diff --git a/examples/omnisvg_example/omnisvg_inference.py b/examples/omnisvg_example/omnisvg_inference.py
new file mode 100644
index 000000000..ef5711625
--- /dev/null
+++ b/examples/omnisvg_example/omnisvg_inference.py
@@ -0,0 +1,64 @@
+import os
+
+import torch
+import torch.nn as nn
+from transformers import AutoConfig, AutoProcessor, AutoTokenizer, Qwen2_5_VLForConditionalGeneration
+
+from QEfficient import QEFFAutoModelForImageTextToText
+
+
+class SketchDecoder(nn.Module):
+    """
+    Autoregressive generative model
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__()
+        self.vocab_size = 196042
+        self.bos_token_id = 151643
+        self.eos_token_id = 196041
+        self.pad_token_id = 151643
+
+        config = AutoConfig.from_pretrained(
+            "Qwen/Qwen2.5-VL-3B-Instruct",
+            vocab_size=self.vocab_size,
+            bos_token_id=self.bos_token_id,
+            eos_token_id=self.eos_token_id,
+            pad_token_id=self.pad_token_id,
+        )
+
+        self.transformer = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2.5-VL-3B-Instruct", config=config, attn_implementation="eager", ignore_mismatched_sizes=True
+        )
+
+        self.transformer.resize_token_embeddings(self.vocab_size)
+
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError("Forward pass not included in open-source version")
+
+
+model_id = "Qwen/Qwen2.5-VL-3B-Instruct"
+sketch_decoder = SketchDecoder()
+weight_path = "/home/dipankar/omnisvg/OmniSVG"
+sketch_weight_file = os.path.join(weight_path, "pytorch_model.bin")
+if not os.path.exists(sketch_weight_file):
+    raise FileNotFoundError(f"pytorch_model.bin not found in {weight_path}")
+sketch_decoder.load_state_dict(torch.load(sketch_weight_file))
+sketch_decoder.transformer.eval()
+qeff_model = QEFFAutoModelForImageTextToText(sketch_decoder.transformer)
+qeff_model.export()
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct", padding_side="left")
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct", padding_side="left")
+path = qeff_model.compile(
+    batch_size=1,
+    prefill_seq_len=128,
+    ctx_len=4096,
+    num_cores=16,
+    num_devices=8,
+    height=354,
+    width=536,
+    mxfp6_matmul=False,
+    aic_enable_depth_first=True,
+    skip_vision=True,
+    mos=1,
+)