diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..fb2f9f0
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+__pycache__
+ingress/Torch-MLIR/examples/**/dumps/*.mlir
diff --git a/ingress/Torch-MLIR/README.md b/ingress/Torch-MLIR/README.md
new file mode 100644
index 0000000..2b35616
--- /dev/null
+++ b/ingress/Torch-MLIR/README.md
@@ -0,0 +1,49 @@
+Using scripts in this directory one can convert a Torch Model to a MLIR module.
+
+The conversion script is written in python and is basically a wrapper around [`torch-mlir` library](https://github.com/llvm/torch-mlir). One need to setup a python virtual environment with torch and torch-mlir libraries
+(`./scripts/install-virtualenv.sh`) to use the script.
+
+In order to convert a model the script has to recieve:
+1. An instance of `torch.nn.Model` with proper state (weights).
+2. Sample input arguments to the model (e.g. empty tensor with proper shape and dtype).
+
+There are two options of how this info can be provided to the converter:
+
+### 1. Instantiate a model in your own script and use a function from the `py_src/export_lib` (recomended)
+
+In this scenario a user is responsible for instantiating a model with proper state in their
+own python script. Then they should import a `generate_mlir` function from `py_src.export_lib`
+and call it in order to get a MLIR module:
+
+```python
+model : nn.Model = get_model()
+sample_args = (get_sample_tensor(),)
+
+# PYTHONPATH=$(pwd)/py_src/
+from export_lib import generate_mlir
+
+mlir_module = generate_mlir(model, sample_args, dialect="linalg")
+print(mlir_module)
+```
+
+### 2. Use `py_src/main.py` or `scripts/generate-mlir.sh` and pass Torch Model parameters via CLI
+
+In this scenario the `py_src/main.py` script is fully responsible for instantiating a torch model
+and converting it to MLIR. User has to pass a proper python entrypoint for model's factory,
+its parameters if needed (`--model-args & --model-kwargs`), and sample model arguments (either
+as `--sample-shapes` or as an entrypoint to a function returning args and kwargs `--sample-fn`).
+
+```
+# note that 'my_module' has to be in $PYTHONPATH
+python py_src/main.py --model-entrypoint my_module:my_factory   \
+                      --module-state-path path/to/state.pth     \
+                      --sample-shapes '1,2,324,float32'         \
+                      --out-mlir res.mlir
+
+# note that 'my_module' has to be in $PYTHONPATH
+./scripts/generate-mlir.sh --model-entrypoint torchvision.models:resnet18      \ 
+                           --sample-fn my_module:generate_resnet18_sample_args \
+                           --out-mlir res.mlir
+```
+
+Look into `examples/` folder for more info.
diff --git a/ingress/Torch-MLIR/examples/dummy_mlp_cli/dummy_mlp.pth b/ingress/Torch-MLIR/examples/dummy_mlp_cli/dummy_mlp.pth
new file mode 100644
index 0000000..28ccd45
Binary files /dev/null and b/ingress/Torch-MLIR/examples/dummy_mlp_cli/dummy_mlp.pth differ
diff --git a/ingress/Torch-MLIR/examples/dummy_mlp_cli/dummy_mlp_factory.py b/ingress/Torch-MLIR/examples/dummy_mlp_cli/dummy_mlp_factory.py
new file mode 100644
index 0000000..88d5d55
--- /dev/null
+++ b/ingress/Torch-MLIR/examples/dummy_mlp_cli/dummy_mlp_factory.py
@@ -0,0 +1,23 @@
+import torch
+import torch.nn as nn
+
+import os
+
+class DummyMLP(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(10, 32),
+            nn.ReLU(),
