diff --git a/.github/actions/build-test-environment/action.yml b/.github/actions/build-test-environment/action.yml
index 723e8a702f..c2524d2c16 100644
--- a/.github/actions/build-test-environment/action.yml
+++ b/.github/actions/build-test-environment/action.yml
@@ -1,41 +1,20 @@
 name: Install packages
 description: This action installs the package and its dependencies for testing
 
-inputs:
-  python-version:
-    description: 'Python version to set up'
-    required: false
-  os:
-    description: 'Operating system to set up'
-    required: false
-
 runs:
   using: "composite"
   steps:
     - name: Install dependencies
       run: |
-        sudo apt install git
         git config --global user.email "CI@example.com"
         git config --global user.name "CI Almighty"
-        python -m venv ${{ github.workspace }}/test_env # Environment used in the caching step
-        python -m pip install -U pip  # Official recommended way
-        source ${{ github.workspace }}/test_env/bin/activate
         pip install tabulate  # This produces summaries at the end
         pip install -e .[test,extractors,streaming_extractors,test_extractors,full]
       shell: bash
-    - name: Force installation of latest dev from key-packages when running dev (not release)
-      run: |
-        source ${{ github.workspace }}/test_env/bin/activate
-        spikeinterface_is_dev_version=$(python -c "import spikeinterface; print(spikeinterface.DEV_MODE)")
-        if [ $spikeinterface_is_dev_version = "True" ]; then
-          echo "Running spikeinterface dev version"
-          pip install --no-cache-dir git+https://github.com/NeuralEnsemble/python-neo
-          pip install --no-cache-dir git+https://github.com/SpikeInterface/probeinterface
-        fi
-          echo "Running tests for release, using pyproject.toml versions of neo and probeinterface"
+    - name: Install git-annex
       shell: bash
-    - name: git-annex install
       run: |
+        pip install datalad-installer
         wget https://downloads.kitenet.net/git-annex/linux/current/git-annex-standalone-amd64.tar.gz
         mkdir /home/runner/work/installation
         mv git-annex-standalone-amd64.tar.gz /home/runner/work/installation/
@@ -44,4 +23,14 @@ runs:
         tar xvzf git-annex-standalone-amd64.tar.gz
         echo "$(pwd)/git-annex.linux" >> $GITHUB_PATH
         cd $workdir
+        git config --global filter.annex.process "git-annex filter-process"  # recommended for efficiency
+    - name: Force installation of latest dev from key-packages when running dev (not release)
+      run: |
+        spikeinterface_is_dev_version=$(python -c "import spikeinterface; print(spikeinterface.DEV_MODE)")
+        if [ $spikeinterface_is_dev_version = "True" ]; then
+          echo "Running spikeinterface dev version"
+          pip install --no-cache-dir git+https://github.com/NeuralEnsemble/python-neo
+          pip install --no-cache-dir git+https://github.com/SpikeInterface/probeinterface
+        fi
+          echo "Running tests for release, using pyproject.toml versions of neo and probeinterface"
       shell: bash
diff --git a/.github/workflows/all-tests.yml b/.github/workflows/all-tests.yml
index dcaec8b272..a9c840d5d5 100644
--- a/.github/workflows/all-tests.yml
+++ b/.github/workflows/all-tests.yml
@@ -47,7 +47,7 @@ jobs:
             echo "$file was changed"
           done
 
-      - name: Set testing environment  # This decides which tests are run and whether to install especial dependencies
+      - name: Set testing environment  # This decides which tests are run and whether to install special dependencies
         shell: bash
         run: |
           changed_files="${{ steps.changed-files.outputs.all_changed_files }}"
diff --git a/.github/workflows/full-test-with-codecov.yml b/.github/workflows/full-test-with-codecov.yml
index 407c614ebf..f8ed2aa7a9 100644
--- a/.github/workflows/full-test-with-codecov.yml
+++ b/.github/workflows/full-test-with-codecov.yml
@@ -45,7 +45,6 @@ jobs:
         env:
           HDF5_PLUGIN_PATH: ${{ github.workspace }}/hdf5_plugin_path_maxwell
         run: |
-          source ${{ github.workspace }}/test_env/bin/activate
           pytest -m "not sorters_external" --cov=./ --cov-report xml:./coverage.xml -vv -ra --durations=0 | tee report_full.txt; test ${PIPESTATUS[0]} -eq 0 || exit 1
           echo "# Timing profile of full tests" >> $GITHUB_STEP_SUMMARY
           python ./.github/scripts/build_job_summary.py report_full.txt >> $GITHUB_STEP_SUMMARY
diff --git a/doc/get_started/quickstart.rst b/doc/get_started/quickstart.rst
index 3d45606a78..1349802ce5 100644
--- a/doc/get_started/quickstart.rst
+++ b/doc/get_started/quickstart.rst
@@ -673,7 +673,7 @@ compute quality metrics (some quality metrics require certain extensions
                               'min_spikes': 0,
                               'window_size_s': 1},
      'snr': {'peak_mode': 'extremum', 'peak_sign': 'neg'},
-     'synchrony': {'synchrony_sizes': (2, 4, 8)}}
+     'synchrony': {}
 
 
 Since the recording is very short, let’s change some parameters to
diff --git a/doc/modules/qualitymetrics/synchrony.rst b/doc/modules/qualitymetrics/synchrony.rst
index d244fd0c0f..696dacbd3c 100644
--- a/doc/modules/qualitymetrics/synchrony.rst
+++ b/doc/modules/qualitymetrics/synchrony.rst
@@ -12,7 +12,7 @@ trains. This way synchronous events can be found both in multi-unit and single-u
 Complexity is calculated by counting the number of spikes (i.e. non-empty bins) that occur at the same sample index,
 within and across spike trains.
 
-Synchrony metrics can be computed for different synchrony sizes (>1), defining the number of simultaneous spikes to count.
+Synchrony metrics are computed for 2, 4 and 8 synchronous spikes.
 
