diff --git a/CHANGELOGS.rst b/CHANGELOGS.rst
index 3cb416dc..9029a24d 100644
--- a/CHANGELOGS.rst
+++ b/CHANGELOGS.rst
@@ -4,6 +4,7 @@ Change Logs
 0.7.16
 ++++++
 
+* :pr:`270`: add export sample code to export a specific model id with the appropriate inputs
 * :pr:`269`: adds one unit test to track a patch fixing broadcast output shape
 * :pr:`267`: patches ``sdpa_attention_forward`` because of a control flow (``transformers>=5.0``)
 * :pr:`266`: makes ``patch_torch`` an integer in ``torch_export_patches`` to enable more patches 
diff --git a/_doc/api/torch_models/code_sample.rst b/_doc/api/torch_models/code_sample.rst
new file mode 100644
index 00000000..ae6df4f3
--- /dev/null
+++ b/_doc/api/torch_models/code_sample.rst
@@ -0,0 +1,7 @@
+
+onnx_diagnostic.torch_models.code_sample
+========================================
+
+.. automodule:: onnx_diagnostic.torch_models.code_sample
+    :members:
+    :no-undoc-members:
diff --git a/_doc/api/torch_models/index.rst b/_doc/api/torch_models/index.rst
index 3279e918..9a472cec 100644
--- a/_doc/api/torch_models/index.rst
+++ b/_doc/api/torch_models/index.rst
@@ -5,6 +5,7 @@ onnx_diagnostic.torch_models
     :maxdepth: 1
     :caption: submodules
 
+    code_sample    
     hghub/index
     llms
     validate
diff --git a/_unittests/ut_torch_models/test_code_sample.py b/_unittests/ut_torch_models/test_code_sample.py
new file mode 100644
index 00000000..d3794086
--- /dev/null
+++ b/_unittests/ut_torch_models/test_code_sample.py
@@ -0,0 +1,100 @@
+import unittest
+import subprocess
+import sys
+import torch
+from onnx_diagnostic.ext_test_case import (
+    ExtTestCase,
+    hide_stdout,
+    requires_torch,
+    requires_experimental,
+    requires_transformers,
+)
+from onnx_diagnostic.helpers.cache_helper import make_dynamic_cache
+from onnx_diagnostic.torch_models.code_sample import code_sample, make_code_for_inputs
+
+
+class TestCodeSample(ExtTestCase):
+    @requires_transformers("4.53")
+    @requires_torch("2.9")
+    @requires_experimental()
+    @hide_stdout()
+    def test_code_sample_tiny_llm_custom(self):
+        code = code_sample(
+            "arnir0/Tiny-LLM",
+            verbose=2,
+            exporter="custom",
+            patch=True,
+            dump_folder="dump_test/validate_tiny_llm_custom",
+            dtype="float16",
+            device="cpu",
+            optimization="default",
+        )
+        filename = self.get_dump_file("test_code_sample_tiny_llm_custom.py")
+        with open(filename, "w") as f:
+            f.write(code)
+        cmds = [sys.executable, "-u", filename]
+        p = subprocess.Popen(cmds, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        res = p.communicate()
+        _out, err = res
+        st = err.decode("ascii", errors="ignore")
+        self.assertNotIn("Traceback", st)
+
+    @requires_transformers("4.53")
+    @requires_torch("2.9")
+    @requires_experimental()
+    @hide_stdout()
+    def test_code_sample_tiny_llm_dynamo(self):
+        code = code_sample(
+            "arnir0/Tiny-LLM",
+            verbose=2,
+            exporter="onnx-dynamo",
+            patch=True,
+            dump_folder="dump_test/validate_tiny_llm_dynamo",
+            dtype="float16",
+            device="cpu",
+            optimization="ir",
+        )
+        filename = self.get_dump_file("test_code_sample_tiny_llm_dynamo.py")
+        with open(filename, "w") as f:
+            f.write(code)
+        cmds = [sys.executable, "-u", filename]
+        p = subprocess.Popen(cmds, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        res = p.communicate()
+        _out, err = res
+        st = err.decode("ascii", errors="ignore")
+        self.assertNotIn("Traceback", st)
+
+    def test_make_code_for_inputs(self):
+        values = [
+            ("dict(a=True)", dict(a=True)),
