diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/README.md b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/README.md
new file mode 100644
index 00000000000..c0f4c555dbb
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/README.md
@@ -0,0 +1,64 @@
+This example provides an end-to-end workflow to quantize DeepSeek models to MXFP4/MXFP8 and evaluate them using a custom vLLM fork.
+
+## Requirement
+```bash
+pip install neural-compressor-pt==3.7
+# auto-round
+pip install auto-round==0.9.1
+# vLLM
+git clone -b fused-moe-ar --single-branch --quiet https://github.com/yiliu30/vllm-fork.git && cd vllm-fork
+VLLM_USE_PRECOMPILED=1 pip install --editable . -vvv
+# other requirements
+pip install -r requirements.txt
+```
+
+### Quantize Model
+- Export model path
+```bash
+export MODEL=deepseek-ai/DeepSeek-R1
+```
+
+- MXFP8
+```bash
+bash run_quant.sh --model $MODEL -t mxfp8 --output_dir ./qmodels
+```
+
+- MXFP4
+```bash
+bash run_quant.sh --model $MODEL -t mxfp4 --output_dir ./qmodels
+```
+
+## Evaluation
+
+### Prompt Tests
+
+Usage: 
+```bash
+bash ./run_generate.sh -s [mxfp4|mxfp8] -tp [tensor_parallel_size] -m [model_path]
+```
+
+- MXFP8
+```bash
+bash ./run_generate.sh -s mxfp8 -tp 8 -m /path/to/ds_mxfp8
+```
+- MXFP4
+```bash
+bash ./run_generate.sh -s mxfp4 -tp 8 -m /path/to/ds_mxfp4
+```
+### Evaluation
+
+
+Usage: 
+```bash
+bash run_evaluation.sh -m [model_path] -s [mxfp4|mxfp8] -t [task_name] -tp [tensor_parallel_size] -b [batch_size]
+```
+```bash
+bash run_evaluation.sh -s mxfp8 -t piqa,hellaswag,mmlu -tp 8 -b 512 -m /path/to/ds_mxfp8
+bash run_evaluation.sh -s mxfp8 -t gsm8k -tp 8 -b 256 -m /path/to/ds_mxfp8
+
+```
+- MXFP4
+```bash
+bash run_evaluation.sh -s mxfp4 -t piqa,hellaswag,mmlu -tp 8 -b 512 -m /path/to/ds_mxfp4
+bash run_evaluation.sh -s mxfp4 -t gsm8k -tp 8 -b 256 -m /path/to/ds_mxfp4
+```
\ No newline at end of file
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/generate.py b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/generate.py
new file mode 100644
index 00000000000..ec150a71576
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/generate.py
@@ -0,0 +1,67 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copied from https://github.com/vllm-project/vllm/
+
+from vllm import LLM, EngineArgs
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+
+
+
+def create_parser():
+    parser = FlexibleArgumentParser()
+    # Add engine args
+    EngineArgs.add_cli_args(parser)
+    parser.set_defaults(model="meta-llama/Llama-3.2-1B-Instruct")
+    # Add sampling params
+    sampling_group = parser.add_argument_group("Sampling parameters")
+    sampling_group.add_argument("--max-tokens", type=int)
+    sampling_group.add_argument("--temperature", type=float)
+    sampling_group.add_argument("--top-p", type=float)
+    sampling_group.add_argument("--top-k", type=int)
+
+    return parser
+
+
+def main(args: dict):
+    # Pop arguments not used by LLM
+    max_tokens = args.pop("max_tokens")
+    temperature = args.pop("temperature")
+    top_p = args.pop("top_p")
+    top_k = args.pop("top_k")
+
+    # Create an LLM
+    llm = LLM(**args)
+
+    # Create a sampling params object
+    sampling_params = llm.get_default_sampling_params()
+    if max_tokens is not None:
+        sampling_params.max_tokens = max_tokens
+    if temperature is not None:
+        sampling_params.temperature = temperature
+    if top_p is not None:
+        sampling_params.top_p = top_p
+    if top_k is not None:
+        sampling_params.top_k = top_k
+
+    # Generate texts from the prompts. The output is a list of RequestOutput
+    # objects that contain the prompt, generated text, and other information.
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+    outputs = llm.generate(prompts, sampling_params)
+    # Print the outputs.
