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README.md 0 → 100644
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# Kimi-K2-Instruct-quantized.w4a16
## 论文
[Kimi-K2-Instruct](https://github.com/MoonshotAI/Kimi-K2/blob/main/tech_report.pdf)
## 模型简介
该模型是通过将**Kimi-K2-Instruct**的权重量化为INT4数据类型而获得的。这种优化将表示权重所需的位数从16 (FP16/BF16) 减少到4，从而减少了GPU内存需求（约减少75%）。这种权重量化还使模型的磁盘大小减少了约75%。
原始的Kimi K2是一个最先进的专家混合（MoE）语言模型，具有320亿个激活参数和总共1万亿个参数。使用Muon优化器训练后，Kimi K2在前沿知识、推理和编码任务中表现出色，同时精心优化了代理能力。
**主要特点**
- INT4量化：该模型已被量化为INT4，显著减少了内存占用，并实现了高吞吐量、低延迟的推理。
- 大规模训练：在一个15.5T令牌上预训练了一个1T参数的MoE模型，且无训练不稳定性。
- MuonClip优化器：我们在前所未有的规模上应用了Muon优化器，并开发了新的优化技术来解决扩展过程中的不稳定性问题。
- 代理智能：特别设计用于工具使用、推理和自主解决问题。
<div align=center>
<img src="./doc/banner.png"/>
</div>
## 环境依赖
| 软件 | 版本 |
| :------: | :------: |
| DTK | 25.04.2 |
| python | 3.10 |
| transformers | 4.57.3 |
| vllm | 0.11 |
| torch | 2.5.1+das.opt1.dtk25041 |
推荐使用镜像: harbor.sourcefind.cn:5443/dcu/admin/base/vllm:0.11.0-ubuntu22.04-dtk25.04.2-1226-das1.7-py3.10-20251226
- 挂载地址`-v`根据实际模型情况修改
```bash
docker run -it \
--shm-size 60g \
--network=host \
--name glm-4.7 \
--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/vllm:0.11.0-ubuntu22.04-dtk25.04.2-1226-das1.7-py3.10-20251226 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。
## 数据集
暂无
## 训练
暂无
## 推理
### vllm
#### 多机推理
1. 环境变量配置
```bash
export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export ALLREDUCE_STREAM_WITH_COMPUTE=1
export VLLM_HOST_IP=x.x.x.x # 对应计算节点的IP，建议选择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 # 根据 ibstat 查看
unset NCCL_ALGO
export NCCL_TOPO_FILE=null
export NCCL_MIN_NCHANNELS=16
export NCCL_MAX_NCHANNELS=16
export NCCL_NET_GDR_READ=1
export VLLM_RPC_TIMEOUT=1800000
# 海光CPU绑定核,intel cpu可不加
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
# 量化相关
export AWQ_MOE_SZ=0
export W4A16_MOE_CUDA=0
export VLLM_USE_LIGHTOP_MOE_ALIGN=0
export VLLM_USE_LIGHTOP=0
export VLLM_USE_PIECEWISE=1
```
2. 启动RAY集群
> x.x.x.x 对应第一步 Master节点的 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
vllm serve RedHatAI/Kimi-K2-Instruct-quantized.w4a16 \
--served-model-name kimi-k2 \
--tensor-parallel-size 16 \
--dtype float16 \
--trust-remote-code \
--port 8001 \
--distributed-executor-backend ray
```
启动完成后可通过以下方式访问：
> "max_tokens" 参数为必选参数，如不设置，可能出现模型长时间没有输出的问题。
```bash
curl http://localhost:8001/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "kimi-k2",
"messages": [
{"role": "system", "content": "You are Kimi, an AI assistant created by Moonshot AI."},
{"role": "user", "content": "Please give a brief self-introduction."}
],
"max_tokens": 512,
"temperature": 0.7
}'
```
## 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-Instruct-quantized.w4a16 | 146B | BW1000 | 16 | [Hugging Face](https://huggingface.co/RedHatAI/Kimi-K2-Instruct-quantized.w4a16) |
## 源码仓库及问题反馈
- https://developer.sourcefind.cn/codes/modelzoo/kimi-k2-instruct-quantized.w4a16_vllm
## 参考资料
- https://github.com/MoonshotAI/Kimi-K2
- https://huggingface.co/RedHatAI/Kimi-K2-Instruct-quantized.w4a16
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# 模型唯一标识
modelCode=1915
# 模型名称
modelName=kimi-k2-instruct-quantized.w4a16_vllm
# 模型描述
modelDescription=kimi-k2-instruct-quantized.w4a16是通过将Kimi-K2-Instruct的权重量化为INT4数据类型而获得的。
# 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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