+            ("dict(a=1)", dict(a=1)),
+            (
+                "dict(a=torch.randint(3, size=(2,), dtype=torch.int64))",
+                dict(a=torch.tensor([2, 3], dtype=torch.int64)),
+            ),
+            (
+                "dict(a=torch.rand((2,), dtype=torch.float16))",
+                dict(a=torch.tensor([2, 3], dtype=torch.float16)),
+            ),
+        ]
+        for res, inputs in values:
+            self.assertEqual(res, make_code_for_inputs(inputs))
+
+        res = make_code_for_inputs(
+            dict(
+                cc=make_dynamic_cache(
+                    [(torch.randn(2, 2, 2, 2), torch.randn(2, 2, 2, 2)) for i in range(2)]
+                )
+            )
+        )
+        self.assertEqual(
+            "dict(cc=make_dynamic_cache([(torch.rand((2, 2, 2, 2), "
+            "dtype=torch.float32),torch.rand((2, 2, 2, 2), dtype=torch.float32)), "
+            "(torch.rand((2, 2, 2, 2), dtype=torch.float32),"
+            "torch.rand((2, 2, 2, 2), dtype=torch.float32))]))",
+            res,
+        )
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)
diff --git a/onnx_diagnostic/_command_lines_parser.py b/onnx_diagnostic/_command_lines_parser.py
index cf7cc863..f4c2eca5 100644
--- a/onnx_diagnostic/_command_lines_parser.py
+++ b/onnx_diagnostic/_command_lines_parser.py
@@ -557,7 +557,7 @@ def get_parser_validate() -> ArgumentParser:
         "--quiet-input-sets",
         default="",
         help="Avoids raising an exception when an input sets does not work with "
-        "the exported model, example: --quiet-input-sets=inputs,inputs22",
+        "the exported model.\nExample: --quiet-input-sets=inputs,inputs22",
     )
     return parser
 
@@ -631,6 +631,94 @@ def _cmd_validate(argv: List[Any]):
             print(f":{k},{v};")
 
 
+def _cmd_export_sample(argv: List[Any]):
+    from .helpers import string_type
+    from .torch_models.validate import get_inputs_for_task, _make_folder_name
+    from .torch_models.code_sample import code_sample
+    from .tasks import supported_tasks
+
+    parser = get_parser_validate()
+    args = parser.parse_args(argv[1:])
+    if not args.task and not args.mid:
+        print("-- list of supported tasks:")
+        print("\n".join(supported_tasks()))
+    elif not args.mid:
+        data = get_inputs_for_task(args.task)
+        if args.verbose:
+            print(f"task: {args.task}")
+        max_length = max(len(k) for k in data["inputs"]) + 1
+        print("-- inputs")
+        for k, v in data["inputs"].items():
+            print(f"  + {k.ljust(max_length)}: {string_type(v, with_shape=True)}")
+        print("-- dynamic_shapes")
+        for k, v in data["dynamic_shapes"].items():
+            print(f"  + {k.ljust(max_length)}: {string_type(v)}")
+    else:
+        # Let's skip any invalid combination if known to be unsupported
+        if (
+            "onnx" not in (args.export or "")
+            and "custom" not in (args.export or "")
+            and (args.opt or "")
+        ):
+            print(f"code-sample - unsupported args: export={args.export!r}, opt={args.opt!r}")
+            return
+        patch_dict = args.patch if isinstance(args.patch, dict) else {"patch": args.patch}
+        code = code_sample(
+            model_id=args.mid,
+            task=args.task,
+            do_run=args.run,
+            verbose=args.verbose,
+            quiet=args.quiet,
+            same_as_pretrained=args.same_as_trained,
+            use_pretrained=args.trained,
+            dtype=args.dtype,
+            device=args.device,
+            patch=patch_dict,
+            rewrite=args.rewrite and patch_dict.get("patch", True),
+            stop_if_static=args.stop_if_static,
+            optimization=args.opt,
+            exporter=args.export,
+            dump_folder=args.dump_folder,
