We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token.
File: README.md (L48-49)
To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2.
Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance.
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We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities.
Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models.
Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training.
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In addition, its training process is remarkably stable.
Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks.
> The total size of DeepSeek-V3 models on Hugging Face is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights.
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> Best results are shown in bold. Scores with a gap not exceeding 0.3 are considered to be at the same level. DeepSeek-V3 achieves the best performance on most benchmarks, especially on math and code tasks.
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| DeepSeek-V3 | **85.5** | **70.0** |
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You can chat with DeepSeek-V3 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in)
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We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/)
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1. **DeepSeek-Infer Demo**: We provide a simple and lightweight demo for FP8 and BF16 inference.
2. **SGLang**: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes, with Multi-Token Prediction [coming soon](https://github.com/sgl-project/sglang/issues/2591).
3. **LMDeploy**: Enables efficient FP8 and BF16 inference for local and cloud deployment.
4. **TensorRT-LLM**: Currently supports BF16 inference and INT4/8 quantization, with FP8 support coming soon.
5. **vLLM**: Support DeepSeek-V3 model with FP8 and BF16 modes for tensor parallelism and pipeline parallelism.
6. **LightLLM**: Supports efficient single-node or multi-node deployment for FP8 and BF16.
7. **AMD GPU**: Enables running the DeepSeek-V3 model on AMD GPUs via SGLang in both BF16 and FP8 modes.
8. **Huawei Ascend NPU**: Supports running DeepSeek-V3 on Huawei Ascend devices in both INT8 and BF16.
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```shell
cd inference
python fp8_cast_bf16.py --input-fp8-hf-path /path/to/fp8_weights --output-bf16-hf-path /path/to/bf16_weights
**File:** README.md (L345-345)
markdown This code repository is licensed under the MIT License. The use of DeepSeek-V3 Base/Chat models is subject to the Model License. DeepSeek-V3 series (including Base and Chat) supports commercial use.
**File:** README_WEIGHTS.md (L62-62)
markdown DeepSeek-V3 natively supports FP8 weight format with 128×128 block scaling.
**File:** LICENSE-MODEL (L37-39)
text
Grant of Copyright License. Subject to the terms and conditions of this License, DeepSeek hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model.
Grant of Patent License. Subject to the terms and conditions of this License and where and as applicable, DeepSeek hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by DeepSeek that are necessarily infringed by its contribution(s). If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for … (truncated)
**File:** LICENSE-MODEL (L79-90)
text You agree not to use the Model or Derivatives of the Model:
In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party;
For military use in any way;
For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
To generate or disseminate verifiably false information and/or content with the purpose of harming others;
To generate or disseminate inappropriate content subject to applicable regulatory requirements;
To generate or disseminate personal identifiable information without due authorization or for unreasonable use;
To defame, disparage or otherwise harass others;
For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; “`
b. 双模式设计： Plan模式：只读分析，生成实现方案（如功能拆解、文件修改计划）。Build模式：实际修改文件、运行命令，支持撤销（/undo）与重做（/redo）。
c. 多场景覆盖：代码问答、功能开发、重构、团队协作（通过/share分享对话）。
2. 典型流程
flowchart TB
A[进入项目目录<br>cd /path/to/project] --> B[启动 OpenCode<br>opencode]
B --> C[初始化项目<br>/init 生成 AGENTS.md]
C --> D[提问 / 说明需求]
D --> E{选择模式}
E -->|Plan 模式| F[生成实现方案<br>只读分析不改动文件]
F --> D
E -->|Build 模式| G[让 OpenCode 修改文件、运行命令]
G --> H{结果满意?}
H -->|不满意| I[用 /undo 撤销调整需求]
I --> D
H -->|满意| J[用 /share 分享对话<br>或提交代码]
三、技术架构与特性
1. 技术栈
a. 核心语言：TypeScript（主语言）、Bun（运行时）、Bubble Tea（TUI框架）。
b. 模块划分： 代理核心（agent.ts）：模型调度、任务规划。
配置系统（config.ts）：管理模型参数、密钥、插件。
交互层：CLI/TUI/LSP多端适配，支持VS Code、Neovim等编辑器。
插件系统：提供工具调用接口，支持自定义扩展。
2. 关键特性
a. 模型无关性：通过JSON配置切换模型，支持本地部署（如Ollama）。
b. 生态集成： GitHub/GitLab Actions：自动化PR审查、代码检查。
LSP支持：增强代码语义理解能力。
MCP协议：接入监控系统（如Netdata）辅助DevOps场景。
四、安装与配置
1. 安装方式
a. 一键脚本：curl -fsSL https://opencode.ai/install | bash
b. 包管理器： npm：npm i -g opencode-ai
macOS：brew install sst/tap/opencode
Arch Linux：paru -S opencode-bin
c. Docker：docker run -it –rm ghcr.io/anomalyco/opencode
2. 配置步骤
a. 初始化项目：opencode session create 生成 AGENTS.md（记录项目理解）。
b. 模型配置：编辑~/.opencode/config.json，指定API密钥（支持OpenAI、Claude等）。
c. 远程开发：服务端启动 opencode server –port 8080，客户端通过 opencode connect 连接。