diff --git a/Makefile b/Makefile
index ef0f2d3..be38ccb 100644
--- a/Makefile
+++ b/Makefile
@@ -9,7 +9,7 @@ install:
 	uv run pre-commit install
 
 install-no-pre-commit:
-	uv pip install ".[dev,distill]"
+	uv pip install ".[dev,distill,inference,train]"
 	uv pip install "torch<2.5.0"
 
 install-base:
diff --git a/model2vec/inference/README.md b/model2vec/inference/README.md
new file mode 100644
index 0000000..6f859e5
--- /dev/null
+++ b/model2vec/inference/README.md
@@ -0,0 +1,18 @@
+# Inference
+
+This subpackage mainly contains helper functions for inference with trained models that have been exported to `scikit-learn` compatible pipelines.
+
+If you're looking for information on how to train a model, see [here](../train/README.md).
+
+# Usage
+
+Let's assume you're using our `potion-edu classifier`.
+
+```python
+from model2vec.inference import StaticModelPipeline
+
+s = StaticModelPipeline.from_pretrained("minishlab/potion-8m-edu-classifier")
+label = s.predict("Attitudes towards cattle in the Alps: a study in letting go.")
+```
+
+This should just work.
diff --git a/model2vec/inference/__init__.py b/model2vec/inference/__init__.py
new file mode 100644
index 0000000..de94e18
--- /dev/null
+++ b/model2vec/inference/__init__.py
@@ -0,0 +1,10 @@
+from model2vec.utils import get_package_extras, importable
+
+_REQUIRED_EXTRA = "inference"
+
+for extra_dependency in get_package_extras("model2vec", _REQUIRED_EXTRA):
+    importable(extra_dependency, _REQUIRED_EXTRA)
+
+from model2vec.inference.model import StaticModelPipeline
+
+__all__ = ["StaticModelPipeline"]
diff --git a/model2vec/inference/model.py b/model2vec/inference/model.py
new file mode 100644
index 0000000..62bb642
--- /dev/null
+++ b/model2vec/inference/model.py
@@ -0,0 +1,143 @@
+from __future__ import annotations
+
+import re
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+import huggingface_hub
+import numpy as np
+import skops.io
+from sklearn.neural_network import MLPClassifier
+from sklearn.pipeline import Pipeline
+
+from model2vec.model import PathLike, StaticModel
+
+_DEFAULT_TRUST_PATTERN = re.compile(r"sklearn\..+")
+_DEFAULT_MODEL_FILENAME = "pipeline.skops"
+
+
+class StaticModelPipeline:
+    def __init__(self, model: StaticModel, head: Pipeline) -> None:
+        """Create a pipeline with a StaticModel encoder."""
+        self.model = model
+        self.head = head
+
+    @classmethod
+    def from_pretrained(
+        cls: type[StaticModelPipeline], path: PathLike, token: str | None = None
+    ) -> StaticModelPipeline:
+        """
+        Load a StaticModel from a local path or huggingface hub path.
+
+        NOTE: if you load a private model from the huggingface hub, you need to pass a token.
+
+        :param path: The path to the folder containing the pipeline, or a repository on the Hugging Face Hub
+        :param token: The token to use to download the pipeline from the hub.
+        :return: The loaded pipeline.
+        """
+        model, head = _load_pipeline(path, token)
+        model.embedding = np.nan_to_num(model.embedding)
+
+        return cls(model, head)
+
+    def save_pretrained(self, path: str) -> None:
+        """Save the model to a folder."""
+        save_pipeline(self, path)
+
+    def push_to_hub(self, repo_id: str, token: str | None = None, private: bool = False) -> None:
+        """
+        Save a model to a folder, and then push that folder to the hf hub.
+
+        :param repo_id: The id of the repository to push to.
+        :param token: The token to use to push to the hub.
+        :param private: Whether the repository should be private.
+        """
+        from model2vec.hf_utils import push_folder_to_hub
+
+        with TemporaryDirectory() as temp_dir:
+            save_pipeline(self, temp_dir)
+            self.model.save_pretrained(temp_dir)
+            push_folder_to_hub(Path(temp_dir), repo_id, private, token)
+
+    def _predict_and_coerce_to_2d(self, X: list[str] | str) -> np.ndarray:
+        """Predict the labels of the input and coerce the output to a matrix."""
+        encoded = self.model.encode(X)
+        if np.ndim(encoded) == 1:
+            encoded = encoded[None, :]
+
+        return encoded
+
+    def predict(self, X: list[str] | str) -> np.ndarray:
+        """Predict the labels of the input."""
+        encoded = self._predict_and_coerce_to_2d(X)
+
+        return self.head.predict(encoded)
+
+    def predict_proba(self, X: list[str] | str) -> np.ndarray:
+        """Predict the probabilities of the labels of the input."""
+        encoded = self._predict_and_coerce_to_2d(X)
+
+        return self.head.predict_proba(encoded)
+
+
+def _load_pipeline(
+    folder_or_repo_path: PathLike, token: str | None = None, trust_remote_code: bool = False
+) -> tuple[StaticModel, Pipeline]:
+    """
+    Load a model and an sklearn pipeline.
+
+    This assumes the following files are present in the repo:
+    - `pipeline.skops`: The head of the pipeline.
+    - `config.json`: The configuration of the model.
+    - `model.safetensors`: The weights of the model.
+    - `tokenizer.json`: The tokenizer of the model.
+
+    :param folder_or_repo_path: The path to the folder containing the pipeline.
+    :param token: The token to use to download the pipeline from the hub. If this is None, you will only
+        be able to load the pipeline from a local folder, public repository, or a repository that you have access to
+        because you are logged in.
+    :param trust_remote_code: Whether to trust the remote code. If this is False,
+        we will only load components coming from `sklearn`. If this is True, we will load all components.
+        If you set this to True, you are responsible for whatever happens.