+            drop_inputs=None if not args.drop else args.drop.split(","),
+            input_options=args.iop,
+            model_options=args.mop,
+            subfolder=args.subfolder,
+            opset=args.opset,
+            runtime=args.runtime,
+            output_names=(
+                None if len(args.outnames.strip()) < 2 else args.outnames.strip().split(",")
+            ),
+        )
+        if args.dump_folder:
+            os.makedirs(args.dump_folder, exist_ok=True)
+            name = (
+                _make_folder_name(
+                    model_id=args.model_id,
+                    exporter=args.exporter,
+                    optimization=args.optimization,
+                    dtype=args.dtype,
+                    device=args.device,
+                    subfolder=args.subfolder,
+                    opset=args.opset,
+                    drop_inputs=None if not args.drop else args.drop.split(","),
+                    same_as_pretrained=args.same_as_pretrained,
+                    use_pretrained=args.use_pretrained,
+                    task=args.task,
+                ).replace("/", "-")
+                + ".py"
+            )
+            fullname = os.path.join(args.dump_folder, name)
+            if args.verbose:
+                print(f"-- prints code in {fullname!r}")
+            print("--")
+            with open(fullname, "w") as f:
+                f.write(code)
+            if args.verbose:
+                print("-- done")
+        else:
+            print(code)
+
+
 def get_parser_stats() -> ArgumentParser:
     parser = ArgumentParser(
         prog="stats",
@@ -960,14 +1048,15 @@ def get_main_parser() -> ArgumentParser:
             Type 'python -m onnx_diagnostic <cmd> --help'
             to get help for a specific command.
 
-            agg        - aggregates statistics from multiple files
-            config     - prints a configuration for a model id
-            find       - find node consuming or producing a result
-            lighten    - makes an onnx model lighter by removing the weights,
-            print      - prints the model on standard output
-            stats      - produces statistics on a model
-            unlighten  - restores an onnx model produces by the previous experiment
-            validate   - validate a model
+            agg          - aggregates statistics from multiple files
+            config       - prints a configuration for a model id
+            exportsample - produces a code to export a model
+            find         - find node consuming or producing a result
+            lighten      - makes an onnx model lighter by removing the weights,
+            print        - prints the model on standard output
+            stats        - produces statistics on a model
+            unlighten    - restores an onnx model produces by the previous experiment
+            validate     - validate a model
             """
         ),
     )
@@ -976,6 +1065,7 @@ def get_main_parser() -> ArgumentParser:
         choices=[
             "agg",
             "config",
+            "exportsample",
             "find",
             "lighten",
             "print",
@@ -998,6 +1088,7 @@ def main(argv: Optional[List[Any]] = None):
         validate=_cmd_validate,
         stats=_cmd_stats,
         agg=_cmd_agg,
+        exportsample=_cmd_export_sample,
     )
 
     if argv is None:
@@ -1020,6 +1111,7 @@ def main(argv: Optional[List[Any]] = None):
                 validate=get_parser_validate,
                 stats=get_parser_stats,
                 agg=get_parser_agg,
+                exportsample=get_parser_validate,
             )
             cmd = argv[0]
             if cmd not in parsers:
diff --git a/onnx_diagnostic/torch_models/code_sample.py b/onnx_diagnostic/torch_models/code_sample.py
new file mode 100644
index 00000000..cdcd8970