 
 
@@ -29,7 +29,7 @@ Example code
 
     import spikeinterface.qualitymetrics as sqm
     # Combine a sorting and recording into a sorting_analyzer
-    synchrony = sqm.compute_synchrony_metrics(sorting_analyzer=sorting_analyzer synchrony_sizes=(2, 4, 8))
+    synchrony = sqm.compute_synchrony_metrics(sorting_analyzer=sorting_analyzer)
     # synchrony is a tuple of dicts with the synchrony metrics for each unit
 
 
diff --git a/pyproject.toml b/pyproject.toml
index fc09ad9198..22fbdc7f22 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -73,7 +73,7 @@ extractors = [
 ]
 
 streaming_extractors = [
-    "ONE-api>=2.7.0", # alf sorter and streaming IBL
+    "ONE-api>=2.7.0,<2.10.0", # alf sorter and streaming IBL
     "ibllib>=2.36.0", # streaming IBL
     # Following dependencies are for streaming with nwb files
     "pynwb>=2.6.0",
diff --git a/src/spikeinterface/postprocessing/template_metrics.py b/src/spikeinterface/postprocessing/template_metrics.py
index 6e7bcf21b8..1969480503 100644
--- a/src/spikeinterface/postprocessing/template_metrics.py
+++ b/src/spikeinterface/postprocessing/template_metrics.py
@@ -63,23 +63,10 @@ class ComputeTemplateMetrics(AnalyzerExtension):
     include_multi_channel_metrics : bool, default: False
         Whether to compute multi-channel metrics
     delete_existing_metrics : bool, default: False
-        If True, any template metrics attached to the `sorting_analyzer` are deleted. If False, any metrics which were previously calculated but are not included in `metric_names` are kept, provided the `metrics_kwargs` are unchanged.
-    metrics_kwargs : dict
-        Additional arguments to pass to the metric functions. Including:
-            * recovery_window_ms: the window in ms after the peak to compute the recovery_slope, default: 0.7
-            * peak_relative_threshold: the relative threshold to detect positive and negative peaks, default: 0.2
-            * peak_width_ms: the width in samples to detect peaks, default: 0.2
-            * depth_direction: the direction to compute velocity above and below, default: "y" (see notes)
-            * min_channels_for_velocity: the minimum number of channels above or below to compute velocity, default: 5
-            * min_r2_velocity: the minimum r2 to accept the velocity fit, default: 0.7
-            * exp_peak_function: the function to use to compute the peak amplitude for the exp decay, default: "ptp"
-            * min_r2_exp_decay: the minimum r2 to accept the exp decay fit, default: 0.5
-            * spread_threshold: the threshold to compute the spread, default: 0.2
-            * spread_smooth_um: the smoothing in um to compute the spread, default: 20
-            * column_range: the range in um in the horizontal direction to consider channels for velocity, default: None
-                - If None, all channels all channels are considered
-                - If 0 or 1, only the "column" that includes the max channel is considered
-                - If > 1, only channels within range (+/-) um from the max channel horizontal position are used
+        If True, any template metrics attached to the `sorting_analyzer` are deleted. If False, any metrics which were previously calculated but are not included in `metric_names` are kept, provided the `metric_params` are unchanged.
+    metric_params : dict of dicts or None, default: None
+        Dictionary with parameters for template metrics calculation.
+        Default parameters can be obtained with: `si.postprocessing.template_metrics.get_default_tm_params()`
 
     Returns
     -------
@@ -100,15 +87,29 @@ class ComputeTemplateMetrics(AnalyzerExtension):
     need_recording = False
     use_nodepipeline = False
     need_job_kwargs = False
+    need_backward_compatibility_on_load = True
 
     min_channels_for_multi_channel_warning = 10
 
+    def _handle_backward_compatibility_on_load(self):
+
+        # For backwards compatibility - this reformats metrics_kwargs as metric_params
+        if (metrics_kwargs := self.params.get("metrics_kwargs")) is not None:
+
+            metric_params = {}
+            for metric_name in self.params["metric_names"]:
+                metric_params[metric_name] = deepcopy(metrics_kwargs)
+            self.params["metric_params"] = metric_params
+
+            del self.params["metrics_kwargs"]
+
     def _set_params(
         self,
         metric_names=None,
         peak_sign="neg",
         upsampling_factor=10,
         sparsity=None,
+        metric_params=None,
         metrics_kwargs=None,
         include_multi_channel_metrics=False,
         delete_existing_metrics=False,
@@ -134,33 +135,24 @@ def _set_params(
         if include_multi_channel_metrics:
             metric_names += get_multi_channel_template_metric_names()
 
-        if metrics_kwargs is None:
-            metrics_kwargs_ = _default_function_kwargs.copy()
-            if len(other_kwargs) > 0:
-                for m in other_kwargs:
-                    if m in metrics_kwargs_:
-                        metrics_kwargs_[m] = other_kwargs[m]
-        else:
-            metrics_kwargs_ = _default_function_kwargs.copy()
-            metrics_kwargs_.update(metrics_kwargs)
+        if metrics_kwargs is not None and metric_params is None:
+            deprecation_msg = "`metrics_kwargs` is deprecated and will be removed in version 0.104.0. Please use `metric_params` instead"
+            deprecation_msg = "`metrics_kwargs` is deprecated and will be removed in version 0.104.0. Please use `metric_params` instead"
+
+            metric_params = {}
+            for metric_name in metric_names:
+                metric_params[metric_name] = deepcopy(metrics_kwargs)
+
+        metric_params_ = get_default_tm_params(metric_names)
+        for k in metric_params_:
+            if metric_params is not None and k in metric_params:
+                metric_params_[k].update(metric_params[k])
 
         metrics_to_compute = metric_names
         tm_extension = self.sorting_analyzer.get_extension("template_metrics")
         if delete_existing_metrics is False and tm_extension is not None:
 
