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+# GLM-4.7-Flash examples
+
+## Environment Preparation
+
+### Model Weight
+
+- `GLM-4.7-Flash`(BF16 version): [Download model weight](https://www.modelscope.cn/models/ZhipuAI/GLM-4.7-Flash).
+
+### Installation
+
+The dependencies required for the NPU runtime environment have been integrated into a Docker image and uploaded to the quay.io platform. You can directly pull it.
+
+```bash
+#Atlas 800 A3
+docker pull quay.io/ascend/sglang:main-cann8.5.0-a3
+#Atlas 800 A2
+docker pull quay.io/ascend/sglang:main-cann8.5.0-910b
+
+#start container
+docker run -itd --shm-size=16g --privileged=true --name ${NAME} \
+--privileged=true --net=host \
+-v /var/queue_schedule:/var/queue_schedule \
+-v /etc/ascend_install.info:/etc/ascend_install.info \
+-v /usr/local/sbin:/usr/local/sbin \
+-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
+-v /usr/local/Ascend/firmware:/usr/local/Ascend/firmware \
+--device=/dev/davinci0:/dev/davinci0  \
+--device=/dev/davinci1:/dev/davinci1  \
+--device=/dev/davinci2:/dev/davinci2  \
+--device=/dev/davinci3:/dev/davinci3  \
+--device=/dev/davinci4:/dev/davinci4  \
+--device=/dev/davinci5:/dev/davinci5  \
+--device=/dev/davinci6:/dev/davinci6  \
+--device=/dev/davinci7:/dev/davinci7  \
+--device=/dev/davinci8:/dev/davinci8  \
+--device=/dev/davinci9:/dev/davinci9  \
+--device=/dev/davinci10:/dev/davinci10  \
+--device=/dev/davinci11:/dev/davinci11  \
+--device=/dev/davinci12:/dev/davinci12  \
+--device=/dev/davinci13:/dev/davinci13  \
+--device=/dev/davinci14:/dev/davinci14  \
+--device=/dev/davinci15:/dev/davinci15  \
+--device=/dev/davinci_manager:/dev/davinci_manager \
+--device=/dev/hisi_hdc:/dev/hisi_hdc \
+--entrypoint=bash \
+quay.io/ascend/sglang:${tag}
+```
+
+Note: When using this image, you need to update Transformers to version 5.3.0.
+
+``` shell
+# reinstall transformers
+pip install transformers==5.3.0
+```
+
+## Running GLM-4.7-Flash
+
+### Running GLM-4.7-Flash on 1 x Atlas 800I A3.
+
+Run the following script to execute online inference.
+
+```shell
+# high performance cpu
+echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
+sysctl -w vm.swappiness=0
+sysctl -w kernel.numa_balancing=0
+sysctl -w kernel.sched_migration_cost_ns=50000
+# bind cpu
+export SGLANG_SET_CPU_AFFINITY=1
+
+unset https_proxy
+unset http_proxy
+unset HTTPS_PROXY
+unset HTTP_PROXY
+unset ASCEND_LAUNCH_BLOCKING
+# cann
+source /usr/local/Ascend/ascend-toolkit/set_env.sh
+source /usr/local/Ascend/nnal/atb/set_env.sh
+
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export STREAMS_PER_DEVICE=32
+export HCCL_BUFFSIZE=1000
+export HCCL_OP_EXPANSION_MODE=AIV
+export HCCL_SOCKET_IFNAME=lo
+export GLOO_SOCKET_IFNAME=lo
+
+python3 -m sglang.launch_server \
+        --model-path $MODEL_PATH \
+        --tp-size 2 \
+        --attention-backend ascend \
+        --device npu \
+        --chunked-prefill-size 16384 \
+        --max-prefill-tokens 150000 \
+        --dtype bfloat16 \
+        --max-running-requests 32 \
+        --trust-remote-code \
+        --host 127.0.0.1 \
+        --mem-fraction-static 0.75 \
+        --port 8000 \
+        --cuda-graph-bs 1 2 4 8 16 32 \
+        --watchdog-timeout 9000
+```
+
+Note: TP size is currently limited to 2 or 4.
+
+### Running GLM-4.7-Flash on 1 x Atlas 800I A3 in slime-ascend.
+
+#### Preparation
+
+- [slime-ascend](https://gitcode.com/Ascend/slime-ascend) code
+
+#### Installation
+
+Run the following commands to install sglang. (Please replace '<slime-ascend-root>' with the path to the root directory of the slime codebase.')
+
+```bash
+git clone -b v0.5.8 https://github.com/sgl-project/sglang.git
+cd sglang
+mv python/pyproject_other.toml python/pyproject.toml
+pip install -e python[srt_npu]
+git checkout . && git checkout sglang-slime
+git am <slime-ascend-root>/docker/npu_patch/v0.2.2/sglang/*
+```
+
+Note: Make sure you are using Transformers 5.3.0.
+
+#### Execution
+
+Run the following script to execute online **inference**.
+
+```shell
+# high performance cpu
+echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
+sysctl -w vm.swappiness=0
+sysctl -w kernel.numa_balancing=0
+sysctl -w kernel.sched_migration_cost_ns=50000
+# bind cpu
+export SGLANG_SET_CPU_AFFINITY=1
+
+unset https_proxy
+unset http_proxy
+unset HTTPS_PROXY
+unset HTTP_PROXY
+unset ASCEND_LAUNCH_BLOCKING
+# cann
+source /usr/local/Ascend/ascend-toolkit/set_env.sh
+source /usr/local/Ascend/nnal/atb/set_env.sh
+
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export STREAMS_PER_DEVICE=32
+export HCCL_BUFFSIZE=1000
+export HCCL_OP_EXPANSION_MODE=AIV
+export HCCL_SOCKET_IFNAME=lo
+export GLOO_SOCKET_IFNAME=lo
+
+python3 -m sglang.launch_server \
+        --model-path $MODEL_PATH \
+        --tp-size 2 \
+        --attention-backend ascend \
+        --device npu \
+        --chunked-prefill-size 16384 \
+        --max-prefill-tokens 150000 \
+        --dtype bfloat16 \
+        --max-running-requests 32 \
+        --trust-remote-code \
+        --host 127.0.0.1 \
+        --mem-fraction-static 0.75 \
+        --port 8000 \
+        --cuda-graph-bs 1 2 4 8 16 32 \
+        --watchdog-timeout 9000
+```
+
+Refer to [Training and Deployment Example](https://gitcode.com/Ascend/slime-ascend/blob/main/docs/ascend_tutorial/examples/glm4.7-30B-A3B.md) for training and deployment.
+
+### Using Benchmark
+
+Refer to [Benchmark and Profiling](../developer_guide/benchmark_and_profiling.md) for details.