diff --git a/examples/converters/gguf2pte.py b/examples/converters/gguf2pte.py
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+++ b/examples/converters/gguf2pte.py
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+'''
+Example to convert .gguf files into .pte format.
+
+1. Load our model using transformers/gguf
+2. Torch export
+3. Executorch lowering and export to .pte
+'''
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from executorch.exir import to_edge_transform_and_lower
+from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner
+from torch.export import export
+import torch
+
+model_id = "bartowski/SmolLM2-135M-Instruct-GGUF" # Here we would have our HF model in GGUF form we wish to convert
+filename = "SmolLM2-135M-Instruct-Q8_0.gguf"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename)
+model = AutoModelForCausalLM.from_pretrained(model_id, gguf_file=filename)
+print(f"Model weights dtype: {model.dtype}")
+model.eval()
+
+# Generate some sample input for our torch export
+sample_inputs = tokenizer("Plants create energy through a process known as", return_tensors="pt",)
+print(sample_inputs)
+print(sample_inputs["input_ids"].shape)
+print(sample_inputs["attention_mask"].shape)
+
+sample_inputs = (sample_inputs["input_ids"], sample_inputs["attention_mask"],)
+
+# Torch export followed by ET lowering and export
+exported_program = export(model, sample_inputs)
+executorch_program = to_edge_transform_and_lower(
+    exported_program,
+    partitioner = [XnnpackPartitioner()]
+).to_executorch()
+
+with open("model.pte", "wb") as file:
+    file.write(executorch_program.buffer)
diff --git a/examples/converters/requirements.txt b/examples/converters/requirements.txt
new file mode 100644
index 00000000000..9e2ead3a5b0
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+++ b/examples/converters/requirements.txt
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+accelerate
+gguf
+setuptools
+transformers
+executorch
+torch