-            existing_params = tm_extension.params["metrics_kwargs"]
-            # checks that existing metrics were calculated using the same params
-            if existing_params != metrics_kwargs_:
-                warnings.warn(
-                    f"The parameters used to calculate the previous template metrics are different"
-                    f"than those used now.\nPrevious parameters: {existing_params}\nCurrent "
-                    f"parameters:  {metrics_kwargs_}\nDeleting previous template metrics..."
-                )
-                tm_extension.params["metric_names"] = []
-                existing_metric_names = []
-            else:
-                existing_metric_names = tm_extension.params["metric_names"]
-
+            existing_metric_names = tm_extension.params["metric_names"]
             existing_metric_names_propogated = [
                 metric_name for metric_name in existing_metric_names if metric_name not in metrics_to_compute
             ]
@@ -171,7 +163,7 @@ def _set_params(
             sparsity=sparsity,
             peak_sign=peak_sign,
             upsampling_factor=int(upsampling_factor),
-            metrics_kwargs=metrics_kwargs_,
+            metric_params=metric_params_,
             delete_existing_metrics=delete_existing_metrics,
             metrics_to_compute=metrics_to_compute,
         )
@@ -273,7 +265,7 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, metri
                             sampling_frequency=sampling_frequency_up,
                             trough_idx=trough_idx,
                             peak_idx=peak_idx,
-                            **self.params["metrics_kwargs"],
+                            **self.params["metric_params"][metric_name],
                         )
                     except Exception as e:
                         warnings.warn(f"Error computing metric {metric_name} for unit {unit_id}: {e}")
@@ -312,7 +304,7 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, metri
                         template_upsampled,
                         channel_locations=channel_locations_sparse,
                         sampling_frequency=sampling_frequency_up,
-                        **self.params["metrics_kwargs"],
+                        **self.params["metric_params"][metric_name],
                     )
                 except Exception as e:
                     warnings.warn(f"Error computing metric {metric_name} for unit {unit_id}: {e}")
@@ -326,8 +318,8 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, metri
 
     def _run(self, verbose=False):
 
-        delete_existing_metrics = self.params["delete_existing_metrics"]
         metrics_to_compute = self.params["metrics_to_compute"]
+        delete_existing_metrics = self.params["delete_existing_metrics"]
 
         # compute the metrics which have been specified by the user
         computed_metrics = self._compute_metrics(
@@ -343,9 +335,21 @@ def _run(self, verbose=False):
         ):
             existing_metrics = tm_extension.params["metric_names"]
 
+        existing_metrics = []
+        # here we get in the loaded via the dict only (to avoid full loading from disk after params reset)
+        tm_extension = self.sorting_analyzer.extensions.get("template_metrics", None)
+        if (
+            delete_existing_metrics is False
+            and tm_extension is not None
+            and tm_extension.data.get("metrics") is not None
+        ):
+            existing_metrics = tm_extension.params["metric_names"]
+
         # append the metrics which were previously computed
         for metric_name in set(existing_metrics).difference(metrics_to_compute):
-            computed_metrics[metric_name] = tm_extension.data["metrics"][metric_name]
+            # some metrics names produce data columns with other names. This deals with that.
+            for column_name in tm_compute_name_to_column_names[metric_name]:
+                computed_metrics[column_name] = tm_extension.data["metrics"][column_name]
 
         self.data["metrics"] = computed_metrics
 
@@ -372,6 +376,35 @@ def _get_data(self):
 )
 
 
+def get_default_tm_params(metric_names):
+    if metric_names is None:
+        metric_names = get_template_metric_names()
+
+    base_tm_params = _default_function_kwargs
+
+    metric_params = {}
+    for metric_name in metric_names:
+        metric_params[metric_name] = deepcopy(base_tm_params)
+
+    return metric_params
+
+
+# a dict converting the name of the metric for computation to the output of that computation
+tm_compute_name_to_column_names = {
+    "peak_to_valley": ["peak_to_valley"],
+    "peak_trough_ratio": ["peak_trough_ratio"],
+    "half_width": ["half_width"],
+    "repolarization_slope": ["repolarization_slope"],
+    "recovery_slope": ["recovery_slope"],
+    "num_positive_peaks": ["num_positive_peaks"],
+    "num_negative_peaks": ["num_negative_peaks"],
+    "velocity_above": ["velocity_above"],
+    "velocity_below": ["velocity_below"],
+    "exp_decay": ["exp_decay"],
+    "spread": ["spread"],
+}
+
+
 def get_trough_and_peak_idx(template):
     """
     Return the indices into the input template of the detected trough
diff --git a/src/spikeinterface/postprocessing/tests/test_template_metrics.py b/src/spikeinterface/postprocessing/tests/test_template_metrics.py
index 5056d4ff2a..1bf49f64c1 100644
--- a/src/spikeinterface/postprocessing/tests/test_template_metrics.py
+++ b/src/spikeinterface/postprocessing/tests/test_template_metrics.py
@@ -1,5 +1,5 @@
 from spikeinterface.postprocessing.tests.common_extension_tests import AnalyzerExtensionCommonTestSuite
-from spikeinterface.postprocessing import ComputeTemplateMetrics
+from spikeinterface.postprocessing import ComputeTemplateMetrics, compute_template_metrics
 import pytest
 import csv
 
@@ -8,6 +8,49 @@
 template_metrics = list(_single_channel_metric_name_to_func.keys())
 
 
+def test_different_params_template_metrics(small_sorting_analyzer):
+    """
+    Computes template metrics using different params, and check that they are
+    actually calculated using the different params.
+    """
+    compute_template_metrics(
+        sorting_analyzer=small_sorting_analyzer,
+        metric_names=["exp_decay", "spread", "half_width"],
+        metric_params={"exp_decay": {"recovery_window_ms": 0.8}, "spread": {"spread_smooth_um": 15}},
+    )
+
+    tm_extension = small_sorting_analyzer.get_extension("template_metrics")
+    tm_params = tm_extension.params["metric_params"]
+
+    assert tm_params["exp_decay"]["recovery_window_ms"] == 0.8
+    assert tm_params["spread"]["recovery_window_ms"] == 0.7
+    assert tm_params["half_width"]["recovery_window_ms"] == 0.7
+
+    assert tm_params["spread"]["spread_smooth_um"] == 15
+    assert tm_params["exp_decay"]["spread_smooth_um"] == 20
+    assert tm_params["half_width"]["spread_smooth_um"] == 20
+
+
+def test_backwards_compat_params_template_metrics(small_sorting_analyzer):
+    """
+    Computes template metrics using the metrics_kwargs keyword
+    """
+    compute_template_metrics(
+        sorting_analyzer=small_sorting_analyzer,
+        metric_names=["exp_decay", "spread"],
+        metrics_kwargs={"recovery_window_ms": 0.8},
+    )
+
+    tm_extension = small_sorting_analyzer.get_extension("template_metrics")
+    tm_params = tm_extension.params["metric_params"]
+
+    assert tm_params["exp_decay"]["recovery_window_ms"] == 0.8
+    assert tm_params["spread"]["recovery_window_ms"] == 0.8
+
+    assert tm_params["spread"]["spread_smooth_um"] == 20
+    assert tm_params["exp_decay"]["spread_smooth_um"] == 20
+
+
 def test_compute_new_template_metrics(small_sorting_analyzer):
     """
     Computes template metrics then computes a subset of template metrics, and checks
@@ -17,6 +60,8 @@ def test_compute_new_template_metrics(small_sorting_analyzer):
     are deleted.
     """
 
