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README.md 0 → 100644
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## GLM-5.1
## 论文
[GLM-5.1: Towards Long-Horizon Tasks](https://z.ai/blog/glm-5.1)
## 模型简介
GLM-5.1 是智谱AI面向智能体工程的下一代旗舰模型，其代码能力相比前代显著增强。它在 SWE-Bench Pro 上达到了当前最优水平，并在 NL2Repo（代码仓库生成）和 Terminal-Bench 2.0（真实终端任务）上大幅领先于 GLM-5。
<div align=center>
<img src="./doc/bench_51.png"/>
</div>
但最具意义的飞跃并不仅限于首次通过的表现。以往的模型（包括 GLM-5）往往过早耗尽其能力储备：它们会快速应用熟悉的技术以获得初步成果，随后便陷入停滞。即使给予更多时间也无济于事。
相比之下，GLM-5.1 被设计为在更长的时间跨度内持续高效地处理智能体任务。该模型在面对模糊问题时具备更佳的判断力，并能在更长时间的会话中保持高效产出。它能够分解复杂问题、运行实验、解读结果，并精准识别障碍点。通过不断回顾自身推理过程并反复迭代调整策略，GLM-5.1 能在数百轮交互和数千次工具调用中持续优化。运行时间越长，结果越好。
## 环境依赖
| 软件 | 版本 |
| :------: |:-------:|
| DTK | 26.04 |
| python | 3.10.12 |
| transformers | 5.2.0 |
| torch | 2.9.0+das.opt1.dtk2604.20260331.g4e3c1e7 |
| vllm | 0.15.1+das.opt1.alpha.dtk2604.torch290.2604042155.gba9f96 |
当前仅支持镜像: harbor.sourcefind.cn:5443/dcu/admin/base/custom:vllm015-ubuntu22.04-dtk26.04-glm5-0408
- 挂载地址`-v`根据实际模型情况修改
```bash
docker run -it \
--shm-size 256g \
--network=host \
--name glm-5.1 \
--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:vllm015-ubuntu22.04-dtk26.04-glm5-0408 bash
```
更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用。
## 数据集
`暂无`
## 训练
`暂无`
## 推理
### vllm
#### 单机推理
1. 加入环境变量
```bash
# 环境变量
rm -rf ~/.cache
rm -rf ~/.triton
export VLLM_TORCH_PROFILER_DIR=/home/GLM-5/
export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export ALLREDUCE_STREAM_WITH_COMPUTE=1
export NCCL_MIN_NCHANNELS=16
export NCCL_MAX_NCHANNELS=16
export Allgather_Base_STREAM_WITH_COMPUTE=1
export SENDRECV_STREAM_WITH_COMPUTE=1
export HIP_KERNEL_EVENT_SYSTENFENCE=1
export VLLM_RPC_TIMEOUT=1800000
export VLLM_USE_PD_SPLIT=1
export VLLM_USE_PIECEWISE=1
export VLLM_REJECT_SAMPLE_OPT=1
export USE_FUSED_RMS_QUANT=0
export USE_FUSED_SILU_MUL_QUANT=1
export VLLM_USE_GLOBAL_CACHE13=1
export VLLM_FUSED_MOE_CHUNK_SIZE=16384
export VLLM_CUSTOM_CACHE=1
export VLLM_USE_OPT_CAT=1
export VLLM_USE_FUSED_FILL_RMS_CAT=1
export VLLM_USE_LIGHTOP_MOE_SUM_MUL_ADD=0
export VLLM_USE_LIGHTOP_RMS_ROPE_CONCAT=0
export VLLM_USE_V32_ENCODE=1
export VLLM_USE_FLASH_MLA=1
export VLLM_DISABLE_DSA=0
export USE_LIGHTOP_TOPK=1
export USE_LIGHTOP_PER_TOKEN_GROUP_QUANT_FP8=1
export USE_LIGHTOP_CONVERT_REQ_INDEX_TO_GLOBAL_INDEX=1
```
2. 启动vllm serve
```bash
vllm serve ZhipuAI/GLM-5.1-FP8 \
--gpu-memory-utilization 0.925 \
--port 8001 \
--tensor-parallel-size 8 \
--tool-call-parser glm47 \
--reasoning-parser glm45 \
--enable-auto-tool-choice \
--kv-cache-dtype fp8_ds_mla \
--served-model-name glm-5.1-fp8 \
--disable-log-requests \
--compilation-config '{"pass_config": {"fuse_act_quant": false}}'
```
3. 启动完成后可通过以下方式访问：
```bash
curl http://localhost:8001/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "glm-5.1-fp8",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Summarize GLM-5 in one sentence."}
],
"max_tokens": 4096,
"temperature": 0.7,
"chat_template_kwargs": {"enable_thinking": false}
}'
```
#### 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 serve
```bash
vllm serve ZhipuAI/GLM-5.1 \
--port 8001 \
--trust-remote-code \
--tensor-parallel-size 32 \
--gpu-memory-utilization 0.85 \
--distributed-executor-backend ray \
--dtype bfloat16 \
--max-model-len 32768 \
--tool-call-parser glm47 \
--reasoning-parser glm45 \
--enable-auto-tool-choice \
--served-model-name glm-5.1
```
4. 启动完成后可通过以下方式访问：
```bash
curl http://localhost:8001/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "glm-5.1",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Summarize GLM-5 in one sentence."}
],
"max_tokens": 4096,
"temperature": 1
}'
```
## 效果展示
<div align=center>
<img src="./doc/result-DCU.jpg"/>
</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|
|:-----:|:----------:|:----------:|:---------------------:|:----------:|
| GLM-5.1 | 754B | BW1000,BW1100 | 32 | [ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-5.1) |
| GLM-5.1-FP8 | 754B | BW1100 | 8 | [ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-5.1-FP8) |
## 源码仓库及问题反馈
- https://developer.sourcefind.cn/codes/modelzoo/glm-5.1_vllm
## 参考资料
- https://github.com/zai-org/GLM-5
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icon.png

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model.properties 0 → 100644
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# 模型唯一标识
modelCode=2322
# 模型名称
modelName=GLM-5.1_vllm
# 模型描述
modelDescription=GLM-5.1 是智谱AI面向智能体工程的下一代旗舰模型，其代码能力相比前代显著增强。它在 SWE-Bench Pro 上达到了当前最优水平，并在 NL2Repo（代码仓库生成）和 Terminal-Bench 2.0（真实终端任务）上大幅领先于 GLM-5。
# 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,BW1100
\ No newline at end of file
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