+    :return: The encoder model and the loaded head
+    :raises FileNotFoundError: If the pipeline file does not exist in the folder.
+    :raises ValueError: If an untrusted type is found in the pipeline, and `trust_remote_code` is False.
+    """
+    folder_or_repo_path = Path(folder_or_repo_path)
+    model_filename = _DEFAULT_MODEL_FILENAME
+    if folder_or_repo_path.exists():
+        head_pipeline_path = folder_or_repo_path / model_filename
+        if not head_pipeline_path.exists():
+            raise FileNotFoundError(f"Pipeline file does not exist in {folder_or_repo_path}")
+    else:
+        head_pipeline_path = huggingface_hub.hf_hub_download(
+            folder_or_repo_path.as_posix(), model_filename, token=token
+        )
+
+    model = StaticModel.from_pretrained(folder_or_repo_path)
+
+    unknown_types = skops.io.get_untrusted_types(file=head_pipeline_path)
+    # If the user does not trust remote code, we should check that the unknown types are trusted.
+    # By default, we trust everything coming from scikit-learn.
+    if not trust_remote_code:
+        for t in unknown_types:
+            if not _DEFAULT_TRUST_PATTERN.match(t):
+                raise ValueError(f"Untrusted type {t}.")
+    head = skops.io.load(head_pipeline_path, trusted=unknown_types)
+
+    return model, head
+
+
+def save_pipeline(pipeline: StaticModelPipeline, folder_path: str | Path) -> None:
+    """
+    Save a pipeline to a folder.
+
+    :param pipeline: The pipeline to save.
+    :param folder_path: The path to the folder to save the pipeline to.
+    """
+    folder_path = Path(folder_path)
+    folder_path.mkdir(parents=True, exist_ok=True)
+    model_filename = _DEFAULT_MODEL_FILENAME
+    head_pipeline_path = folder_path / model_filename
+    skops.io.dump(pipeline.head, head_pipeline_path)
+    pipeline.model.save_pretrained(folder_path)
diff --git a/model2vec/model.py b/model2vec/model.py
index 39ca64a..62255b8 100644
--- a/model2vec/model.py
+++ b/model2vec/model.py
@@ -87,7 +87,7 @@ def normalize(self) -> bool:
     @normalize.setter
     def normalize(self, value: bool) -> None:
         """Update the config if the value of normalize changes."""
-        config_normalize = self.config.get("normalize", False)
+        config_normalize = self.config.get("normalize")
         self._normalize = value
         if config_normalize is not None and value != config_normalize:
             logger.warning(
diff --git a/model2vec/train/README.md b/model2vec/train/README.md
new file mode 100644
index 0000000..e4c9d1a
--- /dev/null
+++ b/model2vec/train/README.md
@@ -0,0 +1,137 @@
+# Training
+
+Aside from [distillation](../../README.md#distillation), `model2vec` also supports training simple classifiers on top of static models, using [pytorch](https://pytorch.org/), [lightning](https://lightning.ai/) and [scikit-learn](https://scikit-learn.org/stable/index.html).
+
+# Installation
+
+To train, make sure you install the training extra:
+
+```
+pip install model2vec[training]
+```
+
+# Quickstart
+
+To train a model, simply initialize it using a `StaticModel`, or from a pre-trained model, as follows:
+
+```python
+from model2vec.distill import distill
+from model2vec.train import StaticModelForClassification
+
+# From a distilled model
+distilled_model = distill("baai/bge-base-en-v1.5")
+classifier = StaticModelForClassification.from_static_model(distilled_model)
+
+# From a pre-trained model: potion is the default
+classifier = StaticModelForClassification.from_pretrained(model_name="minishlab/potion-base-8m")
+```
+
+This creates a very simple classifier: a StaticModel with a single 512-unit hidden layer on top. You can adjust the number of hidden layers and the number units through some parameters on both functions. Note that the default for `from_pretrained` is [potion-base-8m](https://huggingface.co/minishlab/potion-base-8M), our best model to date. This is our recommended path if you're working with general English data.
+
+Now that you have created the classifier, let's just train a model. The example below assumes you have the [`datasets`](https://github.com/huggingface/datasets) library installed.
+
+```python
+import numpy as np
+from datasets import load_dataset
+
+# Load the subj dataset
+ds = load_dataset("setfit/subj")
+train = ds["train"]
+test = ds["test"]
+
+s = perf_counter()
+classifier = classifier.fit(train["text"], train["label"])
+
+predicted = classifier.predict(test["text"])
+print(f"Training took {int(perf_counter() - s)} seconds.")
+# Training took 81 seconds
+accuracy = np.mean([x == y for x, y in zip(predicted, test["label"])]) * 100
+print(f"Achieved {accuracy} test accuracy")
+# Achieved 91.0 test accuracy
+```
+
+As you can see, we got a pretty nice 91% accuracy, with only 81 seconds of training.
+
+The training loop is handled by [`lightning`](https://pypi.org/project/lightning/). By default the training loop splits the data into a train and validation split, with 90% of the data being used for training and 10% for validation. By default, it runs with early stopping on the validation set accuracy, with a patience of 5.
+
+Note that this model is as fast as you're used to from us:
+
+```python
+from time import perf_counter
+
+s = perf_counter()
+classifier.predict(test["text"])
+print(f"Took {int((perf_counter() - s) * 1000)} milliseconds for {len(test)} instances on CPU.")
+# Took 67 milliseconds for 2000 instances on CPU.
+```
+
+# Persistence
+
+You can turn a classifier into a scikit-learn compatible pipeline, as follows:
+
+```python
+pipeline = classifier.to_pipeline()
+```
+
+This pipeline object can be persisted using standard pickle-based methods, such as [joblib](https://joblib.readthedocs.io/en/stable/). This makes it easy to use your model in inferene pipelines (no installing torch!), although `joblib` and `pickle` should not be used to share models outside of your organization.
