTTP requests to containerized Ollama API","CLI commands via `docker exec`"],"output_types":["text responses via HTTP API","container logs for debugging"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-dolphin-mixtral__cap_8","uri":"capability://automation.workflow.tiered.cloud.hosting.via.ollama.cloud.with.usage.based.pricing","name":"tiered cloud hosting via ollama cloud with usage-based pricing","description":"Offers optional cloud-hosted inference via Ollama Cloud (separate from local Ollama), with three pricing tiers: Free (light usage, 1 concurrent model), Pro ($20/month, 50x more usage, 3 concurrent models), and Max ($100/month, 5x more usage than Pro, 10 concurrent models). Cloud hosting abstracts infrastructure management but introduces API latency and usage-based costs compared to local inference.","intents":["I want to scale inference beyond my local hardware without managing cloud infrastructure","I need a managed LLM service with automatic scaling and uptime guarantees","I want to prototype an application with cloud inference before deploying locally"],"best_for":["developers prototyping LLM applications without local GPU hardware","teams needing elastic scaling for variable inference workloads","applications with bursty traffic that don't justify dedicated local hardware"],"limitations":["Cloud inference introduces network latency (typically 100-500ms round-trip) compared to local inference","Usage-based pricing (Pro/Max tiers) can become expensive for high-volume inference; no published per-request pricing","Free tier has strict usage limits; production applications require paid tiers","Vendor lock-in to Ollama Cloud; switching to alternative cloud providers requires code changes","No published SLA or uptime guarantees; reliability depends on Ollama's infrastructure"],"requires":["Ollama Cloud account (free signup)","API key for authentication","Network connectivity to Ollama Cloud endpoints","Payment method for Pro/Max tiers"],"input_types":["HTTP requests to Ollama Cloud API (same format as local API)"],"output_types":["JSON responses with generated text","streaming responses via Server-Sent Events"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-dolphin-mixtral__cap_9","uri":"capability://tool.use.integration.community.integration.ecosystem.with.40.000.third.party.integrations","name":"community integration ecosystem with 40,000+ third-party integrations","description":"Ollama integrates with 40,000+ community-built tools, frameworks, and applications (exact integrations not detailed in documentation), enabling Dolphin Mixtral to be used in existing workflows without custom API wrappers. Integration points include IDE plugins, web frameworks, chatbot platforms, and specialized tools; community maintains most integrations independently.","intents":["I want to use Dolphin Mixtral in my existing IDE, web framework, or chatbot platform without building custom integrations","I need to find pre-built tools that already support Ollama and Dolphin Mixtral","I want to contribute integrations or use community-maintained plugins"],"best_for":["developers using popular frameworks (LangChain, LlamaIndex, etc.) that have Ollama integrations","teams adopting Ollama across multiple tools and platforms","community contributors building Ollama integrations"],"limitations":["No official registry or discovery mechanism for integrations; finding relevant integrations requires web search","Community integrations vary in quality, maintenance, and documentation; no guarantee of compatibility with latest Ollama versions","Integration maintenance is fragmented; breaking changes in Ollama API may break community integrations without immediate fixes","No official support for community integrations; users must rely on community maintainers for bug fixes and updates","Integration quality and feature completeness depend entirely on individual maintainers"],"requires":["Ollama runtime running and accessible to integration tool","Compatible integration tool/framework (varies by integration)","Network connectivity between integration and Ollama instance"],"input_types":["varies by integration (typically HTTP requests to Ollama API)"],"output_types":["varies by integration (typically text responses formatted by integration tool)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Ollama runtime (macOS, Windows, Linux, or Docker)","26GB disk space for 8x7b variant or 80GB for 8x22b variant","GPU with sufficient VRAM (exact requirements not documented; estimate 16GB+ for 8x7b, 48GB+ for 8x22b based on parameter count)","Python 3.8+ or Node.js 14+ for SDK usage (optional; CLI works without SDKs)","Ollama runtime with dolphin-mixtral model loaded","26GB+ disk space and 16GB+ VRAM for 8x7b variant","Text editor or IDE with HTTP client capability (or custom integration layer)","Ollama runtime","26GB disk space for 8x7b or 80GB for 8x22b","Sufficient VRAM (estimate 16GB+ for 8x7b, 48GB+ for 8x22b; exact requirements unknown)"],"failure_modes":["32K token context window is fixed and cannot be extended; documents longer than 32K tokens must be chunked or summarized before input","No benchmark scores published for instruction-following accuracy; claimed improvements over base Mixtral are not quantified","Inference speed not documented; MoE routing adds computational overhead compared to dense models of equivalent parameter count","Single-turn and multi-turn conversation quality depends entirely on Dolphin fine-tuning dataset composition, which is not fully disclosed","No specific coding benchmarks (e.g., HumanEval, MBPP scores) published; coding capability claims are not quantified","Code generation quality depends on prompt engineering; no built-in code validation or syntax checking","32K token context limits multi-file refactoring tasks; large codebases must be split across multiple requests","No language-specific optimizations documented; performance may vary significantly across Python, JavaScript, Rust, etc.","No published benchmarks comparing 8x7b and 8x22b performance; quality differences are not quantified","No automatic model selection logic; users must manually choose variant based on hardware estimates","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.38999999999999996,"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=dolphin-mixtral","compare_url":"https://unfragile.ai/compare?artifact=dolphin-mixtral"}},"signature":"ATOKK0gNN0vkMun8+CW3xCBzxJ4VvAYtsJyP3dmc6IXSl0nQw6ffAo/lolEraEVHygetPD0YwL7OHWLooaGoCg==","signedAt":"2026-06-20T23:27:08.335Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dolphin-mixtral","artifact":"https://unfragile.ai/dolphin-mixtral","verify":"https://unfragile.ai/api/v1/verify?slug=dolphin-mixtral","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"}}