首先安装官方提供镜像
# Pull the latest image
docker pull zhuzilin/slime:latest
# start the docker
docker run -d -it --network=host --gpus all --privileged --ipc=host --shm-size=16g --ulimit memlock=-1 --ulimit stack=67108864 -v /model/:/workspace/infrawaves/ --name
slime f01f4f265d5d /bin/bash 安装slime源码
git clone <https://github.com/THUDM/slime.git>
cd slime
pip install -e .download模型与数据集、验证集
export HF_HUB_ENABLE_HF_TRANSFER=1
export HF_ENDPOINT=https://hf-mirror.com
# Download model weights (GLM-Z1-9B)
hf download zai-org/GLM-Z1-9B-0414 --local-dir /root/GLM-Z1-9B-0414
# Download training dataset (dapo-math-17k)
hf download --repo-type dataset zhuzilin/dapo-math-17k \\
--local-dir /root/dapo-math-17k
# Download evaluation dataset (aime-2024)
hf download --repo-type dataset zhuzilin/aime-2024 \\
--local-dir /root/aime-2024转换hugging face权重为megatron格式
PYTHONPATH=/root/Megatron-LM python tools/convert_hf_to_torch_dist.py \\
${MODEL_ARGS[@]} \\
--hf-checkpoint /root/GLM-Z1-9B-0414 \\
--save /root/GLM-Z1-9B-0414_torch_dist启动脚本
cd /root/slime
bash scripts/run-glm4-9B.sh观察到会有如下的输出
其中，核心的perf信息如下：