--- /dev/null
+++ b/onnx_diagnostic/torch_models/code_sample.py
@@ -0,0 +1,343 @@
+import os
+import textwrap
+import torch
+from typing import Any, Dict, List, Optional, Tuple, Union
+from ..helpers import flatten_object
+from ..helpers.torch_helper import to_any
+from .hghub.model_inputs import _preprocess_model_id
+from .hghub import get_untrained_model_with_inputs
+from .validate import filter_inputs, make_patch_kwargs
+
+
+CODE_SAMPLES = {
+    "imports": "from typing import Any\nimport torch",
+    "get_model_with_inputs": textwrap.dedent(
+        """
+    def get_model_with_inputs(
+        model_id:str,
+        subfolder: str | None = None,
+        dtype: str | torch.dtype | None = None,
+        device: str | torch.device | None = None,
+        same_as_pretrained: bool = False,
+        use_pretrained: bool = False,
+        input_options: dict[str, Any] | None = None,
+        model_options: dict[str, Any] | None = None,
+    ) -> dict[str, Any]:
+        if use_pretrained:
+            import transformers
+            assert same_as_pretrained, (
+                "same_as_pretrained must be True if use_pretrained is True"
+            )
+            # tokenizer = AutoTokenizer.from_pretrained(model_path)
+            model = transformers.AutoModel.from_pretrained(
+                model_id,
+                trust_remote_code=True,
+                subfolder=subfolder,
+                dtype=dtype,
+                device=device,
+            )
+            data = {"model": model}
+            assert not input_options, "Not implemented yet with input_options{input_options}"
+            assert not model_options, "Not implemented yet with input_options{model_options}"
+        else:
+            from onnx_diagnostic.torch_models.hghub import get_untrained_model_with_inputs
+            data = get_untrained_model_with_inputs(
+                model_id,
+                use_pretrained=use_pretrained,
+                same_as_pretrained=same_as_pretrained,
+                inputs_kwargs=input_options,
+                model_kwargs=model_options,
+                subfolder=subfolder,
+                add_second_input=False,
+            )
+            if dtype:
+                data["model"] = data["model"].to(
+                    getattr(torch, dtype) if isinstance(dtype, str) else dtype
+                )
+            if device:
+                data["model"] = data["model"].to(device)
+        return data["model"]
+    """
+    ),
+}
+
+
+def make_code_for_inputs(inputs: Dict[str, torch.Tensor]) -> str:
+    """
+    Creates a code to generate random inputs.
+
+    :param inputs: dictionary
+    :return: code
+    """
+    codes = []
+    for k, v in inputs.items():
+        if isinstance(v, (int, bool, float)):
+            code = f"{k}={v}"
+        elif isinstance(v, torch.Tensor):
+            shape = tuple(map(int, v.shape))
+            if v.dtype in (torch.int32, torch.int64):
+                code = f"{k}=torch.randint({v.max()}, size={shape}, dtype={v.dtype})"
+            elif v.dtype in (torch.float32, torch.float16, torch.bfloat16):
+                code = f"{k}=torch.rand({shape}, dtype={v.dtype})"
+            else:
+                raise ValueError(f"Unexpected dtype = {v.dtype} for k={k!r}")
+        elif v.__class__.__name__ == "DynamicCache":
+            obj = flatten_object(v)
+            cc = [f"torch.rand({tuple(map(int,_.shape))}, dtype={_.dtype})" for _ in obj]
+            va = [f"({a},{b})" for a, b in zip(cc[: len(cc) // 2], cc[len(cc) // 2 :])]
+            va2 = ", ".join(va)
+            code = f"{k}=make_dynamic_cache([{va2}])"
+        else:
+            raise ValueError(f"Unexpected type {type(v)} for k={k!r}")
+        codes.append(code)
+    st = ", ".join(codes)
+    return f"dict({st})"
+
+
+def make_export_code(
+    exporter: str,
