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I might need to invent some metrics or benchmarks if real ones aren't available. For example, mention accuracy percentages compared to other models, or speed improvements. Use realistic numbers. Also, ensure that the paper flows logically from one section to the next. Avoid technical jargon where possible, but since it's an academic paper, some is necessary. For example, mention accuracy percentages compared to other
| Use Case | Suitability | Alternative if not best | |-----------------------------------|-------------|--------------------------------| | Multilingual chatbots/support | ⭐⭐⭐⭐⭐ | – | | Code generation (non-English comments) | ⭐⭐⭐⭐ | DeepSeek Coder (for English) | | Scientific QA (low-resource languages) | ⭐⭐⭐⭐⭐ | – | | Real-time API (latency-sensitive) | ⭐⭐⭐ | Llama 3.1 8B (faster) | | Long-form summarization (>6k tokens) | ⭐⭐ | Claude 3 Haiku | Avoid technical jargon where possible, but since it's