diff --git a/examples/quantization_w4a4_fp4/gpt_oss_example.py b/examples/quantization_w4a4_fp4/gpt_oss_example.py
new file mode 100644
index 000000000..77de89b0d
--- /dev/null
+++ b/examples/quantization_w4a4_fp4/gpt_oss_example.py
@@ -0,0 +1,76 @@
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from torch.utils.data import DataLoader
+import os
+import torch
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.utils import dispatch_for_generation
+
+model_id = "openai/gpt-oss-20b"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+
+# -----------------------------
+# Create calibration dataloader
+# -----------------------------
+DATASET_ID = "HuggingFaceH4/ultrachat_200k"
+DATASET_SPLIT = "train_sft"
+NUM_CALIBRATION_SAMPLES = 32
+MAX_SEQUENCE_LENGTH = 2048
+
+ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]")
+ds = ds.shuffle(seed=42)
+
+def preprocess(example):
+    return {
+        "text": tokenizer.apply_chat_template(
+            example["messages"],
+            tokenize=False,
+        )
+    }
+
+ds = ds.map(preprocess)
+
+def tokenize(sample):
+    return tokenizer(
+        sample["text"],
+        padding=False,
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+        add_special_tokens=False,
+    )
+
+ds = ds.map(tokenize, remove_columns=ds.column_names)
+dataloader = DataLoader(ds, batch_size=1, shuffle=False)
+
+# -----------------------------
+# Quantization recipe
+# -----------------------------
+recipe = QuantizationModifier(
+    targets="Linear",
+    scheme="NVFP4",
+    ignore=["lm_head"],
+)
+
+SAVE_DIR = f"{model_id.split('/')[-1]}-NVFP4"
+
+oneshot(
+    model=model,
+    tokenizer=tokenizer,
+    recipe=recipe,
+    dataset=ds,
+    trust_remote_code_model=True,
+    output_dir=SAVE_DIR,
+)
+
+# Save compressed
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+tokenizer.save_pretrained(SAVE_DIR)
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