+    optimization: Optional[str] = None,
+    patch_kwargs: Optional[Dict[str, Any]] = None,
+    stop_if_static: int = 0,
+    dump_folder: Optional[str] = None,
+    opset: Optional[int] = None,
+    dynamic_shapes: Optional[Dict[str, Any]] = None,
+    output_names: Optional[List[str]] = None,
+    verbose: int = 0,
+) -> Tuple[str, str]:
+    args = [f"dynamic_shapes={dynamic_shapes}"]
+    if output_names:
+        args.append(f"output_names={output_names}")
+    code = []
+    imports = []
+    if dump_folder:
+        code.append(f"os.makedirs({dump_folder!r})")
+        imports.append("import os")
+        filename = os.path.join(dump_folder, "model.onnx")
+    if exporter == "custom":
+        if opset:
+            args.append(f"target_opset={opset}")
+        if optimization:
+            args.append(f"options=OptimizationOptions(patterns={optimization!r})")
+            args.append(f"large_model=True, filename={filename!r}")
+        sargs = ", ".join(args)
+        imports.extend(
+            [
+                "from experimental_experiment.torch_interpreter import to_onnx",
+                "from experimental_experiment.xbuilder import OptimizationOptions",
+            ]
+        )
+        code.extend([f"onx = to_onnx(model, inputs, {sargs})"])
+    elif exporter == "onnx-dynamo":
+        if opset:
+            args.append(f"opset_version={opset}")
+        sargs = ", ".join(args)
+        code.extend([f"epo = torch.onnx.export(model, args=(), kwargs=inputs, {sargs})"])
+        if optimization:
+            imports.append("import onnxscript")
+            code.extend(["onnxscript.optimizer.optimize_ir(epo.model)"])
+            if "os_ort" in optimization:
+                imports.append("import onnxscript.rewriter.ort_fusions as ort_fusions")
+                code.extend(["ort_fusions.optimize_for_ort(epo.model)"])
+        if dump_folder:
+            code.extend([f"epo.save({filename!r})"])
+    else:
+        raise ValueError(f"Unexpected exporter {exporter!r}")
+    if not patch_kwargs:
+        return "\n".join(imports), "\n".join(code)
+
+    imports.append("from onnx_diagnostic.torch_export_patches import torch_export_patches")
+    if stop_if_static:
+        patch_kwargs["stop_if_static"] = stop_if_static
+    sargs = ", ".join(f"{k}={v}" for k, v in patch_kwargs.items())
+    code = [f"with torch_export_patches({sargs}):", *["    " + _ for _ in code]]
+    return "\n".join(imports), "\n".join(code)
+
+
+def code_sample(
+    model_id: str,
+    task: Optional[str] = None,
+    do_run: bool = False,
+    exporter: Optional[str] = None,
+    do_same: bool = False,
+    verbose: int = 0,
+    dtype: Optional[Union[str, torch.dtype]] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    same_as_pretrained: bool = False,
+    use_pretrained: bool = False,
+    optimization: Optional[str] = None,
+    quiet: bool = False,
+    patch: Union[bool, str, Dict[str, bool]] = False,
+    rewrite: bool = False,
+    stop_if_static: int = 1,
+    dump_folder: Optional[str] = None,
+    drop_inputs: Optional[List[str]] = None,
+    input_options: Optional[Dict[str, Any]] = None,
+    model_options: Optional[Dict[str, Any]] = None,
+    subfolder: Optional[str] = None,
+    opset: Optional[int] = None,
+    runtime: str = "onnxruntime",
+    output_names: Optional[List[str]] = None,
+) -> str:
+    """
+    This generates a code to export a model with the proper settings.
+
+    :param model_id: model id to validate
+    :param task: task used to generate the necessary inputs,
+        can be left empty to use the default task for this model
+        if it can be determined
+    :param do_run: checks the model works with the defined inputs
+    :param exporter: exporter the model using this exporter,
+        available list: ``export-strict``, ``export-nostrict``, ...
