diff --git a/.github/workflows/build-test.yml b/.github/workflows/build-test.yml
index 8b44f3ef..9f08440d 100644
--- a/.github/workflows/build-test.yml
+++ b/.github/workflows/build-test.yml
@@ -46,7 +46,7 @@ jobs:
 
       - uses: prefix-dev/setup-pixi@v0.8.1
         with:
-          pixi-version: v0.39.0
+          pixi-version: v0.39.5
           environments: ${{ matrix.environment }}
           # we can freeze the environment and manually bump the dependencies to the
           # latest version time to time.
diff --git a/.github/workflows/clean-skops-user.yml b/.github/workflows/clean-skops-user.yml
deleted file mode 100644
index c32ad9be..00000000
--- a/.github/workflows/clean-skops-user.yml
+++ /dev/null
@@ -1,24 +0,0 @@
-name: clean-skops-user
-
-on:
-  schedule:
-    # * is a special character in YAML so you have to quote this string
-    - cron:  '10 1 * * *'
-
-jobs:
-  clean-skops-user:
-
-    runs-on: ubuntu-latest
-    if: "github.repository == 'skops-dev/skops'"
-
-    # Timeout: https://stackoverflow.com/a/59076067/4521646
-    timeout-minutes: 35
-
-    steps:
-    - uses: actions/checkout@v4
-    - name: Set up Python
-      uses: actions/setup-python@v5
-    - name: Install Requirements
-      run: pip install huggingface_hub
-    - name: run cleanup
-      run: echo "y" | python scripts/clean_skops.py
diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index 83222e6c..cd247d41 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -98,21 +98,6 @@ scikit-learn and all other required dependencies with:
 
    pytest
 
-Certain tests require internet access to run, and they typically take slightly
-longer to run than other tests. If you'd like to skip those tests, you can add
-``-m not network`` to your ``pytest`` command, or ``-m network`` to only run
-those tests. For example, you can run all tests except the ones requiring
-internet with:
-
-.. code:: bash
-
-   pytest -m "not network" skops
-
-Similarly, there is a flag, ``-m inference`` for tests that hit the Hugging Face
-Inference API, which can be quite slow or even hang. Skip these tests as long as
-you don't make any changes to this functionality. If you already skip network
-tests, the inference tests will also be skipped.
-
 
 Releases
 ========
diff --git a/README.rst b/README.rst
index a77ac4ec..7d225121 100644
--- a/README.rst
+++ b/README.rst
@@ -31,26 +31,19 @@ SKOPS
 
 ``skops`` is a Python library helping you share your `scikit-learn
 <https://scikit-learn.org/stable/>`__ based models and put them in production.
-At the moment, it includes tools to easily integrate models on the Hugging Face
-Hub, which allows you to share your models, make them discoverable, and use the
-Hub's API inference and widgets to get outputs of the model without having to
-download or load the model.
-
-- ``skops.hub_utils``: tools to create a model repository to be stored on
-  `Hugging Face Hub <https://hf.co/models>`__, mainly through
-  ``skops.hub_utils.init`` and ``skops.hub_utils.push``. You can see all the
-  models uploaded to the hub using this library `here
-  <https://huggingface.co/models?other=skops>`__. Find out more `here
-  <https://skops.readthedocs.io/en/stable/hf_hub.html>`__.
+At the moment, it includes `skops.io` to securely persist sklearn estimators and
+more, without using ``pickle``. It also includes `skops.card` to create a model
+card explaining what the model does and how it should be used.
+
+- ``skops.io``: Secure persistence of sklearn estimators and more, without using
+  ``pickle``. Visit `the docs
+  <https://skops.readthedocs.io/en/latest/persistence.html>`__ for more
+  information.
 - ``skops.card``: tools to create a model card explaining what the model does
   and how it should be used. The model card can then be stored as the
   ``README.md`` file on the Hugging Face Hub, with pre-populated metadata to
   help Hub understand the model. More information can be found `here
   <https://skops.readthedocs.io/en/stable/model_card.html>`__.
-- ``skops.io``: Secure persistence of sklearn estimators and more, without using
-  ``pickle``. Visit `the docs
-  <https://skops.readthedocs.io/en/latest/persistence.html>`__ for more
-  information.
 
 Please refer to our `documentation <https://skops.readthedocs.io/en/latest/>`_
 on using the library as user, which includes user guides on the above topics as
diff --git a/docs/changes.rst b/docs/changes.rst
index 22c036e0..c0ddb20e 100644
--- a/docs/changes.rst
+++ b/docs/changes.rst
@@ -39,7 +39,7 @@ v0.9
   estimators. :pr:`384` by :user:`Reid Johnson <reidjohnson>`.
 - Fix an issue with visualizing Skops files for `scikit-learn` tree estimators.
   :pr:`386` by :user:`Reid Johnson <reidjohnson>`.
-- :func:`skops.hub_utils.get_model_output` and :func:`skops.hub_utils.push` are
+- ``skops.hub_utils.get_model_output`` and ``skops.hub_utils.push`` are
   deprecated and will be removed in version 0.10. :pr:`396` by `Adrin Jalali`_.
 
 v0.8
@@ -156,7 +156,7 @@ v0.2
   filesystem if it fails for some reason. :pr:`60` by `Adrin Jalali`_
 - When adding figures or tables, it's now possible to set ``folded=True`` to
   render the content inside a details tag. :pr:`108` by `Benjamin Bossan`_.
-- Add :meth:`skops.hub_utils.get_model_output` to get the model's output using
+- Add ``skops.hub_utils.get_model_output`` to get the model's output using
   The Hugging Face Hub's inference API, and return an array with the outputs.
   :pr:`105` by `Adrin Jalali`_.
 
@@ -167,7 +167,7 @@ This is the first release of the library. It include two main modules:
 
 - :mod:`skops.hub_utils`: tools to create a model repository to be stored on
   `Hugging Face Hub <https://hf.co/models>`__, mainly through
-  :func:`skops.hub_utils.init` and :func:`skops.hub_utils.push`.
+  :func:`skops.hub_utils.init` and ``skops.hub_utils.push``.
 - :mod:`skops.card`: tools to create a model card explaining what the model does
   and how it should be used. The model card can then be stored as the
   ``README.md`` file on the Hugging Face Hub, with pre-populated metadata to
diff --git a/docs/community.rst b/docs/community.rst
index 2f7aff3f..0cf0fd34 100644
--- a/docs/community.rst
+++ b/docs/community.rst
@@ -13,16 +13,6 @@ If you'd like to contribute to the project, please make sure you read our
 <https://github.com/skops-dev/skops/blob/main/CONTRIBUTING.rst>`__.
 
 
-Discord
-~~~~~~~
-We also have a place on Hugging Face's discord server. We're happy to see you
-there and answer any questions you might have. You can join using this `invite
-link <http://hf.co/join/discord>`__. Once you join, first you need to accept
-the rules on the server regarding respectful and harassment free communication,
-and then you can head to the ``#role-assignment`` channel where you'll find and
-``Open Source ML`` button. Clicking on that will give you access to a few
-channels and categories, including the ``skops`` category.
-
 Maintainers
 -----------
 Current maintainers of the project are (in alphabetical order):
diff --git a/docs/conf.py b/docs/conf.py
index 4c4d99dd..b63f7467 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -148,6 +148,5 @@ def linkcode_resolve(domain, info):
     "sklearn": ("https://scikit-learn.org/stable/", None),
     "pandas": ("https://pandas.pydata.org/docs/", None),
     "joblib": ("https://joblib.readthedocs.io/en/latest/", None),
-    "huggingface_hub": ("https://huggingface.co/docs/huggingface_hub/main/en", None),
     "fairlearn": ("https://fairlearn.org/v0.8/", None),
 }
diff --git a/docs/examples.rst b/docs/examples.rst
index f7ca5d38..45fc78eb 100644
--- a/docs/examples.rst
+++ b/docs/examples.rst
@@ -6,9 +6,6 @@ Examples of interactions with the Hugging Face Hub
 - Creating the Model Card:
     :ref:`sphx_glr_auto_examples_plot_model_card.py` is an example of using
     skops to create a model card that can be used on the Hugging Face Hub.
-- Putting the Model Card on the Hub:
-    :ref:`sphx_glr_auto_examples_plot_hf_hub.py` is an example of using skops
-    to put a model card on the Hugging Face Hub.
 - Tabular Regression:
     :ref:`sphx_glr_auto_examples_plot_tabular_regression.py` is an example of using skops to serialize a tabular
     regression model and create a model card and a Hugging Face Hub repository.
diff --git a/docs/hf_hub.rst b/docs/hf_hub.rst
deleted file mode 100644
index d116ce56..00000000
--- a/docs/hf_hub.rst
+++ /dev/null
@@ -1,162 +0,0 @@
-.. _hf_hub:
-
-scikit-learn Models on Hugging Face Hub
-=======================================
-
-This library allows you to initialize and create a model repository compatible
-with `Hugging Face Hub <https://huggingface.co/models>`__, which among other
-things, gives you the following benefits:
-
-- Inference API to get model output through REST calls
-- A widget to try the model directly in the browser
-- Metadata tags for better discoverability of the model
-- Collaborating with others on a model through discussions and pull requests
-- Convenient sharing of models with the community
-
-You can see all the models uploaded to the Hugging Face Hub using this library
-`here <https://huggingface.co/models?other=skops>`_.
-
-In terms of files, there are three which a scikit-learn model repo needs to
-have on the Hub:
-
-- ``README.md``: includes certain metadata on top of the file and then a
-  description of the model, aka model card.
-- ``config.json``: contains the configuration needed to run the model.
-- The persisted model file. There are no constraints on the name of the file
-  and the name is configured in ``config.json``. The file needs to be loadable
-  by :func:`joblib.load` or :func:`pickle.load`.
-
-There are certain requirements in terms of information about the model for the
-Hub to be able to load and run the model. For scikit-learn compatible models,
-this information is stored in two places:
-
-- The metadata in ``README.md`` of the model repository, about which you can
-  read `here <https://huggingface.co/docs/hub/models-cards>`__.
-- The configuration stored in ``config.json``.
-
-As a user of ``skops``, you can use the tools in ``skops.hub_utils`` to create
-and persist a ``config.json`` file, and then use it to populate necessary
-metadata in the ``README.md`` file. The metadata in ``README.md`` is used by
-the Hub's backend to understand the type of the model and the kind of task
-which the model tries to solve. An example of a task can be
-``"tabular-classification"`` or ``"text-regression"``.
-
-An example ``config.json`` file looks like this::
-
-    {
-        "sklearn": {
-            "columns": [
-                "petal length (cm)",
-                "petal width (cm)",
-                "sepal length (cm)",
-                "sepal width (cm)",
-            ],
-            "environment": ['scikit-learn="1.1.1"', "numpy"],
-            "example_input": {
-                "petal length (cm)": [1.4, 1.4, 1.3],
-                "petal width (cm)": [0.2, 0.2, 0.2],
-                "sepal length (cm)": [5.1, 4.9, 4.7],
-                "sepal width (cm)": [3.5, 3.0, 3.2],
-            },
-            "model": {"file": "model.pkl"},
-            "task": "tabular-classification",
-        }
-    }
-
-The key ``sklearn`` includes the following sub-keys:
-
-- ``columns``: An ordered list of column names. The order is important as it is
-  used to make sure the input given to the model is what the model expects.
-- ``example_input``: A list of examples to the model. This is in the form of a
-  dictionary of column names to list of values, and is used by the Hugging Face
-  Hub backend to show them in the widget to test the model when visiting the
-  model's page on the Hub.
-- ``environment``: A list of dependencies that the model requires. These
-  packages must be available on conda-forge and are installed before loading
-  the model.
-- ``model.file``: The file name of the persisted model.
-- ``task``: The task of the model.
-
-You almost never need to create or touch this file manually, and it's created
-when you call :func:`skops.hub_utils.init`.
-
-It is recommended to include the script itself that creates the whole output in
-the upload. This way, the results are easily reproducible for others. To achieve
-this, call :func:`skops.hub_utils.add_files`:
-
-.. code:: python
-
-    # contents of train.py
-    ...
-    hub_utils.init(model, dst=local_repo)
-    hub_utils.add_files(__file__, dst=local_repo)  # adds train.py to repo
-    hub_utils.push(...)
-
-You may of course add more files if they're useful.
-
-.. _hf_hub_inference:
-
-Inference without Downloading the Models
-----------------------------------------
-
-You can use the Hugging Face Hub's inference API to get model output without
-downloading the models. The :func:`skops.hub_utils.get_model_output` function
-returns the model output for a given input. It can be used as::
-
-    import skops.hub_utils as hub_utils
-    import pandas as pd
-    data = pd.DataFrame(...)
-    # Load the model from the Hub
-    res = hub_utils.get_model_output("USER/MODEL_ID", data)
-
-In the above code snippet, ``res`` will be a :class:`numpy.ndarray` containing
-the model's output.
-
-.. _hf_hub_gradio:
-..
-   TODO: replace gradio link once gradio provides object.inv
-Easily build user interfaces to your scikit-learn models
---------------------------------------------------------
-`gradio <https://gradio.app/>`__ is a python library that lets you create interfaces on your model.
-It has a class called `Interface <https://gradio.app/docs/#interface>`__ that lets you create application
-interfaces to your machine learning models. Using gradio can have some advantages over the using a plain
-model repository, e.g. the Gradio dataframe component allows uploading a csv for tabular data, unlike the
-widget in the model repository.
-
-``gradio`` is integrated with skops, so you can load an interface with only one
-line of code. During the initialization of the interface, call load method with
-your repository identifier prepended with "huggingface/" will load an
-interface for your model. The interface has a dataframe input that takes samples
-and a dataframe output to return predictions. It also takes the example in the
-repository that is previously pushed with skops.
-Calling `gr.Interface.launch() <https://gradio.app/docs/#launch/>`__ will launch your application.
-
-.. code:: python
-
-    import gradio as gr
-    repo_id = "scikit-learn/tabular-playground"
-    gr.Interface.load(f"huggingface/{repo_id}").launch()
-
-
-You can further customize your UI, add description, title, and more. If you'd
-like to share your demo, you can set ``share`` to True in `gr.Interface.launch() <https://gradio.app/docs/#launch/>`__.
-
-.. code:: python
-
-    title = "Supersoaker Defective Product Prediction"
-    description = ("This model predicts Supersoaker production line failures."
-    "Drag and drop any slice from dataset or edit values as you wish in below"
-    "dataframe component.")
-    gr.Interface.load(f"huggingface/{repo_id}", title = title, description = description)
-
-Sharing your local application has time limitations.
-If you want to share your application continuously, you can deploy it to
-Hugging Face Spaces. You can check out `this blog <https://huggingface.co/blog/gradio-spaces>`__
-on how to do it.
-For more information, please refer to documentation of `gradio <https://gradio.app/docs/>`__.
-
-It's also possible to spawn a gradio space directly from the model repository.
-To achieve this, from the model page, click on ``Deploy`` (top right corner) >
-``Spaces`` > ``Create new Space``, then follow the instructions. After
-finishing, you get a gradio space hosted on Hugging Face Hub, with all the
-benefits that brings.
diff --git a/docs/model_card.rst b/docs/model_card.rst
index 0afdb7d2..32ee1e29 100644
--- a/docs/model_card.rst
+++ b/docs/model_card.rst
@@ -8,8 +8,7 @@ which are a short documentation explaining what the model does, how it's
 trained, and its limitations. `Hugging Face Hub <https://huggingface.co/>`__
 expects a ``README.md`` file containing a certain set of metadata at the
 beginning of it, following with the content of the model card in markdown
-format. The metadata section is used to make models searchable on the Hub, and
-get the inference API and the widgets on the website working.
+format.
 
