Update README.md

README.md

@@ -186,22 +186,6 @@ and use a token with write access, from https://huggingface.co/settings/tokens
 # Model Quality
 We rely on [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate the quality of the quantized model.
 
-Need to install lm-eval from source:
-https://github.com/EleutherAI/lm-evaluation-harness#install
-
-## baseline
-```Shell
-lm_eval --model hf --model_args pretrained=microsoft/Phi-4-mini-instruct --tasks hellaswag --device cuda:0 --batch_size 8
-```
-
-## int4 weight only quantization with hqq (int4wo-hqq)
-```Shell
-export MODEL=pytorch/Qwen3-8B-int4wo-hqq
-# or
-# export MODEL=Qwen/Qwen3-8B
-lm_eval --model hf --model_args pretrained=$MODEL --tasks hellaswag --device cuda:0 --batch_size 8
-```
-
 | Benchmark                        |                |                           |
 |----------------------------------|----------------|---------------------------|
 |                                  | Qwen3-8B       | Qwen3-8B-int4wo           | 
@@ -217,7 +201,26 @@ lm_eval --model hf --model_args pretrained=$MODEL --tasks hellaswag --device cud
 | gsm8k                            | 87.79          | 86.28                     |
 | leaderboard_math_hard (v3)       | 53.7           | 46.83                     |
 | **Overall**                      | 60.02          | 56.33                     |
- 
+
+<details>
+<summary> Reproduce Model Quality Results </summary>
+
+Need to install lm-eval from source:
+https://github.com/EleutherAI/lm-evaluation-harness#install
+
+## baseline
+```Shell
+lm_eval --model hf --model_args pretrained=microsoft/Phi-4-mini-instruct --tasks hellaswag --device cuda:0 --batch_size 8
+```
+
+## int4 weight only quantization with hqq (int4wo-hqq)
+```Shell
+export MODEL=pytorch/Qwen3-8B-int4wo-hqq
+# or
+# export MODEL=Qwen/Qwen3-8B
+lm_eval --model hf --model_args pretrained=$MODEL --tasks hellaswag --device cuda:0 --batch_size 8
+```
+</details>
 
 # Peak Memory Usage
 
@@ -229,7 +232,8 @@ lm_eval --model hf --model_args pretrained=$MODEL --tasks hellaswag --device cud
 | Peak Memory (GB) | 16.47          | 6.27 (62% reduction)           |
 
 
-## Code Example
+<details>
+<summary> Reproduce Peak Memory Usage Results </summary>
 
 We can use the following code to get a sense of peak memory usage during inference:
 
@@ -273,6 +277,8 @@ mem = torch.cuda.max_memory_reserved() / 1e9
 print(f"Peak Memory Usage: {mem:.02f} GB")
 ```
 
+</details>
+
 # Model Performance
 
 Our int4wo is only optimized for batch size 1, so expect some slowdown with larger batch sizes, we expect this to be used in local server deployment for single or a few users where the decode tokens per second will matters more than the time to first token.
@@ -287,6 +293,9 @@ Our int4wo is only optimized for batch size 1, so expect some slowdown with larg
 Note the result of latency (benchmark_latency) is in seconds, and serving (benchmark_serving) is in number of requests per second.
 Int4 weight only is optimized for batch size 1 and short input and output token length, please stay tuned for models optimized for larger batch sizes or longer token length.
 
+<details>
+<summary> Reproduce Model Performance Results </summary>
+
 ## Setup
 
 Get vllm source code:
@@ -356,7 +365,7 @@ Client:
 export MODEL=pytorch/Qwen3-8B-int4wo-hqq
 python benchmarks/benchmark_serving.py --backend vllm --dataset-name sharegpt --tokenizer $MODEL --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json --model $MODEL --num-prompts 1
 ```
-
+</details>
 
 # Disclaimer
 PyTorch has not performed safety evaluations or red teamed the quantized models. Performance characteristics, outputs, and behaviors may differ from the original models. Users are solely responsible for selecting appropriate use cases, evaluating and mitigating for accuracy, safety, and fairness, ensuring security, and complying with all applicable laws and regulations.