graph TD
A[前端层 km-console] --> B[API层 km-rest]
B --> C[业务层 km-biz]
C --> D[核心层 km-core]
D --> E[数据层 km-persistence]
F[任务调度 km-task] --> D
G[指标采集 km-collector] --> D
H[系统监控 km-monitor] --> D
I[共享工具 km-common] --> A
I --> B
I --> C
I --> D
Clawdbot 是一个个人 AI 助手平台，将消息渠道（WhatsApp、Telegram、Discord、Slack、Signal、iMessage 等）连接到在您自己设备上运行的 AI 代理。它作为一个本地优先的系统运行，其中 Gateway 控制平面管理渠道连接、代理执行和会话状态，而 Pi 代理运行时 处理与您工作区、浏览器和系统的工具访问的 AI 交互。
核心功能
多渠道消息集成
● WhatsApp (通过 Baileys WhatsApp Web 协议)
● Telegram (Bot API / grammY)
● Discord (Bot API / discord.js)
● Slack (Bolt)
● Signal、iMessage 等更多渠道
智能代理系统
● Pi 代理运行时：基于 @mariozechner/pi-agent-core 的 RPC 模式集成
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.
File: README.md (L50-52)
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.
File: README.md (L53-54)
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.
File: README.md (L153-153)
> 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.
File: README.md (L214-214)
| DeepSeek-V3 | **85.5** | **70.0** |
File: README.md (L223-223)
You can chat with DeepSeek-V3 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in)
File: README.md (L225-225)
We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/)
File: README.md (L231-238)
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.
File: README.md (L244-247)
```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; “`