+        see below
+    :param do_same: checks the discrepancies of the exported model
+    :param verbose: verbosity level
+    :param dtype: uses this dtype to check the model
+    :param device: do the verification on this device
+    :param same_as_pretrained: use a model equivalent to the trained,
+        this is not always possible
+    :param use_pretrained: use the trained model, not the untrained one
+    :param optimization: optimization to apply to the exported model,
+        depend on the the exporter
+    :param quiet: if quiet, catches exception if any issue
+    :param patch: applies patches (``patch_transformers=True, path_diffusers=True``)
+        if True before exporting
+        see :func:`onnx_diagnostic.torch_export_patches.torch_export_patches`,
+        a string can be used to specify only one of them
+    :param rewrite: applies known rewriting (``patch_transformers=True``) before exporting,
+        see :func:`onnx_diagnostic.torch_export_patches.torch_export_patches`
+    :param stop_if_static: stops if a dynamic dimension becomes static,
+        see :func:`onnx_diagnostic.torch_export_patches.torch_export_patches`
+    :param dump_folder: dumps everything in a subfolder of this one
+    :param drop_inputs: drops this list of inputs (given their names)
+    :param input_options: additional options to define the dummy inputs
+        used to export
+    :param model_options: additional options when creating the model such as
+        ``num_hidden_layers`` or ``attn_implementation``
+    :param subfolder: version or subfolders to uses when retrieving a model id
+    :param opset: onnx opset to use for the conversion
+    :param runtime: onnx runtime to use to check about discrepancies,
+        possible values ``onnxruntime``, ``torch``, ``orteval``,
+        ``orteval10``, ``ref`` only if `do_run` is true
+    :param output_names: output names the onnx exporter should use
+    :return: a code
+
+    .. runpython::
+        :showcode:
+
+        from onnx_diagnostic.torch_models.code_sample import code_sample
+
+        print(
+            code_sample(
+                "arnir0/Tiny-LLM",
+                exporter="onnx-dynamo",
+                optimization="ir",
+                patch=True,
+            )
+        )
+    """
+    model_id, subfolder, same_as_pretrained, use_pretrained = _preprocess_model_id(
+        model_id,
+        subfolder,
+        same_as_pretrained=same_as_pretrained,
+        use_pretrained=use_pretrained,
+    )
+    patch_kwargs = make_patch_kwargs(patch=patch, rewrite=rewrite)
+
+    iop = input_options or {}
+    mop = model_options or {}
+    data = get_untrained_model_with_inputs(
+        model_id,
+        verbose=verbose,
+        task=task,
+        use_pretrained=use_pretrained,
+        same_as_pretrained=same_as_pretrained,
+        inputs_kwargs=iop,
+        model_kwargs=mop,
+        subfolder=subfolder,
+        add_second_input=False,
+    )
+    if drop_inputs:
+        update = {}
+        for k in data:
+            if k.startswith("inputs"):
+                update[k], ds = filter_inputs(
+                    data[k],
+                    drop_names=drop_inputs,
+                    model=data["model"],
+                    dynamic_shapes=data["dynamic_shapes"],
+                )
+        update["dynamic_shapes"] = ds
+        data.update(update)
+
+    update = {}
+    for k in data:
+        if k.startswith("inputs"):
+            v = data[k]
+            if dtype:
+                update[k] = v = to_any(
+                    v, getattr(torch, dtype) if isinstance(dtype, str) else dtype
+                )
+            if device:
+                update[k] = v = to_any(v, device)
+    if update:
+        data.update(update)
+
+    args = [f"{model_id!r}"]
+    if subfolder:
+        args.append(f"subfolder={subfolder!r}")
+    if dtype:
+        args.append(f"dtype={dtype!r}")
+    if device:
+        args.append(f"device={device!r}")
+    if same_as_pretrained:
+        args.append(f"same_as_pretrained={same_as_pretrained!r}")
+    if use_pretrained:
+        args.append(f"use_pretrained={use_pretrained!r}")
+    if input_options:
+        args.append(f"input_options={input_options!r}")
+    if model_options:
+        args.append(f"model_options={model_options!r}")
+    model_args = ", ".join(args)
+    imports, exporter_code = (
+        make_export_code(
+            exporter=exporter,
+            patch_kwargs=patch_kwargs,
+            verbose=verbose,
+            optimization=optimization,
+            stop_if_static=stop_if_static,
+            dump_folder=dump_folder,
+            opset=opset,
+            dynamic_shapes=data["dynamic_shapes"],
+        )
+        if exporter is not None
+        else ([], [])
+    )
+    input_code = make_code_for_inputs(data["inputs"])
+    cache_import = (
+        "from onnx_diagnostic.helpers.cache_helper import make_dynamic_cache"
+        if "dynamic_cache" in input_code
+        else ""
+    )
+
+    pieces = [
+        CODE_SAMPLES["imports"],
+        imports,
+        cache_import,
+        CODE_SAMPLES["get_model_with_inputs"],
+        textwrap.dedent(
+            f"""
+            model = get_model_with_inputs({model_args})
+                        """
+        ),
+        f"inputs = {input_code}",
+        exporter_code,
+    ]
+    code = "\n".join(pieces)  # type: ignore[arg-type]
+    try:
+        import black
+    except ImportError:
+        # No black formatting.
