--- title: openPangu-Embedded-7B-model canonical_url: "https://www.modelscope.ai/models/openpangu/openPangu-Embedded-7B-model" md_url: "https://www.modelscope.ai/models/openpangu/openPangu-Embedded-7B-model.md" repository: openpangu/openPangu-Embedded-7B-model last_updated: 2026-09-22 model_type: - PanguEmbedded architectures: - PanguEmbeddedForCausalLM parameters: 8.0B tensor_type: - BF16 - F32 library_name: - safetensors - pytorch frameworks: - pytorch downloads: 127 stars: 0 --- # openPangu-Embedded-7B-model > openPangu-Embedded-7B-model - An open-source model by openpangu on ModelScope. 开源盘古 Embedded-7B 中文 | English - **Repository**: openpangu/openPangu-Embedded-7B-model - **Parameters**: 8.0B - **Downloads**: 127 - **Stars**: 0 - **Last updated**: 2026-09-22 Source: https://www.modelscope.ai/models/openpangu/openPangu-Embedded-7B-model --- # 开源盘古 Embedded-7B 中文 | [English](README_EN.md) ## 1. 简介 openPangu-Embedded-7B 是基于昇腾 NPU 从零训练的高效大语言模型，参数量为 7B（不含词表Embedding）。openPangu-Embedded-7B 训练了约 19T tokens，具备快慢思考融合能力。 ## 2. 模型架构 | | openPangu-Embedded-7B | | :---------------------------: | :----------------: | | **Architecture** | Dense | | **Parameters (Non-Embedding)** | 7B | | **Number of Layers** | 34 | | **Hidden Dimension** | 12800 | | **Attention Mechanism** | GQA | | **Number of Attention Heads** | 32 for Q，8 for KV | | **Vocabulary Size** | 153k | | **Context Length (Natively)** | 32k | | **Pretraining Tokens** | 19T | ## 3. 测评结果 | 测评集 | 测评指标 | 慢思考 | | :---: | :---: | :---: | | **通用能力** | | | | MMLU-Pro | Exact Match | 76.32 | | CMMLU | Acc | 75.59 | | ArenaHard_v0.1 | w/o style control | 85.80 | | C-Eval | Acc | 83.05 | | GPQA-Diamond | Avg@4 | 70.54 | | **数学能力** | | | | MATH-500 | Avg@1 | 95.00 | | AIME24 | Avg@16 | 71.57 | | AIME25 | Avg@16 | 58.24 | | **代码能力** | | | | LiveCodeBench | Avg@2 (08/24~01/25) | 54.04 | | MBPP+ | Avg@2 | 76.06 | **注：** 评测过程中system prompt 为空，且不添加任何额外的思维链（CoT）提示。评测采用 128k 的序列长度进行。 ## 4. 部署和使用 ### 4.1 环境准备 ##### 硬件规格 Atlas 800T A2 (64GB)，驱动与固件安装包获取请参照 [[Atlas 800T A2](https://www.hiascend.com/hardware/firmware-drivers/community?product=4&model=26&cann=8.2.RC1.alpha003&driver=Ascend+HDK+25.0.RC1)]。 ##### 软件环境 - 操作系统：Linux（推荐 openEuler>=24.03） - CANN==8.1.RC1，安装准备及流程请参照 [[CANN Install]](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/82RC1alpha002/softwareinst/instg/instg_0001.html?Mode=PmIns&OS=Ubuntu&Software=cannToolKit) - python==3.10 - torch==2.1.0 - torch-npu==2.1.0.post12 - transformers==4.53.2 以上软件配套经过验证，理论可以支持更高版本，如有疑问，可以提交 issue。 ### 4.2 权重完整性校验 请参考以下方法对下载内容进行完整性校验，hash 值存储在 checklist.chk 文件中。 ``` #!/usr/bin/env bash ARCH=$(uname -m) MODEL_PATH="${TARGET_FOLDER}/${MODEL_FOLDER_PATH}" cd "$MODEL_PATH" || exit 1 if [ "$ARCH" = "arm64" ]; then sha256sum checklist.chk else sha256sum -c checklist.chk fi ``` ### 4.3 推理样例 下述内容提供 openPangu-Embedded-7B 在 `transformers` 框架上进行推理的一个简单示例： >运行前请修改 generate.py，添加模型路径。 ```bash cd inference python generate.py ``` openPangu-Embedded-7B 模型默认为慢思考模式，可以通过以下手段切换至快思考模式： - 在代码实例`generate.py`中，`no_thinking_prompt`变量的定义展示了切换至快思考模式的具体实现：通过在用户输入末尾添加`/no_think`标记，可将当前轮次切换至快思考模式。处于该模式时，`thinking_content`将为空值。 ### 4.4 使用推理框架 vllm_ascend：参考[[vllm_ascend_for_openpangu_embedded_7b.zh]](inference/vllm_ascend_for_openpangu_embedded_7b.zh.md) ## 5. 模型许可证 除文件中对开源许可证另有约定外，openPangu-Embedded-7B 模型根据 OPENPANGU MODEL LICENSE AGREEMENT VERSION 1.0 授权，旨在允许使用并促进人工智能技术的进一步发展。有关详细信息，请参阅模型存储库根目录中的 [LICENSE](https://huggingface.co/openpangu/openPangu-Embedded-7B-model/blob/main/LICENSE) 文件。 ## 6. 免责声明 由于 openPangu-Embedded-7B（“模型”）所依赖的技术固有的技术限制，以及人工智能生成的内容是由盘古自动生成的，华为无法对以下事项做出任何保证： - 尽管该模型的输出由 AI 算法生成，但不能排除某些信息可能存在缺陷、不合理或引起不适的可能性，生成的内容不代表华为的态度或立场； - 无法保证该模型 100% 准确、可靠、功能齐全、及时、安全、无错误、不间断、持续稳定或无任何故障； - 该模型的输出内容不构成任何建议或决策，也不保证生成的内容的真实性、完整性、准确性、及时性、合法性、功能性或实用性。生成的内容不能替代医疗、法律等领域的专业人士回答您的问题。生成的内容仅供参考，不代表华为的任何态度、立场或观点。您需要根据实际情况做出独立判断，华为不承担任何责任。 ## 7. 反馈 如果有任何意见和建议，请提交issue或联系 openPangu@huawei.com。
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