完善readme

README.md

@@ -24,7 +24,25 @@ fastllm是纯c++实现的高性能大模型推理库
 在光源可拉取推理的docker镜像，拉取方式如下：
 
 ```
-docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:glm-ft-v1.0
+docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk23.10.1-py38
+```
+
+### 容器启动
+
+模型推理容器启动命令参考如下，用户根据需要修改：
+
+```
+# <container_name> 自定义容器名
+# <project_path> 当前工程所在路径
+docker run -it --name=<container_name> -v <project_path>:/work -w /work --privileged -v /opt/hyhal:/opt/hyhal --device=/dev/kfd --device=/dev/dri --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --ipc=host --network host --shm-size=16G --group-add video image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk23.10.1-py38 /bin/bash
+```
+
+### 加载环境
+
+进入容器后执行如下命令，加载运行环境变量
+
+```
+source /opt/dtk/cuda/env.sh
 ```
 
 ### 编译

tools/scripts/chatglm_export.py

@@ -3,7 +3,7 @@ from transformers import AutoTokenizer, AutoModel
 from fastllm_pytools import torch2flm
 
 if __name__ == "__main__":
-    model_path = "/home/ZhipuAI/chatglm3-6b"
+    model_path = "THUDM/chatglm2-6b"
     tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
     model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
     model = model.eval()