+        return code
+
+    return black.format_str(code, mode=black.Mode())
diff --git a/onnx_diagnostic/torch_models/validate.py b/onnx_diagnostic/torch_models/validate.py
index fca1cfbd..e9706f4f 100644
--- a/onnx_diagnostic/torch_models/validate.py
+++ b/onnx_diagnostic/torch_models/validate.py
@@ -293,6 +293,33 @@ def shrink_config(cfg: Dict[str, Any]) -> Dict[str, Any]:
     return new_cfg
 
 
+def make_patch_kwargs(
+    patch: Union[bool, str, Dict[str, bool]] = False,
+    rewrite: bool = False,
+) -> Dict[str, Any]:
+    """Creates patch arguments."""
+    default_patch = dict(patch_transformers=True, patch_diffusers=True, patch=True)
+    if isinstance(patch, bool):
+        patch_kwargs = default_patch if patch else dict(patch=False)
+    elif isinstance(patch, str):
+        patch_kwargs = {"patch": True, **{p: True for p in patch.split(",")}}  # noqa: C420
+    else:
+        assert isinstance(patch, dict), f"Unable to interpret patch={patch!r}"
+        patch_kwargs = patch.copy()
+        if "patch" not in patch_kwargs:
+            if any(patch_kwargs.values()):
+                patch_kwargs["patch"] = True
+        elif len(patch) == 1 and patch.get("patch", False):
+            patch_kwargs.update(default_patch)
+
+    assert not rewrite or patch_kwargs.get("patch", False), (
+        f"rewrite={rewrite}, patch={patch}, patch_kwargs={patch_kwargs} "
+        f"patch must be True to enable rewriting, "
+        f"if --patch=0 was specified on the command line, rewrites are disabled."
+    )
+    return patch_kwargs
+
+
 def validate_model(
     model_id: str,
     task: Optional[str] = None,
@@ -420,25 +447,8 @@ def validate_model(
         use_pretrained=use_pretrained,
     )
     time_preprocess_model_id = time.perf_counter() - main_validation_begin
-    default_patch = dict(patch_transformers=True, patch_diffusers=True, patch=True)
-    if isinstance(patch, bool):
-        patch_kwargs = default_patch if patch else dict(patch=False)
-    elif isinstance(patch, str):
-        patch_kwargs = {"patch": True, **{p: True for p in patch.split(",")}}  # noqa: C420
-    else:
-        assert isinstance(patch, dict), f"Unable to interpret patch={patch!r}"
-        patch_kwargs = patch.copy()
-        if "patch" not in patch_kwargs:
-            if any(patch_kwargs.values()):
-                patch_kwargs["patch"] = True
-        elif len(patch) == 1 and patch.get("patch", False):
-            patch_kwargs.update(default_patch)
+    patch_kwargs = make_patch_kwargs(patch=patch, rewrite=rewrite)
 
-    assert not rewrite or patch_kwargs.get("patch", False), (
-        f"rewrite={rewrite}, patch={patch}, patch_kwargs={patch_kwargs} "
-        f"patch must be True to enable rewriting, "
-        f"if --patch=0 was specified on the command line, rewrites are disabled."
-    )
     summary = version_summary()
     summary.update(
         dict(