 Metadata
 --------
@@ -17,13 +16,7 @@ Metadata
 The metadata part of the file needs to follow the specifications `here
 <https://huggingface.co/docs/hub/models-cards#model-card-metadata>`__. It
 includes simple attributes of your models such as the task you're solving,
-dataset you trained the model with, evaluation results and more. When the model
-is hosted on the Hub, information in metadata like task name or dataset help
-your model be discovered on the `Hugging Face Hub
-<https://huggingface.co/models>`__. The task identifiers should follow the task
-taxonomy defined in Hugging Face Hub, as it enables the inference widget on the
-model page. An example to task identifier can be ``"tabular-classification"``
-or ``"text-regression"``.
+dataset you trained the model with, evaluation results and more.
 
 Here's an example of the metadata section of the ``README.md`` file:
 
diff --git a/examples/plot_california_housing.py b/examples/plot_california_housing.py
index e20c067a..a77f1a5c 100644
--- a/examples/plot_california_housing.py
+++ b/examples/plot_california_housing.py
@@ -39,7 +39,6 @@
 # ``python -m pip install jupyter matplotlib pandas scikit-learn skops``
 
 # %%
-import os
 from operator import itemgetter
 from pathlib import Path
 from tempfile import mkdtemp
@@ -47,7 +46,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import sklearn
 from matplotlib.patches import Rectangle
 from sklearn.compose import ColumnTransformer
 from sklearn.datasets import fetch_california_housing
@@ -66,8 +64,7 @@
 from sklearn.preprocessing import FunctionTransformer
 from sklearn.tree import DecisionTreeRegressor
 
-import skops
-from skops import card, hub_utils
+from skops import card
 from skops import io as sio
 
 # %%
@@ -1663,23 +1660,6 @@
 # just add the same section name again and the value would be overwritten
 # by the new content.
 
-# %%
-# Another useful thing for readers of the card to have is a small code
-# snippet that shows how to load the model. To add this, we use the
-# ``model_card.add_get_started_code`` method. We add a short
-# description, but that’s optional, as well as the model format (remember,
-# we used the skops format here) and the file name. Regarding the latter,
-# since the file is saved in a temporary directory, we should strip that
-# away from the name by calling ``file_name.name``, since another
-# user would not have the file in the exact same temporary location.
-
-# %%
-model_card.add_get_started_code(
-    description="Run the code below to load the model",
-    model_format="skops",
-    file_name=file_name.name,
-)
-
 # %%
 # Another convenience method we should make use of is the
 # ``model_card.add_metrics`` method. This will store the metrics
@@ -1766,103 +1746,7 @@
 # %%
 # Now the model card is saved as a markdown file in the temporary
 # directory, together with the gradient boosting model and the figures we
-# added earlier. We could now share the contents of that folder with
-# people who might be interested in our model by sending them the contents
-# of that directory.
-
-# %%
-# Upload the model to Hugging Face Hub
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-# %%
-# A perhaps better way to share these results is to upload it somewhere
-# for other people to discover and use. Hugging Face provides a platform
-# for doing this, the `Hugging Face
-# Hub <https://huggingface.co/docs/hub/index>`__. Even though it’s mostly
-# known for deep learning models, it also works with scikit-learn. So
-# let’s upload our model and model card there.
-
-# %%
-# First, let’s create yet another temporary directory. This one will
-# contain everything we want to upload to the Hugging Face Hub repository:
-
-# %%
-hub_dir = Path(mkdtemp())
-
-# %%
-# Next we use the ``hub_utils`` provided by skops to initialize and
-# push the repository. For now, let’s create the initial repo using
-# ``hub_utils.init``. Note that we pass the full path to the model
-# file here. We also pass the requirements (which are scikit-learn,
-# pandas, and skops in this case), the kind of task we’re solving,
-# i.e. tabular regression, and a sample of our data (only the first 3 rows
-# will actually be used).
-
-# %%
-requirements = [
-    f"scikit-learn=={sklearn.__version__}",
-    f"pandas=={pd.__version__}",
-    f"skops=={skops.__version__}",
-]
-
-hub_utils.init(
-    model=file_name,
-    requirements=requirements,
-    dst=hub_dir,
-    task="tabular-regression",
-    data=df_test,
-)
-
-# %%
-# When we take a look at the directory of the repo, we find the following:
-
-# %%
-os.listdir(hub_dir)
-
-# %%
-# So the model was automatically copied to the directory (which is why we
-# needed to pass the full path to it), and a ``config.json`` was
-# created, which contains useful metadata about our model.
-
-# %%
-# Remember that we can attach the metadata to our model card? Let’s do
-# this now. To achieve this, we load the metadata from the
-# ``config.json`` using the function ``metadata_from_config``
-# that is provided by skops:
-
-# %%
-metadata = card.metadata_from_config(hub_dir / "config.json")
-metadata
-
-# %%
-# Let’s attach the metadata to our model card and save it again:
-
-# %%
-model_card.metadata = metadata
-model_card.save(temp_dir / "README.md")
-
-# %%
-# So now the ``README.md`` contains the metadata. However, the model
-# card and the figures we created are not part of the repository directory
-# yet, they are still in the temporary directory we created earler. So
-# let’s fix this. We use the ``add_files`` function to do this:
-
-# %%
-hub_utils.add_files(
-    temp_dir / "README.md",
-    temp_dir / "geographic.png",
-    "permutation-importances.png",
-    dst=hub_dir,
-)
-
-# %%
-os.listdir(hub_dir)
-
-# %%
-# Creating the Repo and Pushing to Hugging Face Hub
-# You can use the tools available in ``huggingface_hub`` to create a repo and
-# push the contents of the repo folder to that repo. For more information visit
-# https://huggingface.co/docs/huggingface_hub/index
+# added earlier.
 
 # %%
 # Conclusion
@@ -1875,11 +1759,6 @@
 # known features of scikit-learn, and trained a machine learning model
 # that performs well.
 
-# %%
-# But we didn’t stop there. We also leveraged skops and the Hugging Face
-# Hub to share our results with a wider public, ensuring that the model
-# artifact is safe to use and that our task is well documented.
-
 # %%
 # If you have any feedback or suggestions for improvement, feel free to
 # reach out to the skops team, e.g. by visiting our `discord
diff --git a/examples/plot_custom_model_card.py b/examples/plot_custom_model_card.py
index 1385836f..32548b76 100644
--- a/examples/plot_custom_model_card.py
+++ b/examples/plot_custom_model_card.py
@@ -128,17 +128,6 @@
     section="Regression on California Housing dataset/Model Description/Model Diagram",
 )
 
-# %%
-# Add getting started code
-# ------------------------
-
-model_card.add_get_started_code(
-    section="Regression on California Housing dataset/Usage/Getting Started",
-    description="Load the model using the snippet below.",
-    file_name=model_file_name,
-    model_format="skops",
-)
-
 # %%
 # Add partial dependence plot
 # ---------------------------
diff --git a/examples/plot_model_card.py b/examples/plot_model_card.py
deleted file mode 100644
index 7f098ee6..00000000
--- a/examples/plot_model_card.py
+++ /dev/null
@@ -1,189 +0,0 @@
-"""
-scikit-learn model cards
-------------------------
-
-This guide demonstrates how you can use this package to create a model card on a
-scikit-learn compatible model and save it.
-"""
-
-# %%
-# Imports
-# =======
-# First we will import everything required for the rest of this document.
-
-import pickle
-from pathlib import Path
-from tempfile import mkdtemp, mkstemp
-
-import pandas as pd
-import sklearn
-from sklearn.datasets import load_breast_cancer
-from sklearn.ensemble import HistGradientBoostingClassifier
-from sklearn.experimental import enable_halving_search_cv  # noqa
-from sklearn.inspection import permutation_importance
-from sklearn.metrics import (
-    ConfusionMatrixDisplay,
-    accuracy_score,
-    classification_report,
-    confusion_matrix,
-    f1_score,
-)
-from sklearn.model_selection import HalvingGridSearchCV, train_test_split
-
-from skops import hub_utils
-from skops.card import Card, metadata_from_config
-
-# %%
-# Data
-# ====
-# We load breast cancer dataset from sklearn.
-
-X, y = load_breast_cancer(as_frame=True, return_X_y=True)
-X_train, X_test, y_train, y_test = train_test_split(
-    X, y, test_size=0.3, random_state=42
-)
-print("X's summary: ", X.describe())
-print("y's summary: ", y.describe())
-
-# %%
-# Train a Model
-# =============
-# Using the above data, we train a model. To select the model, we use
-# :class:`~sklearn.model_selection.HalvingGridSearchCV` with a parameter grid
-# over :class:`~sklearn.ensemble.HistGradientBoostingClassifier`.
-
-param_grid = {
-    "max_leaf_nodes": [5, 10, 15],
-    "max_depth": [2, 5, 10],
-}
-
-model = HalvingGridSearchCV(
-    estimator=HistGradientBoostingClassifier(),
-    param_grid=param_grid,
-    random_state=42,
-    n_jobs=-1,
-).fit(X_train, y_train)
-model.score(X_test, y_test)
-
-
-# %%
-# Initialize a repository to save our files in
-# ============================================
-# We will now initialize a repository and save our model
-_, pkl_name = mkstemp(prefix="skops-", suffix=".pkl")
-
-with open(pkl_name, mode="bw") as f:
-    pickle.dump(model, file=f)
-
-local_repo = mkdtemp(prefix="skops-")
-
-hub_utils.init(
-    model=pkl_name,
-    requirements=[f"scikit-learn={sklearn.__version__}"],
-    dst=local_repo,
-    task="tabular-classification",
-    data=X_test,
-)
-
-# %%
-# Create a model card
-# ====================
-# We now create a model card, and populate its metadata with information which
-# is already provided in ``config.json``, which itself is created by the call to
-# :func:`.hub_utils.init` above. We will see below how we can populate the model
-# card with useful information.
-
-model_card = Card(model, metadata=metadata_from_config(Path(local_repo)))
-
-# %%
-# Add more information
-# ====================
-# So far, the model card does not tell viewers a lot about the model. Therefore,
-# we add more information about the model, like a description and what its
-# license is.
-
-model_card.metadata.license = "mit"
-limitations = "This model is not ready to be used in production."
-model_description = (
-    "This is a `HistGradientBoostingClassifier` model trained on breast cancer "
-    "dataset. It's trained with `HalvingGridSearchCV`, with parameter grids on "
-    "`max_leaf_nodes` and `max_depth`."
-)
-model_card_authors = "skops_user"
-citation_bibtex = "**BibTeX**\n\n```\n@inproceedings{...,year={2020}}\n```"
-model_card.add(
-    **{  # type: ignore
-        "Citation": citation_bibtex,
-        "Model Card Authors": model_card_authors,
-        "Model description": model_description,
-        "Model description/Intended uses & limitations": limitations,
-    }
-)
-
-# %%
-# Add plots, metrics, and tables to our model card
-# ================================================
-# Furthermore, to better understand the model performance, we should evaluate it
-# on certain metrics and add those evaluations to the model card. In this
-# particular example, we want to calculate the accuracy and the F1 score. We
-# calculate those using sklearn and then add them to the model card by calling
-# :meth:`.Card.add_metrics`. But this is not all, we can also add matplotlib
-# figures to the model card, e.g. a plot of the confusion matrix. To achieve
-# this, we create the plot using sklearn, save it locally, and then add it using
-# :meth:`.Card.add_plot` method. Finally, we can also add some useful tables to
-# the model card, e.g. the results from the grid search and the classification
-# report. Those can be added using :meth:`.Card.add_table`
-
-y_pred = model.predict(X_test)
-eval_descr = (
-    "The model is evaluated on test data using accuracy and F1-score with "
-    "macro average."
-)
-model_card.add(**{"Model description/Evaluation Results": eval_descr})  # type: ignore
-
-accuracy = accuracy_score(y_test, y_pred)
-f1 = f1_score(y_test, y_pred, average="micro")
-model_card.add_metrics(**{"accuracy": accuracy, "f1 score": f1})
-
-cm = confusion_matrix(y_test, y_pred, labels=model.classes_)
-disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)
-disp.plot()
-
-disp.figure_.savefig(Path(local_repo) / "confusion_matrix.png")
-model_card.add_plot(
-    **{"Model description/Evaluation Results/Confusion Matrix": "confusion_matrix.png"}
-)
-
-importances = permutation_importance(model, X_test, y_test, n_repeats=10)
-model_card.add_permutation_importances(
-    importances,
-    X_test.columns,
-    plot_file="importance.png",
-    plot_name="Permutation Importance",
-)
-
-cv_results = model.cv_results_
-clf_report = classification_report(
-    y_test, y_pred, output_dict=True, target_names=["malignant", "benign"]
-)
-# The classification report has to be transformed into a DataFrame first to have
-# the correct format. This requires removing the "accuracy", which was added
-# above anyway.
-del clf_report["accuracy"]
-clf_report = pd.DataFrame(clf_report).T.reset_index()
-model_card.add_table(
-    folded=True,
-    **{
-        "Model description/Evaluation Results/Hyperparameter search results": (
-            cv_results
-        ),
-        "Model description/Evaluation Results/Classification report": clf_report,
-    },
-)
-
-# %%
-# Save model card
-# ===============
-# We can simply save our model card by providing a path to :meth:`.Card.save`.
-
-model_card.save(Path(local_repo) / "README.md")
diff --git a/examples/plot_tabular_regression.py b/examples/plot_tabular_regression.py
index 762939b3..5ca2e8ce 100644
--- a/examples/plot_tabular_regression.py
+++ b/examples/plot_tabular_regression.py
@@ -16,7 +16,6 @@
 from tempfile import mkdtemp, mkstemp
 
 import matplotlib.pyplot as plt
-import sklearn
 from sklearn.datasets import load_diabetes
 from sklearn.linear_model import LinearRegression
 from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
@@ -25,7 +24,7 @@
 from sklearn.preprocessing import StandardScaler
 
 import skops.io as sio
-from skops import card, hub_utils
+from skops import card
 
 # %%
 # Data
@@ -70,27 +69,13 @@
 
 local_repo = mkdtemp(prefix="skops-")
 
-hub_utils.init(
-    model=pkl_name,
-    requirements=[f"scikit-learn={sklearn.__version__}"],
-    dst=local_repo,
-    task="tabular-regression",
-    data=X_test,
-)
-
-if "__file__" in locals():  # __file__ not defined during docs built
-    # Add this script itself to the files to be uploaded for reproducibility
-    hub_utils.add_files(__file__, dst=local_repo)
-
 # %%
 # Create a model card
 # ===================
-# We now create a model card, and populate its metadata with information which
-# is already provided in ``config.json``, which itself is created by the call to
-# :func:`.hub_utils.init` above. We will see below how we can populate the model
+# We now create a model card. We will see below how we can populate the model
 # card with useful information.
 
