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LICENSE

+Modified MIT License
+
+Copyright (c) 2026 Moonshot AI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the “Software”), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+Our only modification part is that, if the Software (or any derivative works
+thereof) is used for any of your commercial products or services that have
+more than 100 million monthly active users, or more than 20 million US dollars
+(or equivalent in other currencies) in monthly revenue, you shall prominently
+display "Kimi K2.5" on the user interface of such product or service.

README.md

+# Kimi-K2-Thinking
+## 论文
+[Kimi-K2-Thinking technical blog](https://moonshotai.github.io/Kimi-K2/thinking.html)
+
+## 模型简介
+
+Kimi K2 Thinking是最新、能力最强的开源推理模型版本。自Kimi K2起，我们将其构建为一个能够逐步推理并动态调用工具的推理智能体。通过在“人类最后考试”（Humanity's Last Exam, HLE）、“浏览复杂度评估”（BrowseComp）等多项基准测试中显著扩展多步推理深度，并保持对200–300次顺序调用的稳定工具使用，Kimi K2 Thinking树立了新的行业标杆。同时，K2 Thinking作为原生INT4量化模型，拥有256k上下文窗口，实现了推理延迟和GPU内存占用的无损降低。
+
+**核心特性**:  
+- 深度推理与工具协同：经过端到端训练，能够将思维链推理与函数调用交错进行，支持自主研究、代码生成与写作工作流，可在数百个步骤中保持逻辑连贯，不发生偏离。  
+- 原生 INT4 量化：在后训练阶段采用量化感知训练技术，在低延迟模式下实现无损的2倍推理加速。  
+- 稳定的长程任务执行能力：可在连续多达200–300次工具调用过程中保持连贯的目标导向行为，超越了此前模型在30–50步后性能衰减的局限。  
+
+
+## 环境依赖
+| 软件 |                    版本                     |
+| :------: |:-----------------------------------------:|
+| DTK |                   26.04                   |
+| python |                  3.10.12                  |
+| transformers |                4.57.6                 |
+| vllm |       0.15.1+das.opt1.alpha.dtk2604.20260220.g2799735a       |
+| triton | 3.3.0+das.opt2.dtk2604.torch291.20260210.g1329924c |
+| torch | 2.9.0+das.opt1.dtk2604.20260206.g275d08c2 |
+
+推荐使用镜像: harbor.sourcefind.cn:5443/dcu/admin/base/custom:vllm0.15.1-ubuntu22.04-dtk26.04-0130-py3.10-20260220
+
+
+- 挂载地址`-v` 根据实际模型情况修改
+```bash
+docker run -it \
+    --shm-size 200g \
+    --network=host \
+    --name Kimi-K2-Thinking \
+    --privileged \
+    --device=/dev/kfd \
+    --device=/dev/dri \
+    --device=/dev/mkfd \
+    --group-add video \
+    --cap-add=SYS_PTRACE \
+    --security-opt seccomp=unconfined \
+    -u root \
+    -v /opt/hyhal/:/opt/hyhal/:ro \
+    -v /path/your_code_data/:/path/your_code_data/ \
+    harbor.sourcefind.cn:5443/dcu/admin/base/custom:vllm0.15.1-ubuntu22.04-dtk26.04-0130-py3.10-20260220 bash
+```
+更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用。
+
+关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装，pycountry库需要单独安装：
+```
+pip install pycountry
+```
+
+## 数据集
+暂无
+
+## 训练
+暂无
+
+## 推理
+### vllm
+#### 多机推理
+1. 加入环境变量
+> 请注意：
+> 每个节点上的环境变量都写到.sh文件中，保存后各个计算节点分别source`.sh`文件
+>
+> VLLM_HOST_IP：节点本地通信口ip，尽量选择IB网卡的IP，**避免出现rccl超时问题**
+>
+> NCCL_SOCKET_IFNAME和 GLOO_SOCKET_IFNAME：节点本地通信网口ip对应的名称
+>
+> 通信口和ip查询方法：ifconfig
+>
+> IB口状态查询：ibstat  !!!一定要active激活状态才可用，各个节点要保持统一
+
+```bash
+export ALLREDUCE_STREAM_WITH_COMPUTE=1
+export VLLM_HOST_IP=x.x.x.x # 对应计算节点的IP，选择IB口SOCKET_IFNAME对应IP地址
+export NCCL_SOCKET_IFNAME=ibxxxx
+export GLOO_SOCKET_IFNAME=ibxxxx
+export NCCL_IB_HCA=mlx5_0:1 # 环境中的IB网卡名字
+unset NCCL_ALGO
+export NCCL_MIN_NCHANNELS=16
+export NCCL_MAX_NCHANNELS=16
+export NCCL_NET_GDR_READ=1
+export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export VLLM_SPEC_DECODE_EAGER=1
+export VLLM_MLA_DISABLE=0
+export VLLM_USE_FLASH_MLA=1
+export VLLM_RPC_TIMEOUT=1800000
+
+# 海光CPU绑定核
+export VLLM_NUMA_BIND=1
+export VLLM_RANK0_NUMA=0
+export VLLM_RANK1_NUMA=1
+export VLLM_RANK2_NUMA=2
+export VLLM_RANK3_NUMA=3
+export VLLM_RANK4_NUMA=4
+export VLLM_RANK5_NUMA=5
+export VLLM_RANK6_NUMA=6
+export VLLM_RANK7_NUMA=7
+```
+
+2. 启动RAY集群
+> x.x.x.x 对应第一步 VLLM_HOST_IP
+
+```bash
+# head节点执行
+ray start --head --node-ip-address=x.x.x.x --port=6379 --num-gpus=8 --num-cpus=32
+# worker节点执行
+ray start --address='x.x.x.x:6379' --num-gpus=8 --num-cpus=32
+```
+
+3. 启动vllm server
+
+```bash
+## serve启动
+
+vllm serve moonshotai/Kimi-K2-Thinking \
+    -tp 32 \
+    --distributed-executor-backend ray \
+    --mm-encoder-tp-mode data \
+    --trust-remote-code \
+    --tool-call-parser kimi_k2 \
+    --reasoning-parser kimi_k2
+
+## client访问
+curl http://localhost:8000/v1/chat/completions   \
+    -H "Content-Type: application/json"  \
+    -d '{
+        "model": "moonshotai/Kimi-K2-Thinking",
+        "messages": [
+          {"role": "user", "content": "which one is bigger, 9.11 or 9.9? think carefully"}
+        ],
+        "temperature": 0.6
+    }'
+```
+
+## 效果展示
+<div align=center>
+    <img src="./doc/result.png"/>
+</div>
+
+### 精度
+DCU与GPU精度一致，推理框架：vllm。
+
+## 预训练权重
+|  模型名称  | 权重大小 | DCU型号  | 最低卡数需求 |         下载地址          |
+|:------:|:----:|:----------:|:------:|:---------------------:|
+| Kimi-K2-Thinking | 1T | BW1000 |   32   | [Hugging Face](https://huggingface.co/moonshotai/Kimi-K2-Thinking) |
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/kimi-k2-thinking_vllm
+
+## 参考资料
+- https://github.com/MoonshotAI/Kimi-K2

model.properties

+# 模型唯一标识
+modelCode=2159
+# 模型名称
+modelName=Kimi-K2-Thinking_vllm 
+# 模型描述
+modelDescription=Kimi K2 Thinking是最新、能力最强的开源推理模型版本。
+# 运行过程
+processType=推理
+# 算法类别
+appCategory=对话问答
+# 框架类型
+frameType=vllm
+# 加速卡类型
+accelerateType=BW1000
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