+
+If you want to persist your pipeline to the Hugging Face hub, you can use our built-in functions:
+
+```python
+pipeline.save_pretrained(path)
+pipeline.push_to_hub("my_cool/project")
+```
+
+Later, you can load these as follows:
+
+```python
+from model2vec.inference import StaticModelPipeline
+
+pipeline = StaticModelPipeline.from_pretrained("my_cool/project")
+```
+
+Loading pipelines in this way is _extremely_ fast. It takes only 30ms to load a pipeline from disk.
+
+# Results
+
+The main results are detailed in our training blogpost, but we'll do a comparison with vanilla model2vec here. In a vanilla model2vec classifier, you just put a scikit-learn `LogisticRegressionCV` on top of the model encoder. In contrast, training a `StaticModelForClassification` fine-tunes the full model, including the `StaticModel` weights.
+
+We use 14 classification datasets, using 1000 examples from the train set, and the full test set. No parameters were tuned on any validation set. All datasets were taken from the [Setfit organization on Hugging Face](https://huggingface.co/datasets/SetFit).
+
+| dataset_name               |   model2vec logreg |   setfit |  model2vec full finetune |
+|:---------------------------|---------------------------------------------:|-------------------------------------------------:|--------------------------------------:|
+| 20_newgroups               |                                     0.545312 |                                         0.595426 |                              0.555459 |
+| ade                        |                                     0.715725 |                                         0.788789 |                              0.740307 |
+| ag_news                    |                                     0.860154 |                                         0.880142 |                              0.858304 |
+| amazon_counterfactual      |                                     0.637754 |                                         0.873249 |                              0.744288 |
+| bbc                        |                                     0.955719 |                                         0.965823 |                              0.965018 |
+| emotion                    |                                     0.516267 |                                         0.598852 |                              0.586328 |
+| enron_spam                 |                                     0.951975 |                                         0.974498 |                              0.964994 |
+| hatespeech_offensive       |                                     0.543758 |                                         0.659873 |                              0.592587 |
+| imdb                       |                                     0.839002 |                                         0.860037 |                              0.846198 |
+| massive_scenario           |                                     0.797779 |                                         0.814601 |                              0.822825 |
+| senteval_cr                |                                     0.743436 |                                         0.8526   |                              0.745863 |
+| sst5                       |                                     0.290249 |                                         0.393179 |                              0.363071 |
+| student                    |                                     0.806069 |                                         0.889399 |                              0.837581 |
+| subj                       |                                     0.878394 |                                         0.937955 |                              0.88941  |
+| tweet_sentiment_extraction |                                     0.638664 |                                         0.755296 |                              0.632009 |
+
+|                |   logreg   |  full finetune |
+|:---------------------------|-----------:|---------------:|
+| average                    |   0.714    |    0.742       |
+
+As you can see, full fine-tuning brings modest performance improvements in some cases, but very large ones in other cases, leading to a pretty large increase in average score. Our advice is to test both if you can use `potion-base-8m`, and to use full fine-tuning if you are starting from another base model.
+
+# Bring your own architecture
+
+Our training architecture is set up to be extensible, with each task having a specific class. Right now, we only offer `StaticModelForClassification`, but in the future we'll also offer regression, etc.
+
+The core functionality of the `StaticModelForClassification` is contained in a couple of functions:
+
+* `construct_head`: This function constructs the classifier on top of the staticmodel. For example, if you want to create a model that has LayerNorm, just subclass, and replace this function. This should be the main function to update if you want to change model behavior.
+* `train_test_split`: governs the train test split before classification.
+* `prepare_dataset`: Selects the `torch.Dataset` that will be used in the `Dataloader` during training.
+* `_encode`: The encoding function used in the model.
+* `fit`: contains all the lightning-related fitting logic.
+
+The training of the model is done in a `lighting.LightningModule`, which can be modified but is very basic.
diff --git a/model2vec/train/__init__.py b/model2vec/train/__init__.py
new file mode 100644
index 0000000..c70f803
--- /dev/null
+++ b/model2vec/train/__init__.py
@@ -0,0 +1,10 @@
+from model2vec.utils import get_package_extras, importable
+
+_REQUIRED_EXTRA = "train"
+
+for extra_dependency in get_package_extras("model2vec", _REQUIRED_EXTRA):
+    importable(extra_dependency, _REQUIRED_EXTRA)
+
+from model2vec.train.classifier import StaticModelForClassification
+
+__all__ = ["StaticModelForClassification"]
diff --git a/model2vec/train/base.py b/model2vec/train/base.py
new file mode 100644
index 0000000..65f4b45
--- /dev/null
+++ b/model2vec/train/base.py
@@ -0,0 +1,161 @@
+from __future__ import annotations
+
+from typing import Any, TypeVar
+
+import numpy as np
+import torch
+from tokenizers import Encoding, Tokenizer
+from torch import nn
+from torch.nn.utils.rnn import pad_sequence
+from torch.utils.data import DataLoader, Dataset
+
+from model2vec import StaticModel
+
+
+class FinetunableStaticModel(nn.Module):
+    def __init__(self, *, vectors: torch.Tensor, tokenizer: Tokenizer, out_dim: int = 2, pad_id: int = 0) -> None:
+        """
+        Initialize a trainable StaticModel from a StaticModel.
+
+        :param vectors: The embeddings of the staticmodel.
+        :param tokenizer: The tokenizer.
+        :param out_dim: The output dimension of the head.
+        :param pad_id: The padding id. This is set to 0 in almost all model2vec models
+        """
+        super().__init__()
+        self.pad_id = pad_id
+        self.out_dim = out_dim
+        self.embed_dim = vectors.shape[1]
+        self.vectors = vectors
+
+        self.embeddings = nn.Embedding.from_pretrained(vectors.clone().float(), freeze=False, padding_idx=pad_id)
+        self.head = self.construct_head()
+        self.w = self.construct_weights()
+        self.tokenizer = tokenizer
+
+    def construct_weights(self) -> nn.Parameter:
+        """Construct the weights for the model."""
+        weights = torch.zeros(len(self.vectors))
+        weights[self.pad_id] = -10_000
+        return nn.Parameter(weights)
+
+    def construct_head(self) -> nn.Sequential:
+        """Method should be overridden for various other classes."""
+        return nn.Sequential(nn.Linear(self.embed_dim, self.out_dim))
+
+    @classmethod
+    def from_pretrained(
+        cls: type[ModelType], out_dim: int = 2, model_name: str = "minishlab/potion-base-8m", **kwargs: Any
+    ) -> ModelType:
+        """Load the model from a pretrained model2vec model."""
+        model = StaticModel.from_pretrained(model_name)
+        return cls.from_static_model(model, out_dim, **kwargs)
+
+    @classmethod
+    def from_static_model(cls: type[ModelType], model: StaticModel, out_dim: int = 2, **kwargs: Any) -> ModelType:
+        """Load the model from a static model."""
+        model.embedding = np.nan_to_num(model.embedding)
+        embeddings_converted = torch.from_numpy(model.embedding)
+        return cls(
+            vectors=embeddings_converted,
+            pad_id=model.tokenizer.token_to_id("[PAD]"),
+            out_dim=out_dim,
+            tokenizer=model.tokenizer,
+            **kwargs,
+        )
+
+    def _encode(self, input_ids: torch.Tensor) -> torch.Tensor:
+        """
+        A forward pass and mean pooling.
+
+        This function is analogous to `StaticModel.encode`, but reimplemented to allow gradients
+        to pass through.
+
+        :param input_ids: A 2D tensor of input ids. All input ids are have to be within bounds.
+        :return: The mean over the input ids, weighted by token weights.
+        """
+        w = self.w[input_ids]
+        w = torch.sigmoid(w)
+        zeros = (input_ids != self.pad_id).float()
+        w = w * zeros
+        # Add a small epsilon to avoid division by zero
+        length = zeros.sum(1) + 1e-16
+        embedded = self.embeddings(input_ids)
+        # Simulate actual mean
+        # Zero out the padding
+        embedded = torch.bmm(w[:, None, :], embedded).squeeze(1)
+        # embedded = embedded.sum(1)
+        embedded = embedded / length[:, None]
+
+        return nn.functional.normalize(embedded)
+
+    def forward(self, input_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        """Forward pass through the mean, and a classifier layer after."""
+        encoded = self._encode(input_ids)
+        return self.head(encoded), encoded
+
+    def tokenize(self, texts: list[str], max_length: int | None = 512) -> torch.Tensor:
+        """
+        Tokenize a bunch of strings into a single padded 2D tensor.
+
+        Note that this is not used during training.
+
+        :param texts: The texts to tokenize.
+        :param max_length: If this is None, the sequence lengths are truncated to 512.
+        :return: A 2D padded tensor
+        """
+        encoded: list[Encoding] = self.tokenizer.encode_batch_fast(texts, add_special_tokens=False)
+        encoded_ids: list[torch.Tensor] = [torch.Tensor(encoding.ids[:max_length]).long() for encoding in encoded]
+        return pad_sequence(encoded_ids, batch_first=True)
+
+    @property
+    def device(self) -> str:
+        """Get the device of the model."""
+        return self.embeddings.weight.device
+
+    def to_static_model(self) -> StaticModel:
+        """Convert the model to a static model."""
+        emb = self.embeddings.weight.detach().cpu().numpy()
+        w = torch.sigmoid(self.w).detach().cpu().numpy()
+
+        return StaticModel(emb * w[:, None], self.tokenizer, normalize=True)
+
+
+class TextDataset(Dataset):
+    def __init__(self, tokenized_texts: list[list[int]], targets: torch.Tensor) -> None:
+        """
+        A dataset of texts.
+
+        :param tokenized_texts: The tokenized texts. Each text is a list of token ids.
+        :param targets: The targets.
+        :raises ValueError: If the number of labels does not match the number of texts.
+        """
+        if len(targets) != len(tokenized_texts):
+            raise ValueError("Number of labels does not match number of texts.")
+        self.tokenized_texts = tokenized_texts
+        self.targets = targets
+
+    def __len__(self) -> int:
+        """Return the length of the dataset."""
+        return len(self.tokenized_texts)
+
+    def __getitem__(self, index: int) -> tuple[list[int], torch.Tensor]:
+        """Gets an item."""
+        return self.tokenized_texts[index], self.targets[index]
+
+    @staticmethod
+    def collate_fn(batch: list[tuple[list[list[int]], int]]) -> tuple[torch.Tensor, torch.Tensor]:
+        """Collate function."""
+        texts, targets = zip(*batch)
+
+        tensors = [torch.LongTensor(x) for x in texts]
+        padded = pad_sequence(tensors, batch_first=True, padding_value=0)
+
+        return padded, torch.stack(targets)
+
+    def to_dataloader(self, shuffle: bool, batch_size: int = 32) -> DataLoader:
+        """Convert the dataset to a DataLoader."""
+        return DataLoader(self, collate_fn=self.collate_fn, shuffle=shuffle, batch_size=batch_size)
+
+
+ModelType = TypeVar("ModelType", bound=FinetunableStaticModel)
diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py
new file mode 100644
index 0000000..7155164
--- /dev/null
+++ b/model2vec/train/classifier.py
@@ -0,0 +1,295 @@
+from __future__ import annotations
+
+import logging
+from collections import Counter
+from tempfile import TemporaryDirectory
+
+import lightning as pl
+import numpy as np
+import torch
+from lightning.pytorch.callbacks import Callback, EarlyStopping
+from lightning.pytorch.utilities.types import OptimizerLRScheduler
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+from sklearn.pipeline import make_pipeline
+from tokenizers import Tokenizer
+from torch import nn
+from tqdm import trange
+
+from model2vec.inference import StaticModelPipeline
+from model2vec.train.base import FinetunableStaticModel, TextDataset
+
+logger = logging.getLogger(__name__)
+
+_RANDOM_SEED = 42
+
+
+class StaticModelForClassification(FinetunableStaticModel):
+    def __init__(
+        self,
+        *,
+        vectors: torch.Tensor,
+        tokenizer: Tokenizer,
+        n_layers: int = 1,
+        hidden_dim: int = 512,
+        out_dim: int = 2,
+        pad_id: int = 0,
+    ) -> None:
+        """Initialize a standard classifier model."""
+        self.n_layers = n_layers
+        self.hidden_dim = hidden_dim
+        # Alias: Follows scikit-learn. Set to dummy classes
+        self.classes_: list[str] = [str(x) for x in range(out_dim)]
+        super().__init__(vectors=vectors, out_dim=out_dim, pad_id=pad_id, tokenizer=tokenizer)
+
+    @property
+    def classes(self) -> list[str]:
+        """Return all clasess in the correct order."""
+        return self.classes_
+
+    def construct_head(self) -> nn.Sequential:
+        """Constructs a simple classifier head."""
+        if self.n_layers == 0:
+            return nn.Sequential(nn.Linear(self.embed_dim, self.out_dim))
+        modules = [
+            nn.Linear(self.embed_dim, self.hidden_dim),
+            nn.ReLU(),
+        ]
+        for _ in range(self.n_layers - 1):
+            modules.extend([nn.Linear(self.hidden_dim, self.hidden_dim), nn.ReLU()])
+        modules.extend([nn.Linear(self.hidden_dim, self.out_dim)])
+
+        for module in modules:
+            if isinstance(module, nn.Linear):
+                nn.init.kaiming_uniform_(module.weight)
+                nn.init.zeros_(module.bias)
+
+        return nn.Sequential(*modules)
+
+    def predict(self, X: list[str], show_progress_bar: bool = False, batch_size: int = 1024) -> np.ndarray:
+        """Predict a class for a set of texts."""
+        pred: list[str] = []
+        for batch in trange(0, len(X), batch_size, disable=not show_progress_bar):
+            logits = self._predict_single_batch(X[batch : batch + batch_size])
+            pred.extend([self.classes[idx] for idx in logits.argmax(1)])
+
+        return np.asarray(pred)
+
+    @torch.no_grad()
+    def _predict_single_batch(self, X: list[str]) -> torch.Tensor:
+        input_ids = self.tokenize(X)
+        vectors, _ = self.forward(input_ids)
+        return vectors
+
+    def predict_proba(self, X: list[str], show_progress_bar: bool = False, batch_size: int = 1024) -> np.ndarray:
+        """Predict the probability of each class."""
+        pred: list[np.ndarray] = []
+        for batch in trange(0, len(X), batch_size, disable=not show_progress_bar):
+            logits = self._predict_single_batch(X[batch : batch + batch_size])
+            pred.append(torch.softmax(logits, dim=1).numpy())
+
+        return np.concatenate(pred)
+
+    def fit(
+        self,
+        X: list[str],
+        y: list[str],
+        learning_rate: float = 1e-3,
+        batch_size: int | None = None,
+        early_stopping_patience: int | None = 5,
+        test_size: float = 0.1,
+        device: str = "auto",
+    ) -> StaticModelForClassification:
+        """
+        Fit a model.
+
+        This function creates a Lightning Trainer object and fits the model to the data.
+        We use early stopping. After training, the weigths of the best model are loaded back into the model.
+
+        This function seeds everything with a seed of 42, so the results are reproducible.
+        It also splits the data into a train and validation set, again with a random seed.
+
+        :param X: The texts to train on.
+        :param y: The labels to train on.
+        :param learning_rate: The learning rate.
+        :param batch_size: The batch size.
+            If this is None, a good batch size is chosen automatically.
+        :param early_stopping_patience: The patience for early stopping.
+            If this is None, early stopping is disabled.
+        :param test_size: The test size for the train-test split.
+        :param device: The device to train on. If this is "auto", the device is chosen automatically.
+        :return: The fitted model.
+        """
+        pl.seed_everything(_RANDOM_SEED)
+        logger.info("Re-initializing model.")
+        self._initialize(y)
+
+        train_texts, validation_texts, train_labels, validation_labels = self._train_test_split(
+            X, y, test_size=test_size
+        )
+
+        if batch_size is None:
+            batch_size = max(min(32, len(train_texts) // 10), 512)
+            logger.info("Batch size automatically set to %d.", batch_size)
+
+        logger.info("Preparing train dataset.")
+        train_dataset = self._prepare_dataset(train_texts, train_labels)
+        logger.info("Preparing validation dataset.")
+        val_dataset = self._prepare_dataset(validation_texts, validation_labels)
+
+        c = _ClassifierLightningModule(self, learning_rate=learning_rate)
+
+        n_train_batches = len(train_dataset) // batch_size
+        callbacks: list[Callback] = []
+        if early_stopping_patience is not None:
+            callback = EarlyStopping(monitor="val_accuracy", mode="max", patience=early_stopping_patience)
+            callbacks.append(callback)
+
+        # If the dataset is small, we check the validation set every epoch.
+        # If the dataset is large, we check the validation set every 250 batches.
+        if n_train_batches < 250:
+            val_check_interval = None
+            check_val_every_epoch = 1
+        else:
+            val_check_interval = max(250, 2 * len(val_dataset) // batch_size)
+            check_val_every_epoch = None
+
+        with TemporaryDirectory() as tempdir:
+            trainer = pl.Trainer(
+                max_epochs=500,
+                callbacks=callbacks,
+                val_check_interval=val_check_interval,
+                check_val_every_n_epoch=check_val_every_epoch,
+                accelerator=device,
+                default_root_dir=tempdir,
+            )
+
+            trainer.fit(
+                c,
+                train_dataloaders=train_dataset.to_dataloader(shuffle=True, batch_size=batch_size),
+                val_dataloaders=val_dataset.to_dataloader(shuffle=False, batch_size=batch_size),
+            )
+            best_model_path = trainer.checkpoint_callback.best_model_path  # type: ignore
+            best_model_weights = torch.load(best_model_path, weights_only=True)
+
+        state_dict = {}
+        for weight_name, weight in best_model_weights["state_dict"].items():
+            state_dict[weight_name.removeprefix("model.")] = weight
+
+        self.load_state_dict(state_dict)
+        self.eval()
+
+        return self
+
+    def _initialize(self, y: list[str]) -> None:
+        """Sets the out dimensionality, the classes and initializes the head."""
+        classes = sorted(set(y))
+        self.classes_ = classes
+
+        if len(self.classes) != self.out_dim:
+            self.out_dim = len(self.classes)
+
+        self.head = self.construct_head()
+        self.embeddings = nn.Embedding.from_pretrained(self.vectors.clone(), freeze=False, padding_idx=self.pad_id)
+        self.w = self.construct_weights()
+        self.train()
+
+    def _prepare_dataset(self, X: list[str], y: list[str], max_length: int = 512) -> TextDataset:
+        """Prepare a dataset."""
+        # This is a speed optimization.
+        # assumes a mean token length of 10, which is really high, so safe.
+        truncate_length = max_length * 10
+        X = [x[:truncate_length] for x in X]
+        tokenized: list[list[int]] = [
+            encoding.ids[:max_length] for encoding in self.tokenizer.encode_batch_fast(X, add_special_tokens=False)
+        ]
+        labels_tensor = torch.Tensor([self.classes.index(label) for label in y]).long()
+
+        return TextDataset(tokenized, labels_tensor)
+
+    @staticmethod
+    def _train_test_split(
+        X: list[str], y: list[str], test_size: float
+    ) -> tuple[list[str], list[str], list[str], list[str]]:
+        """Split the data."""
+        label_counts = Counter(y)
+        if min(label_counts.values()) < 2:
+            logger.info("Some classes have less than 2 samples. Stratification is disabled.")
+            return train_test_split(X, y, test_size=test_size, random_state=42, shuffle=True)
+        return train_test_split(X, y, test_size=test_size, random_state=42, shuffle=True, stratify=y)
+
+    def to_pipeline(self) -> StaticModelPipeline:
+        """Convert the model to an sklearn pipeline."""
+        static_model = self.to_static_model()
+
+        random_state = np.random.RandomState(_RANDOM_SEED)
+        n_items = len(self.classes)
+        X = random_state.randn(n_items, static_model.dim)
+        y = self.classes
+
+        converted = make_pipeline(MLPClassifier(hidden_layer_sizes=(self.hidden_dim,) * self.n_layers))
+        converted.fit(X, y)
+        mlp_head: MLPClassifier = converted[-1]
+
+        for index, layer in enumerate([module for module in self.head if isinstance(module, nn.Linear)]):
+            mlp_head.coefs_[index] = layer.weight.detach().cpu().numpy().T
+            mlp_head.intercepts_[index] = layer.bias.detach().cpu().numpy()
+        # Below is necessary to ensure that the converted model works correctly.
+        # In scikit-learn, a binary classifier only has a single vector of output coefficients
+        # and a single intercept. We use two output vectors.
+        # To convert correctly, we need to set the outputs correctly, and fix the activation function.
+        # Make sure n_outputs is set to > 1.
+        mlp_head.n_outputs_ = self.out_dim
+        # Set to softmax
+        mlp_head.out_activation_ = "softmax"
+
+        return StaticModelPipeline(static_model, converted)
+
+
+class _ClassifierLightningModule(pl.LightningModule):
+    def __init__(self, model: StaticModelForClassification, learning_rate: float) -> None:
+        """Initialize the lightningmodule."""
+        super().__init__()
+        self.model = model
+        self.learning_rate = learning_rate
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Simple forward pass."""
+        return self.model(x)
+
+    def training_step(self, batch: tuple[torch.Tensor, torch.Tensor], batch_idx: int) -> torch.Tensor:
+        """Simple training step using cross entropy loss."""
+        x, y = batch
+        head_out, _ = self.model(x)
+        loss = nn.functional.cross_entropy(head_out, y).mean()
+
+        self.log("train_loss", loss)
+        return loss
+
+    def validation_step(self, batch: tuple[torch.Tensor, torch.Tensor], batch_idx: int) -> torch.Tensor:
+        """Simple validation step using cross entropy loss and accuracy."""
+        x, y = batch
+        head_out, _ = self.model(x)
+        loss = nn.functional.cross_entropy(head_out, y).mean()
+        accuracy = (head_out.argmax(1) == y).float().mean()
+
+        self.log("val_loss", loss)
+        self.log("val_accuracy", accuracy, prog_bar=True)
+
+        return loss
+
+    def configure_optimizers(self) -> OptimizerLRScheduler:
+        """Simple Adam optimizer."""
