{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-solar","slug":"solar","name":"Solar (10.7B)","type":"model","url":"https://ollama.com/library/solar","page_url":"https://unfragile.ai/solar","categories":["text-writing"],"tags":["ollama","open-source","upstage"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-solar__cap_0","uri":"capability://text.generation.language.single.turn.instruction.following.chat.completion","name":"single-turn instruction-following chat completion","description":"Generates contextually relevant text responses to user prompts using a Transformer architecture with Depth Up-Scaling (DUS) technique that integrates Mistral 7B weights into upscaled Llama 2 layers. Processes input via standard chat message format (role/content fields) and outputs coherent text completions optimized for single-turn interactions without multi-turn conversation state management. Inference is performed locally via Ollama runtime or cloud-hosted via Ollama Cloud with GPU acceleration.","intents":["I need a local language model that can answer questions and generate text without cloud dependencies","I want to run a capable 10B-parameter model on consumer hardware for chatbot applications","I need to integrate a text generation model into my application via REST API or Python/JavaScript SDK","I want to benchmark a model that claims to outperform 30B-parameter models on instruction-following tasks"],"best_for":["developers building local-first LLM applications on resource-constrained hardware","teams prototyping chatbot assistants without cloud API costs or latency concerns","researchers comparing instruction-tuned model performance in the 10-30B parameter range","solo developers deploying models via Ollama for offline-first use cases"],"limitations":["Designed explicitly for single-turn conversation only — no built-in multi-turn state management or conversation history handling","Hard context window limit of 4,096 tokens prevents processing of long documents or extended dialogue histories","No tool-calling, function-calling, or structured output capabilities documented","No vision or multimodal input support — text-only model","Inference latency and throughput benchmarks not publicly documented, making performance comparison difficult","Training dataset composition and size unknown, limiting ability to assess potential biases or domain coverage"],"requires":["Ollama runtime (macOS, Windows, Linux, or Docker)","Minimum 8GB RAM for local inference (exact VRAM requirements unknown)","Optional: GPU with CUDA/Metal support for accelerated inference","For cloud deployment: Ollama Cloud account with Pro or Max tier for concurrent model access","Python 3.7+ for Python SDK or Node.js 14+ for JavaScript SDK integration"],"input_types":["text (natural language prompts)","chat message format with role/content fields"],"output_types":["text (natural language generation)","streaming text tokens via Ollama API"],"categories":["text-generation-language","chatbots-assistants"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-solar__cap_1","uri":"capability://automation.workflow.local.first.model.inference.via.ollama.runtime","name":"local-first model inference via ollama runtime","description":"Executes the Solar model entirely on local hardware through Ollama's runtime environment, supporting multiple interface patterns: CLI commands, REST API endpoints on localhost:11434, and language-specific SDKs (Python `ollama` package, JavaScript `ollama` npm package). Model weights are stored as quantized GGUF format (6.1GB artifact) and loaded into memory for inference without transmitting data to external servers, enabling offline-first operation and zero API latency.","intents":["I need to run a language model locally without sending data to cloud APIs for privacy or compliance reasons","I want to integrate model inference into my application via REST API without managing containers or Kubernetes","I need to switch between different models (Solar, Llama, Mistral) using a unified interface","I want to avoid per-token billing and latency overhead of cloud LLM APIs"],"best_for":["enterprises with data privacy requirements preventing cloud API usage","developers building offline-capable applications or edge deployments","teams prototyping multiple models rapidly without cloud infrastructure setup","cost-sensitive projects where per-token billing becomes prohibitive at scale"],"limitations":["Inference performance depends entirely on local hardware — no auto-scaling or load balancing across machines","Requires manual model management (downloading, updating, switching versions) via Ollama CLI","No built-in monitoring, logging, or observability beyond basic runtime output","Single-machine deployment model limits horizontal scaling for high-concurrency scenarios","Ollama Cloud tier required for cloud hosting, adding operational complexity vs. managed services","GPU acceleration requires compatible hardware (NVIDIA CUDA, Apple Metal, or AMD ROCm) — CPU-only inference significantly slower"],"requires":["Ollama runtime installed (macOS 11+, Windows 10+, Linux with Docker, or Docker Desktop)","8GB+ RAM