-model_card = card.Card(model, metadata=card.metadata_from_config(Path(local_repo)))
+model_card = card.Card(model)
 
 # %%
 # Add more information
@@ -99,7 +84,6 @@
 # we add more information about the model, like a description and what its
 # license is.
 
-model_card.metadata.license = "mit"
 limitations = (
     "This model is made for educational purposes and is not ready to be used in"
     " production."
diff --git a/examples/plot_text_classification.py b/examples/plot_text_classification.py
index e8ca8dc3..e5c6fdb3 100644
--- a/examples/plot_text_classification.py
+++ b/examples/plot_text_classification.py
@@ -17,7 +17,6 @@
 from tempfile import mkdtemp, mkstemp
 
 import pandas as pd
-import sklearn
 from sklearn.datasets import fetch_20newsgroups
 from sklearn.feature_extraction.text import CountVectorizer
 from sklearn.metrics import (
@@ -31,7 +30,7 @@
 from sklearn.naive_bayes import MultinomialNB
 from sklearn.pipeline import Pipeline
 
-from skops import card, hub_utils
+from skops import card
 
 # %%
 # Data
@@ -87,23 +86,13 @@
 
 local_repo = mkdtemp(prefix="skops-")
 
-hub_utils.init(
-    model=pkl_name,
-    requirements=[f"scikit-learn={sklearn.__version__}"],
-    dst=local_repo,
-    task="text-classification",
-    data=X_test,
-)
-
 # %%
 # Create a model card
 # ===================
-# We now create a model card, and populate its metadata with information which
-# is already provided in ``config.json``, which itself is created by the call to
-# :func:`.hub_utils.init` above. We will see below how we can populate the model
+# We now create a model card. We will see below how we can populate the model
 # card with useful information.
 
-model_card = card.Card(model, metadata=card.metadata_from_config(Path(local_repo)))
+model_card = card.Card(model)
 
 # %%
 # Add more information
@@ -112,7 +101,6 @@
 # we add more information about the model, like a description and what its
 # license is.
 
-model_card.metadata.license = "mit"
 limitations = "This model is not ready to be used in production."
 model_description = (
     "This is a Multinomial Naive Bayes model trained on 20 news groups dataset."
diff --git a/pixi.lock b/pixi.lock
index e7a9893d..ef954d09 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -219,14 +219,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda
-      - pypi: https://files.pythonhosted.org/packages/28/89/60f51ad71f63aaaa7e51a2a2ad37919985a341a1d267070f212cdf6c2d22/charset_normalizer-3.4.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
-      - pypi: https://files.pythonhosted.org/packages/c6/b2/454d6e7f0158951d8a78c2e1eb4f69ae81beb8dca5fee9809c6c99e9d0d0/fsspec-2024.10.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/95/9b/3068fb3ae0b498eb66960ca5f4d92a81c91458cacd4dc17bfa6d40ce90fb/huggingface_hub-0.26.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/ce/d9/5f4c13cecde62396b0d3fe530a50ccea91e7dfc1ccf0e09c228841bb5ba8/urllib3-2.2.3-py3-none-any.whl
       - pypi: .
       osx-64:
       - conda: https://conda.anaconda.org/conda-forge/osx-64/_py-xgboost-mutex-2.0-cpu_0.tar.bz2
@@ -357,14 +350,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.6-h915ae27_0.conda
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-      - pypi: https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl
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       - pypi: .
       osx-arm64:
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@@ -495,14 +481,7 @@ environments:
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       win-64:
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   requires_dist:
   - numpy>=1.23.5
   - pytest ; extra == 'test'
@@ -19831,7 +19173,7 @@ packages:
   - jupytext ; extra == 'doc'
   - myst-nb ; extra == 'doc'
   - pooch ; extra == 'doc'
-  - jupyterlite-sphinx>=0.16.5 ; extra == 'doc'
+  - jupyterlite-sphinx>=0.17.1 ; extra == 'doc'
   - jupyterlite-pyodide-kernel ; extra == 'doc'
   - mypy==1.10.0 ; extra == 'dev'
   - typing-extensions ; extra == 'dev'
@@ -19843,9 +19185,9 @@ packages:
   - doit>=0.36.0 ; extra == 'dev'
   - pydevtool ; extra == 'dev'
   requires_python: '>=3.10'
-- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/scipy/1.15.0.dev0/scipy-1.15.0.dev0-cp313-cp313-macosx_10_13_x86_64.whl
+- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/scipy/1.16.0.dev0/scipy-1.16.0.dev0-cp313-cp313-macosx_10_13_x86_64.whl
   name: scipy
-  version: 1.15.0.dev0
+  version: 1.16.0.dev0
   requires_dist:
   - numpy>=1.23.5
   - pytest ; extra == 'test'
@@ -19873,7 +19215,7 @@ packages:
   - jupytext ; extra == 'doc'
   - myst-nb ; extra == 'doc'
   - pooch ; extra == 'doc'
-  - jupyterlite-sphinx>=0.16.5 ; extra == 'doc'
+  - jupyterlite-sphinx>=0.17.1 ; extra == 'doc'
   - jupyterlite-pyodide-kernel ; extra == 'doc'
   - mypy==1.10.0 ; extra == 'dev'
   - typing-extensions ; extra == 'dev'
@@ -19885,9 +19227,9 @@ packages:
   - doit>=0.36.0 ; extra == 'dev'
   - pydevtool ; extra == 'dev'
   requires_python: '>=3.10'
-- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/scipy/1.15.0.dev0/scipy-1.15.0.dev0-cp313-cp313-macosx_12_0_arm64.whl
+- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/scipy/1.16.0.dev0/scipy-1.16.0.dev0-cp313-cp313-macosx_12_0_arm64.whl
   name: scipy
-  version: 1.15.0.dev0
+  version: 1.16.0.dev0
   requires_dist:
   - numpy>=1.23.5
   - pytest ; extra == 'test'
@@ -19915,7 +19257,7 @@ packages:
   - jupytext ; extra == 'doc'
   - myst-nb ; extra == 'doc'
   - pooch ; extra == 'doc'
-  - jupyterlite-sphinx>=0.16.5 ; extra == 'doc'
+  - jupyterlite-sphinx>=0.17.1 ; extra == 'doc'
   - jupyterlite-pyodide-kernel ; extra == 'doc'
   - mypy==1.10.0 ; extra == 'dev'
   - typing-extensions ; extra == 'dev'
@@ -19927,9 +19269,9 @@ packages:
   - doit>=0.36.0 ; extra == 'dev'
   - pydevtool ; extra == 'dev'
   requires_python: '>=3.10'
-- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/scipy/1.15.0.dev0/scipy-1.15.0.dev0-cp313-cp313-win_amd64.whl
+- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/scipy/1.16.0.dev0/scipy-1.16.0.dev0-cp313-cp313-win_amd64.whl
   name: scipy
-  version: 1.15.0.dev0
+  version: 1.16.0.dev0
   requires_dist:
   - numpy>=1.23.5
   - pytest ; extra == 'test'
@@ -19957,7 +19299,7 @@ packages:
   - jupytext ; extra == 'doc'
   - myst-nb ; extra == 'doc'
   - pooch ; extra == 'doc'
-  - jupyterlite-sphinx>=0.16.5 ; extra == 'doc'
+  - jupyterlite-sphinx>=0.17.1 ; extra == 'doc'
   - jupyterlite-pyodide-kernel ; extra == 'doc'
   - mypy==1.10.0 ; extra == 'dev'
   - typing-extensions ; extra == 'dev'
@@ -20470,10 +19812,9 @@ packages:
   timestamp: 1733216901349
 - pypi: .
   name: skops
-  version: 0.11.dev0
-  sha256: dcbfbe445939c6a829b254426dd38dc111bdc8b60190c209c354253ffab5c76e
+  version: 0.12.dev0
+  sha256: 35c757b6b516b1ffd6c44d851692b2278f893c8df5808a2f6266d3f542616c4a
   requires_dist:
-  - huggingface-hub>=0.17.0
   - packaging>=17.0
   - scikit-learn>=1.1
   - tabulate>=0.8.8
@@ -21081,22 +20422,6 @@ packages:
   - pkg:pypi/tornado?source=hash-mapping
   size: 645144
   timestamp: 1732616217328
-- pypi: https://files.pythonhosted.org/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl
-  name: tqdm
-  version: 4.67.1
-  sha256: 26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2
-  requires_dist:
-  - colorama ; platform_system == 'Windows'
-  - pytest>=6 ; extra == 'dev'
-  - pytest-cov ; extra == 'dev'
-  - pytest-timeout ; extra == 'dev'
-  - pytest-asyncio>=0.24 ; extra == 'dev'
-  - nbval ; extra == 'dev'
-  - requests ; extra == 'discord'
-  - slack-sdk ; extra == 'slack'
-  - requests ; extra == 'telegram'
-  - ipywidgets>=6 ; extra == 'notebook'
-  requires_python: '>=3.7'
 - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_0.conda
   sha256: 8a64fa0f19022828513667c2c7176cfd125001f3f4b9bc00d33732e627dd2592
   md5: 3df84416a021220d8b5700c613af2dc5
@@ -21707,17 +21032,6 @@ packages:
   - pkg:pypi/unicodedata2?source=hash-mapping
   size: 365699
   timestamp: 1729705064037
-- pypi: https://files.pythonhosted.org/packages/ce/d9/5f4c13cecde62396b0d3fe530a50ccea91e7dfc1ccf0e09c228841bb5ba8/urllib3-2.2.3-py3-none-any.whl
-  name: urllib3
-  version: 2.2.3
-  sha256: ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac
-  requires_dist:
-  - brotli>=1.0.9 ; platform_python_implementation == 'CPython' and extra == 'brotli'
-  - brotlicffi>=0.8.0 ; platform_python_implementation != 'CPython' and extra == 'brotli'
-  - h2>=4,<5 ; extra == 'h2'
-  - pysocks>=1.5.6,!=1.5.7,<2.0 ; extra == 'socks'
-  - zstandard>=0.18.0 ; extra == 'zstd'
-  requires_python: '>=3.8'
 - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.3-pyhd8ed1ab_1.conda
   sha256: 416e30a1c3262275f01a3e22e783118d9e9d2872a739a9ed860d06fa9c7593d5
   md5: 4a2d8ef7c37b8808c5b9b750501fffce
diff --git a/pyproject.toml b/pyproject.toml
index 830e1b6b..fd69ed0a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,7 +38,6 @@ classifiers = [
 license = {file = "LICENSE"}
 dependencies = [
     "scikit-learn>=1.1",
-    "huggingface-hub>=0.17.0",
     "tabulate>=0.8.8",
     "packaging>=17.0",
 ]
@@ -90,10 +89,6 @@ filterwarnings = [
     # BaseEstimator._validate_data deprecation warning in sklearn 1.6 #TODO can be removed when a new release of quantile-forest is out
     "ignore:`BaseEstimator._validate_data` is deprecated in 1.6 and will be removed in 1.7:FutureWarning",
 ]
-markers = [
-    "network: marks tests as requiring internet (deselect with '-m \"not network\"')",
-    "inference: marks tests that call inference API (deselect with '-m \"not inference\"')",
-]
 addopts = "--cov=skops --cov-report=term-missing --doctest-modules"
 
 [tool.coverage.run]
diff --git a/skops/card/__init__.py b/skops/card/__init__.py
index 0febe42a..bf1a0c47 100644
--- a/skops/card/__init__.py
+++ b/skops/card/__init__.py
@@ -1,4 +1,4 @@
-from ._model_card import Card, metadata_from_config
+from ._model_card import Card
 from ._parser import parse_modelcard
 
-__all__ = ["Card", "metadata_from_config", "parse_modelcard"]
+__all__ = ["Card", "parse_modelcard"]
diff --git a/skops/card/_model_card.py b/skops/card/_model_card.py
index 61e09f0f..efa19b86 100644
--- a/skops/card/_model_card.py
+++ b/skops/card/_model_card.py
@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import json
 import re
 import shutil
 import sys
@@ -12,10 +11,9 @@
 from hashlib import sha256
 from pathlib import Path
 from reprlib import Repr
-from typing import Any, Iterator, List, Literal, Optional, Sequence, Union
+from typing import Any, Iterator, List, Literal, Optional, Sequence
 
 import joblib
-from huggingface_hub import ModelCardData
 from sklearn.utils import estimator_html_repr
 from tabulate import tabulate  # type: ignore
 
