diff --git a/examples/auto_deploy/README.md b/examples/auto_deploy/README.md
index 343ad8c87f35..8cdf46239b7b 100644
--- a/examples/auto_deploy/README.md
+++ b/examples/auto_deploy/README.md
@@ -92,18 +92,6 @@ The script supports flexible configs:
 
 The default level is `INFO`.
 
-### Model Evaluation with LM Evaluation Harness
-
-lm-evaluation-harness is supported. To run the evaluation, please use the following command:
-
-```bash
-# model is defined the same as above. Other config args can also be specified in the model_args (comma separated).
-# You can specify any tasks supported with lm-evaluation-harness.
-cd examples/auto_deploy
-python lm_eval_ad.py \
---model autodeploy --model_args model=meta-llama/Meta-Llama-3.1-8B-Instruct,world_size=2 --tasks mmlu
-```
-
 ### Mixed-precision Quantization using Model Optimizer
 
 Model Optimizer [AutoQuantize](https://nvidia.github.io/Model-Optimizer/reference/generated/modelopt.torch.quantization.model_quant.html#modelopt.torch.quantization.model_quant.auto_quantize) algorithm is a PTQ algorithm from ModelOpt which quantizes a model by searching for the best quantization format per-layer while meeting the performance constraint specified by the user. This way, `AutoQuantize` enables to trade-off model accuracy for performance.
@@ -339,4 +327,4 @@ the current progress in AutoDeploy and where you can help.
 ## Disclaimer
 
 This project is under active development and is currently released as beta feature. The code is
-subject to change, and may include backward-incompatible updates.
+subject to change, and may include backward-incompatible updates.
\ No newline at end of file