+    print("-" * 50)
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}")
+        print("-" * 50)
+
+
+if __name__ == "__main__":
+    parser = create_parser()
+    args: dict = vars(parser.parse_args())
+    main(args)
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/quantize.py b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/quantize.py
new file mode 100644
index 00000000000..2192948ce73
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/quantize.py
@@ -0,0 +1,113 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import transformers
+import logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+topologies_config = {
+    "mxfp8": {
+        "scheme": "MXFP8",
+        "fp_layers": "lm_head",
+        "iters": 0,
+    },
+    "mxfp4": {
+        "scheme": "MXFP4",
+        "fp_layers": "lm_head,self_attn",
+        "iters": 0,
+    },
+}
+
+
+def get_model_and_tokenizer(model_name):
+    # Load model and tokenizer
+    fp32_model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        device_map="cpu",
+        trust_remote_code=True,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_name,
+        trust_remote_code=True,
+    )
+    return fp32_model, tokenizer
+
+
+def quant_model(args):
+    from neural_compressor.torch.quantization import (
+        AutoRoundConfig,
+        convert,
+        prepare,
+    )
+
+    config = topologies_config[args.t]
+    export_format = "auto_round" if args.use_autoround_format else "llm_compressor"
+    output_dir = f"{args.output_dir}/quantized_model_{args.t}"
+    fp32_model, tokenizer = get_model_and_tokenizer(args.model)
+    quant_config = AutoRoundConfig(
+        tokenizer=tokenizer,
+        scheme=config["scheme"],
+        enable_torch_compile=args.enable_torch_compile,
+        iters=config["iters"],
+        fp_layers=config["fp_layers"],
+        export_format=export_format,
+        output_dir=output_dir,
+    )
+
+    # quantizer execute
+    model = prepare(model=fp32_model, quant_config=quant_config)
+    inc_model = convert(model)
+    logger.info(f"Quantized model saved to {output_dir}")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    # Parse command-line arguments
+    parser = argparse.ArgumentParser(description="Select a quantization scheme.")
+    parser.add_argument(
+        "--model",
+        type=str,
+        help="Path to the pre-trained model or model identifier from Hugging Face Hub.",
+    )
+    parser.add_argument(
+        "-t",
+        type=str,
+        choices=topologies_config.keys(),
+        default="mxfp4",
+        help="Quantization scheme to use. Available options: " + ", ".join(topologies_config.keys()),
+    )
+
+    parser.add_argument(
+        "--enable_torch_compile",
+        action="store_true",
+        help="Enable torch compile for the model.",
+    )
+    parser.add_argument(
+        "--use_autoround_format",
+        action="store_true",
+        help="Use AutoRound format for saving the quantized model.",
+    )
+
+    parser.add_argument(
+        "--skip_attn",
+        action="store_true",
+        help="Skip quantize attention layers.",
+    )
+    parser.add_argument(
+        "--iters",
+        type=int,
+        default=0,
+        help="Number of iterations for quantization.",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="./",
+        help="Directory to save the quantized model.",
+    )
+
+    args = parser.parse_args()
+
+    quant_model(args)
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/requirements.txt b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/requirements.txt
new file mode 100644
index 00000000000..80392549f26
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/requirements.txt
@@ -0,0 +1,2 @@
+lm-eval==0.4.9.1
+loguru
\ No newline at end of file
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/run_evaluation.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/run_evaluation.sh
new file mode 100644
index 00000000000..1d805c7872b
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/run_evaluation.sh
@@ -0,0 +1,120 @@
+#!/bin/bash
+set -e
+
+# Usage: ./run_evaluation.sh -m [model_path] -s [mxfp4|mxfp8] -t [task_name] -tp [tensor_parallel_size] -b [batch_size]
+# Default values
+MODEL_PATH=""
+SCHEME="mxfp8"
+TASK_NAME="piqa,hellaswag,mmlu"
+TP_SIZE=8
+BATCH_SIZE=512
+
+# Function to display usage
+usage() {
+    echo "Usage: $0 -m [model_path] -s [mxfp4|mxfp8] -t [task_name] -tp [tensor_parallel_size] -b [batch_size]"
+    echo "  -m: Path to the quantized model (required)"
+    echo "  -s: Quantization scheme (mxfp4 or mxfp8, default: mxfp8)"
+    echo "  -t: Task name(s) to evaluate (default: piqa,hellaswag,mmlu)"
+    echo "  -tp: Tensor parallelism size (default: 8)"
+    echo "  -b: Batch size (default: 512)"
+    echo ""
+    echo "Examples:"
+    echo "  $0 -m /path/to/model -s mxfp4 -t gsm8k -tp 4 -b 256"
+    echo "  $0 -m /path/to/model -s mxfp8 -t piqa,hellaswag -tp 8 -b 512"
+}
+
+# Parse command-line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        -m)
+            MODEL_PATH="$2"
+            shift 2
+            ;;
+        -s)
+            SCHEME="$2"
+            shift 2
+            ;;
+        -t)
+            TASK_NAME="$2"
+            shift 2
+            ;;
+        -tp)
+            TP_SIZE="$2"
+            shift 2
+            ;;
+        -b)
+            BATCH_SIZE="$2"
+            shift 2
+            ;;
+        -h|--help)
+            usage
+            exit 0
+            ;;
+        *)
+            echo "Invalid option: $1" >&2
+            usage
+            exit 1
+            ;;
+    esac
+done
+
+# Validate required arguments
+if [[ -z "$MODEL_PATH" ]]; then
+    echo "Error: Model path (-m) is required."
+    usage
+    exit 1
+fi
+
+# Extract model name and set output directory
+MODEL_NAME=$(basename ${MODEL_PATH})
+OUTPUT_DIR="${MODEL_NAME}-tp${TP_SIZE}-eval"
+
+# Create output directory
+mkdir -p ${OUTPUT_DIR}
+
+# Set environment variables based on the quantization scheme
+if [[ "$SCHEME" == "mxfp4" ]]; then
+    VLLM_AR_MXFP4_MODULAR_MOE=1
+    VLLM_MXFP4_PRE_UNPACK_TO_FP8=1
+    VLLM_MXFP4_PRE_UNPACK_WEIGHTS=0
+    VLLM_ENABLE_STATIC_MOE=0
+    VLLM_USE_DEEP_GEMM=0
+    VLLM_ENABLE_AR_EXT=1
+elif [[ "$SCHEME" == "mxfp8" ]]; then
+    VLLM_AR_MXFP4_MODULAR_MOE=0
+    VLLM_MXFP4_PRE_UNPACK_TO_FP8=0
+    VLLM_MXFP4_PRE_UNPACK_WEIGHTS=0
+    VLLM_ENABLE_STATIC_MOE=0
+    VLLM_USE_DEEP_GEMM=0
+    VLLM_ENABLE_AR_EXT=1
+else
+    echo "Error: Invalid quantization scheme (-s). Must be 'mxfp4' or 'mxfp8'."
+    usage
+    exit 1
+fi
+
+# Run evaluation
+echo "Evaluating model: ${MODEL_PATH}"
+echo "Quantization scheme: ${SCHEME}"
+echo "Tasks: ${TASK_NAME}"
+echo "Tensor parallelism size: ${TP_SIZE}"
+echo "Batch size: ${BATCH_SIZE}"
+echo "Output directory: ${OUTPUT_DIR}"
+
+VLLM_WORKER_MULTIPROC_METHOD=spawn \
+VLLM_ENABLE_AR_EXT=$VLLM_ENABLE_AR_EXT \
+VLLM_AR_MXFP4_MODULAR_MOE=$VLLM_AR_MXFP4_MODULAR_MOE \
+VLLM_MXFP4_PRE_UNPACK_TO_FP8=$VLLM_MXFP4_PRE_UNPACK_TO_FP8 \
+VLLM_MXFP4_PRE_UNPACK_WEIGHTS=$VLLM_MXFP4_PRE_UNPACK_WEIGHTS \
+VLLM_ENABLE_STATIC_MOE=$VLLM_ENABLE_STATIC_MOE \
+VLLM_USE_DEEP_GEMM=$VLLM_USE_DEEP_GEMM \
+VLLM_ENABLE_V1_MULTIPROCESSING=1 \
+lm_eval --model vllm \
+  --model_args "pretrained=${MODEL_PATH},tensor_parallel_size=${TP_SIZE},max_model_len=8192,max_num_batched_tokens=32768,max_num_seqs=128,add_bos_token=True,gpu_memory_utilization=0.8,dtype=bfloat16,max_gen_toks=2048,enable_prefix_caching=False" \
+  --tasks $TASK_NAME \
+  --batch_size $BATCH_SIZE \
+  --log_samples \
+  --limit 64 \
+  --seed 42 \
+  --output_path ${OUTPUT_DIR} \
+  --show_config 2>&1 | tee ${OUTPUT_DIR}/log.txt
\ No newline at end of file
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/run_generate.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/run_generate.sh
new file mode 100644
index 00000000000..c9ee73ce182
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/run_generate.sh
@@ -0,0 +1,118 @@
+#!/bin/bash
+set -e
+
+# Model Testing Script
+# Usage: ./run_generate.sh -s [mxfp4|mxfp8] -m [model_path] -tp [tensor_parallel_size]
+
+# Default values
+QUANT_TYPE="mxfp8"
+MODEL_PATH="/path/to/quantized_model"
+TP_SIZE=8
+
+# Function to display usage
+usage() {
+    echo "Usage: $0 -s [mxfp4|mxfp8] -m [model_path] -tp [tensor_parallel_size]"
+    echo "  -s: Quantization scheme (mxfp4 or mxfp8, default: mxfp8)"
+    echo "  -m: Path to quantized model (required)"
+    echo "  -tp: Tensor parallelism size (default: 8)"
+    echo ""
+    echo "Examples:"
+    echo "  $0 -s mxfp4 -m /path/to/my/model -tp 4"
+    echo "  $0 -m /path/to/my/model"
+    echo "  $0 -s mxfp8 -m /path/to/my/model"
+}
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        -s)
+            QUANT_TYPE="$2"
+            shift 2
+            ;;
+        -m)
+            MODEL_PATH="$2"
+            shift 2
+            ;;
+        -tp)
+            TP_SIZE="$2"
+            shift 2
+            ;;
+        -h)
+            usage
+            exit 0
+            ;;
+        *)
+            echo "Invalid option: $1" >&2
+            usage
+            exit 1
+            ;;
+    esac
+done
+
+
+# Validate quantization type
+QUANT_TYPE_UPPER=$(echo "$QUANT_TYPE" | tr '[:lower:]' '[:upper:]')
+if [[ "$QUANT_TYPE_UPPER" != "MXFP4" && "$QUANT_TYPE_UPPER" != "MXFP8" ]]; then
+    echo "Error: Quantization type must be mxfp4 or mxfp8"
+    usage
+    exit 1
+fi
+
+# Validate model path
+if [[ "$MODEL_PATH" == "/path/to/quantized_model" ]]; then
+    echo "Error: Model path is required (-m)"
+    usage
+    exit 1
+fi
+
+if [[ ! -d "$MODEL_PATH" ]]; then
+    echo "Error: Model path '$MODEL_PATH' does not exist or is not a directory"
+    exit 1
+fi
+
+# Validate TP_SIZE is a number
+if ! [[ "$TP_SIZE" =~ ^[0-9]+$ ]] || [ "$TP_SIZE" -lt 1 ]; then
+    echo "Error: Tensor parallelism size must be a positive integer"
+    exit 1
+fi
+
+echo "Running $QUANT_TYPE_UPPER test with:"
+echo "  Model: $MODEL_PATH"
+echo "  Tensor Parallelism: $TP_SIZE"
+echo ""
+
+# Set environment variables based on quantization type
+if [[ "$QUANT_TYPE_UPPER" == "MXFP4" ]]; then
+    export VLLM_AR_MXFP4_MODULAR_MOE=1
+    export VLLM_MXFP4_PRE_UNPACK_TO_FP8=1
+    echo "Using MXFP4 configuration"
+else
+    export VLLM_AR_MXFP4_MODULAR_MOE=0
+    export VLLM_MXFP4_PRE_UNPACK_TO_FP8=0
+    echo "Using MXFP8 configuration"
+fi
+
+# Common environment variables
+export VLLM_ENABLE_AR_EXT=1
+export VLLM_ENABLE_STATIC_MOE=0
+export VLLM_MXFP4_PRE_UNPACK_WEIGHTS=0
+export VLLM_USE_DEEP_GEMM=0
+export VLLM_ENABLE_V1_MULTIPROCESSING=0
+
+echo "Environment variables set:"
+echo "  VLLM_AR_MXFP4_MODULAR_MOE=$VLLM_AR_MXFP4_MODULAR_MOE"
+echo "  VLLM_MXFP4_PRE_UNPACK_TO_FP8=$VLLM_MXFP4_PRE_UNPACK_TO_FP8"
+echo "  VLLM_ENABLE_AR_EXT=$VLLM_ENABLE_AR_EXT"
+echo ""
+
+# Run the model
+echo "Starting model generation..."
+VLLM_WORKER_MULTIPROC_METHOD=spawn \
+python generate.py \
+    --model "${MODEL_PATH}" \
+    --tensor_parallel_size $TP_SIZE \
+    --max-tokens 16 \
+    --max-num-seqs 4 \
+    --gpu_memory_utilization 0.75 \
+    --no-enable-prefix-caching \
+    --enable_expert_parallel
\ No newline at end of file
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/run_quant.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/run_quant.sh
new file mode 100644
index 00000000000..435132a97f2
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/deepseek/run_quant.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+set -e
+
+MODEL=""
+TARGET=""
+OUTPUT_DIR=""
+
+usage() {
+  echo "Usage: $0 --model MODEL -t [mxfp4|mxfp8] --output_dir DIR"
+  echo "  --model      Hugging Face model ID or local path"
+  echo "  -t           quantization target (e.g. mxfp8, mxfp4)"
+  echo "  --output_dir output directory for quantized model"
+  exit 1
+}
+
+while [[ $# -gt 0 ]]; do
+  case "$1" in
+    --model)
+      MODEL="$2"
+      shift 2
+      ;;
+    -t)
+      TARGET="$2"
+      shift 2
+      ;;
+    --output_dir)
+      OUTPUT_DIR="$2"
+      shift 2
+      ;;
+    -h|--help)
+      usage
+      ;;
+    *)
+      echo "Unknown option: $1"
+      usage
+      ;;
+  esac
+done
+
+[ -z "$MODEL" ] && echo "Error: --model is required" && usage
+[ -z "$TARGET" ] && echo "Error: -t is required" && usage
+[ -z "$OUTPUT_DIR" ] && echo "Error: --output_dir is required" && usage
+
+python quantize.py \
+  --model "$MODEL" \
+  -t "$TARGET" \
+  --use_autoround_format \
+  --output_dir "$OUTPUT_DIR"
\ No newline at end of file
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/README.md b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/README.md
new file mode 100644
index 00000000000..8f8b17af102
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/README.md
@@ -0,0 +1,67 @@
+This example provides an end-to-end workflow to quantize Qwen models to MXFP4/MXFP8 and evaluate them using a custom vLLM fork.
+
+## Requirement
+```bash
+pip install neural-compressor-pt==3.7
+# auto-round
+pip install auto-round==0.9.1
+# vLLM
+git clone -b fused-moe-ar --single-branch --quiet https://github.com/yiliu30/vllm-fork.git && cd vllm-fork
+VLLM_USE_PRECOMPILED=1 pip install --editable . -vvv
+# other requirements
+pip install -r requirements.txt
+```
+
+### Quantize Model
+- Export model path
+```bash
+export MODEL=Qwen/Qwen3-235B-A22B
+```
+> [!TIP]
+> For quicker experimentation (shorter quantization and evaluation time, lower memory),
+> you can start with the smaller `export MODEL=Qwen/Qwen3-30B-A3B` model before moving to larger variants.
+
+- MXFP8
+```bash
+bash run_quant.sh --model $MODEL -t mxfp8 --output_dir ./qmodels
+```
+
+- MXFP4
+```bash
+bash run_quant.sh --model $MODEL -t mxfp4 --output_dir ./qmodels
+```
+
+## Evaluation
+
+### Prompt Tests
+
+Usage: 
+```bash
+bash ./run_generate.sh -s [mxfp4|mxfp8] -tp [tensor_parallel_size] -m [model_path]
+```
+
+- MXFP8
+```bash
+bash ./run_generate.sh -s mxfp8 -tp 4 -m /path/to/qwen_mxfp8
+```
+- MXFP4
+```bash
+bash ./run_generate.sh -s mxfp4 -tp 4 -m /path/to/qwen_mxfp4
+```
+### Evaluation
+
+
+Usage: 
+```bash
+bash run_evaluation.sh -m [model_path] -s [mxfp4|mxfp8] -t [task_name] -tp [tensor_parallel_size] -b [batch_size]
+```
+```bash
+bash run_evaluation.sh -s mxfp8 -t piqa,hellaswag,mmlu -tp 4 -b 512 -m /path/to/qwen_mxfp8
+bash run_evaluation.sh -s mxfp8 -t gsm8k -tp 4 -b 256 -m /path/to/qwen_mxfp8
+
+```
+- MXFP4
+```bash
+bash run_evaluation.sh -s mxfp4 -t piqa,hellaswag,mmlu -tp 4 -b 512 -m /path/to/qwen_mxfp4
+bash run_evaluation.sh -s mxfp4 -t gsm8k -tp 4 -b 256 -m /path/to/qwen_mxfp4
+```
\ No newline at end of file
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/generate.py b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/generate.py
new file mode 100644
index 00000000000..ec150a71576
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/generate.py
@@ -0,0 +1,67 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copied from https://github.com/vllm-project/vllm/
+
+from vllm import LLM, EngineArgs
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+
+
+
+def create_parser():
+    parser = FlexibleArgumentParser()
+    # Add engine args
+    EngineArgs.add_cli_args(parser)
+    parser.set_defaults(model="meta-llama/Llama-3.2-1B-Instruct")
+    # Add sampling params
+    sampling_group = parser.add_argument_group("Sampling parameters")
+    sampling_group.add_argument("--max-tokens", type=int)
+    sampling_group.add_argument("--temperature", type=float)
+    sampling_group.add_argument("--top-p", type=float)
+    sampling_group.add_argument("--top-k", type=int)
+
+    return parser
+
+
+def main(args: dict):
+    # Pop arguments not used by LLM
+    max_tokens = args.pop("max_tokens")
+    temperature = args.pop("temperature")
+    top_p = args.pop("top_p")
+    top_k = args.pop("top_k")
+
+    # Create an LLM
+    llm = LLM(**args)
+
+    # Create a sampling params object
+    sampling_params = llm.get_default_sampling_params()
+    if max_tokens is not None:
+        sampling_params.max_tokens = max_tokens
+    if temperature is not None:
+        sampling_params.temperature = temperature
+    if top_p is not None:
+        sampling_params.top_p = top_p
+    if top_k is not None:
+        sampling_params.top_k = top_k
+
+    # Generate texts from the prompts. The output is a list of RequestOutput
+    # objects that contain the prompt, generated text, and other information.
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+    outputs = llm.generate(prompts, sampling_params)
+    # Print the outputs.
+    print("-" * 50)
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}")
+        print("-" * 50)
+
+
+if __name__ == "__main__":
+    parser = create_parser()
+    args: dict = vars(parser.parse_args())
+    main(args)
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/quantize.py b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/quantize.py
new file mode 100644
index 00000000000..0b5bf23ab6e
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/quantize.py
@@ -0,0 +1,113 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import transformers
+import logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+topologies_config = {
+    "mxfp8": {
+        "scheme": "MXFP8",
+        "fp_layers": "lm_head,mlp.gate",
+        "iters": 0,
+    },
+    "mxfp4": {
+        "scheme": "MXFP4",
+        "fp_layers": "lm_head,mlp.gate,self_attn",
+        "iters": 200,
+    },
+}
+
+
+def get_model_and_tokenizer(model_name):
+    # Load model and tokenizer
+    fp32_model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        device_map="cpu",
+        trust_remote_code=True,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_name,
+        trust_remote_code=True,
+    )
+    return fp32_model, tokenizer
+
+
+def quant_model(args):
+    from neural_compressor.torch.quantization import (
+        AutoRoundConfig,
+        convert,
+        prepare,
+    )
+
+    config = topologies_config[args.t]
+    export_format = "auto_round" if args.use_autoround_format else "llm_compressor"
+    output_dir = f"{args.output_dir}/quantized_model_{args.t}"
+    fp32_model, tokenizer = get_model_and_tokenizer(args.model)
+    quant_config = AutoRoundConfig(
+        tokenizer=tokenizer,
+        scheme=config["scheme"],
+        enable_torch_compile=args.enable_torch_compile,
+        iters=config["iters"],
+        fp_layers=config["fp_layers"],
+        export_format=export_format,
+        output_dir=output_dir,
+    )
+
+    # quantizer execute
+    model = prepare(model=fp32_model, quant_config=quant_config)
+    inc_model = convert(model)
+    logger.info(f"Quantized model saved to {output_dir}")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    # Parse command-line arguments
+    parser = argparse.ArgumentParser(description="Select a quantization scheme.")
+    parser.add_argument(
+        "--model",
+        type=str,
+        help="Path to the pre-trained model or model identifier from Hugging Face Hub.",
+    )
+    parser.add_argument(
+        "-t",
+        type=str,
+        choices=topologies_config.keys(),
+        default="mxfp4",
+        help="Quantization scheme to use. Available options: " + ", ".join(topologies_config.keys()),
+    )
+
+    parser.add_argument(
+        "--enable_torch_compile",
+        action="store_true",
+        help="Enable torch compile for the model.",
+    )
+    parser.add_argument(
+        "--use_autoround_format",
+        action="store_true",
+        help="Use AutoRound format for saving the quantized model.",
+    )
+
+    parser.add_argument(
+        "--skip_attn",
+        action="store_true",
+        help="Skip quantize attention layers.",
+    )
+    parser.add_argument(
+        "--iters",
+        type=int,
+        default=0,
+        help="Number of iterations for quantization.",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="./",
+        help="Directory to save the quantized model.",
+    )
+
+    args = parser.parse_args()
+
+    quant_model(args)
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/requirements.txt b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/requirements.txt
new file mode 100644
index 00000000000..80392549f26
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/requirements.txt
@@ -0,0 +1,2 @@
+lm-eval==0.4.9.1
+loguru
\ No newline at end of file
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/run_evaluation.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/run_evaluation.sh
new file mode 100644
index 00000000000..1d805c7872b
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/run_evaluation.sh
@@ -0,0 +1,120 @@
+#!/bin/bash
+set -e
+
+# Usage: ./run_evaluation.sh -m [model_path] -s [mxfp4|mxfp8] -t [task_name] -tp [tensor_parallel_size] -b [batch_size]
+# Default values
+MODEL_PATH=""
+SCHEME="mxfp8"
+TASK_NAME="piqa,hellaswag,mmlu"
+TP_SIZE=8
+BATCH_SIZE=512
+
+# Function to display usage
+usage() {
+    echo "Usage: $0 -m [model_path] -s [mxfp4|mxfp8] -t [task_name] -tp [tensor_parallel_size] -b [batch_size]"
+    echo "  -m: Path to the quantized model (required)"
+    echo "  -s: Quantization scheme (mxfp4 or mxfp8, default: mxfp8)"
+    echo "  -t: Task name(s) to evaluate (default: piqa,hellaswag,mmlu)"
+    echo "  -tp: Tensor parallelism size (default: 8)"
+    echo "  -b: Batch size (default: 512)"
+    echo ""
+    echo "Examples:"
+    echo "  $0 -m /path/to/model -s mxfp4 -t gsm8k -tp 4 -b 256"
+    echo "  $0 -m /path/to/model -s mxfp8 -t piqa,hellaswag -tp 8 -b 512"
+}
+
+# Parse command-line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        -m)
+            MODEL_PATH="$2"
+            shift 2
+            ;;
+        -s)
+            SCHEME="$2"
+            shift 2
+            ;;
+        -t)
+            TASK_NAME="$2"
+            shift 2
+            ;;
+        -tp)
+            TP_SIZE="$2"
+            shift 2
+            ;;
+        -b)
+            BATCH_SIZE="$2"
+            shift 2
+            ;;
+        -h|--help)
+            usage
+            exit 0
+            ;;
+        *)
+            echo "Invalid option: $1" >&2
+            usage
+            exit 1
+            ;;
+    esac
+done
+
+# Validate required arguments
+if [[ -z "$MODEL_PATH" ]]; then
+    echo "Error: Model path (-m) is required."
+    usage
+    exit 1
+fi
+
+# Extract model name and set output directory
+MODEL_NAME=$(basename ${MODEL_PATH})
+OUTPUT_DIR="${MODEL_NAME}-tp${TP_SIZE}-eval"
+
+# Create output directory
+mkdir -p ${OUTPUT_DIR}
+
+# Set environment variables based on the quantization scheme
+if [[ "$SCHEME" == "mxfp4" ]]; then
+    VLLM_AR_MXFP4_MODULAR_MOE=1
+    VLLM_MXFP4_PRE_UNPACK_TO_FP8=1
+    VLLM_MXFP4_PRE_UNPACK_WEIGHTS=0
+    VLLM_ENABLE_STATIC_MOE=0
+    VLLM_USE_DEEP_GEMM=0
+    VLLM_ENABLE_AR_EXT=1
+elif [[ "$SCHEME" == "mxfp8" ]]; then
+    VLLM_AR_MXFP4_MODULAR_MOE=0
+    VLLM_MXFP4_PRE_UNPACK_TO_FP8=0
+    VLLM_MXFP4_PRE_UNPACK_WEIGHTS=0
+    VLLM_ENABLE_STATIC_MOE=0
+    VLLM_USE_DEEP_GEMM=0
+    VLLM_ENABLE_AR_EXT=1
+else
+    echo "Error: Invalid quantization scheme (-s). Must be 'mxfp4' or 'mxfp8'."
+    usage
+    exit 1
+fi
+
+# Run evaluation
+echo "Evaluating model: ${MODEL_PATH}"
+echo "Quantization scheme: ${SCHEME}"
+echo "Tasks: ${TASK_NAME}"
+echo "Tensor parallelism size: ${TP_SIZE}"
+echo "Batch size: ${BATCH_SIZE}"
+echo "Output directory: ${OUTPUT_DIR}"
+
+VLLM_WORKER_MULTIPROC_METHOD=spawn \
+VLLM_ENABLE_AR_EXT=$VLLM_ENABLE_AR_EXT \
+VLLM_AR_MXFP4_MODULAR_MOE=$VLLM_AR_MXFP4_MODULAR_MOE \
+VLLM_MXFP4_PRE_UNPACK_TO_FP8=$VLLM_MXFP4_PRE_UNPACK_TO_FP8 \
+VLLM_MXFP4_PRE_UNPACK_WEIGHTS=$VLLM_MXFP4_PRE_UNPACK_WEIGHTS \
+VLLM_ENABLE_STATIC_MOE=$VLLM_ENABLE_STATIC_MOE \
+VLLM_USE_DEEP_GEMM=$VLLM_USE_DEEP_GEMM \
+VLLM_ENABLE_V1_MULTIPROCESSING=1 \
+lm_eval --model vllm \
+  --model_args "pretrained=${MODEL_PATH},tensor_parallel_size=${TP_SIZE},max_model_len=8192,max_num_batched_tokens=32768,max_num_seqs=128,add_bos_token=True,gpu_memory_utilization=0.8,dtype=bfloat16,max_gen_toks=2048,enable_prefix_caching=False" \
+  --tasks $TASK_NAME \
+  --batch_size $BATCH_SIZE \
+  --log_samples \
+  --limit 64 \
+  --seed 42 \
+  --output_path ${OUTPUT_DIR} \
+  --show_config 2>&1 | tee ${OUTPUT_DIR}/log.txt
\ No newline at end of file
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/run_generate.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/run_generate.sh
new file mode 100644
index 00000000000..c9ee73ce182
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/run_generate.sh
@@ -0,0 +1,118 @@
+#!/bin/bash
+set -e
+
+# Model Testing Script
+# Usage: ./run_generate.sh -s [mxfp4|mxfp8] -m [model_path] -tp [tensor_parallel_size]
+
+# Default values
+QUANT_TYPE="mxfp8"
+MODEL_PATH="/path/to/quantized_model"
+TP_SIZE=8
+
+# Function to display usage
+usage() {
+    echo "Usage: $0 -s [mxfp4|mxfp8] -m [model_path] -tp [tensor_parallel_size]"
+    echo "  -s: Quantization scheme (mxfp4 or mxfp8, default: mxfp8)"
+    echo "  -m: Path to quantized model (required)"
+    echo "  -tp: Tensor parallelism size (default: 8)"
+    echo ""
+    echo "Examples:"
+    echo "  $0 -s mxfp4 -m /path/to/my/model -tp 4"
+    echo "  $0 -m /path/to/my/model"
+    echo "  $0 -s mxfp8 -m /path/to/my/model"
+}
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        -s)
+            QUANT_TYPE="$2"
+            shift 2
+            ;;
+        -m)
+            MODEL_PATH="$2"
+            shift 2
+            ;;
+        -tp)
+            TP_SIZE="$2"
+            shift 2
+            ;;
+        -h)
+            usage
+            exit 0
+            ;;
+        *)
+            echo "Invalid option: $1" >&2
+            usage