+            nn.Linear(32, 2)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+def make_dummy_mlp():
+    return DummyMLP()
+
+if __name__ == "__main__":
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    torch.save(make_dummy_mlp().state_dict(), os.path.join(script_dir, "dummy_mlp.pth"))
diff --git a/ingress/Torch-MLIR/examples/dummy_mlp_cli/export_bash.sh b/ingress/Torch-MLIR/examples/dummy_mlp_cli/export_bash.sh
new file mode 100755
index 0000000..bf247b2
--- /dev/null
+++ b/ingress/Torch-MLIR/examples/dummy_mlp_cli/export_bash.sh
@@ -0,0 +1,10 @@
+#!/usr/bin/env bash
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+ROOT_DIR=$SCRIPT_DIR/../../scripts/
+
+PYTHONPATH=$PYTHONPATH:$SCRIPT_DIR $ROOT_DIR/generate-mlir.sh --model-entrypoint dummy_mlp_factory:make_dummy_mlp \
+  --model-state-path $SCRIPT_DIR/dummy_mlp.pth \
+  --sample-shapes "1,10,float32" \
+  --dialect linalg \
+  --out-mlir $SCRIPT_DIR/dummy_mlp_sh.mlir
diff --git a/ingress/Torch-MLIR/examples/dummy_mlp_cli/export_py.sh b/ingress/Torch-MLIR/examples/dummy_mlp_cli/export_py.sh
new file mode 100755
index 0000000..7d15911
--- /dev/null
+++ b/ingress/Torch-MLIR/examples/dummy_mlp_cli/export_py.sh
@@ -0,0 +1,10 @@
+#!/usr/bin/env bash
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+ROOT_DIR=$SCRIPT_DIR/../../py_src/
+
+PYTHONPATH=$PYTHONPATH:$ROOT_DIR:$SCRIPT_DIR python $ROOT_DIR/main.py --model-entrypoint dummy_mlp_factory:make_dummy_mlp \
+  --model-state-path $SCRIPT_DIR/dummy_mlp.pth \
+  --sample-shapes "1,10,float32" \
+  --dialect linalg \
+  --out-mlir $SCRIPT_DIR/dummy_mlp.mlir
diff --git a/ingress/Torch-MLIR/examples/dummy_mlp_python/export.py b/ingress/Torch-MLIR/examples/dummy_mlp_python/export.py
new file mode 100644
index 0000000..523946a
--- /dev/null
+++ b/ingress/Torch-MLIR/examples/dummy_mlp_python/export.py
@@ -0,0 +1,25 @@
+import torch
+import torch.nn as nn
+
+from export_lib.export import generate_mlir
+
+class DummyMLP(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(10, 32),
+            nn.ReLU(),
+            nn.Linear(32, 2)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+def main():
+    model = DummyMLP()
+    dummy_input = torch.randn(1, 10)
+    mlir_mod = generate_mlir(model, (dummy_input,), {})
+    print(mlir_mod)
+
+if __name__ == "__main__":
+    main()
diff --git a/ingress/Torch-MLIR/examples/dummy_mlp_python/run.sh b/ingress/Torch-MLIR/examples/dummy_mlp_python/run.sh
new file mode 100755
index 0000000..62ff78e
--- /dev/null
+++ b/ingress/Torch-MLIR/examples/dummy_mlp_python/run.sh
@@ -0,0 +1,4 @@
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+ROOT_DIR=$SCRIPT_DIR/../../py_src/
+
+PYTHONPATH=$ROOT_DIR python $SCRIPT_DIR/export.py
diff --git a/ingress/Torch-MLIR/examples/resnet_18_cli/export_bash.sh b/ingress/Torch-MLIR/examples/resnet_18_cli/export_bash.sh
new file mode 100755
index 0000000..b5e0f5f
--- /dev/null
+++ b/ingress/Torch-MLIR/examples/resnet_18_cli/export_bash.sh
@@ -0,0 +1,9 @@
+#!/usr/bin/env bash
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+ROOT_DIR=$SCRIPT_DIR/../../scripts/
+
+$ROOT_DIR/generate-mlir.sh --model-entrypoint torchvision.models:resnet18 \
+  --sample-shapes "1,3,224,224,float32" \
+  --dialect linalg \
+  --out-mlir $SCRIPT_DIR/resnet_18_sh.mlir
diff --git a/ingress/Torch-MLIR/examples/resnet_18_cli/export_py.sh b/ingress/Torch-MLIR/examples/resnet_18_cli/export_py.sh
new file mode 100755
index 0000000..22f3931
--- /dev/null
+++ b/ingress/Torch-MLIR/examples/resnet_18_cli/export_py.sh