+    small_sorting_analyzer.delete_extension("template_metrics")
+
     # calculate just exp_decay
     small_sorting_analyzer.compute({"template_metrics": {"metric_names": ["exp_decay"]}})
     template_metric_extension = small_sorting_analyzer.get_extension("template_metrics")
@@ -47,7 +92,7 @@ def test_compute_new_template_metrics(small_sorting_analyzer):
 
     # check that, when parameters are changed, the old metrics are deleted
     small_sorting_analyzer.compute(
-        {"template_metrics": {"metric_names": ["exp_decay"], "metrics_kwargs": {"recovery_window_ms": 0.6}}}
+        {"template_metrics": {"metric_names": ["exp_decay"], "metric_params": {"recovery_window_ms": 0.6}}}
     )
 
 
diff --git a/src/spikeinterface/postprocessing/unit_locations.py b/src/spikeinterface/postprocessing/unit_locations.py
index 3f6dd47eec..df19458316 100644
--- a/src/spikeinterface/postprocessing/unit_locations.py
+++ b/src/spikeinterface/postprocessing/unit_locations.py
@@ -26,7 +26,7 @@ class ComputeUnitLocations(AnalyzerExtension):
     ----------
     sorting_analyzer : SortingAnalyzer
         A SortingAnalyzer object
-    method : "center_of_mass" | "monopolar_triangulation" | "grid_convolution", default: "center_of_mass"
+    method : "monopolar_triangulation" |  "center_of_mass" | "grid_convolution", default: "monopolar_triangulation"
         The method to use for localization
     **method_kwargs : dict, default: {}
         Kwargs which are passed to the method function. These can be found in the docstrings of `compute_center_of_mass`, `compute_grid_convolution` and `compute_monopolar_triangulation`.
diff --git a/src/spikeinterface/qualitymetrics/__init__.py b/src/spikeinterface/qualitymetrics/__init__.py
index 9d604f6ae2..754c82d8e3 100644
--- a/src/spikeinterface/qualitymetrics/__init__.py
+++ b/src/spikeinterface/qualitymetrics/__init__.py
@@ -6,4 +6,4 @@
     get_default_qm_params,
 )
 from .pca_metrics import get_quality_pca_metric_list
-from .misc_metrics import get_synchrony_counts
+from .misc_metrics import _get_synchrony_counts
diff --git a/src/spikeinterface/qualitymetrics/misc_metrics.py b/src/spikeinterface/qualitymetrics/misc_metrics.py
index 8dfd41cf88..6007de379c 100644
--- a/src/spikeinterface/qualitymetrics/misc_metrics.py
+++ b/src/spikeinterface/qualitymetrics/misc_metrics.py
@@ -520,7 +520,7 @@ def compute_sliding_rp_violations(
 )
 
 
-def get_synchrony_counts(spikes, synchrony_sizes, all_unit_ids):
+def _get_synchrony_counts(spikes, synchrony_sizes, all_unit_ids):
     """
     Compute synchrony counts, the number of simultaneous spikes with sizes `synchrony_sizes`.
 
@@ -528,10 +528,10 @@ def get_synchrony_counts(spikes, synchrony_sizes, all_unit_ids):
     ----------
     spikes : np.array
         Structured numpy array with fields ("sample_index", "unit_index", "segment_index").
-    synchrony_sizes : numpy array
-        The synchrony sizes to compute. Should be pre-sorted.
     all_unit_ids : list or None, default: None
         List of unit ids to compute the synchrony metrics. Expecting all units.
+    synchrony_sizes : None or np.array, default: None
+        The synchrony sizes to compute. Should be pre-sorted.
 
     Returns
     -------
@@ -565,37 +565,38 @@ def get_synchrony_counts(spikes, synchrony_sizes, all_unit_ids):
     return synchrony_counts
 
 
-def compute_synchrony_metrics(sorting_analyzer, synchrony_sizes=(2, 4, 8), unit_ids=None):
+def compute_synchrony_metrics(sorting_analyzer, unit_ids=None, synchrony_sizes=None):
     """
     Compute synchrony metrics. Synchrony metrics represent the rate of occurrences of
-    "synchrony_size" spikes at the exact same sample index.
+    spikes at the exact same sample index, with synchrony sizes 2, 4 and 8.
 
     Parameters
     ----------
     sorting_analyzer : SortingAnalyzer
         A SortingAnalyzer object.
-    synchrony_sizes : list or tuple, default: (2, 4, 8)
-        The synchrony sizes to compute.
     unit_ids : list or None, default: None
         List of unit ids to compute the synchrony metrics. If None, all units are used.
+    synchrony_sizes: None, default: None
+        Deprecated argument. Please use private `_get_synchrony_counts` if you need finer control over number of synchronous spikes.
 
     Returns
     -------
     sync_spike_{X} : dict
         The synchrony metric for synchrony size X.
-        Returns are as many as synchrony_sizes.
 
     References
     ----------
     Based on concepts described in [Grün]_
     This code was adapted from `Elephant - Electrophysiology Analysis Toolkit <https://github.com/NeuralEnsemble/elephant/blob/master/elephant/spike_train_synchrony.py#L245>`_
     """
-    assert min(synchrony_sizes) > 1, "Synchrony sizes must be greater than 1"
-    # Sort the synchrony times so we can slice numpy arrays, instead of using dicts
-    synchrony_sizes_np = np.array(synchrony_sizes, dtype=np.int16)
-    synchrony_sizes_np.sort()
 