@@ -55,63 +53,6 @@ def _clean_table(table: str) -> str:
     return table
 
 
-def metadata_from_config(config_path: Union[str, Path]) -> ModelCardData:
-    """Construct a ``ModelCardData`` object from a ``config.json`` file.
-
-    Most information needed for the metadata section of a ``README.md`` file on
-    Hugging Face Hub is included in the ``config.json`` file. This utility
-    function constructs a :class:`huggingface_hub.ModelCardData` object which
-    can then be passed to the :class:`~skops.card.Card` object.
-
-    This method populates the following attributes of the instance:
-
-    - ``library_name``: It needs to be ``"sklearn"`` for scikit-learn
-        compatible models.
-    - ``tags``: Set to a list, containing ``"sklearn"`` and the task of the
-        model. You can then add more tags to this list.
-    - ``widget``: It is populated with the example data to be used by the
-        widget component of the Hugging Face Hub widget, on the model's
-        repository page.
-
-    Parameters
-    ----------
-    config_path: str, or Path
-        Filepath to the ``config.json`` file, or the folder including that
-        file.
-
-    Returns
-    -------
-    card_data: huggingface_hub.ModelCardData
-        :class:`huggingface_hub.ModelCardData` object.
-
-    """
-    config_path = Path(config_path)
-    if not config_path.is_file():
-        config_path = config_path / "config.json"
-
-    with open(config_path) as f:
-        config = json.load(f)
-    card_data = ModelCardData(
-        model_format=config.get("sklearn", {}).get("model_format", {})
-    )
-    card_data.library_name = "sklearn"
-    card_data.tags = ["sklearn", "skops"]
-    task = config.get("sklearn", {}).get("task", None)
-    if task:
-        card_data.tags += [task]
-    card_data.model_file = config.get("sklearn", {}).get("model", {}).get("file")  # type: ignore
-
-    example_input = config.get("sklearn", {}).get("example_input", None)
-    # Documentation on what the widget expects:
-    # https://huggingface.co/docs/hub/models-widgets-examples
-    if example_input:
-        if "tabular" in task:
-            card_data.widget = [{"structuredData": example_input}]  # type: ignore
-        # TODO: add text data example here.
-
-    return card_data
-
-
 def split_subsection_names(key: str) -> list[str]:
     r"""Split a string containing multiple sections into a list of strings for
     each.
@@ -148,32 +89,6 @@ def split_subsection_names(key: str) -> list[str]:
     return [part.replace(placeholder, "/") for part in parts]
 
 
-def _getting_started_code(
-    file_name: str, model_format: Literal["pickle", "skops"], indent: str = "    "
-) -> list[str]:
-    # get lines of code required to load the model
-    lines = [
-        "import json",
-        "import pandas as pd",
-    ]
-    if model_format == "skops":
-        lines += ["import skops.io as sio"]
-    else:
-        lines += ["import joblib"]
-
-    if model_format == "skops":
-        lines += [f'model = sio.load("{file_name}")']
-    else:  # pickle
-        lines += [f'model = joblib.load("{file_name}")']
-
-    lines += [
-        'with open("config.json") as f:',
-        indent + "config = json.load(f)",
-        'model.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))',
-    ]
-    return lines
-
-
 @dataclass
 class Section:
     """Building block of the model card.
@@ -375,6 +290,12 @@ class Card:
     model: pathlib.Path, str, or sklearn estimator object
         ``Path``/``str`` of the model or the actual model instance that will be
         documented. If a ``Path`` or ``str`` is provided, model will be loaded.
+        Note that a a "get started" code block will be added to the card only if
+        the model is a ``Path`` or ``str``.
+
+    model_format: Literal["pickle", "skops"] or None (default=None)
+        The format of the model file. If ``None``, the format will be inferred
+        from the file extension of the model file if possible.
 
     model_diagram: bool or "auto" or str, default="auto"
         If using the skops template, setting this to ``True`` or ``"auto"`` will
@@ -389,16 +310,6 @@ class Card:
         diagram can, however, always be added later using
         :meth:`Card.add_model_plot`.
 
-    metadata: ModelCardData, optional
-        :class:`huggingface_hub.ModelCardData` object. The contents of this
-        object are saved as metadata at the beginning of the output file, and
-        used by Hugging Face Hub.
-
-        You can use :func:`~skops.card.metadata_from_config` to create an
-        instance pre-populated with necessary information based on the contents
-        of the ``config.json`` file, which itself is created by
-        :func:`skops.hub_utils.init`.
-
     template: "skops", dict, or None (default="skops")
         Whether to add default sections or not. The template can be a predefined
         template, which at the moment can only be the string ``"skops"``, which
@@ -419,10 +330,6 @@ class Card:
     model: estimator object
         The scikit-learn compatible model that will be documented.
 
-    metadata: ModelCardData
-        Metadata to be stored at the beginning of the saved model card, as
-        metadata to be understood by the Hugging Face Hub.
-
     Examples
     --------
     >>> from sklearn.metrics import (
@@ -439,7 +346,6 @@ class Card:
     >>> X, y = load_iris(return_X_y=True)
     >>> model = LogisticRegression(solver="liblinear", random_state=0).fit(X, y)
     >>> model_card = Card(model)
-    >>> model_card.metadata.license = "mit"
     >>> y_pred = model.predict(X)
     >>> model_card.add_metrics(**{
     ...     "accuracy": accuracy_score(y, y_pred),
@@ -480,13 +386,13 @@ class Card:
     def __init__(
         self,
         model,
+        model_format: Literal["pickle", "skops"] | None = None,
         model_diagram: bool | Literal["auto"] | str = "auto",
-        metadata: ModelCardData | None = None,
         template: Literal["skops"] | dict[str, str] | None = "skops",
         trusted: Optional[List[str]] = None,
     ) -> None:
         self.model = model
-        self.metadata = metadata or ModelCardData()
+        self.model_format = model_format
         self.template = template
         self.trusted = trusted
 
@@ -524,7 +430,6 @@ def _populate_template(self, model_diagram: bool | Literal["auto"] | str):
             self.add(folded=False, **SKOPS_TEMPLATE)
             # for the skops template, automatically add some default sections
             self.add_hyperparams()
-            self.add_get_started_code()
 
             if (model_diagram is True) or (model_diagram == "auto"):
                 self.add_model_plot()
@@ -915,101 +820,6 @@ def _add_hyperparams(
         )
         self._add_single(section_name, section)
 
-    def add_get_started_code(
-        self,
-        section: str = "How to Get Started with the Model",
-        description: str | None = None,
-        file_name: str | None = None,
-        model_format: Literal["pickle", "skops"] | None = None,
-    ) -> Self:
-        """Add getting started code
-
-        This code can be copied by users to load the model and make predictions
-        with it.
-
-        Parameters
-        ----------
-        section : str (default="How to Get Started with the Model")
-            The section that the code for loading the model should be added to.
-            By default, the section is set to fit the skops model card template.
-            If you're using a different template, you may have to choose a
-            different section name.
-
-        description : str or None, default=None
-            An optional description to be added before the code. If you're using
-            the default skops template, a standard text is used. Pass a string
-            here if you want to use your own text instead. Leave this empty to
-            not add any description.
-
-        file_name : str or None, default=None
-            The file name of the model. If no file name is indicated, there will
-            be an attempt to read the file name from the card's metadata. If
-            that fails, an error is raised and you have to pass this argument
-            explicitly.
-
-        model_format : "skops", "pickle", or None, default=None
-            The model format used to store the model.If format is indicated,
-            there will be an attempt to read the model format from the card's
-            metadata. If that fails, an error is raised and you have to pass
-            this argument explicitly.
-
-        Returns
-        -------
-        self : object
-            Card object.
-
-        """
-        if file_name is None:
-            file_name = self.metadata.to_dict().get("model_file")
-
-        if model_format is None:
-            model_format = (
-                self.metadata.to_dict().get("sklearn", {}).get("model_format")
-            )
-
-        if model_format and (model_format not in ("pickle", "skops")):
-            msg = (
-                f"Invalid model format '{model_format}', should be one of "
-                "'pickle' or 'skops'"
-            )
-            raise ValueError(msg)
-
-        if (not file_name) or (not model_format):
-            return self
-
-        self._add_get_started_code(
-            section,
-            file_name=file_name,
-            model_format=model_format,
-            description=description,
-        )
-
-        return self
-
-    def _add_get_started_code(
-        self,
-        section_name: str,
-        file_name: str,
-        model_format: Literal["pickle", "skops"],
-        description: str | None,
-        indent: str = "    ",
-    ) -> None:
-        """Add getting started code to the corresponding section"""
-        lines = _getting_started_code(
-            file_name, model_format=model_format, indent=indent
-        )
-        lines = ["```python"] + lines + ["```"]
-        code = "\n".join(lines)
-
-        if description:
-            content = f"{description}\n\n{code}"
-        else:
-            content = code
-
-        title = split_subsection_names(section_name)[-1]
-        section = Section(title=title, content=content)
-        self._add_single(section_name, section)
-
     def add_plot(
         self,
         *,
@@ -1309,16 +1119,6 @@ def _add_metrics(
         section = TableSection(title=title, content=description, table=table)
         self._add_single(section_name, section)
 
-    def _generate_metadata(self, metadata: ModelCardData) -> Iterator[str]:
-        """Yield metadata in yaml format"""
-        # Repr attributes can be used to control the behavior of repr
-        aRepr = Repr()
-        aRepr.maxother = 79
-        aRepr.maxstring = 79
-
-        for key, val in metadata.to_dict().items() if metadata else {}:
-            yield aRepr.repr(f"metadata.{key}={val},").strip('"').strip("'")
-
     def _generate_content(
         self,
         data: dict[str, Section],
@@ -1391,16 +1191,6 @@ def __repr__(self) -> str:
         else:
             model_repr = None
 
-        # repr for metadata
-        metadata_reprs = []
-        for key, val in self.metadata.to_dict().items() if self.metadata else {}:
-            if key == "widget":
-                metadata_reprs.append("metadata.widget=[{...}],")
-                continue
-
-            metadata_reprs.append(self._format_repr(f"metadata.{key}", repr(val)))
-        metadata_repr = "\n".join(metadata_reprs)
-
         # repr for contents
         content_reprs = []
         for title, section in self._iterate_content(self._data):
@@ -1417,18 +1207,13 @@ def __repr__(self) -> str:
         complete_repr = "Card(\n"
         if model_repr:
             complete_repr += textwrap.indent(model_repr, "  ") + "\n"
-        if metadata_reprs:
-            complete_repr += textwrap.indent(metadata_repr, "  ") + "\n"
         if content_reprs:
             complete_repr += textwrap.indent(content_repr, "  ") + "\n"
         complete_repr += ")"
         return complete_repr
 
     def _generate_card(self, destination_path: Path | None = None) -> Iterator[str]:
-        """Yield sections of the model card, including the metadata."""
-        if self.metadata.to_dict():
-            yield f"---\n{self.metadata.to_yaml()}\n---"
-
+        """Yield sections of the model card."""
         for line in self._generate_content(
             self._data, destination_path=destination_path
         ):
@@ -1454,11 +1239,6 @@ def save(self, path: str | Path, copy_files: bool = False) -> None:
             before creating the repository. Without this path the README will
             have an absolute path to the plot that won't exist in the
             repository.
-
-        Notes
-        -----
-        The keys in model card metadata can be seen `here
-        <https://huggingface.co/docs/hub/models-cards#model-card-metadata>`__.
         """
         with open(path, "w", encoding="utf-8") as f:
             if not isinstance(path, Path):
diff --git a/skops/card/_parser.py b/skops/card/_parser.py
index 06fbc29f..35a0ad41 100644
--- a/skops/card/_parser.py
+++ b/skops/card/_parser.py
@@ -11,10 +11,8 @@
 import json
 import subprocess
 from pathlib import Path
-from tempfile import mkdtemp
-from typing import Any, Literal
+from typing import Literal
 
-import yaml  # type: ignore
 from packaging.version import Version
 
 from skops.card import Card
@@ -209,55 +207,6 @@ def check_pandoc_installed(
         )
 
 
-def _card_with_detached_metainfo(path: str | Path) -> tuple[str | Path, dict[str, Any]]:
-    """Detach the possibly existing yaml part of the model card
-
-    Model cards always have a markdown part and optionally a yaml part at the
-    head, delimited by "---". Obviously, pandoc cannot parse that. Therefore, we
-    detach the yaml part and return it as a separate dict, only leaving
-    (hopefully) valid markdown.
-
-    path : str or pathlib.Path
-        The path to the model card file.
-
-    Returns
-    -------
-    file : path
-        The path to the model card without any yaml metainfo. If the model card
-        didn't contain that metainfo to begin with, this is just the path to the
-        original model card. If it did contain metainfo, this is a path to a new
-        temporary file with the metainfo removed.
-
-    metainfo : dict
-        The metainfo from the yaml part as a parsed dict. If no metainfo was
-        present, the dict is empty.
-    """
-    with open(path, "r") as f:
-        text = f.read()
-
-    sep_start, sep_end = "---\n", "\n---"
-
-    metainfo: dict[str, Any] = {}
-    if not text.startswith(sep_start):  # no metainfo:
-        return path, metainfo
-
-    idx_separator = text.find(sep_end)
-    if idx_separator < len(sep_start):  # pragma: no cover
-        # separator shouldn't come earlier than this
-        return path, metainfo
-
-    # https://black.readthedocs.io/en/stable/faq.html#why-are-flake8-s-e203-and-w503-violated
-    text_clean = text[idx_separator + len(sep_end) :]  # noqa: E203
-    metainfo = yaml.safe_load(  # type: ignore
-        text[len(sep_start) : idx_separator]  # noqa: E203
-    )
-
-    file = Path(mkdtemp()) / "tmp-model-card.md"
-    with open(file, "w") as f:
-        f.write(text_clean)
-    return file, metainfo
-
-
 def parse_modelcard(path: str | Path) -> Card:
     """Read a model card and return a Card object
 
@@ -328,8 +277,6 @@ def parse_modelcard(path: str | Path) -> Card:
     """
     check_pandoc_installed()
 
-    path, metainfo = _card_with_detached_metainfo(path)
-
     proc = subprocess.run(
         ["pandoc", "-t", "json", "-s", str(path)],
         capture_output=True,
@@ -338,7 +285,4 @@ def parse_modelcard(path: str | Path) -> Card:
 
     parser = PandocParser(source)
     card = parser.generate()
-    for key, val in metainfo.items():
-        setattr(card.metadata, key, val)
-
     return card
diff --git a/skops/card/default_template.md b/skops/card/default_template.md
index 91141dfe..4e2f4ad0 100644
--- a/skops/card/default_template.md
+++ b/skops/card/default_template.md
@@ -37,15 +37,6 @@ You can find the details about evaluation process and the evaluation results.
 
 {{ eval_results | default("[More Information Needed]", true)}}
 
-# How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-```python
-{{ get_started_code | default("[More Information Needed]", true)}}
-```
-
-
 # Model Card Authors
 
 This model card is written by following authors:
diff --git a/skops/card/tests/test_card.py b/skops/card/tests/test_card.py
index 750bebb4..6b07965d 100644
--- a/skops/card/tests/test_card.py
+++ b/skops/card/tests/test_card.py
@@ -9,15 +9,13 @@
 import numpy as np
 import pytest
 import sklearn
-from huggingface_hub import ModelCardData, metadata_load
 from sklearn.datasets import load_iris
 from sklearn.inspection import permutation_importance
 from sklearn.linear_model import LinearRegression, LogisticRegression
 from sklearn.metrics import f1_score, make_scorer
 from sklearn.neighbors import KNeighborsClassifier
 
-from skops import hub_utils
-from skops.card import Card, metadata_from_config
+from skops.card import Card
 from skops.card._model_card import (
     CONTENT_PLACEHOLDER,
     SKOPS_TEMPLATE,
@@ -113,38 +111,20 @@ def iris_skops_file(iris_estimator):
 def _create_model_card_from_saved_model(
     destination_path,
     iris_estimator,
-    iris_data,
-    save_file,
 ):
-    X, y = iris_data
-    hub_utils.init(
-        model=save_file,
-        requirements=[f"scikit-learn=={sklearn.__version__}"],
-        dst=destination_path,
-        task="tabular-classification",
-        data=X,
-    )
-    card = Card(iris_estimator, metadata=metadata_from_config(destination_path))
+    card = Card(iris_estimator)
     card.save(Path(destination_path) / "README.md")
     return card
 
 
 @pytest.fixture
-def skops_model_card_metadata_from_config(
-    destination_path, iris_estimator, iris_skops_file, iris_data
-):
-    yield _create_model_card_from_saved_model(
-        destination_path, iris_estimator, iris_data, iris_skops_file
-    )
+def skops_model_card(destination_path, iris_estimator):
+    yield _create_model_card_from_saved_model(destination_path, iris_estimator)
 
 
 @pytest.fixture
-def pkl_model_card_metadata_from_config(
-    destination_path, iris_estimator, iris_pkl_file, iris_data
-):
-    yield _create_model_card_from_saved_model(
-        destination_path, iris_estimator, iris_data, iris_pkl_file
-    )
+def pkl_model_card(destination_path, iris_estimator):
+    yield _create_model_card_from_saved_model(destination_path, iris_estimator)
 
 
 @pytest.fixture
@@ -158,15 +138,13 @@ def test_save_model_card(destination_path, model_card):
     assert (Path(destination_path) / "README.md").exists()
 
 
-def test_model_caching(
-    skops_model_card_metadata_from_config, iris_skops_file, destination_path
-):
+def test_model_caching(skops_model_card, iris_skops_file, destination_path):
     """Tests that the model card caches the model to avoid loading it multiple times"""
 
     new_model = LogisticRegression(random_state=4321)
     # mock _load_model, it still loads the model but we can track call count
     mock_load_model = mock.Mock(side_effect=load)
-    card = Card(iris_skops_file, metadata=metadata_from_config(destination_path))
+    card = Card(iris_skops_file)
     with mock.patch("skops.card._model_card._load_model", mock_load_model):
         model1 = card.get_model()
         model2 = card.get_model()
@@ -620,177 +598,6 @@ def test_permutation_importances_with_description(
         assert section.format() == expected
 
 
-class TestAddGetStartedCode:
-    """Tests for getting started code"""
-
-    @pytest.fixture
-    def metadata(self):
-        # dummy ModelCardData using pickle
-        class Metadata:
-            def to_dict(self):
-                return {
-                    "model_file": "my-model.pickle",
-                    "sklearn": {
-                        "model_format": "pickle",
-                    },
-                }
-
-        return Metadata()
-
-    @pytest.fixture
-    def model_card(self, metadata):
-        model = fit_model()
-        card = Card(model, metadata=metadata)
-        return card
-
-    @pytest.fixture
-    def metadata_skops(self):
-        # dummy ModelCardData using skops
-        class Metadata:
-            def to_dict(self):
-                return {
-                    "model_file": "my-model.skops",
-                    "sklearn": {
-                        "model_format": "skops",
-                    },
-                }
-
-        return Metadata()
-
-    @pytest.fixture
-    def model_card_skops(self, metadata_skops):
-        model = fit_model()
-        card = Card(model, metadata=metadata_skops)
-        return card
-
-    @pytest.fixture
-    def text_pickle(self):
-        return (
-            "```python\n"
-            "import json\n"
-            "import pandas as pd\n"
-            "import joblib\n"
-            'model = joblib.load("my-model.pickle")\n'
-            'with open("config.json") as f:\n'
-            "    config = json.load(f)\n"
-            'model.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))\n'
-            "```"
-        )
-
-    @pytest.fixture
-    def text_skops(self):
-        return (
-            "```python\n"
-            "import json\n"
-            "import pandas as pd\n"
-            "import skops.io as sio\n"
-            'model = sio.load("my-model.skops")\n'
-            'with open("config.json") as f:\n'
-            "    config = json.load(f)\n"
-            'model.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))\n'
-            "```"
-        )
-
-    def test_default_pickle(self, model_card, text_pickle):
-        # by default, don't add a table, as there are no metrics
-        result = model_card.select("How to Get Started with the Model").format()
-        assert result == text_pickle
-
-    def test_default_skops(self, model_card_skops, text_skops):
-        # by default, don't add a table, as there are no metrics
-        result = model_card_skops.select("How to Get Started with the Model").format()
-        assert result == text_skops
-
-    def test_no_metadata_file_name(self):
-        model = fit_model()
-        card = Card(model, metadata=None)
-        card.add_get_started_code()  # does not raise
-
-    def test_no_metadata_file_format(self):
-        class Metadata:
-            def to_dict(self):
-                return {
-                    "model_file": "my-model.skops",
-                    # missing file format entry
-                }
-
-        model = fit_model()
-        card = Card(model, metadata=Metadata())
-        card.add_get_started_code()  # does not raise
-
-    def test_other_section(self, model_card, text_pickle):
-        model_card.add_get_started_code(section="Other section")
-        result = model_card.select("Other section").format()
-        assert result == text_pickle
-
-    def test_use_description(self, model_card):
-        model_card.add_get_started_code(description="Awesome code")
-        result = model_card.select("How to Get Started with the Model").format()
-        assert result.startswith("Awesome code")
-
-    def test_other_filename(self, model_card, text_pickle):
-        model_card.add_get_started_code(file_name="foobar.pkl")
-        text = text_pickle.replace("my-model.pickle", "foobar.pkl")
-        result = model_card.select("How to Get Started with the Model").format()
-        assert result == text
-
-    def test_explicitly_set_other_model_format(self, model_card, text_skops):
-        model_card.add_get_started_code(model_format="skops")
-        result = model_card.select("How to Get Started with the Model").format()
-        # file name is still "my-model.pickle", only the loading code changes
-        text = text_skops.replace(".skops", ".pickle")
-        assert result == text
-
-    def test_invalid_model_format_passed(self, model_card):
-        # json is not a valid model format
-        msg = "Invalid model format 'json', should be one of 'pickle' or 'skops'"
-        with pytest.raises(ValueError, match=msg):
-            model_card.add_get_started_code(model_format="json")
-
-    def test_invalid_model_format_passed_via_metadata(self):
-        # metadata contains invalid model format json
-        class Metadata:
-            def to_dict(self):
-                return {
-                    "model_file": "my-model.skops",
-                    "sklearn": {
-                        "model_format": "json",
-                    },
-                }
-
-        model = fit_model()
-
-        msg = "Invalid model format 'json', should be one of 'pickle' or 'skops'"
-        with pytest.raises(ValueError, match=msg):
-            Card(model, metadata=Metadata())
-
-    @pytest.mark.parametrize("template", CUSTOM_TEMPLATES)
-    def test_custom_template_no_section_uses_default(self, template, text_pickle):
-        model = fit_model()
-
-        class Metadata:
-            def to_dict(self):
-                return {
-                    "model_file": "my-model.pickle",
-                    "sklearn": {
-                        "model_format": "pickle",
-                    },
-                }
-
-        model_card = Card(model, metadata=Metadata(), template=template)
-        model_card.add_get_started_code()
-        result = model_card.select("How to Get Started with the Model").format()
-        assert result == text_pickle
-
-    def test_add_twice(self, model_card):
-        # it's possible to add the section twice, even if it doesn't make a lot
-        # of sense
-        text1 = model_card.select("How to Get Started with the Model").format()
-        model_card.add_get_started_code(section="Other section")
-        text2 = model_card.select("Other section").format()
-        assert text1 == text2
-
-
 class TestRender:
     def test_render(self, model_card, destination_path):
         file_name = destination_path / "README.md"
@@ -801,18 +608,6 @@ def test_render(self, model_card, destination_path):
         rendered = model_card.render()
         assert loaded == rendered
 
-    def test_render_with_metadata(self, model_card):
-        model_card.metadata.foo = "something"
-        model_card.metadata.bar = "something else"
-        rendered = model_card.render()
-        expected = textwrap.dedent("""
-            ---
-            foo: something
-            bar: something else
-            ---
-            """).strip()
-        assert rendered.startswith(expected)
-
 
 class TestSelect:
     """Selecting sections from the model card"""
@@ -1167,49 +962,6 @@ def test_add_plot_with_alt_text(self, destination_path, model_card):
         assert plot_content == "![the figure](fig1.png)"
 
 
-class TestMetadata:
-    def test_adding_metadata(self, model_card):
-        # test if the metadata is added to the card
-        model_card.metadata.tags = "dummy"
-        metadata = list(model_card._generate_metadata(model_card.metadata))
-        assert len(metadata) == 1
-        assert metadata[0] == "metadata.tags=dummy,"
-
-    def test_metadata_from_config_tabular_data(
-        self, pkl_model_card_metadata_from_config, destination_path
-    ):
-        # test if widget data is correctly set in the README
-        metadata = metadata_load(local_path=Path(destination_path) / "README.md")
-        assert "widget" in metadata
-
-        expected_data = [
-            {
-                "structuredData": {
-                    "petal length (cm)": [1.4, 1.4, 1.3],
-                    "petal width (cm)": [0.2, 0.2, 0.2],
-                    "sepal length (cm)": [5.1, 4.9, 4.7],
-                    "sepal width (cm)": [3.5, 3.0, 3.2],
-                }
-            },
-        ]
-        assert metadata["widget"] == expected_data
-
-        for tag in ["sklearn", "skops", "tabular-classification"]:
-            assert tag in metadata["tags"]
-
-    def test_metadata_model_format_pkl(
-        self, pkl_model_card_metadata_from_config, destination_path
-    ):
-        metadata = metadata_load(local_path=Path(destination_path) / "README.md")
-        assert metadata["model_format"] == "pickle"
-
-    def test_metadata_model_format_skops(
-        self, skops_model_card_metadata_from_config, destination_path
-    ):
-        metadata = metadata_load(local_path=Path(destination_path) / "README.md")
-        assert metadata["model_format"] == "skops"
-
-
 @pytest.mark.xfail(reason="dynamic adjustment when model changes not implemented yet")
 class TestModelDynamicUpdate:
     def test_model_related_sections_updated_dynamically_skops_template(
@@ -1359,32 +1111,6 @@ def test_without_model_attribute(self, card: Card, meth, expected_lines):
         result = meth(card)
         assert reprs_equal(expected, result)
 
-    @pytest.mark.parametrize("meth", [repr, str])
-    def test_with_metadata(self, card: Card, meth, expected_lines):
-        metadata = ModelCardData(
-            language="fr",
-            license="bsd",
-            library_name="sklearn",
-            tags=["sklearn", "tabular-classification"],
-            foo={"bar": 123},
-            widget=[{"something": "very-long"}],
-        )
-        card.metadata = metadata
-
-        # metadata comes after model line, i.e. position 2
-        extra_lines = [
-            "  metadata.language=fr,",
-            "  metadata.license=bsd,",
-            "  metadata.library_name=sklearn,",
-            "  metadata.tags=['sklearn', 'tabular-classification'],",
-            "  metadata.foo={'bar': 123},",
-            "  metadata.widget=[{...}],",
-        ]
-        expected = "\n".join(expected_lines[:2] + extra_lines + expected_lines[2:])
-        result = meth(card)
-
-        assert reprs_equal(expected, result)
-
 
 class TestCardModelAttributeIsPath:
     def path_to_card(self, path, suffix):
@@ -1660,15 +1386,6 @@ def test_add_metrics(self, card):
         assert "accuracy" in content
         assert "0.1" in content
 
-    def test_add_get_started_code(self, card):
-        card.add_get_started_code(
-            section="Getting Started",
-            file_name="foobar.skops",
-            model_format="skops",
-        )
-        content = card.select("Getting Started").content
-        assert "load" in content
-
     def test_custom_template_all_sections_present(self, template, card):
         # model_card contains all default sections
         for key in template:
@@ -1853,7 +1570,6 @@ def card(self):
         card.add_model_plot()
         card.add_hyperparams()
         card.add_metrics(accuracy=0.1)
-        card.add_get_started_code()
         return card
 
     def test_toc(self, card):
diff --git a/skops/hub_utils/__init__.py b/skops/hub_utils/__init__.py
deleted file mode 100644
index c7c05aa0..00000000
--- a/skops/hub_utils/__init__.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from ._hf_hub import (
-    add_files,
-    download,
-    get_config,
-    get_model_output,
-    get_requirements,
-    init,
-    push,
-    update_env,
-)
-
-__all__ = [
-    "add_files",
-    "download",
-    "get_config",
-    "get_requirements",
-    "get_model_output",
-    "init",
-    "push",
-    "update_env",
-]
diff --git a/skops/hub_utils/_hf_hub.py b/skops/hub_utils/_hf_hub.py
deleted file mode 100644
index d1d1eff6..00000000
--- a/skops/hub_utils/_hf_hub.py
+++ /dev/null
@@ -1,756 +0,0 @@
-"""
-This module contains utilities to push a model to the hub and pull from the
-hub.
-"""
-from __future__ import annotations
-
-import collections
-import itertools
-import json
-import os
-import shutil
-import warnings
-from pathlib import Path
-from typing import Any, List, Literal, MutableMapping, Optional, Sequence, Union
-
-import numpy as np
-from huggingface_hub import HfApi, InferenceClient, snapshot_download
-from sklearn.utils import check_array
-
-SUPPORTED_TASKS = [
-    "tabular-classification",
-    "tabular-regression",
-    "text-classification",
-    "text-regression",
-]
-
-
-def _validate_folder(path: Union[str, Path]) -> None:
-    """Validate the contents of a folder.
-
-    This function checks if the contents of a folder make a valid repo for a
-    scikit-learn based repo on the Hugging Face Hub.
-
-    A valid repository is one which is understood by the Hub as well as this
-    library to run and use the model. Otherwise anything can be put as a model
-    repository on the Hub and use it as a `git` and `git lfs` server.
-
-    Raises a ``TypeError`` if invalid.
-
-    Parameters
-    ----------
-    path: str or Path
-        The location of the repo.
-
-    Raises
-    ------
-    TypeError
-        Raised when the passed path is invalid.
-
-    Returns
-    -------
-    None
-    """
-    path = Path(path)
-    if not path.is_dir():
-        raise TypeError("The given path is not a directory.")
-
-    config_path = path / "config.json"
-    if not config_path.exists():
-        raise TypeError("Configuration file `config.json` missing.")
-
-    with open(config_path, "r") as f:
-        config = json.load(f)
-
-    model_path = config.get("sklearn", {}).get("model", {}).get("file", None)
-    if not model_path:
-        raise TypeError(
-            "Model file not configured in the configuration file. It should be stored"
-            " in the hf_hub.sklearn.model key."
-        )
-
-    if not (path / model_path).exists():
-        raise TypeError(f"Model file {model_path} does not exist.")
-
-
-def _get_example_input_from_tabular_data(data):
-    """Returns the example input of a model for a tabular task.
-
-    The input is converted into a dictionary which is then stored in the config
-    file.
-
-    Parameters
-    ----------
-    data: array-like
-        The input needs to be either a ``pandas.DataFrame``, a 2D
-        ``numpy.ndarray`` or a list/tuple that can be converted to a 2D
-        ``numpy.ndarray``. The first 3 rows are used as example input.
-
-    Returns
-    -------
-    example_input: dict of lists
-        The example input of the model as accepted by Hugging Face's backend.
-    """
-    try:
-        import pandas as pd
-
-        if isinstance(data, pd.DataFrame):
-            return {x: data[x][:3].to_list() for x in data.columns}
-    except ImportError:
-        # pandas is not installed, the data cannot be a pandas DataFrame
-        pass
-
-    # here we convert the first three rows of `data` to a dict of lists
-    # to be stored in the config file
-    if isinstance(data, (np.ndarray, list, tuple)):
-        data_slice = data[:3]
-        # This will raise a ValueError if the array is not 2D
-        data_slice_array = check_array(data_slice, ensure_2d=True)
-        return {
-            f"x{x}": data_slice_array[:, x].tolist()
-            for x in range(data_slice_array.shape[1])
-        }
-
-    raise ValueError(
-        "The data is not a pandas.DataFrame, a 2D numpy.ndarray or a "
-        "list/tuple that can be converted to a 2D numpy.ndarray."
-    )
-
-
-def _get_example_input_from_text_data(data: Sequence[str]):
-    """Returns the example input of a model for a text task.
-
-    The input is converted into a dictionary which is then stored in the config
-    file.
-
-    Parameters
-    ----------
-    data: Sequence[str]
-        A sequence of strings. The first 3 elements are used as example input.
-
-    Returns
-    -------
-    example_input: dict of lists
-        The example input of the model as accepted by Hugging Face's backend.
-    """
-
-    def _head(data, n):
-        is_data_subscriptable = hasattr(data, "__getitem__")
-        if is_data_subscriptable:
-            return data[:n]
-
-        return list(itertools.islice(data, n))
-
-    def _is_sequence_of_strings(data):
-        return not isinstance(data, str) and all(isinstance(x, str) for x in data)
-
-    error_message = "The data needs to be a sequence of strings."
-    try:
-        data_head = _head(data, n=3)
-        if _is_sequence_of_strings(data_head):
-            return {"data": data_head}
-        else:
-            raise ValueError(error_message)
-    except TypeError as e:
-        raise ValueError(error_message) from e
-
-
-def _get_column_names(data):
-    """Returns the column names of the input.
-
-    If data is not a ``pandas.DataFrame``, column names are assumed to be
-    ``x0`` to ``xn-1``, where ``n`` is the number of columns.
-
-    Parameters
-    ----------
-    data: array-like
-        The data whose columns names are to be returned. Must be a
-        ``pandas.DataFrame``, a 2D ``numpy.ndarray`` or a list/tuple that can
-        be converted to a 2D ``numpy.ndarray``.
-
-    Returns
-    -------
-    columns: list of strings
-        A list of strings. Each string is a column name.
-    """
-    try:
-        import pandas as pd
-
-        if isinstance(data, pd.DataFrame):
-            return list(data.columns)
-    except ImportError:
-        # pandas is not installed, the data cannot be a pandas DataFrame
-        pass
-
-    # TODO: this is going to fail for Structured Arrays. We can add support for
-    # them later if we see need for it.
-    if isinstance(data, (np.ndarray, list, tuple)):
-        # This will raise a ValueError if the array is not 2D
-        data_array = check_array(data, ensure_2d=True)
-        return [f"x{x}" for x in range(data_array.shape[1])]
-
-    raise ValueError(
-        "The data is not a pandas.DataFrame, a 2D numpy.ndarray or a "
-        "list/tuple that can be converted to a 2D numpy.ndarray."
-    )
-
-
-def _create_config(
-    *,
-    model_path: Union[str, Path],
-    requirements: List[str],
-    dst: Union[str, Path],
-    task: Literal[
-        "tabular-classification",
-        "tabular-regression",
-        "text-classification",
-        "text-regression",
-    ],
-    data,
-    model_format: Literal[
-        "skops",
-        "pickle",
-        "auto",
-    ] = "auto",
-) -> None:
-    """Write the configuration into a ``config.json`` file.
-
-    Parameters
-    ----------
-    model_path : str, or Path
-        The relative path (from the repo root) to the model file.
-
-    requirements : list of str
-        A list of required packages. The versions are then extracted from the
-        current environment.
-
-    dst : str, or Path
-        The path to an existing folder where the config file should be created.
-
-    task: "tabular-classification", "tabular-regression",
-    "text-classification", /
-            or "text-regression"
-        The task of the model, which determines the input and output type of
-        the model. It can be one of: ``tabular-classification``,
-        ``tabular-regression``, ``text-classification``, ``text-regression``.
-
-    data: array-like or sequence
-        The input to the model. This is used for two purposes:
-
-            1. Save an example input to the model, which is used by
-               HuggingFace's backend and shown in the widget of the model's
-               page.
-            2. Store the columns and their order of the input, which is used by
-               HuggingFace's backend to pass the data in the right form to the
-               model.
-
-        The first 3 input values are used as example inputs. If the task is
-        ``tabular-classification`` or ``tabular-regression``, then data is
-        expected to be an array-like. Otherwise, it is expected to be an
-        sequence of strings.
-
-    model_format: str (default="auto")
-        The format used to persist the model. Can be ``"auto"``, ``"skops"``
-        or ``"pickle"``. Defaults to ``"auto"``, which would mean:
-
-        - ``"pickle"`` if the extension is one of ``{".pickle", ".pkl", ".joblib"}``
-        - ``"skops"`` if the extension is ``".skops"``
-    """
-
-    # so that we don't have to explicitly add keys and they're added as a
-    # dictionary if they are not found
-    # see: https://stackoverflow.com/a/13151294/2536294
-    def recursively_default_dict() -> MutableMapping:
-        return collections.defaultdict(recursively_default_dict)
-
-    if model_format == "auto":
-        extension = Path(model_path).suffix
-        if extension in [".pkl", ".pickle", ".joblib"]:
-            model_format = "pickle"
-        elif extension == ".skops":
-            model_format = "skops"
-    if model_format not in ["skops", "pickle"]:
-        raise ValueError(
-            "Cannot determine the input file format. Please indicate the format using"
-            " the `model_format` argument."
-        )
-
-    config = recursively_default_dict()
-    config["sklearn"]["model"]["file"] = str(model_path)
-    config["sklearn"]["environment"] = requirements
-    config["sklearn"]["task"] = task
-    config["sklearn"]["model_format"] = model_format
-
-    if "tabular" in task:
-        config["sklearn"]["example_input"] = _get_example_input_from_tabular_data(data)
-        config["sklearn"]["columns"] = _get_column_names(data)
-    elif "text" in task:
-        config["sklearn"]["example_input"] = _get_example_input_from_text_data(data)
-
-    dump_json(Path(dst) / "config.json", config)
-
-
-def _check_model_file(path: str | Path) -> Path:
-    """Perform sanity checks on the model file
-
-    Parameters
-    ----------
-    path : str or Path
-      The model path
-
-    Returns
-    -------
-    path : Path
-      The model path as a  ``pathlib.Path``.
-
-    Raises
-    ------
-    OSError
-      If the model file does not exist.
-    """
-    if not os.path.exists(path):
-        raise OSError(f"Model file '{path}' does not exist.")
-
-    if os.path.getsize(path) == 0:
-        raise RuntimeError(f"Model file '{path}' is empty.")
-
-    return Path(path)
-
-
-def init(
-    *,
-    model: Union[str, Path],
-    requirements: List[str],
-    dst: Union[str, Path],
-    task: Literal[
-        "tabular-classification",
-        "tabular-regression",
-        "text-classification",
-        "text-regression",
-    ],
-    data,
-    model_format: Literal[
-        "skops",
-        "pickle",
-        "auto",
-    ] = "auto",
-) -> None:
-    """Initialize a scikit-learn based Hugging Face repo.
-
-    Given a pickled model and a set of required packages, this function
-    initializes a folder to be a valid Hugging Face scikit-learn based repo.
-
-    Parameters
-    ----------
-    model: str, or Path
-        The path to a model pickle file.
-
-    requirements: list of str
-        A list of required packages. The versions are then extracted from the
-        current environment.
-
-    dst: str, or Path
-        The path to a non-existing or empty folder which is to be initialized.
-
-    task: str
-        The task of the model, which determines the input and output type of
-        the model. It can be one of: ``tabular-classification``,
-        ``tabular-regression``, ``text-classification``, ``text-regression``.
-
-    data: array-like
-        The input to the model. This is used for two purposes:
-
-            1. Save an example input to the model, which is used by
-               HuggingFace's backend and shown in the widget of the model's
-               page.
-            2. Store the columns and their order of the input, which is used by
-               HuggingFace's backend to pass the data in the right form to the
-               model.
-
-        The first 3 input values are used as example inputs.
-
-        If ``task`` is ``"tabular-classification"`` or ``"tabular-regression"``,
-        the data needs to be a :class:`pandas.DataFrame` or a
-        :class:`numpy.ndarray`. If ``task`` is ``"text-classification"`` or
-        ``"text-regression"``, the data needs to be a ``list`` of strings.
-
-    model_format: str (default="auto")
-        The format the model was persisted in. Can be ``"auto"``, ``"skops"``
-        or ``"pickle"``. Defaults to ``"auto"`` that relies on file extension.
-    """
-    dst = Path(dst)
-    if dst.exists() and bool(next(dst.iterdir(), None)):
-        raise OSError("None-empty dst path already exists!")
-
-    if task not in SUPPORTED_TASKS:
-        raise ValueError(
-            f"Task {task} not supported. Supported tasks are: {SUPPORTED_TASKS}"
-        )
-
-    model = _check_model_file(model)
-
-    dst.mkdir(parents=True, exist_ok=True)
-
-    try:
-        shutil.copy2(src=model, dst=dst)
-
-        model_name = model.name
-        _create_config(
-            model_path=model_name,
-            requirements=requirements,
-            dst=dst,
-            task=task,
-            data=data,
-            model_format=model_format,
-        )
-    except Exception:
-        shutil.rmtree(dst)
-        raise
-
-
-def add_files(*files: str | Path, dst: str | Path, exist_ok: bool = False) -> None:
-    """Add files to initialized repo.
-
-    After having called :func:`.hub_utils.init`, use this function to add
-    arbitrary files to be uploaded in addition to the model and model card.
-
-    In particular, it can be useful to upload the script itself that produces
-    those artifacts by calling ``hub_utils.add_files([__file__], dst=...)``.
-
-    Parameters
-    ----------
-    *files : str or Path
-        The files to be added.
-
-    dst : str or Path
-        Path to the initialized repo, same as used during
-        :func:`.hub_utils.init`.
-
-    exist_ok : bool (default=False)
-        Whether it's okay or not to add a file that already exists. If
-        ``True``, override the files, otherwise raise a ``FileExistsError``.
-
-    Raises
-    ------
-    FileNotFoundError
-        When the target folder or the files to be added are not found.
-
-    FileExistsError
-        When a file is added that already exists at the target location and
-        ``exist_ok=False``.
-
-    """
-    dst = Path(dst)
-    # check dst exists
-    if not dst.exists():
-        msg = f"Could not find '{dst}', did you run 'skops.hub_utils.init' first?"
-        raise FileNotFoundError(msg)
-
-    src_files = [Path(file) for file in files]
-    # check that source files exist
-    for file in src_files:
-        if not file.exists():
-            msg = f"File '{file}' could not be found."
-            raise FileNotFoundError(msg)
-
-    dst_files = [dst / Path(file).name for file in files]
-    for src_file, dst_file in zip(src_files, dst_files):
-        # check if target file already exists
-        if dst_file.exists() and not exist_ok:
-            msg = f"File '{src_file.name}' already found at '{dst}'."
-            raise FileExistsError(msg)
