Solar (10.7B) vs Google Translate
Side-by-side comparison to help you choose.
| Feature | Solar (10.7B) | Google Translate |
|---|---|---|
| Type | Model | Product |
| UnfragileRank | 24/100 | 33/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 0 |
| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 8 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Uses Depth Up-Scaling (DUS) technique to integrate Mistral 7B weights into upscaled Llama 2 architecture, achieving claimed state-of-the-art performance for models under 30B parameters without requiring larger model sizes or additional training compute. Distributed via Ollama as quantized 6.1GB artifact enabling local execution without cloud dependencies.
vs alternatives: Smaller than Mixtral 8X7B (56B) and other 30B+ models while claiming superior instruction-following performance, making it ideal for resource-constrained deployments; faster inference than larger models with comparable quality on single-turn tasks.
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.
Unique: Ollama abstracts away GGUF quantization format handling and GPU/CPU dispatch logic behind unified CLI and REST API interfaces, allowing developers to swap models without code changes. Supports streaming responses via Server-Sent Events (SSE) for real-time token generation without waiting for full completion.
vs alternatives: Simpler deployment than vLLM or TensorRT-LLM for single-model serving; more accessible than llama.cpp for non-expert users while maintaining comparable inference speed through native GGUF optimization.
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.
Unique: Ollama Cloud uses GPU-hour billing model instead of token-based pricing, making it cost-effective for variable-length outputs and unpredictable workloads. Maintains identical API surface to local Ollama, enabling zero-code migration between local and cloud deployments.
vs alternatives: Cheaper than OpenAI API for high-volume inference; simpler deployment than self-hosted vLLM clusters; more cost-predictable than token-based cloud LLM services for long-form generation tasks.
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.
Unique: Combines Depth Up-Scaling (DUS) architecture with instruction-tuning to achieve claimed performance parity with 5-6x larger models, but lacks published benchmark scores or methodology documentation to substantiate claims. No independent verification available.
vs alternatives: If benchmark claims are accurate, offers 5-6x parameter efficiency vs. Mixtral 8X7B and 70B models; however, unverified claims make direct comparison impossible without custom evaluation.
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.
Unique: Ollama abstracts GGUF quantization format handling completely, allowing non-expert users to deploy quantized models without understanding compression trade-offs. Automatic GPU/CPU dispatch based on available hardware without manual configuration.
vs alternatives: Simpler than managing raw GGUF files with llama.cpp; more transparent than proprietary quantization formats used by other model providers; smaller artifact size (6.1GB) than full-precision models enabling consumer hardware deployment.
Translates written text input from one language to another using neural machine translation. Supports over 100 language pairs with context-aware processing for more natural output than statistical models.
Translates spoken language in real-time by capturing audio input and converting it to translated text or speech output. Enables live conversation between speakers of different languages.
Captures images using a device camera and translates visible text within the image to a target language. Useful for translating signs, menus, documents, and other printed or displayed text.
Translates entire documents by uploading files in various formats. Preserves original formatting and layout while translating content.
Automatically detects and translates web pages directly in the browser without requiring manual copy-paste. Provides seamless in-page translation with one-click activation.
Provides offline access to translation dictionaries for quick word and phrase lookups without requiring internet connection. Enables fast reference for individual terms.
Automatically detects the source language of input text and translates it to a target language without requiring manual language selection. Handles mixed-language content.
Google Translate scores higher at 33/100 vs Solar (10.7B) at 24/100. Solar (10.7B) leads on ecosystem, while Google Translate is stronger on quality.
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Converts text written in non-Latin scripts (e.g., Arabic, Chinese, Cyrillic) into Latin characters while also providing translation. Useful for reading unfamiliar writing systems.