-    res = namedtuple("synchrony_metrics", [f"sync_spike_{size}" for size in synchrony_sizes_np])
+    if synchrony_sizes is not None:
+        warning_message = "Custom `synchrony_sizes` is deprecated; the `synchrony_metrics` will be computed using `synchrony_sizes = [2,4,8]`"
+        warnings.warn(warning_message, DeprecationWarning, stacklevel=2)
+
+    synchrony_sizes = np.array([2, 4, 8])
+
+    res = namedtuple("synchrony_metrics", [f"sync_spike_{size}" for size in synchrony_sizes])
 
     sorting = sorting_analyzer.sorting
 
@@ -606,10 +607,10 @@ def compute_synchrony_metrics(sorting_analyzer, synchrony_sizes=(2, 4, 8), unit_
 
     spikes = sorting.to_spike_vector()
     all_unit_ids = sorting.unit_ids
-    synchrony_counts = get_synchrony_counts(spikes, synchrony_sizes_np, all_unit_ids)
+    synchrony_counts = _get_synchrony_counts(spikes, synchrony_sizes, all_unit_ids)
 
     synchrony_metrics_dict = {}
-    for sync_idx, synchrony_size in enumerate(synchrony_sizes_np):
+    for sync_idx, synchrony_size in enumerate(synchrony_sizes):
         sync_id_metrics_dict = {}
         for i, unit_id in enumerate(all_unit_ids):
             if unit_id not in unit_ids:
@@ -623,7 +624,7 @@ def compute_synchrony_metrics(sorting_analyzer, synchrony_sizes=(2, 4, 8), unit_
     return res(**synchrony_metrics_dict)
 
 
-_default_params["synchrony"] = dict(synchrony_sizes=(2, 4, 8))
+_default_params["synchrony"] = dict()
 
 
 def compute_firing_ranges(sorting_analyzer, bin_size_s=5, percentiles=(5, 95), unit_ids=None):
diff --git a/src/spikeinterface/qualitymetrics/pca_metrics.py b/src/spikeinterface/qualitymetrics/pca_metrics.py
index 4c68dfea59..ca21f1e45f 100644
--- a/src/spikeinterface/qualitymetrics/pca_metrics.py
+++ b/src/spikeinterface/qualitymetrics/pca_metrics.py
@@ -6,6 +6,7 @@
 from copy import deepcopy
 import platform
 from tqdm.auto import tqdm
+from warnings import warn
 
 import numpy as np
 
@@ -52,6 +53,7 @@ def get_quality_pca_metric_list():
 def compute_pc_metrics(
     sorting_analyzer,
     metric_names=None,
+    metric_params=None,
     qm_params=None,
     unit_ids=None,
     seed=None,
@@ -70,7 +72,7 @@ def compute_pc_metrics(
     metric_names : list of str, default: None
         The list of PC metrics to compute.
         If not provided, defaults to all PC metrics.
-    qm_params : dict or None
+    metric_params : dict or None
         Dictionary with parameters for each PC metric function.
     unit_ids : list of int or None
         List of unit ids to compute metrics for.
@@ -86,6 +88,14 @@ def compute_pc_metrics(
     pc_metrics : dict
         The computed PC metrics.
     """
+
+    if qm_params is not None and metric_params is None:
+        deprecation_msg = (
+            "`qm_params` is deprecated and will be removed in version 0.104.0. Please use metric_params instead"
+        )
+        warn(deprecation_msg, category=DeprecationWarning, stacklevel=2)
+        metric_params = qm_params
+
     pca_ext = sorting_analyzer.get_extension("principal_components")
     assert pca_ext is not None, "calculate_pc_metrics() need extension 'principal_components'"
 
@@ -93,8 +103,8 @@ def compute_pc_metrics(
 
     if metric_names is None:
         metric_names = _possible_pc_metric_names.copy()
-    if qm_params is None:
-        qm_params = _default_params
+    if metric_params is None:
+        metric_params = _default_params
 
     extremum_channels = get_template_extremum_channel(sorting_analyzer)
 
@@ -147,7 +157,7 @@ def compute_pc_metrics(
         pcs = dense_projections[np.isin(all_labels, neighbor_unit_ids)][:, :, neighbor_channel_indices]
         pcs_flat = pcs.reshape(pcs.shape[0], -1)
 
-        func_args = (pcs_flat, labels, non_nn_metrics, unit_id, unit_ids, qm_params, max_threads_per_process)
+        func_args = (pcs_flat, labels, non_nn_metrics, unit_id, unit_ids, metric_params, max_threads_per_process)
         items.append(func_args)
 
     if not run_in_parallel and non_nn_metrics:
@@ -184,7 +194,7 @@ def compute_pc_metrics(
             units_loop = tqdm(units_loop, desc=f"calculate {metric_name} metric", total=len(unit_ids))
 
         func = _nn_metric_name_to_func[metric_name]
-        metric_params = qm_params[metric_name] if metric_name in qm_params else {}
+        metric_params = metric_params[metric_name] if metric_name in metric_params else {}
 
         for _, unit_id in units_loop:
             try:
@@ -213,7 +223,7 @@ def compute_pc_metrics(
 
 
 def calculate_pc_metrics(
-    sorting_analyzer, metric_names=None, qm_params=None, unit_ids=None, seed=None, n_jobs=1, progress_bar=False
+    sorting_analyzer, metric_names=None, metric_params=None, unit_ids=None, seed=None, n_jobs=1, progress_bar=False
 ):
     warnings.warn(
         "The `calculate_pc_metrics` function is deprecated and will be removed in 0.103.0. Please use compute_pc_metrics instead",
@@ -224,7 +234,7 @@ def calculate_pc_metrics(
     pc_metrics = compute_pc_metrics(
         sorting_analyzer,
         metric_names=metric_names,
-        qm_params=qm_params,
+        metric_params=metric_params,
         unit_ids=unit_ids,
         seed=seed,
         n_jobs=n_jobs,
@@ -977,16 +987,16 @@ def _compute_isolation(pcs_target_unit, pcs_other_unit, n_neighbors: int):
 
 
 def pca_metrics_one_unit(args):
-    (pcs_flat, labels, metric_names, unit_id, unit_ids, qm_params, max_threads_per_process) = args
+    (pcs_flat, labels, metric_names, unit_id, unit_ids, metric_params, max_threads_per_process) = args
 
     if max_threads_per_process is None:
-        return _pca_metrics_one_unit(pcs_flat, labels, metric_names, unit_id, unit_ids, qm_params)
+        return _pca_metrics_one_unit(pcs_flat, labels, metric_names, unit_id, unit_ids, metric_params)
     else:
         with threadpool_limits(limits=int(max_threads_per_process)):
-            return _pca_metrics_one_unit(pcs_flat, labels, metric_names, unit_id, unit_ids, qm_params)
+            return _pca_metrics_one_unit(pcs_flat, labels, metric_names, unit_id, unit_ids, metric_params)
 
