Update README.md

README.md

@@ -188,10 +188,6 @@ print(f"Peak Memory Usage: {mem:.02f} GB")
 
 Note the result of latency (benchmark_latency) is in seconds, and serving (benchmark_serving) is in number of requests per second.
 
-## Download dataset
-Download sharegpt dataset: `wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json`
-
-Other datasets can be found in: https://github.com/vllm-project/vllm/tree/main/benchmarks
 ## benchmark_latency
 
 Need to install vllm nightly to get some recent changes
@@ -199,7 +195,12 @@ Need to install vllm nightly to get some recent changes
 pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
 ```
 
-Run the following under `vllm` source code root folder:
+Get vllm source code:
+```
+git clone [email protected]:vllm-project/vllm.git
+```
+
+Run the following under `vllm` root folder:
 
 ### baseline
 ```
@@ -215,7 +216,15 @@ python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model
 
 We also benchmarked the throughput in a serving environment.
 
-Run the following under `vllm` source code root folder:
+Download sharegpt dataset: `wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json`
+Other datasets can be found in: https://github.com/vllm-project/vllm/tree/main/benchmarks
+
+Get vllm source code:
+```
+git clone [email protected]:vllm-project/vllm.git
+```
+
+Run the following under `vllm` root folder:
 
 ### baseline
 Server: