diff --git a/examples/quantization_w4a4_fp4/qwen3_example.py b/examples/quantization_w4a4_fp4/qwen3_example.py
new file mode 100644
index 000000000..6f6f8c774
--- /dev/null
+++ b/examples/quantization_w4a4_fp4/qwen3_example.py
@@ -0,0 +1,68 @@
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+
+# Load model.
+MODEL_ID = "Qwen/Qwen3-32B"
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID, torch_dtype="auto", trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+
+DATASET_ID = "HuggingFaceH4/ultrachat_200k"
+DATASET_SPLIT = "train_sft"
+
+# Select number of samples. 512 samples is a good place to start.
+# Increasing the number of samples can improve accuracy.
+NUM_CALIBRATION_SAMPLES = 256
+MAX_SEQUENCE_LENGTH = 2048
+
+# Load dataset and preprocess.
+ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]")
+ds = ds.shuffle(seed=42)
+
+
+def preprocess(example):
+    formatted = tokenizer.apply_chat_template(
+        example["messages"], tokenize=False, add_generation_prompt=False
+    )
+    return {"text": formatted[: MAX_SEQUENCE_LENGTH * 4]}
+
+
+ds = ds.map(preprocess)
+
+
+# Tokenize inputs.
+def tokenize(sample):
+    return tokenizer(
+        sample["text"],
+        padding="max_length",
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+        return_tensors="pt",
+        add_special_tokens=False,
+    )
+
+
+ds = ds.map(tokenize, remove_columns=ds.column_names, batched=True)
+
+# Configure the quantization algorithm and scheme.
+# In this case, we:
+#   * quantize the weights to fp4 with per group 16 via ptq
+#   * calibrate a global_scale for activations, which will be used to
+#       quantize activations to fp4 on the fly
+recipe = QuantizationModifier(targets="Linear", scheme="NVFP4", ignore=["lm_head"])
+
+
+# SAVE_DIR is the directory where the compressed model will be saved.
+SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-NVFP4"
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    output_dir=SAVE_DIR,
+)