@@ -0,0 +1,9 @@
+#!/usr/bin/env bash
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+ROOT_DIR=$SCRIPT_DIR/../../py_src/
+
+python $ROOT_DIR/main.py --model-entrypoint torchvision.models:resnet18 \
+  --sample-shapes "1,3,224,224,float32" \
+  --dialect linalg \
+  --out-mlir $SCRIPT_DIR/resnet_18.mlir
diff --git a/ingress/Torch-MLIR/generate-mlir.py b/ingress/Torch-MLIR/generate-mlir.py
deleted file mode 100644
index 888e6dd..0000000
--- a/ingress/Torch-MLIR/generate-mlir.py
+++ /dev/null
@@ -1,72 +0,0 @@
-#!/usr/bin/env python3
-
-import argparse
-import os
-import torch
-import torch.nn as nn
-from torch_mlir import fx
-from torch_mlir.fx import OutputType
-
-# Parse arguments for selecting which model to load and which MLIR dialect to generate
-def parse_args():
-    parser = argparse.ArgumentParser(description="Generate MLIR for Torch-MLIR models.")
-    parser.add_argument(
-        "--model",
-        type=str,
-        required=True,
-        help="Path to the Torch model file.",
-    )
-    parser.add_argument(
-        "--dialect",
-        type=str,
-        choices=["torch", "linalg", "stablehlo", "tosa"],
-        default="linalg",
-        help="MLIR dialect to generate.",
-    )
-    return parser.parse_args()
-
-# Functin to load the Torch model
-def load_torch_model(model_path):
-
-    if not os.path.exists(model_path):
-        raise FileNotFoundError(f"Model file {model_path} does not exist.")
-    
-    model = torch.load(model_path)
-    return model
-
-# Function to generate MLIR from the Torch model
-# See: https://github.com/MrSidims/PytorchExplorer/blob/main/backend/server.py#L237
-def generate_mlir(model, dialect):
-
-    # Convert the Torch model to MLIR
-    output_type = None
-    if dialect == "torch":
-        output_type = OutputType.TORCH
-    elif dialect == "linalg":
-        output_type = OutputType.LINALG
-    elif dialect == "stablehlo":
-        output_type = OutputType.STABLEHLO
-    elif dialect == "tosa":
-        output_type = OutputType.TOSA
-    else:
-        raise ValueError(f"Unsupported dialect: {dialect}")
-
-    module = fx.export_and_import(model, "", output_type=output_type)
-    return module
-
-# Main function to execute the script
-def main():
-    args = parse_args()
-    
-    # Load the Torch model
-    model = load_torch_model(args.model)
-    
-    # Generate MLIR from the model
-    mlir_module = generate_mlir(model, args.dialect)
-    
-    # Print or save the MLIR module
-    print(mlir_module)
-
-# Entry point for the script
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/ingress/Torch-MLIR/generate-mlir.sh b/ingress/Torch-MLIR/generate-mlir.sh
deleted file mode 100755
index 0a079c6..0000000
--- a/ingress/Torch-MLIR/generate-mlir.sh
+++ /dev/null
@@ -1,42 +0,0 @@
-#!/usr/bin/env bash
-
-# Command line argument for model to load and MLIR dialect to generate
-while getopts "m:d:" opt; do
-  case $opt in
-    m)
-      MODEL=$OPTARG
-      ;;
-    d)
-      DIALECT=$OPTARG
-      ;;
-    *)
-      echo "Usage: $0 [-m model] [-d dialect]"
-      exit 1
-      ;;
-  esac
-done
-if [ -z "$MODEL" ]; then
-  echo "Model not specified. Please provide a model using -m option."
-  exit 1
-fi
-if [ -z "$DIALECT" ]; then
-  DIALECT="linalg"
-fi
-
-# Enable local virtualenv created by install-virtualenv.sh
-if [ ! -d "torch-mlir-venv" ]; then
-  echo "Virtual environment not found. Please run install-virtualenv.sh first."
