[Examples] Add Gemma 4 E4B NVFP4A16 quantization example

examples/quantization_w4a16_fp4/nvfp4/gemma4_example.py

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+# Gemma 4 requires transformers >= 5.5.0 (model_type: gemma4).
+# If your llmcompressor pins an older version, install with:
+#   pip install llmcompressor
+#   pip install transformers>=5.5
+
+from compressed_tensors.offload import dispatch_model
+from transformers import AutoModelForImageTextToText, AutoProcessor
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+
+# Load model.
+MODEL_ID = "google/gemma-4-E4B-it"
+model = AutoModelForImageTextToText.from_pretrained(MODEL_ID, dtype="auto")
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+
+# Configure the quantization algorithm and scheme.
+# In this case, we:
+#   * quantize the weights to fp4 with per group 16 via ptq
+#   * skip the vision encoder, audio encoder, embedding projections, and lm_head
+recipe = QuantizationModifier(
+    targets="Linear",
+    scheme="NVFP4A16",
+    ignore=[
+        "lm_head",
+        "re:.*vision_tower.*",
+        "re:.*audio_tower.*",
+        "re:.*embed_vision.*",
+        "re:.*embed_audio.*",
+    ],
+)
+
+# Apply quantization.
+oneshot(model=model, recipe=recipe)
+
+print("\n\n========== SAMPLE GENERATION ==============")
+dispatch_model(model)
+messages = [
+    {"role": "user", "content": "Hello my name is"},
+]
+text = processor.apply_chat_template(
+    messages, add_generation_prompt=True, tokenize=False
+)
+inputs = processor(text=text, return_tensors="pt").to(model.device)
+output = model.generate(**inputs, max_new_tokens=100)
+print(processor.decode(output[0], skip_special_tokens=True))
+print("==========================================\n\n")
+
+# Save to disk in compressed-tensors format.
+SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-NVFP4A16"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+processor.save_pretrained(SAVE_DIR)