diff --git a/test/modules/model/LlamaPrefill/model.py b/test/modules/model/LlamaPrefill/model.py
new file mode 100644
index 00000000..cae392ca
--- /dev/null
+++ b/test/modules/model/LlamaPrefill/model.py
@@ -0,0 +1,88 @@
+# User input
+prompt = "Lily picked up a flower."
+model_name = "Maykeye/TinyLLama-v0"
+
+captured_input = ()
+
+import copy, inspect, types
+
+from transformers import LlamaForCausalLM
+
+forward_org = LlamaForCausalLM.forward
+
+
+def capture_and_forward(self, *args, **kwargs):
+    global captured_input
+
+    # Prepare args tuple for TICO.convert()
+    # Get arg_names in positional args order using inspect
+    sig = inspect.signature(forward_org)
+    args_names = [
+        # signature includes `self`` and `kwargs``.
+        # Just retrieve the ordinary positional inputs only
+        name
+        for name in sig.parameters.keys()
+        if name not in ("self", "kwargs")
+    ]
+
+    args_dict = dict(zip(args_names, args))
+    args_dict.update(kwargs)
+
+    def populate_args(args_dict, filter):
+        for key in filter:
+            args_dict.pop(key, None)
+        args_tuple = tuple(args_dict.get(name, None) for name in args_names)
+        return copy.deepcopy(args_tuple)
+
+    if args_dict["past_key_values"].get_seq_length() == 0:
+        input_to_remove = [
+            "past_key_values",
+            "use_cache",
+            "attention_mask",
+            "cache_position",
+        ]
+        captured_input = populate_args(args_dict, input_to_remove)
+
+    return forward_org(self, *args, **kwargs)
+
+
+# Tokenizer
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
+inputs = tokenizer(
+    prompt,
+    return_tensors="pt",
+    padding="max_length",
+    max_length=32,
+    truncation=True,
+)
+
+
+# Generator
+import torch
+
+from transformers import AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained(model_name)
+model.eval()
+model.forward = types.MethodType(capture_and_forward, model)
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=32,
+        do_sample=False,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_text)
+
+# Tico
+import tico
+
+model = AutoModelForCausalLM.from_pretrained(model_name)
+model.eval()
+circle_model = tico.convert(model, captured_input)
+circle_model.save(f"tinyllama.prefill.circle")
diff --git a/test/modules/model/LlamaPrefill/requirements.txt b/test/modules/model/LlamaPrefill/requirements.txt
new file mode 100644
index 00000000..5393938f
--- /dev/null
+++ b/test/modules/model/LlamaPrefill/requirements.txt
@@ -0,0 +1 @@
+transformers>=4.50.1