--- title: Qwen2-72B canonical_url: "https://www.modelscope.cn/models/qwen/Qwen2-72B" md_url: "https://www.modelscope.cn/models/qwen/Qwen2-72B.md" repository: qwen/Qwen2-72B chinese_name: "千问2-72B" last_updated: 2025-04-23 license: other pipeline_tag: text-generation tasks: - text-generation model_type: - qwen2 architectures: - Qwen2ForCausalLM parameters: 72.7B tensor_type: - BF16 library_name: - safetensors - pytorch frameworks: - Pytorch language: - en inference_backends: - "deploy_task text/emb" - "lmdeploy 0.9.1" - "lmdeploy_turbomind 0.9.1" - "sglang 0.5.2" - "vllm 0.9.2" downloads: 217187 stars: 67 tags: - pretrained --- # Qwen2-72B > Qwen2-72B - qwen 在 ModelScope 开源的模型。Qwen2是Qwen系列模型的重大升级。Qwen2在⼤规模模型上实现了⾮常⼤幅度的效果提升，并增⼤了上下⽂⻓度⽀持。 qwen/Qwen2-72B 是 ModelScope 魔搭社区上的 72.7B 参数text-generation模型，采用 other 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。 - **Repository**: qwen/Qwen2-72B - **License**: other - **Tasks**: text-generation - **Parameters**: 72.7B - **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2 - **Tags**: pretrained - **Downloads**: 217187 - **Stars**: 67 - **Last updated**: 2025-04-23 Source: https://www.modelscope.cn/models/qwen/Qwen2-72B --- # Qwen2-72B ## Introduction Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the 72B Qwen2 base language model. Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Model Details Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. ## Requirements The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error: ``` KeyError: 'qwen2' ``` ## Usage We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model. ## Performance The evaluation of base models mainly focuses on the model performance of natural language understanding, general question answering, coding, mathematics, scientific knowledge, reasoning, multilingual capability, etc. The datasets for evaluation include: **English Tasks**: MMLU (5-shot), MMLU-Pro (5-shot), GPQA (5shot), Theorem QA (5-shot), BBH (3-shot), HellaSwag (10-shot), Winogrande (5-shot), TruthfulQA (0-shot), ARC-C (25-shot) **Coding Tasks**: EvalPlus (0-shot) (HumanEval, MBPP, HumanEval+, MBPP+), MultiPL-E (0-shot) (Python, C++, JAVA, PHP, TypeScript, C#, Bash, JavaScript) **Math Tasks**: GSM8K (4-shot), MATH (4-shot) **Chinese Tasks**: C-Eval (5-shot), CMMLU (5-shot) **Multilingual Tasks**: Multi-Exam (M3Exam 5-shot, IndoMMLU 3-shot, ruMMLU 5-shot, mMMLU 5-shot), Multi-Understanding (BELEBELE 5-shot, XCOPA 5-shot, XWinograd 5-shot, XStoryCloze 0-shot, PAWS-X 5-shot), Multi-Mathematics (MGSM 8-shot), Multi-Translation (Flores-101 5-shot) #### Qwen2-72B performance | Datasets | DeepSeek-V2 | Mixtral-8x22B | Llama-3-70B | Qwen1.5-72B | Qwen1.5-110B | **Qwen2-72B** | | :--------| :---------: | :------------: | :------------: | :------------: | :------------: |:------------: | |Architecture | MoE | MoE | Dense | Dense | Dense | Dense | |#Activated Params | 21B | 39B | 70B | 72B | 110B | 72B | |#Params | 236B | 140B | 70B | 72B | 110B | 72B| | ***English*** | | | | | | | |MMLU |78.5 | 77.8 | 79.5 | 77.5 | 80.4 | **84.2** | |MMLU-Pro | - | 49.5 | 52.8 | 45.8 | 49.4 | **55.6** | |GPQA | -| 34.3 | 36.3 | 36.3 | 35.9 | **37.9** | |Theorem QA | -| 35.9 | 32.3 | 29.3 | 34.9 | **43.1** | |BBH | 78.9 |78.9 | 81.0 | 65.5 | 74.8 | **82.4** | |HellaSwag | 87.8 | **88.7** | 88.0 | 86.0 | 87.5 | 87.6 | |WindoGrande | 84.8|85.0 | **85.3** | 83.0 | 83.5 | 85.1 | |ARC-C | 70.0| **70.7** | 68.8 | 65.9 | 69.6 | 68.9 | |TruthfulQA | 42.2 | 51.0 | 45.6 | **59.6** | 49.6 | 54.8 | | ***Coding*** | | | | | | | |HumanEval | 45.7 | 46.3 | 48.2 | 46.3 | 54.3 | **64.6** | |MBPP |73.9 | 71.7 | 70.4 | 66.9 | 70.9 | **76.9** | |EvalPlus | 55.0 | 54.1 | 54.8 | 52.9 | 57.7 | **65.4** | |MultiPL-E |44.4 | 46.7 | 46.3 | 41.8 | 52.7 | **59.6** | | ***Mathematics*** | | | | | | | |GSM8K | 79.2 | 83.7 | 83.0 | 79.5 | 85.4 | **89.5** | |MATH | 43.6 | 41.7 | 42.5 | 34.1 | 49.6 | **51.1** | | ***Chinese*** | | | | | | | |C-Eval | 81.7 | 54.6 | 65.2 | 84.1 | 89.1 | **91.0** | |CMMLU | 84.0 | 53.4 | 67.2 | 83.5 | 88.3 | **90.1** | | ***Multilingual*** | | | | | | | |Mulit-Exam | 67.5 | 63.5 | 70.0 | 66.4 | 75.6 | **76.6** | |Multi-Understanding | 77.0 | 77.7 | 79.9 | 78.2 | 78.2 | **80.7** | |Multi-Mathematics | 58.8 | 62.9 | 67.1 | 61.7 | 64.4 | **76.0** | |Multi-Translation | 36.0 | 23.3 | **38.0** | 35.6 | 36.2 | 37.8 | ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen2, title={Qwen2 Technical Report}, year={2024} } ```