+        optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-3)
+        scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
+            optimizer,
+            mode="min",
+            factor=0.5,
+            patience=3,
+            verbose=True,
+            min_lr=1e-6,
+            threshold=0.03,
+            threshold_mode="rel",
+        )
+
+        return {"optimizer": optimizer, "lr_scheduler": {"scheduler": scheduler, "monitor": "val_loss"}}
diff --git a/model2vec/utils.py b/model2vec/utils.py
index 235c3d9..f11f079 100644
--- a/model2vec/utils.py
+++ b/model2vec/utils.py
@@ -88,7 +88,7 @@ def importable(module: str, extra: str) -> None:
         import_module(module)
     except ImportError:
         raise ImportError(
-            f"`{module}`, is required. Please reinstall model2vec with the `distill` extra. `pip install model2vec[{extra}]`"
+            f"`{module}`, is required. Please reinstall model2vec with the `{extra}` extra. `pip install model2vec[{extra}]`"
         )
 
 
diff --git a/pyproject.toml b/pyproject.toml
index fbf1157..1f4efec 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -54,8 +54,12 @@ dev = [
     "pytest-cov",
     "ruff",
 ]
+
 distill = ["torch", "transformers", "scikit-learn"]
 onnx = ["onnx", "torch"]
+# train also installs inference
+train = ["torch", "lightning", "scikit-learn", "skops"]
+inference = ["scikit-learn", "skops"]
 
 [project.urls]
 "Homepage" = "https://github.com/MinishLab"
diff --git a/tests/conftest.py b/tests/conftest.py
index ced1abc..6220329 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -5,16 +5,22 @@
 import numpy as np
 import pytest
 import torch
+from sklearn.neural_network import MLPClassifier
+from sklearn.pipeline import make_pipeline
 from tokenizers import Tokenizer
 from tokenizers.models import WordLevel
 from tokenizers.pre_tokenizers import Whitespace
 from transformers import AutoModel, AutoTokenizer
 
+from model2vec.inference import StaticModelPipeline
+from model2vec.model import StaticModel
+from model2vec.train import StaticModelForClassification
 
-@pytest.fixture
+
+@pytest.fixture(scope="session")
 def mock_tokenizer() -> Tokenizer:
     """Create a mock tokenizer."""
-    vocab = ["word1", "word2", "word3", "[UNK]", "[PAD]"]
+    vocab = ["[PAD]", "word1", "word2", "word3", "[UNK]"]
     unk_token = "[UNK]"
 
     model = WordLevel(vocab={word: idx for idx, word in enumerate(vocab)}, unk_token=unk_token)
@@ -62,7 +68,7 @@ def __call__(self, *args: Any, **kwargs: Any) -> Any:
     return MockPreTrainedModel()
 
 
-@pytest.fixture
+@pytest.fixture(scope="session")
 def mock_vectors() -> np.ndarray:
     """Create mock vectors."""
     return np.array([[0.1, 0.2], [0.2, 0.3], [0.3, 0.4], [0.0, 0.0], [0.0, 0.0]])
@@ -72,3 +78,21 @@ def mock_vectors() -> np.ndarray:
 def mock_config() -> dict[str, str]:
     """Create a mock config."""
     return {"some_config": "value"}
+
+
+@pytest.fixture(scope="session")
+def mock_inference_pipeline(mock_trained_pipeline: StaticModelForClassification) -> StaticModelPipeline:
+    """Mock pipeline."""
+    return mock_trained_pipeline.to_pipeline()
+
+
+@pytest.fixture(scope="session")
+def mock_trained_pipeline() -> StaticModelForClassification:
+    """Mock staticmodelforclassification."""
+    tokenizer = AutoTokenizer.from_pretrained("tests/data/test_tokenizer").backend_tokenizer
+    torch.random.manual_seed(42)
+    vectors_torched = torch.randn(len(tokenizer.get_vocab()), 12)
+    s = StaticModelForClassification(vectors=vectors_torched, tokenizer=tokenizer, hidden_dim=12).to("cpu")
+    s.fit(["dog", "cat"], ["a", "b"], device="cpu")
+
+    return s
diff --git a/tests/test_inference.py b/tests/test_inference.py
new file mode 100644
index 0000000..9f4618d
--- /dev/null
+++ b/tests/test_inference.py
@@ -0,0 +1,50 @@
+import os
+import re
+from tempfile import TemporaryDirectory
+from unittest.mock import patch
+
+import pytest
+
+from model2vec.inference import StaticModelPipeline
+
+
+def test_init_predict(mock_inference_pipeline: StaticModelPipeline) -> None:
+    """Test successful initialization of StaticModelPipeline."""
+    assert mock_inference_pipeline.predict("dog").tolist() == ["b"]
+    assert mock_inference_pipeline.predict(["dog"]).tolist() == ["b"]
+
+
+def test_init_predict_proba(mock_inference_pipeline: StaticModelPipeline) -> None:
+    """Test successful initialization of StaticModelPipeline."""
+    assert mock_inference_pipeline.predict_proba("dog").argmax() == 1
+    assert mock_inference_pipeline.predict_proba(["dog"]).argmax(1).tolist() == [1]
+
+
+def test_roundtrip_save(mock_inference_pipeline: StaticModelPipeline) -> None:
+    """Test saving and loading the pipeline."""
+    with TemporaryDirectory() as temp_dir:
+        mock_inference_pipeline.save_pretrained(temp_dir)
+        loaded = StaticModelPipeline.from_pretrained(temp_dir)
+        assert loaded.predict("dog") == ["b"]
+        assert loaded.predict(["dog"]) == ["b"]
+        assert loaded.predict_proba("dog").argmax() == 1
+        assert loaded.predict_proba(["dog"]).argmax(1).tolist() == [1]
+
+
+@patch("model2vec.inference.model._DEFAULT_TRUST_PATTERN", re.compile("torch"))
+def test_roundtrip_save_mock_trust_pattern(mock_inference_pipeline: StaticModelPipeline) -> None:
+    """Test saving and loading the pipeline."""
+    with TemporaryDirectory() as temp_dir:
+        mock_inference_pipeline.save_pretrained(temp_dir)
+        with pytest.raises(ValueError):
+            StaticModelPipeline.from_pretrained(temp_dir)
+
+
+def test_roundtrip_save_file_gone(mock_inference_pipeline: StaticModelPipeline) -> None:
+    """Test saving and loading the pipeline."""