minimum (exact requirements vary by quantization and hardware)","Optional: NVIDIA GPU with CUDA 11.8+ or Apple Silicon for GPU acceleration","For REST API integration: HTTP client library (curl, requests, axios, etc.)","For Python SDK: Python 3.7+ and `ollama` package (pip install ollama)","For JavaScript SDK: Node.js 14+ and `ollama` npm package"],"input_types":["text prompts (CLI)","JSON chat message format (REST API)","Python dict/list structures (Python SDK)","JavaScript objects (JavaScript SDK)"],"output_types":["text completions (CLI stdout)","JSON response with generated text (REST API)","Python strings or async generators (Python SDK)","JavaScript strings or async iterables (JavaScript SDK)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-solar__cap_2","uri":"capability://automation.workflow.cloud.hosted.model.inference.via.ollama.cloud","name":"cloud-hosted model inference via ollama cloud","description":"Provides managed cloud hosting of the Solar model through Ollama Cloud platform with GPU acceleration, eliminating local hardware requirements while maintaining the same REST API and SDK interfaces as local Ollama. Pricing tiers (Free, Pro, Max) control concurrent model instances and total GPU compute time allocation, with usage measured in GPU-hours rather than tokens, enabling predictable cost scaling for variable workloads.","intents":["I want to deploy Solar without managing local hardware or Ollama runtime infrastructure","I need auto-scaling inference capacity that grows with traffic without manual provisioning","I want to avoid GPU hardware costs while maintaining low-latency inference","I need to share a single model deployment across multiple team members or applications"],"best_for":["teams without GPU hardware or infrastructure expertise","applications with variable traffic patterns requiring elastic scaling","organizations preferring managed services over self-hosted infrastructure","prototyping and development teams wanting quick deployment without DevOps overhead"],"limitations":["Cloud hosting introduces network latency compared to local inference (typically 50-200ms round-trip)","Pricing based on GPU compute time rather than tokens, making cost prediction difficult for variable-length outputs","Free tier limited to single concurrent model — Pro/Max tiers required for production multi-model deployments","Data transmitted to Ollama Cloud servers, violating strict data privacy requirements (unlike local-only deployment)","Vendor lock-in to Ollama Cloud platform — no easy migration to other managed LLM services","No documented SLA, uptime guarantees, or support tiers for production deployments"],"requires":["Ollama Cloud account (free tier available)","Internet connectivity for API requests","API key for authentication (generated in Ollama Cloud dashboard)","For Pro tier: $10/month subscription (3 concurrent models, 50x Free tier usage)","For Max tier: $50/month subscription (10 concurrent models, 5x Pro tier usage)","Same SDK/API integration as local Ollama (Python, JavaScript, REST)"],"input_types":["text prompts (same format as local Ollama)","JSON chat message format (REST API)","Python/JavaScript SDK message objects"],"output_types":["JSON response with generated text (REST API)","Python strings or async generators (Python SDK)","JavaScript strings or async iterables (JavaScript SDK)","Streaming responses via Server-Sent Events"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-solar__cap_3","uri":"capability://text.generation.language.instruction.tuned.text.generation.with.state.of.the.art.benchmark.performance","name":"instruction-tuned text generation with state-of-the-art benchmark performance","description":"Solar is fine-tuned using instruction-tuning methodology (specific approach undocumented) to follow user directives and generate contextually appropriate responses. Claims state-of-the-art performance for models under 30B parameters on the 'H6 benchmark' (benchmark definition unknown), reportedly outperforming Mixtral 8X7B (56B parameters) despite being 5.3x smaller. Performance claims are unverified by independent benchmarks and lack published scores.","intents":["I need a small model that can follow complex instructions as well as much larger models","I want to verify if Solar actually outperforms larger models like Mixtral on standard benchmarks","I need a model optimized for instruction-following rather than raw language modeling","I want to compare Solar's performance against other 10-30B parameter models"],"best_for":["researchers benchmarking instruction-tuned models in the 10-30B parameter range","teams evaluating whether smaller models can replace larger ones for instruction-following tasks","developers building applications where instruction-following quality is critical"],"limitations":["H6 benchmark definition not provided in documentation — unclear what tasks/metrics are measured","No published benchmark scores or comparison data available — claims are unverified","Specific instruction-tuning methodology not documented, making reproducibility impossible","No analysis of failure modes, edge cases, or task categories where