-  exit 1
-fi
-source torch-mlir-venv/bin/activate
-
-# Find script directory
-SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
-
-# Use the Python script to generate MLIR
-echo "Generating MLIR for model '$MODEL' with dialect '$DIALECT'..."
-python $SCRIPT_DIR/generate-mlir.py --model "$MODEL" --dialect "$DIALECT"
-if [ $? -ne 0 ]; then
-  echo "Failed to generate MLIR for model '$MODEL'."
-  exit 1
-fi
diff --git a/ingress/Torch-MLIR/py_src/__init__.py b/ingress/Torch-MLIR/py_src/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/ingress/Torch-MLIR/py_src/export_lib/__init__.py b/ingress/Torch-MLIR/py_src/export_lib/__init__.py
new file mode 100644
index 0000000..fce4293
--- /dev/null
+++ b/ingress/Torch-MLIR/py_src/export_lib/__init__.py
@@ -0,0 +1,8 @@
+# Examples package initialization
+"""
+Example scripts for Torch-MLIR usage.
+"""
+
+from .export import load_torch_model, generate_sample_args, generate_mlir
+
+__all__ = ['load_torch_model', 'generate_sample_args', 'generate_mlir']
diff --git a/ingress/Torch-MLIR/py_src/export_lib/export.py b/ingress/Torch-MLIR/py_src/export_lib/export.py
new file mode 100644
index 0000000..928ffa6
--- /dev/null
+++ b/ingress/Torch-MLIR/py_src/export_lib/export.py
@@ -0,0 +1,58 @@
+import os
+import torch
+from torch_mlir import fx
+from torch_mlir.fx import OutputType
+
+from .utils import parse_shape_str, load_callable_symbol, generate_fake_tensor
+
+def load_torch_model(entrypoint_path, model_state_path=None, *args, **kwargs):
+    entrypoint = load_callable_symbol(entrypoint_path)
+    model = entrypoint(*args, **kwargs)
+    if model_state_path is not None:
+        state_dict = load_model_state(model_state_path)
+        model.load_state_dict(state_dict)
+    return model
+
+# Function to load the Torch model
+def load_model_state(model_path):
+    if not os.path.exists(model_path):
+        raise FileNotFoundError(f"Model file {model_path} does not exist.")
+
+    model = torch.load(model_path)
+    return model
+
+
+def generate_sample_args(shape_str, sample_fn_path) -> tuple[tuple, dict]:
+    """
+    Generate sample arguments for the model's 'forward' method.
+    (Required by torch_mlir.fx.export_and_import)
+    """
+    if sample_fn_path is None:
+        shape, dtype = parse_shape_str(shape_str)
+        return (generate_fake_tensor(shape, dtype),), {}
+
+    return load_callable_symbol(sample_fn_path)()
+
+
+def generate_mlir(model, sample_args, sample_kwargs=None, dialect="linalg"):
+    # Convert the Torch model to MLIR
+    output_type = None
+    if dialect == "torch":
+        output_type = OutputType.TORCH
+    elif dialect == "linalg":
+        output_type = OutputType.LINALG_ON_TENSORS
+    elif dialect == "stablehlo":
+        output_type = OutputType.STABLEHLO
+    elif dialect == "tosa":
+        output_type = OutputType.TOSA
+    else:
+        raise ValueError(f"Unsupported dialect: {dialect}")
+
+    if sample_kwargs is None:
+        sample_kwargs = {}
+
+    model.eval()
+    module = fx.export_and_import(
+        model, *sample_args, output_type=output_type, **sample_kwargs
+    )
+    return module
diff --git a/ingress/Torch-MLIR/py_src/export_lib/utils.py b/ingress/Torch-MLIR/py_src/export_lib/utils.py
new file mode 100644
index 0000000..0534e60
--- /dev/null
+++ b/ingress/Torch-MLIR/py_src/export_lib/utils.py
@@ -0,0 +1,78 @@
+import importlib
+import importlib.util
+import sys
+import os
+
+import torch
+from torch._subclasses.fake_tensor import FakeTensorMode
+
+from typing import Callable
+
+
+def load_callable_symbol(entry: str) -> Callable:
+    """
+    Load a callable python symbol from a module or a file.
