diff --git a/examples/multimodal_audio/qwen2_audio.py b/examples/multimodal_audio/qwen2_audio.py
new file mode 100644
index 0000000000..e385a18117
--- /dev/null
+++ b/examples/multimodal_audio/qwen2_audio.py
@@ -0,0 +1,134 @@
+from compressed_tensors.offload import dispatch_model
+from datasets import load_dataset
+from transformers import AutoProcessor, Qwen2AudioForConditionalGeneration
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import GPTQModifier
+
+# Select model and load it.
+MODEL_ID = "Qwen/Qwen2-Audio-7B-Instruct"
+model = Qwen2AudioForConditionalGeneration.from_pretrained(MODEL_ID, dtype="auto")
+processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
+
+# Select calibration dataset.
+DATASET_ID = "MLCommons/peoples_speech"
+DATASET_SUBSET = "test"
+DATASET_SPLIT = "test"
+
+# Select number of samples. 512 samples is a good place to start.
+# Increasing the number of samples can improve accuracy.
+NUM_CALIBRATION_SAMPLES = 512
+MAX_SEQUENCE_LENGTH = 2048
+
+# Load raw dataset for generation testing.
+raw_ds = load_dataset(
+    DATASET_ID,
+    DATASET_SUBSET,
+    split=f"{DATASET_SPLIT}[:1]",
+)
+
+# Load dataset for calibration.
+ds = load_dataset(
+    DATASET_ID,
+    DATASET_SUBSET,
+    split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]",
+)
+
+
+def preprocess(example):
+    # Qwen2Audio uses a chat template format
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "audio", "audio_url": "placeholder"},
+            ],
+        },
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "What did the person say?"},
+            ],
+        },
+        {
+            "role": "assistant",
+            "content": [
+                {"type": "text", "text": example["text"]},
+            ],
+        },
+    ]
+
+    # Apply chat template
+    text = processor.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=False
+    )
+
+    # Process using the processor (it handles audio token expansion)
+    inputs = processor(
+        text=text,
+        audio=[example["audio"]["array"]],
+        sampling_rate=example["audio"]["sampling_rate"],
+        return_tensors="pt",
+    )
+
+    # Strip batch dimension and return
+    return {key: value[0] for key, value in inputs.items()}
+
+
+ds = ds.map(preprocess, remove_columns=ds.column_names)
+
+# Recipe
+recipe = GPTQModifier(
+    targets="Linear",
+    scheme="W4A16",
+    ignore=["lm_head", "re:audio_tower.*"],
+)
+
+# Apply algorithms.
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+)
+
+# Confirm generations of the model before quantization.
+print("========== SAMPLE GENERATION ==============")
+dispatch_model(model)
+raw_sample = raw_ds[0]
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "audio", "audio_url": "placeholder"},
+        ],
+    },
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "What did the person say?"},
+        ],
+    },
+]
+text_prompt = processor.apply_chat_template(
+    conversation, tokenize=False, add_generation_prompt=True
+)
+inputs = processor(
+    text=text_prompt,
+    audio=[raw_sample["audio"]["array"]],
+    sampling_rate=raw_sample["audio"]["sampling_rate"],
+    return_tensors="pt",
+).to(model.device)
+
+output = model.generate(**inputs, max_new_tokens=100)
+print(processor.batch_decode(output, skip_special_tokens=True)[0])
+print("==========================================\n\n")
+# that's where you have a lot of windows in the south no actually that's passive solar
+# and passive solar is something that was developed and designed in the 1960s and 70s
+# and it was a great thing for what it was at the time but it's not a passive house
+
+# Save to disk compressed.
+SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-W4A16-G128"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+processor.save_pretrained(SAVE_DIR)