 
-def _pca_metrics_one_unit(pcs_flat, labels, metric_names, unit_id, unit_ids, qm_params):
+def _pca_metrics_one_unit(pcs_flat, labels, metric_names, unit_id, unit_ids, metric_params):
     pc_metrics = {}
     # metrics
     if "isolation_distance" in metric_names or "l_ratio" in metric_names:
@@ -1015,7 +1025,7 @@ def _pca_metrics_one_unit(pcs_flat, labels, metric_names, unit_id, unit_ids, qm_
     if "nearest_neighbor" in metric_names:
         try:
             nn_hit_rate, nn_miss_rate = nearest_neighbors_metrics(
-                pcs_flat, labels, unit_id, **qm_params["nearest_neighbor"]
+                pcs_flat, labels, unit_id, **metric_params["nearest_neighbor"]
             )
         except:
             nn_hit_rate = np.nan
@@ -1024,7 +1034,7 @@ def _pca_metrics_one_unit(pcs_flat, labels, metric_names, unit_id, unit_ids, qm_
         pc_metrics["nn_miss_rate"] = nn_miss_rate
 
     if "silhouette" in metric_names:
-        silhouette_method = qm_params["silhouette"]["method"]
+        silhouette_method = metric_params["silhouette"]["method"]
         if "simplified" in silhouette_method:
             try:
                 unit_silhouette_score = simplified_silhouette_score(pcs_flat, labels, unit_id)
diff --git a/src/spikeinterface/qualitymetrics/quality_metric_calculator.py b/src/spikeinterface/qualitymetrics/quality_metric_calculator.py
index b6a50d60f5..d71450853f 100644
--- a/src/spikeinterface/qualitymetrics/quality_metric_calculator.py
+++ b/src/spikeinterface/qualitymetrics/quality_metric_calculator.py
@@ -6,6 +6,7 @@
 from copy import deepcopy
 
 import numpy as np
+from warnings import warn
 
 from spikeinterface.core.job_tools import fix_job_kwargs
 from spikeinterface.core.sortinganalyzer import register_result_extension, AnalyzerExtension
@@ -15,7 +16,7 @@
     compute_pc_metrics,
     _misc_metric_name_to_func,
     _possible_pc_metric_names,
-    compute_name_to_column_names,
+    qm_compute_name_to_column_names,
 )
 from .misc_metrics import _default_params as misc_metrics_params
 from .pca_metrics import _default_params as pca_metrics_params
@@ -31,7 +32,7 @@ class ComputeQualityMetrics(AnalyzerExtension):
         A SortingAnalyzer object.
     metric_names : list or None
         List of quality metrics to compute.
-    qm_params : dict or None
+    metric_params : dict of dicts or None
         Dictionary with parameters for quality metrics calculation.
         Default parameters can be obtained with: `si.qualitymetrics.get_default_qm_params()`
     skip_pc_metrics : bool, default: False
@@ -54,10 +55,18 @@ class ComputeQualityMetrics(AnalyzerExtension):
     need_recording = False
     use_nodepipeline = False
     need_job_kwargs = True
+    need_backward_compatibility_on_load = True
+
+    def _handle_backward_compatibility_on_load(self):
+        # For backwards compatibility - this renames qm_params as metric_params
+        if (qm_params := self.params.get("qm_params")) is not None:
+            self.params["metric_params"] = qm_params
+            del self.params["qm_params"]
 
     def _set_params(
         self,
         metric_names=None,
+        metric_params=None,
         qm_params=None,
         peak_sign=None,
         seed=None,
@@ -65,6 +74,12 @@ def _set_params(
         delete_existing_metrics=False,
         metrics_to_compute=None,
     ):
+        if qm_params is not None and metric_params is None:
+            deprecation_msg = (
+                "`qm_params` is deprecated and will be removed in version 0.104.0 Please use metric_params instead"
+            )
+            metric_params = qm_params
+            warn(deprecation_msg, category=DeprecationWarning, stacklevel=2)
 
         if metric_names is None:
             metric_names = list(_misc_metric_name_to_func.keys())
@@ -80,12 +95,12 @@ def _set_params(
                 if "drift" in metric_names:
                     metric_names.remove("drift")
 
-        qm_params_ = get_default_qm_params()
-        for k in qm_params_:
-            if qm_params is not None and k in qm_params:
-                qm_params_[k].update(qm_params[k])
-            if "peak_sign" in qm_params_[k] and peak_sign is not None:
-                qm_params_[k]["peak_sign"] = peak_sign
+        metric_params_ = get_default_qm_params()
+        for k in metric_params_:
+            if metric_params is not None and k in metric_params:
+                metric_params_[k].update(metric_params[k])
+            if "peak_sign" in metric_params_[k] and peak_sign is not None:
+                metric_params_[k]["peak_sign"] = peak_sign
 
         metrics_to_compute = metric_names
         qm_extension = self.sorting_analyzer.get_extension("quality_metrics")
@@ -101,7 +116,7 @@ def _set_params(
             metric_names=metric_names,
             peak_sign=peak_sign,
             seed=seed,
-            qm_params=qm_params_,
+            metric_params=metric_params_,
             skip_pc_metrics=skip_pc_metrics,
             delete_existing_metrics=delete_existing_metrics,
             metrics_to_compute=metrics_to_compute,
@@ -141,7 +156,7 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, metri
         """
         import pandas as pd
 
-        qm_params = self.params["qm_params"]
+        metric_params = self.params["metric_params"]
         # sparsity = self.params["sparsity"]
         seed = self.params["seed"]
 
@@ -177,7 +192,7 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, metri
 
             func = _misc_metric_name_to_func[metric_name]
 