+
+    Parameters
+    ----------
+    entry : str
+        A string specifying the module or file and the attribute path,
+        in the format 'module_or_path:attr', e.g.
+        'torchvision.models:resnet18' or '/path/to/model.py:build_model'.
+
+    Returns
+    -------
+    Callable
+    """
+    if ":" not in entry:
+        raise ValueError("Entry must be like 'module_or_path:attr'")
+
+    left, right = entry.split(":", 1)
+    attr_path = right.split(".")
+
+    if os.path.exists(left) and left.endswith(".py"):
+        mod_dir = os.path.abspath(os.path.dirname(left))
+        mod_name = os.path.splitext(os.path.basename(left))[0]
+        sys_path_was = list(sys.path)
+        try:
+            if mod_dir not in sys.path:
+                sys.path.insert(0, mod_dir)
+            spec = importlib.util.spec_from_file_location(mod_name, left)
+            if spec is None or spec.loader is None:
+                raise ImportError(f"Cannot load spec from {left}")
+            module = importlib.util.module_from_spec(spec)
+            spec.loader.exec_module(module)
+        finally:
+            sys.path = sys_path_was
+    else:
+        module = importlib.import_module(left)
+
+    obj = module
+    for name in attr_path:
+        obj = getattr(obj, name)
+
+    return obj
+
+
+def parse_shape_str(shape: str) -> tuple[tuple[int], torch.dtype]:
+    """
+    Parse a shape string into a shape tuple and a torch dtype.
+
+    Parameters
+    ----------
+    shape : str
+        A string representing the shape and dtype, e.g. '1,3,224,224,float32'.
+    """
+    *shapes, dtype = shape.split(",")
+    tdtype = getattr(torch, dtype)
+    if tdtype is None:
+        raise ValueError(f"Unsupported dtype: {dtype}")
+    if "?" in shapes:
+        raise ValueError(f"Dynamic shapes are not supported yet: {shape}")
+    return (tuple(map(int, shapes)), tdtype)
+
+
+def generate_fake_tensor(shape: tuple[int], dtype: torch.dtype) -> torch.Tensor:
+    """Generate a fake tensor (has no actual buffer) with the given shape and dtype."""
+    with FakeTensorMode():
+        return torch.empty(shape, dtype=dtype)
diff --git a/ingress/Torch-MLIR/py_src/main.py b/ingress/Torch-MLIR/py_src/main.py
new file mode 100644
index 0000000..feddf4f
--- /dev/null
+++ b/ingress/Torch-MLIR/py_src/main.py
@@ -0,0 +1,98 @@
+#!/usr/bin/env python3
+
+import argparse
+from export_lib.export import load_torch_model, generate_sample_args, generate_mlir
+
+# Parse arguments for selecting which model to load and which MLIR dialect to generate
+def parse_args():
+    parser = argparse.ArgumentParser(description="Generate MLIR for Torch-MLIR models.")
+    parser.add_argument(
+        "--model-entrypoint",
+        type=str,
+        required=True,
+        help="Path to the model entrypoint, e.g. 'torchvision.models:resnet18' or '/path/to/model.py:build_model'.",
+    )
+    parser.add_argument(
+        "--model-state-path",
+        type=str,
+        required=False,
+        help="Path to a state file of the Torch model (usually has .pt or .pth extension).",
+    )
+    parser.add_argument(
+        "--model-args",
+        type=str,
+        required=False,
+        default="[]",
+        help=""
+        "Positional arguments to pass to the model's entrypoint "
+        "(note that this argument will be passed to an 'eval',"
+        " so the string should contain a valid python code).",
+    )
+    parser.add_argument(
+        "--model-kwargs",
+        type=str,
+        required=False,
+        default="{}",
+        help=""
+        "Keyword arguments to pass to the model's entrypoint "
+        "(note that this argument will be passed to an 'eval',"
+        " so the string should contain a valid python code).",
+    )
+    parser.add_argument(
+        "--sample-shapes",
+        type=str,
+        required=False,
+        help="Tensor shapes/dtype that the 'forward' method of the model will be called with,"
+        " e.g. '1,3,224,224,float32'. Must be specified if '--sample-fn' is not given.",
+    )
+    parser.add_argument(
+        "--sample-fn",
+        type=str,
+        required=False,
+        help="Path to a function that generates sample arguments for the model's 'forward' method."
