. . . . . . . . . . . . . . . . . . . . 13 2.6.4 Llama3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3 零代码本地部署 AI 后端 13 3.1 大模型 Llama3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.2 步骤 2：安装 Llama . . . . . . . . . . . . . . . . . . . . . 14 3.1.3 使用 Llama3 . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2 大模型 phi-3 . . . . . . . . . . . . . . . . . . . 32 5.5 构建第一个私人知识库 . . . . . . . . . . . . . . . . . . . . . . . . 34 5.6 MaxKB 配置本地 llama3 . . . . . . . . . . . . . . . . . . . . . . 37 5.7 创建知识库应用 . . . . . . . . . . . . . . . . . .

includ- ing DeepSeek 67B (DeepSeek-AI, 2024) (our previous release), Qwen1.5 72B (Bai et al., 2023), LLaMA3 70B (AI@Meta, 2024), and Mixtral 8x22B (Mistral, 2024). We evaluate all these models with our internal Compared with LLaMA3 70B, DeepSeek-V2 is trained on fewer than a quarter of English tokens. Therefore, we acknowledge that DeepSeek-V2 still has a slight gap in basic English capabilities with LLaMA3 70B. However still demonstrates comparable code and math capability with LLaMA3 70B. Also, as a bilingual language model, DeepSeek-V2 outperforms LLaMA3 15 70B overwhelmingly on Chinese benchmarks. Finally, it is