-            params = qm_params[metric_name] if metric_name in qm_params else {}
+            params = metric_params[metric_name] if metric_name in metric_params else {}
             res = func(sorting_analyzer, unit_ids=non_empty_unit_ids, **params)
             # QM with uninstall dependencies might return None
             if res is not None:
@@ -205,7 +220,7 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, metri
                 # sparsity=sparsity,
                 progress_bar=progress_bar,
                 n_jobs=n_jobs,
-                qm_params=qm_params,
+                metric_params=metric_params,
                 seed=seed,
             )
             for col, values in pc_metrics.items():
@@ -246,7 +261,7 @@ def _run(self, verbose=False, **job_kwargs):
         # append the metrics which were previously computed
         for metric_name in set(existing_metrics).difference(metrics_to_compute):
             # some metrics names produce data columns with other names. This deals with that.
-            for column_name in compute_name_to_column_names[metric_name]:
+            for column_name in qm_compute_name_to_column_names[metric_name]:
                 computed_metrics[column_name] = qm_extension.data["metrics"][column_name]
 
         self.data["metrics"] = computed_metrics
diff --git a/src/spikeinterface/qualitymetrics/quality_metric_list.py b/src/spikeinterface/qualitymetrics/quality_metric_list.py
index 375dd320ae..fc7e92b50d 100644
--- a/src/spikeinterface/qualitymetrics/quality_metric_list.py
+++ b/src/spikeinterface/qualitymetrics/quality_metric_list.py
@@ -55,7 +55,7 @@
 }
 
 # a dict converting the name of the metric for computation to the output of that computation
-compute_name_to_column_names = {
+qm_compute_name_to_column_names = {
     "num_spikes": ["num_spikes"],
     "firing_rate": ["firing_rate"],
     "presence_ratio": ["presence_ratio"],
diff --git a/src/spikeinterface/qualitymetrics/tests/test_metrics_functions.py b/src/spikeinterface/qualitymetrics/tests/test_metrics_functions.py
index 4c0890b62b..18b49cd862 100644
--- a/src/spikeinterface/qualitymetrics/tests/test_metrics_functions.py
+++ b/src/spikeinterface/qualitymetrics/tests/test_metrics_functions.py
@@ -39,7 +39,7 @@
     compute_firing_ranges,
     compute_amplitude_cv_metrics,
     compute_sd_ratio,
-    get_synchrony_counts,
+    _get_synchrony_counts,
     compute_quality_metrics,
 )
 
@@ -69,7 +69,7 @@ def test_compute_new_quality_metrics(small_sorting_analyzer):
     assert calculated_metrics == ["snr"]
 
     small_sorting_analyzer.compute(
-        {"quality_metrics": {"metric_names": list(qm_params.keys()), "qm_params": qm_params}}
+        {"quality_metrics": {"metric_names": list(qm_params.keys()), "metric_params": qm_params}}
     )
     small_sorting_analyzer.compute({"quality_metrics": {"metric_names": ["snr"]}})
 
@@ -96,13 +96,13 @@ def test_compute_new_quality_metrics(small_sorting_analyzer):
     # check that, when parameters are changed, the data and metadata are updated
     old_snr_data = deepcopy(quality_metric_extension.get_data()["snr"].values)
     small_sorting_analyzer.compute(
-        {"quality_metrics": {"metric_names": ["snr"], "qm_params": {"snr": {"peak_mode": "peak_to_peak"}}}}
+        {"quality_metrics": {"metric_names": ["snr"], "metric_params": {"snr": {"peak_mode": "peak_to_peak"}}}}
     )
     new_quality_metric_extension = small_sorting_analyzer.get_extension("quality_metrics")
     new_snr_data = new_quality_metric_extension.get_data()["snr"].values
 
     assert np.all(old_snr_data != new_snr_data)
-    assert new_quality_metric_extension.params["qm_params"]["snr"]["peak_mode"] == "peak_to_peak"
+    assert new_quality_metric_extension.params["metric_params"]["snr"]["peak_mode"] == "peak_to_peak"
 
     # check that all quality metrics are deleted when parents are recomputed, even after
     # recomputation
@@ -280,10 +280,10 @@ def test_unit_id_order_independence(small_sorting_analyzer):
     }
 
     quality_metrics_1 = compute_quality_metrics(
-        small_sorting_analyzer, metric_names=get_quality_metric_list(), qm_params=qm_params
+        small_sorting_analyzer, metric_names=get_quality_metric_list(), metric_params=qm_params
     )
     quality_metrics_2 = compute_quality_metrics(
-        small_sorting_analyzer_2, metric_names=get_quality_metric_list(), qm_params=qm_params
+        small_sorting_analyzer_2, metric_names=get_quality_metric_list(), metric_params=qm_params
     )
 
     for metric, metric_2_data in quality_metrics_2.items():
@@ -352,7 +352,7 @@ def test_synchrony_counts_no_sync():
     one_spike["sample_index"] = spike_times
     one_spike["unit_index"] = spike_units
 
-    sync_count = get_synchrony_counts(one_spike, np.array((2)), [0])
+    sync_count = _get_synchrony_counts(one_spike, np.array([2, 4, 8]), [0])
 
     assert np.all(sync_count[0] == np.array([0]))
 
@@ -372,7 +372,7 @@ def test_synchrony_counts_one_sync():
     two_spikes["sample_index"] = np.concatenate((spike_indices, added_spikes_indices))
     two_spikes["unit_index"] = np.concatenate((spike_labels, added_spikes_labels))
 
-    sync_count = get_synchrony_counts(two_spikes, np.array((2)), [0, 1])
+    sync_count = _get_synchrony_counts(two_spikes, np.array([2, 4, 8]), [0, 1])
 
     assert np.all(sync_count[0] == np.array([1, 1]))
 
@@ -392,7 +392,7 @@ def test_synchrony_counts_one_quad_sync():
     four_spikes["sample_index"] = np.concatenate((spike_indices, added_spikes_indices))
     four_spikes["unit_index"] = np.concatenate((spike_labels, added_spikes_labels))
 
-    sync_count = get_synchrony_counts(four_spikes, np.array((2, 4)), [0, 1, 2, 3])
+    sync_count = _get_synchrony_counts(four_spikes, np.array([2, 4, 8]), [0, 1, 2, 3])
 
     assert np.all(sync_count[0] == np.array([1, 1, 1, 1]))
     assert np.all(sync_count[1] == np.array([1, 1, 1, 1]))
@@ -409,7 +409,7 @@ def test_synchrony_counts_not_all_units():
     three_spikes["sample_index"] = np.concatenate((spike_indices, added_spikes_indices))
     three_spikes["unit_index"] = np.concatenate((spike_labels, added_spikes_labels))
 
-    sync_count = get_synchrony_counts(three_spikes, np.array((2)), [0, 1, 2])
+    sync_count = _get_synchrony_counts(three_spikes, np.array([2, 4, 8]), [0, 1, 2])
 
     assert np.all(sync_count[0] == np.array([0, 1, 1]))
 