+        " The function should return a tuple of (args, kwargs). If this is given, '--sample-shapes' is ignored.",
+    )
+    parser.add_argument(
+        "--dialect",
+        type=str,
+        choices=["torch", "linalg", "stablehlo", "tosa"],
+        default="linalg",
+        help="MLIR dialect to generate.",
+    )
+    parser.add_argument(
+        "--out-mlir",
+        type=str,
+        required=False,
+        help="Path to save the generated MLIR module.",
+    )
+    return parser.parse_args()
+
+
+# Main function to execute the script
+def main():
+    args = parse_args()
+
+    # Load the Torch model
+    model = load_torch_model(
+        args.model_entrypoint,
+        args.model_state_path,
+        *eval(args.model_args),
+        **eval(args.model_kwargs)
+    )
+    sample_args, sample_kwargs = generate_sample_args(
+        args.sample_shapes, args.sample_fn
+    )
+    # Generate MLIR from the model
+    mlir_module = generate_mlir(model, sample_args, sample_kwargs, args.dialect)
+
+    # Print or save the MLIR module
+    if args.out_mlir:
+        with open(args.out_mlir, "w") as f:
+            f.write(str(mlir_module))
+    else:
+        print(mlir_module)
+
+
+# Entry point for the script
+if __name__ == "__main__":
+    main()
diff --git a/ingress/Torch-MLIR/scripts/generate-mlir.sh b/ingress/Torch-MLIR/scripts/generate-mlir.sh
new file mode 100755
index 0000000..080cc05
--- /dev/null
+++ b/ingress/Torch-MLIR/scripts/generate-mlir.sh
@@ -0,0 +1,16 @@
+#!/usr/bin/env bash
+
+# Find script directory
+SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
+PY_SCRIPT_DIR=$SCRIPT_DIR/../py_src/
+VENV_DIR="$SCRIPT_DIR/../torch-mlir-venv"
+
+# Enable local virtualenv created by install-virtualenv.sh
+if [ ! -d "$VENV_DIR" ]; then
+  echo "Virtual environment not found. Please run install-virtualenv.sh first."
+  exit 1
+fi
+source $VENV_DIR/bin/activate
+
+# Use the Python script to generate MLIR
+PYTHONPATH=$PYTHONPATH:$PY_SCRIPT_DIR python "$PY_SCRIPT_DIR/main.py" "$@"
diff --git a/ingress/Torch-MLIR/install-virtualenv.sh b/ingress/Torch-MLIR/scripts/install-virtualenv.sh
similarity index 94%
rename from ingress/Torch-MLIR/install-virtualenv.sh
rename to ingress/Torch-MLIR/scripts/install-virtualenv.sh
index f48019c..43b9124 100755
--- a/ingress/Torch-MLIR/install-virtualenv.sh
+++ b/ingress/Torch-MLIR/scripts/install-virtualenv.sh
@@ -9,6 +9,7 @@ else
   DEVICE_TYPE=$(lspci | grep VGA)
 fi
 
+SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
 
 # Install torch-mlir inside a virtual environment
 echo "First ensure uv is installed"
@@ -16,7 +17,7 @@ echo "First ensure uv is installed"
 python -m pip install uv --upgrade
 
 echo "Preparing the virtual environment"
-python -m uv venv torch-mlir-venv --python 3.12
+python -m uv venv $SCRIPT_DIR/../torch-mlir-venv --python 3.12
 
 #echo "Preparing the virtual environment"
 #python3 -m venv torch-mlir-venv