-
-        shutil.copy2(src_file, dst_file)
-
-
-def dump_json(path, content):
-    with open(Path(path), mode="w") as f:
-        json.dump(content, f, sort_keys=True, indent=4)
-
-
-def update_env(
-    *, path: Union[str, Path], requirements: Union[List[str], None] = None
-) -> None:
-    """Update the environment requirements of a repo.
-
-    This function takes the path to the repo, and updates the requirements of
-    running the scikit-learn based model in the repo.
-
-    Parameters
-    ----------
-    path: str, or Path
-        The path to an existing local repo.
-
-    requirements: list of str, optional
-        The list of required packages for the model. If none is passed, the
-        list of existing requirements is used and their versions are updated.
-
-    """
-
-    with open(Path(path) / "config.json") as f:
-        config = json.load(f)
-
-    config["sklearn"]["environment"] = requirements
-
-    dump_json(Path(path) / "config.json", config)
-
-
-def push(
-    *,
-    repo_id: str,
-    source: Union[str, Path],
-    token: str | None = None,
-    commit_message: str | None = None,
-    create_remote: bool = False,
-    private: bool | None = None,
-) -> None:
-    """Pushes the contents of a model repo to Hugging Face Hub.
-
-    This function validates the contents of the folder before pushing it to the
-    Hub.
-
-    Parameters
-    ----------
-    repo_id: str
-        The ID of the destination repository in the form of ``OWNER/REPO_NAME``.
-
-    source: str or Path
-        A folder where the contents of the model repo are located.
-
-    token: str, optional
-        A token to push to the Hub. If not provided, the user should be already
-        logged in using ``huggingface-cli login``.
-
-    commit_message: str, optional
-        The commit message to be used when pushing to the repo.
-
-    create_remote: bool, default=False
-        Whether to create the remote repository if it doesn't exist. If the
-        remote repository doesn't exist and this parameter is ``False``, it
-        raises an error. Otherwise it checks if the remote repository exists,
-        and would create it if it doesn't.
-
-    private: bool, default=None
-        Whether the remote repository should be public or private. If ``True``
-        or ``False`` is passed, this method will set the private/public status
-        of the remote repository, regardless of it already existing or not. If
-        ``None``, no change is applied.
-
-        .. versionadded:: 0.3
-
-    Returns
-    -------
-    None
-
-    Raises
-    ------
-    TypeError
-        This function raises a ``TypeError`` if the contents of the source
-        folder do not make a valid Hugging Face Hub scikit-learn based repo.
-    """
-    warnings.warn(
-        "Creating repos on hf.co is subject to strict rate limits now and therefore"
-        " this feature is to be removed from this library in version 0.10. You can"
-        " use tools directly available in the huggingface_hub library instead to"
-        " create and push files.",
-        FutureWarning,
-    )
-    _validate_folder(path=source)
-    client = HfApi()
-
-    if create_remote:
-        client.create_repo(
-            repo_id=repo_id, token=token, repo_type="model", exist_ok=True
-        )
-
-    if private is not None:
-        client.update_repo_visibility(repo_id=repo_id, private=private, token=token)
-
-    client.upload_folder(
-        repo_id=repo_id,
-        path_in_repo=".",
-        folder_path=source,
-        commit_message=commit_message,
-        commit_description=None,
-        token=token,
-        repo_type=None,
-        revision=None,
-        create_pr=False,
-    )
-
-
-def get_config(path: Union[str, Path]) -> dict[str, Any]:
-    """Returns the configuration of a project.
-
-    Parameters
-    ----------
-    path: str
-        The path to the directory holding the project and its ``config.json``
-        configuration file.
-
-    Returns
-    -------
-    config: dict
-        A dictionary which holds the configs of the project.
-    """
-    with open(Path(path) / "config.json", "r") as f:
-        config = json.load(f)
-    return config
-
-
-def get_requirements(path: Union[str, Path]) -> List[str]:
-    """Returns the requirements of a project.
-
-    Parameters
-    ----------
-    path: str
-        The path to the director holding the project and its ``config.json``
-        configuration file.
-
-    Returns
-    -------
-    requirements: list of str
-        The list of requirements which can be passed to the package manager to
-        be installed.
-    """
-    config = get_config(path)
-    return config.get("sklearn", dict()).get("environment", list())
-
-
-def download(
-    *,
-    repo_id: str,
-    dst: Union[str, Path],
-    revision: str | None = None,
-    token: str | None = None,
-    keep_cache: bool = True,
-    **kwargs: Any,
-) -> None:
-    """Download a repository into a directory.
-
-    The directory needs to be an empty or a non-existing one.
-
-    Parameters
-    ----------
-    repo_id: str
-        The ID of the Hugging Face Hub repository in the form of
-        ``OWNER/REPO_NAME``.
-
-    dst: str, or Path
-        The directory to which the files are downloaded.
-
-    revision: str, optional
-        The revision of the project to download. This can be a git tag, branch,
-        or a git commit hash. By default the latest revision of the default
-        branch is downloaded.
-
-    token: str, optional
-        The token to be used to download the files. Only required if the
-        repository is private.
-
-    keep_cache: bool, default=True
-        Whether the cached data should be kept or removed after download. By
-        default a copy of the cached files will be created in the ``dst``
-        folder. If ``False``, the cache will be removed after the contents are
-        copied. Note that the cache is git based and by default new files are
-        only downloaded if there is a new revision of them on the hub. If you
-        keep the cache, the old files are not removed after downloading the
-        newer versions of them.
-
-    kwargs: dict
-        Other parameters to be passed to
-        :func:`huggingface_hub.snapshot_download`.
-
-    Returns
-    -------
-    None
-    """
-    dst = Path(dst)
-    if dst.exists() and bool(next(dst.iterdir(), None)):
-        raise OSError("None-empty dst path already exists!")
-
-    # remove the folder only if it's empty and it exists
-    if dst.exists():
-        dst.rmdir()
-
-    # TODO: Switch from use_auth_token to token once huggingface_hub<0.11 is
-    # dropped. Until then, we ignore the mypy type check, because mypy doesn't
-    # see that use_auth_token is handled by the decorator of snapshot_download.
-    cached_folder = snapshot_download(
-        repo_id=repo_id, revision=revision, use_auth_token=token, **kwargs  # type: ignore
-    )
-    shutil.copytree(cached_folder, dst)
-    if not keep_cache:
-        shutil.rmtree(path=cached_folder)
-
-
-# TODO(v0.10): remove this function
-def get_model_output(repo_id: str, data: Any, token: Optional[str] = None) -> Any:
-    """Returns the output of the model using Hugging Face Hub's inference API.
-
-    See the :ref:`User Guide <hf_hub_inference>` for more details.
-
-    .. deprecated:: 0.9
-        Will be removed in version 0.10. Use ``huggingface_hub.InferenceClient``
-        instead.
-
-    Parameters
-    ----------
-    repo_id: str
-        The ID of the Hugging Face Hub repository in the form of
-        ``OWNER/REPO_NAME``.
-
-    data: Any
-        The input to be given to the model. This can be a
-        :class:`pandas.DataFrame` or a :class:`numpy.ndarray`. If possible, you
-        should always pass a :class:`pandas.DataFrame` with correct column
-        names.
-
-    token: str, optional
-        The token to be used to call the inference API. Only required if the
-        repository is private.
-
-    Returns
-    -------
-    output: numpy.ndarray
-        The output of the model.
-
-    Notes
-    -----
-    If there are warnings or exceptions during inference, this function raises
-    a :class:`RuntimeError` including the original errors and warnings
-    returned from the server.
-
-    Also note that if the model repo is private, the inference API would not be
-    available.
-    """
-    warnings.warn(
-        "This feature is no longer free on hf.co and therefore this function will"
-        " be removed in the next release. Use `huggingface_hub.InferenceClient`"
-        " instead.",
-        FutureWarning,
-    )
-    model_info = HfApi().model_info(repo_id=repo_id, use_auth_token=token)  # type: ignore
-    if not model_info.pipeline_tag:
-        raise ValueError(
-            f"Repo {repo_id} has no pipeline tag. You should set a valid 'task' in"
-            " config.json and README.md files. This is automatically done for you if"
-            " you pass a valid task to the skops.hub_utils.init() and"
-            " skops.card.metadata_from_util() functions to generate those files."
-        )
-
-    try:
-        inputs = {"data": data.to_dict(orient="list")}
-    except AttributeError:
-        # the input is not a pandas DataFrame
-        inputs = {f"x{i}": data[:, i] for i in range(data.shape[1])}
-        inputs = {"data": inputs}
-
-    client = InferenceClient(token=token)
-    res_bytes = client.post(json={"inputs": inputs}, model=repo_id)
-    res = json.loads(res_bytes.decode("utf-8"))
-
-    if isinstance(res, list):
-        return np.array(res)
-    else:
-        raise RuntimeError(f"There were errors or warnings during inference: {res}")
diff --git a/skops/hub_utils/tests/common.py b/skops/hub_utils/tests/common.py
deleted file mode 100644
index f06f3b6a..00000000
--- a/skops/hub_utils/tests/common.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# This is the token for the skops user on the hub, used for the CI.
-HF_HUB_TOKEN = "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"
diff --git a/skops/hub_utils/tests/test_hf_hub.py b/skops/hub_utils/tests/test_hf_hub.py
deleted file mode 100644
index b4609dba..00000000
--- a/skops/hub_utils/tests/test_hf_hub.py
+++ /dev/null
@@ -1,654 +0,0 @@
-import json
-import os
-import pickle
-import re
-import shutil
-import tempfile
-import warnings
-from importlib import metadata
-from pathlib import Path
-from uuid import uuid4
-
-import numpy as np
-import pandas as pd
-import pytest
-import sklearn
-from flaky import flaky
-from huggingface_hub import HfApi
-from sklearn.datasets import load_diabetes, load_iris
-from sklearn.linear_model import LinearRegression, LogisticRegression
-
-from skops import card
-from skops.hub_utils import (
-    add_files,
-    get_config,
-    get_model_output,
-    get_requirements,
-    init,
-    push,
-    update_env,
-)
-from skops.hub_utils._hf_hub import (
-    _create_config,
-    _get_column_names,
-    _get_example_input_from_tabular_data,
-    _get_example_input_from_text_data,
-    _validate_folder,
-)
-from skops.hub_utils.tests.common import HF_HUB_TOKEN
-from skops.io import dump
-
-iris = load_iris(as_frame=True, return_X_y=False)
-diabetes = load_diabetes(as_frame=True, return_X_y=False)
-
-IS_SKLEARN_DEV_BUILD = "dev" in sklearn.__version__
-
-
-@pytest.fixture
-def temp_path():
-    with tempfile.TemporaryDirectory(prefix="skops-test-temp-path") as temp_path:
-        yield temp_path
-
-
-@pytest.fixture(scope="session")
-def repo_path():
-    with tempfile.TemporaryDirectory(prefix="skops-test-sample-repo") as repo_path:
-        yield Path(repo_path)
-
-
-@pytest.fixture
-def destination_path():
-    with tempfile.TemporaryDirectory(prefix="skops-test") as dir_path:
-        yield Path(dir_path)
-
-
-def get_classifier():
-    X, y = iris.data, iris.target
-    clf = LogisticRegression(solver="newton-cg").fit(X, y)
-    return clf
-
-
-def get_regressor():
-    X, y = diabetes.data, diabetes.target
-    model = LinearRegression().fit(X, y)
-    return model
-
-
-@pytest.fixture(scope="session")
-def classifier(repo_path, config_json):
-    # Create a simple model file for the purpose of testing
-    clf = get_classifier()
-    config_path, file_format = config_json
-    model_file = CONFIG[file_format]["sklearn"]["model"]["file"]
-    path = repo_path / model_file
-
-    try:
-        if file_format == "pickle":
-            with open(path, "wb") as f:
-                pickle.dump(clf, f)
-        elif file_format == "skops":
-            dump(clf, path)
-        yield path
-    finally:
-        path.unlink(missing_ok=True)
-
-
-CONFIG = {
-    "pickle": {
-        "sklearn": {
-            "environment": ['scikit-learn="1.1.1"'],
-            "model": {"file": "model.pickle"},
-        }
-    },
-    "skops": {
-        "sklearn": {
-            "environment": ['scikit-learn="1.1.1"'],
-            "model": {"file": "model.skops"},
-        }
-    },
-}
-
-
-@pytest.fixture(scope="session", params=["skops", "pickle"])
-def config_json(repo_path, request):
-    path = repo_path / "config.json"
-    try:
-        with open(path, "w") as f:
-            json.dump(CONFIG[request.param], f)
-        yield path, request.param
-    finally:
-        path.unlink(missing_ok=True)
-
-
-def test_validate_format(classifier):
-    dir_path = tempfile.mkdtemp()
-    shutil.rmtree(dir_path)
-    with pytest.raises(ValueError, match="Cannot determine the input file*"):
-        init(
-            model=classifier,
-            requirements=["scikit-learn"],
-            dst=dir_path,
-            task="tabular-classification",
-            data=iris.data,
-            model_format="dummy",
-        )
-
-
-def test_validate_folder(config_json):
-    config_path, file_format = config_json
-    _, file_path = tempfile.mkstemp()
-    dir_path = tempfile.mkdtemp()
-    with pytest.raises(TypeError, match="The given path is not a directory."):
-        _validate_folder(path=file_path)
-
-    with pytest.raises(TypeError, match="Configuration file `config.json` missing."):
-        _validate_folder(path=dir_path)
-
-    with open(Path(dir_path) / "config.json", "w") as f:
-        json.dump(dict(), f)
-
-    with pytest.raises(
-        TypeError, match="Model file not configured in the configuration file."
-    ):
-        _validate_folder(path=dir_path)
-
-    shutil.copy2(config_path, dir_path)
-    model_file = CONFIG[file_format]["sklearn"]["model"]["file"]
-    with pytest.raises(TypeError, match=f"Model file {model_file} does not exist."):
-        _validate_folder(path=dir_path)
-
-    (Path(dir_path) / model_file).touch()
-
-    # this should now work w/o an error
-    _validate_folder(path=dir_path)
-
-
-@pytest.mark.parametrize(
-    "data, task, expected_config",
-    [
-        (
-            iris.data,
-            "tabular-classification",
-            {
-                "sklearn": {
-                    "columns": [
-                        "petal length (cm)",
-                        "petal width (cm)",
-                        "sepal length (cm)",
-                        "sepal width (cm)",
-                    ],
-                    "environment": ['scikit-learn="1.1.1"', "numpy"],
-                    "example_input": {
-                        "petal length (cm)": [1.4, 1.4, 1.3],
-                        "petal width (cm)": [0.2, 0.2, 0.2],
-                        "sepal length (cm)": [5.1, 4.9, 4.7],
-                        "sepal width (cm)": [3.5, 3.0, 3.2],
-                    },
-                    "model": {"file": "model.pkl"},
-                    "task": "tabular-classification",
-                }
-            },
-        ),
-        (
-            ["test", "text", "problem", "random"],
-            "text-classification",
-            {
-                "sklearn": {
