Add benchmarking command.

README.md

@@ -6,7 +6,7 @@ license: apache-2.0
 P-EAGLE is a parallel-drafting speculative decoding model that generates K draft tokens in a single forward pass. It transforms EAGLE—the state-of-the-art speculative decoding method—from autoregressive to parallel draft generation.
 
 ### Model Details
-The model architecture is illustrated in the following figure. Specifically, we trained a 4-layer P-EAGLE for GPT-OSS 120B as the target model.
+The model architecture is illustrated in the following figure. Specifically, we trained a 4-layer P-EAGLE for GPT-OSS 120B as the target model, with number of parallel-token prediction as 8.  
 
 <img src="https://cdn-uploads.huggingface.co/production/uploads/64ab5fe189aa67e4a251b6b4/UBBMgZvXkOduu_LpUunQy.png" width="50%">
 
@@ -48,8 +48,29 @@ CUDA_VISIBLE_DEVICES=0 VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8=1 \
 ```
 
 ### Evaluation
-From vllm-bench, with speculation length of 5, we see the following acceptance length.
+From vllm-bench, with speculation length of 5 and max-new-token of 2048, we see the following acceptance length. 
 - **MT-Bench**: 2.68.
+- **HumanEval**: 3.15.
+- **GSM-8K**: 3.55.
+
+The command to run benchmarking is shown as below.
+
+```
+vllm bench serve \
+    --backend openai-chat \
+    --base-url http://localhost:8040 \
+    --endpoint /v1/chat/completions \
+    --model openai/gpt-oss-120b \
+    --dataset-name custom \
+    --dataset-path /home/ubuntu/eval_datasets/humaneval_custom.jsonl \
+    --custom-output-len 2048 \
+    --num-prompts 164 \
+    --max-concurrency 1 \
+    --request-rate inf \
+    --temperature 0 \
+    --save-result \
+    --save-detailed \
+```
 
 ### Ciatation
 ```