Solar underperforms","Benchmark comparison to Mixtral 8X7B lacks context (different training data, fine-tuning approaches, evaluation protocols)","No independent third-party benchmark verification (e.g., HELM, LMSys Chatbot Arena)"],"requires":["Understanding that performance claims are manufacturer-provided, not independently verified","Willingness to run custom benchmarks to validate claims for your specific use case","Baseline models for comparison (Mixtral 8X7B, Llama 2 70B, etc.) if verifying claims"],"input_types":["text instructions and prompts","chat message format with system/user/assistant roles"],"output_types":["text responses following instruction directives","structured or unstructured text depending on prompt"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-solar__cap_4","uri":"capability://data.processing.analysis.quantized.model.distribution.and.format.abstraction","name":"quantized model distribution and format abstraction","description":"Solar is distributed via Ollama as a quantized GGUF artifact (6.1GB file size), abstracting away quantization scheme details and bit-depth from users. Ollama handles GGUF format loading, memory mapping, and GPU/CPU dispatch automatically, allowing developers to load and run the model without understanding quantization internals. Exact quantization scheme (Q4, Q5, Q8, etc.) is not documented.","intents":["I want to download and run a large language model without managing quantization formats or compression trade-offs","I need to understand the memory footprint and inference speed implications of the quantized model","I want to use the same model in different quantization formats for different hardware constraints"],"best_for":["developers unfamiliar with quantization who want simple model deployment","teams needing predictable model sizes for storage and memory planning","users deploying on resource-constrained hardware (laptops, edge devices)"],"limitations":["Quantization scheme and bit-depth not documented — impossible to assess quality loss vs. inference speed trade-offs","Single quantized format distributed (6.1GB) — no alternative quantization options provided","No published comparison of quantized vs. full-precision model quality or inference speed","Quantization methodology (symmetric, asymmetric, per-channel, etc.) unknown","No control over quantization parameters — users cannot customize for their hardware/quality requirements"],"requires":["Ollama runtime to handle GGUF format loading and dispatch","Sufficient disk space for 6.1GB model artifact","Understanding that quantization trade-offs (quality vs. speed) are opaque"],"input_types":["GGUF binary format (handled transparently by Ollama)"],"output_types":["Loaded model in GPU/CPU memory ready for inference"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":21,"verified":false,"data_access_risk":"high","permissions":["Ollama runtime (macOS, Windows, Linux, or Docker)","Minimum 8GB RAM for local inference (exact VRAM requirements unknown)","Optional: GPU with CUDA/Metal support for accelerated inference","For cloud deployment: Ollama Cloud account with Pro or Max tier for concurrent model access","Python 3.7+ for Python SDK or Node.js 14+ for JavaScript SDK integration","Ollama runtime installed (macOS 11+, Windows 10+, Linux with Docker, or Docker Desktop)","8GB+ RAM minimum (exact requirements vary by quantization and hardware)","Optional: NVIDIA GPU with CUDA 11.8+ or Apple Silicon for GPU acceleration","For REST API integration: HTTP client library (curl, requests, axios, etc.)","For Python SDK: Python 3.7+ and `ollama` package (pip install ollama)"],"failure_modes":["Designed explicitly for single-turn conversation only — no built-in multi-turn state management or conversation history handling","Hard context window limit of 4,096 tokens prevents processing of long documents or extended dialogue histories","No tool-calling, function-calling, or structured output capabilities documented","No vision or multimodal input support — text-only model","Inference latency and throughput benchmarks not publicly documented, making performance comparison difficult","Training dataset composition and size unknown, limiting ability to assess potential biases or domain coverage","Inference performance depends entirely on local hardware — no auto-scaling or load balancing across machines","Requires manual model management (downloading, updating, switching versions) via Ollama CLI","No built-in monitoring, logging, or observability beyond basic runtime output","Single-machine deployment model limits horizontal scaling for high-concurrency scenarios","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"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=solar","compare_url":"https://unfragile.ai/compare?artifact=solar"}},"signature":"n/kbY3E/3fzuaSO29iE/9e3GZabnP9fnPcp1YDyj5pKGqeddQ5iOJdC5Dvo3CzRhtfhUABTDLSmifQmJMQbkDg==","signedAt":"2026-06-22T15:54:05.374Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/solar","artifact":"https://unfragile.ai/solar","verify":"https://unfragile.ai/api/v1/verify?slug=solar","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"}}