diff --git a/examples/quantization_non_uniform/quantization_importance_aware.py b/examples/quantization_non_uniform/quantization_importance_aware.py
new file mode 100644
index 0000000000..f415aa6c1b
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+++ b/examples/quantization_non_uniform/quantization_importance_aware.py
@@ -0,0 +1,94 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor import oneshot
+from llmcompressor.utils import dispatch_for_generation
+
+MODEL_ID = "Qwen/Qwen3-32B"
+
+# Load model.
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+# Configure the importance-aware mixed-precision quantization recipe.
+# In this case, we:
+#   * Keep the first 3 layers (0,1,2) and last 3 layers (51,62,63) in full precision
+#       (not quantized) due to their high sensitivity.
+#   * Exclude lm_head from quantization to preserve output quality.
+#   * Quantize weights of specific middle layers' self-attention and MLP blocks to fp4:
+#       - Layers: 15-24, 31, 46-48, 50, 56-60
+#       - Modules: k_proj, o_proj, q_proj, v_proj, down_proj, gate_proj, up_proj
+#       - Scheme: fp4, symmetric, per-group (group_size=16), static (PTQ)
+#   * Quantize weights of other intermediate layers to fp8:
+#       - Layers: 3-14, 25-30, 32-45, 49, 52-55, 61
+#       - Same modules as above
+#       - Scheme: fp8, symmetric, per-channel, static (PTQ)
+#   * Additionally, dynamically quantize input activations for fp8-weighted layers:
+#       - Activations quantized to fp8, symmetric, per-token, dynamic range
+
+# Define layer groups for readability and line-length compliance
+
+fp4_group = "15|16|17|18|19|20|21|22|23|24|31|46|47|48|50|56|57|58|59|60"
+fp8_group = (
+    "3|4|5|6|7|8|9|10|11|12|13|14|25|26|27|28|29|30|"
+    "32|33|34|35|36|37|38|39|40|41|42|43|44|45|49|52|53|54|55|61"
+)
+
+recipe = f"""
+quant_stage:
+    quant_modifiers:
+        QuantizationModifier:
+            ignore:
+                - "lm_head"
+                - 're:.*layers\\.0\\..*'
+                - 're:.*layers\\.1\\..*'
+                - 're:.*layers\\.2\\..*'
+                - 're:.*layers\\.51\\..*'
+                - 're:.*layers\\.62\\..*'
+                - 're:.*layers\\.63\\..*'
+            config_groups:
+                group_0:
+                    weights:
+                        num_bits: 4
+                        type: float
+                        strategy: tensor_group
+                        dynamic: false
+                        symmetric: true
+                        group_size: 16
+                    targets:
+                        - 're:.*layers\\.({fp4_group})\\.self_attn\\.[kqov]_proj'
+                        - 're:.*layers\\.({fp4_group})\\.mlp\\.(gate|up|down)_proj'
+                group_1:
+                    weights:
+                        num_bits: 8
+                        type: float
+                        strategy: channel
+                        dynamic: false
+                        symmetric: true
+                    input_activations:
+                        num_bits: 8
+                        type: float
+                        strategy: token
+                        dynamic: true
+                        symmetric: true
+                    targets:
+                        - 're:.*layers\\.({fp8_group})\\.self_attn\\.[kqov]_proj'
+                        - 're:.*layers\\.({fp8_group})\\.mlp\\.(gate|up|down)_proj'
+"""
+
+# Apply quantization.
+oneshot(model=model, recipe=recipe)
+
+print("\n\n")
+print("========== SAMPLE GENERATION ==============")
+dispatch_for_generation(model)
+input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to(
+    model.device
+)
+output = model.generate(input_ids, max_new_tokens=100)
+print(tokenizer.decode(output[0]))
+print("==========================================\n\n")
+
+# Save to disk in compressed-tensors format.
+SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-Importance-Aware-Mix-Quantization"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+tokenizer.save_pretrained(SAVE_DIR)