+            exit 1
+            ;;
+    esac
+done
+
+
+# Validate quantization type
+QUANT_TYPE_UPPER=$(echo "$QUANT_TYPE" | tr '[:lower:]' '[:upper:]')
+if [[ "$QUANT_TYPE_UPPER" != "MXFP4" && "$QUANT_TYPE_UPPER" != "MXFP8" ]]; then
+    echo "Error: Quantization type must be mxfp4 or mxfp8"
+    usage
+    exit 1
+fi
+
+# Validate model path
+if [[ "$MODEL_PATH" == "/path/to/quantized_model" ]]; then
+    echo "Error: Model path is required (-m)"
+    usage
+    exit 1
+fi
+
+if [[ ! -d "$MODEL_PATH" ]]; then
+    echo "Error: Model path '$MODEL_PATH' does not exist or is not a directory"
+    exit 1
+fi
+
+# Validate TP_SIZE is a number
+if ! [[ "$TP_SIZE" =~ ^[0-9]+$ ]] || [ "$TP_SIZE" -lt 1 ]; then
+    echo "Error: Tensor parallelism size must be a positive integer"
+    exit 1
+fi
+
+echo "Running $QUANT_TYPE_UPPER test with:"
+echo "  Model: $MODEL_PATH"
+echo "  Tensor Parallelism: $TP_SIZE"
+echo ""
+
+# Set environment variables based on quantization type
+if [[ "$QUANT_TYPE_UPPER" == "MXFP4" ]]; then
+    export VLLM_AR_MXFP4_MODULAR_MOE=1
+    export VLLM_MXFP4_PRE_UNPACK_TO_FP8=1
+    echo "Using MXFP4 configuration"
+else
+    export VLLM_AR_MXFP4_MODULAR_MOE=0
+    export VLLM_MXFP4_PRE_UNPACK_TO_FP8=0
+    echo "Using MXFP8 configuration"
+fi
+
+# Common environment variables
+export VLLM_ENABLE_AR_EXT=1
+export VLLM_ENABLE_STATIC_MOE=0
+export VLLM_MXFP4_PRE_UNPACK_WEIGHTS=0
+export VLLM_USE_DEEP_GEMM=0
+export VLLM_ENABLE_V1_MULTIPROCESSING=0
+
+echo "Environment variables set:"
+echo "  VLLM_AR_MXFP4_MODULAR_MOE=$VLLM_AR_MXFP4_MODULAR_MOE"
+echo "  VLLM_MXFP4_PRE_UNPACK_TO_FP8=$VLLM_MXFP4_PRE_UNPACK_TO_FP8"
+echo "  VLLM_ENABLE_AR_EXT=$VLLM_ENABLE_AR_EXT"
+echo ""
+
+# Run the model
+echo "Starting model generation..."
+VLLM_WORKER_MULTIPROC_METHOD=spawn \
+python generate.py \
+    --model "${MODEL_PATH}" \
+    --tensor_parallel_size $TP_SIZE \
+    --max-tokens 16 \
+    --max-num-seqs 4 \
+    --gpu_memory_utilization 0.75 \
+    --no-enable-prefix-caching \
+    --enable_expert_parallel
\ No newline at end of file
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/run_quant.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/run_quant.sh
new file mode 100644
index 00000000000..435132a97f2
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/qwen/run_quant.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+set -e
+
+MODEL=""
+TARGET=""
+OUTPUT_DIR=""
+
+usage() {
+  echo "Usage: $0 --model MODEL -t [mxfp4|mxfp8] --output_dir DIR"
+  echo "  --model      Hugging Face model ID or local path"
+  echo "  -t           quantization target (e.g. mxfp8, mxfp4)"
+  echo "  --output_dir output directory for quantized model"
+  exit 1
+}
+
+while [[ $# -gt 0 ]]; do
+  case "$1" in
+    --model)
+      MODEL="$2"
+      shift 2
+      ;;
+    -t)
+      TARGET="$2"
+      shift 2
+      ;;
+    --output_dir)
+      OUTPUT_DIR="$2"
+      shift 2
+      ;;
+    -h|--help)
+      usage
+      ;;
+    *)
+      echo "Unknown option: $1"
+      usage
+      ;;
+  esac
+done
+
+[ -z "$MODEL" ] && echo "Error: --model is required" && usage
+[ -z "$TARGET" ] && echo "Error: -t is required" && usage
+[ -z "$OUTPUT_DIR" ] && echo "Error: --output_dir is required" && usage
+
+python quantize.py \
+  --model "$MODEL" \
+  -t "$TARGET" \
+  --use_autoround_format \
+  --output_dir "$OUTPUT_DIR"
\ No newline at end of file