-                    "environment": ['scikit-learn="1.1.1"', "numpy"],
-                    "example_input": {"data": ["test", "text", "problem"]},
-                    "model": {"file": "model.pkl"},
-                    "task": "text-classification",
-                }
-            },
-        ),
-    ],
-)
-def test_create_config(data, task, expected_config):
-    dir_path = tempfile.mkdtemp()
-    _create_config(
-        model_path="model.pkl",
-        requirements=['scikit-learn="1.1.1"', "numpy"],
-        dst=dir_path,
-        task=task,
-        data=data,
-    )
-
-    with open(Path(dir_path) / "config.json") as f:
-        config = json.load(f)
-        for key in ["environment", "model", "task"]:
-            assert config["sklearn"][key] == expected_config["sklearn"][key]
-
-        keys = ["example_input"]
-        if "tabular" in task:
-            # text data doesn't introduce any "columns" in the configuration
-            keys += ["columns"]
-        for key in keys:
-            assert sorted(config["sklearn"][key]) == sorted(
-                expected_config["sklearn"][key]
-            )
-
-
-def test_create_config_invalid_text_data(temp_path):
-    with pytest.raises(ValueError, match="The data needs to be a sequence of strings."):
-        _create_config(
-            model_path="model.pkl",
-            requirements=['scikit-learn="1.1.1"', "numpy"],
-            task="text-classification",
-            data=[1, 2, 3],
-            dst=temp_path,
-        )
-
-
-def test_atomic_init(classifier, temp_path):
-    with pytest.raises(ValueError):
-        # this fails since we're passing an invalid task.
-        init(
-            model=classifier,
-            requirements=["scikit-learn"],
-            dst=temp_path,
-            task="tabular-classification",
-            data="invalid",
-        )
-
-    # this passes even though the above init has failed once, on the same
-    # destination path.
-    init(
-        model=classifier,
-        requirements=["scikit-learn"],
-        dst=temp_path,
-        task="tabular-classification",
-        data=iris.data,
-    )
-
-
-def test_init_invalid_task(classifier, temp_path):
-    with pytest.raises(
-        ValueError, match="Task invalid not supported. Supported tasks are"
-    ):
-        init(
-            model=classifier,
-            requirements=["scikit-learn"],
-            dst=temp_path,
-            task="invalid",
-            data=iris.data,
-        )
-
-
-def test_init(classifier, config_json):
-    config_path, file_format = config_json
-    # create a temp directory and delete it, we just need a unique name.
-    dir_path = tempfile.mkdtemp()
-    shutil.rmtree(dir_path)
-
-    version = metadata.version("scikit-learn")
-    init(
-        model=classifier,
-        requirements=[f'scikit-learn="{version}"'],
-        dst=dir_path,
-        task="tabular-classification",
-        data=iris.data,
-    )
-    _validate_folder(path=dir_path)
-
-    # it should fail a second time since the folder is no longer empty.
-    with pytest.raises(OSError, match="None-empty dst path already exists!"):
-        init(
-            model=classifier,
-            requirements=[f'scikit-learn="{version}"'],
-            dst=dir_path,
-            task="tabular-classification",
-            data=iris.data,
-        )
-
-
-def test_init_no_warning_or_error(classifier, config_json):
-    config_path, file_format = config_json
-    # for the happy path, there should be no warning
-    dir_path = tempfile.mkdtemp()
-    shutil.rmtree(dir_path)
-    version = metadata.version("scikit-learn")
-
-    with warnings.catch_warnings():
-        warnings.simplefilter("error")
-        init(
-            model=classifier,
-            requirements=[f'scikit-learn="{version}"'],
-            dst=dir_path,
-            task="tabular-classification",
-            data=iris.data,
-        )
-
-
-def test_model_file_does_not_exist_raises(repo_path, config_json):
-    config_path, file_format = config_json
-    # when the model file does not exist, raise an OSError
-    model_path = repo_path / "foobar.pickle"
-    dir_path = tempfile.mkdtemp()
-    shutil.rmtree(dir_path)
-    version = metadata.version("scikit-learn")
-
-    msg = re.escape(f"Model file '{model_path}' does not exist.")
-    with pytest.raises(OSError, match=msg):
-        init(
-            model=model_path,
-            requirements=[f'scikit-learn="{version}"'],
-            dst=dir_path,
-            task="tabular-classification",
-            data=iris.data,
-        )
-    model_path.unlink(missing_ok=True)
-
-
-def test_init_empty_model_file_errors(repo_path, config_json):
-    config_path, file_format = config_json
-    # when model file is empty, warn users
-    model_path = Path(repo_path / "foobar.pickle")
-    model_path.touch()
-
-    dir_path = tempfile.mkdtemp()
-    shutil.rmtree(dir_path)
-    version = metadata.version("scikit-learn")
-
-    with pytest.raises(
-        RuntimeError, match=re.escape(f"Model file '{model_path}' is empty.")
-    ):
-        init(
-            model=model_path,
-            requirements=[f'scikit-learn="{version}"'],
-            dst=dir_path,
-            task="tabular-classification",
-            data=iris.data,
-        )
-    model_path.unlink(missing_ok=True)
-
-
-def test_push_deprecation():
-    with pytest.raises(Exception):
-        with pytest.warns(FutureWarning, match="Creating repos on hf.co is subject"):
-            push(repo_id="dummy", source=".")
-
-
-@pytest.fixture
-def repo_path_for_inference():
-    # Create a separate path for test_inference so that the test does not have
-    # any side-effect on existing tests
-    with tempfile.TemporaryDirectory(prefix="skops-test-sample-repo") as repo_path:
-        yield Path(repo_path)
-
-
-@pytest.mark.network
-@pytest.mark.inference
-@pytest.mark.skipif(
-    IS_SKLEARN_DEV_BUILD, reason="Inference tests cannot run with sklearn dev build"
-)
-@flaky(max_runs=3)
-@pytest.mark.parametrize(
-    "model_func, data, task",
-    [
-        (get_classifier, iris, "tabular-classification"),
-        (get_regressor, diabetes, "tabular-regression"),
-    ],
-    ids=["classifier", "regressor"],
-)
-def test_inference(
-    config_json,
-    model_func,
-    data,
-    task,
-    repo_path_for_inference,
-    destination_path,
-):
-    # test inference backend for classifier and regressor models. This test can
-    # take a lot of time and be flaky.
-    config_path, file_format = config_json
-    if file_format != "pickle":
-        pytest.skip(
-            f"Inference only supports pickle at the moment. Given format: {file_format}"
-        )
-
-    client = HfApi()
-    repo_path = repo_path_for_inference
-    model_file = CONFIG[file_format]["sklearn"]["model"]["file"]
-    model = model_func()
-    model_path = repo_path / model_file
-
-    with open(model_path, "wb") as f:
-        pickle.dump(model, f)
-
-    version = metadata.version("scikit-learn")
-    init(
-        model=model_path,
-        requirements=[f'scikit-learn="{version}"'],
-        dst=destination_path,
-        task=task,
-        data=data.data,
-    )
-
-    # TODO: remove when card init at repo init is merged
-    model_card = card.Card(
-        model, metadata=card.metadata_from_config(Path(destination_path))
-    )
-    model_card.save(Path(destination_path) / "README.md")
-
-    user = client.whoami(token=HF_HUB_TOKEN)["name"]
-    repo_id = f"{user}/test-{uuid4()}"
-
-    with pytest.warns(FutureWarning, match="Creating repos on hf.co is subject"):
-        push(
-            repo_id=repo_id,
-            source=destination_path,
-            token=HF_HUB_TOKEN,
-            commit_message="test message",
-            create_remote=True,
-            # api-inference doesn't support private repos for community projects.
-            private=False,
-        )
-
-    X_test = data.data.head(5)
-    y_pred = model.predict(X_test)
-    with pytest.warns(FutureWarning):
-        output = get_model_output(repo_id, data=X_test, token=HF_HUB_TOKEN)
-
-    # cleanup
-    client.delete_repo(repo_id=repo_id, token=HF_HUB_TOKEN)
-    model_path.unlink(missing_ok=True)
-
-    assert np.allclose(output, y_pred)
-
-
-def test_get_model_output_deprecated():
-    with pytest.raises(Exception):
-        with pytest.warns(FutureWarning, match="This feature is no longer free"):
-            get_model_output("dummy", data=iris.data)
-
-
-def test_get_config(repo_path, config_json):
-    config_path, file_format = config_json
-    config = get_config(repo_path)
-
-    assert config == CONFIG[file_format]
-    assert get_requirements(repo_path) == ['scikit-learn="1.1.1"']
-
-
-def test_update_env(repo_path, config_json):
-    # sanity check
-    assert get_requirements(repo_path) == ['scikit-learn="1.1.1"']
-    update_env(path=repo_path, requirements=['scikit-learn="1.1.2"'])
-    assert get_requirements(repo_path) == ['scikit-learn="1.1.2"']
-
-
-def test_get_example_input_from_tabular_data():
-    with pytest.raises(
-        ValueError,
-        match=(
-            "The data is not a pandas.DataFrame, a 2D numpy.ndarray or a "
-            "list/tuple that can be converted to a 2D numpy.ndarray."
-        ),
-    ):
-        _get_example_input_from_tabular_data("random")
-    with pytest.raises(ValueError):
-        _get_example_input_from_tabular_data(["a", "b", "c"])
-
-    examples = _get_example_input_from_tabular_data(np.ones((5, 10)))
-    # the result is a dictionary of column name: list of values
-    assert len(examples) == 10
-    assert len(examples["x0"]) == 3
-
-    examples = _get_example_input_from_tabular_data(np.ones((5, 10)).tolist())
-    # the result is a dictionary of column name: list of values
-    assert len(examples) == 10
-    assert len(examples["x0"]) == 3
-
-    examples = _get_example_input_from_tabular_data(
-        pd.DataFrame(np.ones((5, 10)), columns=[f"column{x}" for x in range(10)])
-    )
-    # the result is a dictionary of column name: list of values
-    assert len(examples) == 10
-    assert len(examples["column0"]) == 3
-
-
-@pytest.mark.parametrize(
-    "data, expected_length",
-    [
-        (["a", "b", "c", "d"], 3),
-        (np.array(["a", "b", "c", "d"]), 3),
-        (set(["a", "b", "c", "d"]), 3),
-        (tuple(["a", "b", "c", "d"]), 3),
-        (["a"], 1),
-        ([], 0),
-    ],
-)
-def test_get_example_input_from_text_data(data, expected_length):
-    example_input = _get_example_input_from_text_data(data)
-    assert len(example_input["data"]) == expected_length
-
-
-@pytest.mark.parametrize("data", ["random", [1, 2, 3], 420])
-def test_get_example_input_from_text_data_invalid_text_data(data):
-    with pytest.raises(ValueError, match="The data needs to be a sequence of strings."):
-        _get_example_input_from_text_data(data)
-
-
-def test_get_column_names():
-    with pytest.raises(
-        ValueError,
-        match=(
-            "The data is not a pandas.DataFrame, a 2D numpy.ndarray or a "
-            "list/tuple that can be converted to a 2D numpy.ndarray."
-        ),
-    ):
-        _get_column_names("random")
-    with pytest.raises(ValueError):
-        _get_column_names(["a", "b", "c"])
-
-    X_array = np.ones((5, 10), dtype=np.float32)
-    expected_columns = [f"x{x}" for x in range(10)]
-    assert _get_column_names(X_array) == expected_columns
-
-    expected_columns = [f"column{x}" for x in range(10)]
-    X_df = pd.DataFrame(X_array, columns=expected_columns)
-    assert _get_column_names(X_df) == expected_columns
-
-
-def test_get_example_input_from_tabular_data_pandas_not_installed(pandas_not_installed):
-    # use pandas_not_installed fixture from conftest.py to pretend that pandas
-    # is not installed and check that the function does not raise when pandas
-    # import fails
-    _get_example_input_from_tabular_data(np.ones((5, 10)))
-
-
-def test_get_column_names_pandas_not_installed(pandas_not_installed):
-    # use pandas_not_installed fixture from conftest.py to pretend that pandas
-    # is not installed and check that the function does not raise when pandas
-    # import fails
-    _get_column_names(np.ones((5, 10)))
-
-
-class TestAddFiles:
-    @pytest.fixture
-    def init_path(self, classifier, config_json):
-        # create temporary directory
-        dir_path = tempfile.mkdtemp()
-        shutil.rmtree(dir_path)
-
-        version = metadata.version("scikit-learn")
-        init(
-            model=classifier,
-            requirements=[f'scikit-learn="{version}"'],
-            dst=dir_path,
-            task="tabular-classification",
-            data=iris.data,
-        )
-        yield dir_path
-
-    @pytest.fixture
-    def some_file_0(self, temp_path):
-        filename = Path(temp_path) / "file0.txt"
-        with open(filename, "w") as f:
-            f.write("")
-        yield filename
-
-    @pytest.fixture
-    def some_file_1(self, temp_path):
-        filename = Path(temp_path) / "file1.txt"
-        with open(filename, "w") as f:
-            f.write("")
-        yield filename
-
-    def test_adding_one_file_path(self, init_path, some_file_0):
-        add_files(some_file_0, dst=init_path)
-        assert os.path.exists(Path(init_path) / some_file_0.name)
-
-    def test_adding_two_file_paths(self, init_path, some_file_0, some_file_1):
-        add_files(some_file_0, some_file_1, dst=init_path)
-        assert os.path.exists(Path(init_path) / some_file_0.name)
-        assert os.path.exists(Path(init_path) / some_file_1.name)
-
-    def test_adding_one_file_str(self, init_path, some_file_0):
-        add_files(str(some_file_0), dst=init_path)
-        assert os.path.exists(Path(init_path) / some_file_0.name)
-
-    def test_adding_two_files_str(self, init_path, some_file_0, some_file_1):
-        add_files(str(some_file_0), str(some_file_1), dst=init_path)
-        assert os.path.exists(Path(init_path) / some_file_0.name)
-        assert os.path.exists(Path(init_path) / some_file_1.name)
-
-    def test_adding_str_and_path(self, init_path, some_file_0, some_file_1):
-        add_files(str(some_file_0), some_file_1, dst=init_path)
-        assert os.path.exists(Path(init_path) / some_file_0.name)
-        assert os.path.exists(Path(init_path) / some_file_1.name)
-
-    def test_dst_does_not_exist_raises(self, some_file_0):
-        dst = tempfile.mkdtemp()
-        shutil.rmtree(dst)
-        msg = (
-            rf"Could not find \'{re.escape(dst)}\', did you run "
-            r"\'skops.hub_utils.init\' first\?"
-        )
-        with pytest.raises(FileNotFoundError, match=msg):
-            add_files(some_file_0, dst=dst)
-
-    def test_file_does_not_exist_raises(self, init_path, some_file_0):
-        non_existing_file = "foobar.baz"
-        msg = r"File \'foobar.baz\' could not be found."
-        with pytest.raises(FileNotFoundError, match=msg):
-            add_files(some_file_0, non_existing_file, dst=init_path)
-
-    def test_adding_existing_file_works_if_exist_ok(self, init_path, some_file_0):
-        add_files(some_file_0, dst=init_path)
-        assert os.path.exists(Path(init_path) / some_file_0.name)
-        add_files(some_file_0, dst=init_path, exist_ok=True)
-        assert os.path.exists(Path(init_path) / some_file_0.name)
-
-    def test_adding_existing_file_raises(self, init_path, some_file_0):
-        # first time around no warning
-        with warnings.catch_warnings():
-            warnings.simplefilter("error")
-            add_files(some_file_0, dst=init_path, exist_ok=False)
-
-        msg = (
-            f"File '{re.escape(some_file_0.name)}' already found "
-            f"at '{re.escape(init_path)}'."
-        )
-        with pytest.raises(FileExistsError, match=msg):
-            add_files(some_file_0, dst=init_path)