@@ -610,9 +610,9 @@ def test_calculate_rp_violations(sorting_analyzer_violations):
 def test_synchrony_metrics(sorting_analyzer_simple):
     sorting_analyzer = sorting_analyzer_simple
     sorting = sorting_analyzer.sorting
-    synchrony_sizes = (2, 3, 4)
-    synchrony_metrics = compute_synchrony_metrics(sorting_analyzer, synchrony_sizes=synchrony_sizes)
-    print(synchrony_metrics)
+    synchrony_metrics = compute_synchrony_metrics(sorting_analyzer)
+
+    synchrony_sizes = np.array([2, 4, 8])
 
     # check returns
     for size in synchrony_sizes:
@@ -625,10 +625,8 @@ def test_synchrony_metrics(sorting_analyzer_simple):
         sorting_sync = add_synchrony_to_sorting(sorting, sync_event_ratio=sync_level)
         sorting_analyzer_sync = create_sorting_analyzer(sorting_sync, sorting_analyzer.recording, format="memory")
 
-        previous_synchrony_metrics = compute_synchrony_metrics(
-            previous_sorting_analyzer, synchrony_sizes=synchrony_sizes
-        )
-        current_synchrony_metrics = compute_synchrony_metrics(sorting_analyzer_sync, synchrony_sizes=synchrony_sizes)
+        previous_synchrony_metrics = compute_synchrony_metrics(previous_sorting_analyzer)
+        current_synchrony_metrics = compute_synchrony_metrics(sorting_analyzer_sync)
         print(current_synchrony_metrics)
         # check that all values increased
         for i, col in enumerate(previous_synchrony_metrics._fields):
@@ -647,22 +645,17 @@ def test_synchrony_metrics_unit_id_subset(sorting_analyzer_simple):
 
     unit_ids_subset = [3, 7]
 
-    synchrony_sizes = (2,)
-    (synchrony_metrics,) = compute_synchrony_metrics(
-        sorting_analyzer_simple, synchrony_sizes=synchrony_sizes, unit_ids=unit_ids_subset
-    )
+    synchrony_metrics = compute_synchrony_metrics(sorting_analyzer_simple, unit_ids=unit_ids_subset)
 
-    assert list(synchrony_metrics.keys()) == [3, 7]
+    assert list(synchrony_metrics.sync_spike_2.keys()) == [3, 7]
+    assert list(synchrony_metrics.sync_spike_4.keys()) == [3, 7]
+    assert list(synchrony_metrics.sync_spike_8.keys()) == [3, 7]
 
 
 def test_synchrony_metrics_no_unit_ids(sorting_analyzer_simple):
 
-    # all_unit_ids = sorting_analyzer_simple.sorting.unit_ids
-
-    synchrony_sizes = (2,)
-    (synchrony_metrics,) = compute_synchrony_metrics(sorting_analyzer_simple, synchrony_sizes=synchrony_sizes)
-
-    assert np.all(list(synchrony_metrics.keys()) == sorting_analyzer_simple.unit_ids)
+    synchrony_metrics = compute_synchrony_metrics(sorting_analyzer_simple)
+    assert np.all(list(synchrony_metrics.sync_spike_2.keys()) == sorting_analyzer_simple.unit_ids)
 
 
 @pytest.mark.sortingcomponents
diff --git a/src/spikeinterface/qualitymetrics/tests/test_quality_metric_calculator.py b/src/spikeinterface/qualitymetrics/tests/test_quality_metric_calculator.py
index a6415c58e8..60f0490f51 100644
--- a/src/spikeinterface/qualitymetrics/tests/test_quality_metric_calculator.py
+++ b/src/spikeinterface/qualitymetrics/tests/test_quality_metric_calculator.py
@@ -24,14 +24,14 @@ def test_compute_quality_metrics(sorting_analyzer_simple):
     metrics = compute_quality_metrics(
         sorting_analyzer,
         metric_names=["snr"],
-        qm_params=dict(isi_violation=dict(isi_threshold_ms=2)),
+        metric_params=dict(isi_violation=dict(isi_threshold_ms=2)),
         skip_pc_metrics=True,
         seed=2205,
     )
     # print(metrics)
 
     qm = sorting_analyzer.get_extension("quality_metrics")
-    assert qm.params["qm_params"]["isi_violation"]["isi_threshold_ms"] == 2
+    assert qm.params["metric_params"]["isi_violation"]["isi_threshold_ms"] == 2
     assert "snr" in metrics.columns
     assert "isolation_distance" not in metrics.columns
 
@@ -40,7 +40,7 @@ def test_compute_quality_metrics(sorting_analyzer_simple):
     metrics = compute_quality_metrics(
         sorting_analyzer,
         metric_names=None,
-        qm_params=dict(isi_violation=dict(isi_threshold_ms=2)),
+        metric_params=dict(isi_violation=dict(isi_threshold_ms=2)),
         skip_pc_metrics=False,
         seed=2205,
     )
@@ -54,7 +54,7 @@ def test_compute_quality_metrics_recordingless(sorting_analyzer_simple):
     metrics = compute_quality_metrics(
         sorting_analyzer,
         metric_names=None,
-        qm_params=dict(isi_violation=dict(isi_threshold_ms=2)),
+        metric_params=dict(isi_violation=dict(isi_threshold_ms=2)),
         skip_pc_metrics=False,
         seed=2205,
     )
@@ -68,7 +68,7 @@ def test_compute_quality_metrics_recordingless(sorting_analyzer_simple):
     metrics_norec = compute_quality_metrics(
         sorting_analyzer_norec,
         metric_names=None,
-        qm_params=dict(isi_violation=dict(isi_threshold_ms=2)),
+        metric_params=dict(isi_violation=dict(isi_threshold_ms=2)),
         skip_pc_metrics=False,
         seed=2205,
     )
@@ -101,7 +101,7 @@ def test_empty_units(sorting_analyzer_simple):
     metrics_empty = compute_quality_metrics(
         sorting_analyzer_empty,
         metric_names=None,
-        qm_params=dict(isi_violation=dict(isi_threshold_ms=2)),
+        metric_params=dict(isi_violation=dict(isi_threshold_ms=2)),
         skip_pc_metrics=True,
         seed=2205,
     )