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Qwen2-1.5B
Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology
阿里巴巴开源的15亿参数规模的大语言模型，是小规模参数语言模型中表现最强的一个。与其它小规模参数模型相比，该模型在不同评测结果上都取得了非常好的结果。下图是该模型与其它模型的对比结果：
| Datasets | Phi-2 | Gemma-2B | MiniCPM | Qwen1.5-1.8B | Qwen2-0.5B | Qwen2-1.5B |
|---|---|---|---|---|---|---|
| #Non-Emb Params | 2.5B | 2.0B | 2.4B | 1.3B | 0.35B | 1.3B |
| MMLU | 52.7 | 42.3 | 53.5 | 46.8 | 45.4 | 56.5 |
| MMLU-Pro | - | 15.9 | - | - | 14.7 | 21.8 |
| Theorem QA | - | - | - | - | 8.9 | 15.0 |
| HumanEval | 47.6 | 22.0 | 50.0 | 20.1 | 22.0 | 31.1 |
| MBPP | 55.0 | 29.2 | 47.3 | 18.0 | 22.0 | 37.4 |
| GSM8K | 57.2 | 17.7 | 53.8 | 38.4 | 36.5 | 58.5 |
| MATH | 3.5 | 11.8 | 10.2 | 10.1 | 10.7 | 21.7 |
| BBH | 43.4 | 35.2 | 36.9 | 24.2 | 28.4 | 37.2 |
| HellaSwag | 73.1 | 71.4 | 68.3 | 61.4 | 49.3 | 66.6 |
| Winogrande | 74.4 | 66.8 | - | 60.3 | 56.8 | 66.2 |
| ARC-C | 61.1 | 48.5 | - | 37.9 | 31.5 | 43.9 |
| TruthfulQA | 44.5 | 33.1 | - | 39.4 | 39.7 | 45.9 |
| C-Eval | 23.4 | 28.0 | 51.1 | 59.7 | 58.2 | 70.6 |
| CMMLU | 24.2 | - | 51.1 | 57.8 | 55.1 | 70.3 |
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