+    with TemporaryDirectory() as temp_dir:
+        mock_inference_pipeline.save_pretrained(temp_dir)
+        # Rename the file to abc.pipeline, so that it looks like it was downloaded from the hub
+        os.unlink(os.path.join(temp_dir, "pipeline.skops"))
+        with pytest.raises(FileNotFoundError):
+            StaticModelPipeline.from_pretrained(temp_dir)
diff --git a/tests/test_trainable.py b/tests/test_trainable.py
new file mode 100644
index 0000000..dc9bb81
--- /dev/null
+++ b/tests/test_trainable.py
@@ -0,0 +1,145 @@
+from tempfile import TemporaryDirectory
+
+import numpy as np
+import pytest
+import torch
+from tokenizers import Tokenizer
+
+from model2vec.model import StaticModel
+from model2vec.train import StaticModelForClassification
+from model2vec.train.base import FinetunableStaticModel, TextDataset
+
+
+@pytest.mark.parametrize("n_layers", [0, 1, 2, 3])
+def test_init_predict(n_layers: int, mock_vectors: np.ndarray, mock_tokenizer: Tokenizer) -> None:
+    """Test successful initialization of StaticModelForClassification."""
+    vectors_torched = torch.from_numpy(mock_vectors)
+    s = StaticModelForClassification(vectors=vectors_torched, tokenizer=mock_tokenizer, n_layers=n_layers)
+    assert s.vectors.shape == mock_vectors.shape
+    assert s.w.shape[0] == mock_vectors.shape[0]
+    assert s.classes == s.classes_
+    assert s.classes == ["0", "1"]
+
+    head = s.construct_head()
+    assert head[0].in_features == mock_vectors.shape[1]
+    head = s.construct_head()
+    assert head[0].in_features == mock_vectors.shape[1]
+    assert head[-1].out_features == 2
+
+
+def test_init_base_class(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer) -> None:
+    """Test successful initialization of the base class."""
+    vectors_torched = torch.from_numpy(mock_vectors)
+    s = FinetunableStaticModel(vectors=vectors_torched, tokenizer=mock_tokenizer)
+    assert s.vectors.shape == mock_vectors.shape
+    assert s.w.shape[0] == mock_vectors.shape[0]
+
+    head = s.construct_head()
+    assert head[0].in_features == mock_vectors.shape[1]
+
+
+def test_init_base_from_model(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer) -> None:
+    """Test initializion from a static model."""
+    model = StaticModel(vectors=mock_vectors, tokenizer=mock_tokenizer)
+    s = FinetunableStaticModel.from_static_model(model)
+    assert s.vectors.shape == mock_vectors.shape
+    assert s.w.shape[0] == mock_vectors.shape[0]
+
+    with TemporaryDirectory() as temp_dir:
+        model.save_pretrained(temp_dir)
+        s = FinetunableStaticModel.from_pretrained(model_name=temp_dir)
+        assert s.vectors.shape == mock_vectors.shape
+        assert s.w.shape[0] == mock_vectors.shape[0]
+
+
+def test_init_classifier_from_model(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer) -> None:
+    """Test initializion from a static model."""
+    model = StaticModel(vectors=mock_vectors, tokenizer=mock_tokenizer)
+    s = StaticModelForClassification.from_static_model(model)
+    assert s.vectors.shape == mock_vectors.shape
+    assert s.w.shape[0] == mock_vectors.shape[0]
+
+    with TemporaryDirectory() as temp_dir:
+        model.save_pretrained(temp_dir)
+        s = StaticModelForClassification.from_pretrained(model_name=temp_dir)
+        assert s.vectors.shape == mock_vectors.shape
+        assert s.w.shape[0] == mock_vectors.shape[0]
+
+
+def test_encode(mock_trained_pipeline: StaticModelForClassification) -> None:
+    """Test the encode function."""
+    result = mock_trained_pipeline._encode(torch.tensor([[0, 1], [1, 0]]).long())
+    assert result.shape == (2, 12)
+    assert torch.allclose(result[0], result[1])
+
+
+def test_tokenize(mock_trained_pipeline: StaticModelForClassification) -> None:
+    """Test the encode function."""
+    result = mock_trained_pipeline.tokenize(["dog dog", "cat"])
+    assert result.shape == torch.Size([2, 2])
+    assert result[1, 1] == 0
+
+
+def test_device(mock_trained_pipeline: StaticModelForClassification) -> None:
+    """Get the device."""
+    assert mock_trained_pipeline.device == torch.device(type="cpu")  # type: ignore  # False positive
+    assert mock_trained_pipeline.device == mock_trained_pipeline.w.device
+
+
+def test_conversion(mock_trained_pipeline: StaticModelForClassification) -> None:
+    """Test the conversion to numpy."""
+    staticmodel = mock_trained_pipeline.to_static_model()
+    with torch.no_grad():
+        result_1 = mock_trained_pipeline._encode(torch.tensor([[0, 1], [1, 0]]).long()).numpy()
+    result_2 = staticmodel.embedding[[[0, 1], [1, 0]]].mean(0)
+    result_2 /= np.linalg.norm(result_2, axis=1, keepdims=True)
+
+    assert np.allclose(result_1, result_2)
+
+
+def test_textdataset_init() -> None:
+    """Test the textdataset init."""
+    dataset = TextDataset([[0], [1]], torch.arange(2))
+    assert len(dataset) == 2
+
+
+def test_textdataset_init_incorrect() -> None:
+    """Test the textdataset init."""
+    with pytest.raises(ValueError):
+        TextDataset([[0]], torch.arange(2))
+
+
+def test_predict(mock_trained_pipeline: StaticModelForClassification) -> None:
+    """Test the predict function."""
+    result = mock_trained_pipeline.predict(["dog cat", "dog"]).tolist()
+    assert result == ["b", "b"]
+
+
+def test_predict_proba(mock_trained_pipeline: StaticModelForClassification) -> None:
+    """Test the predict function."""
+    result = mock_trained_pipeline.predict_proba(["dog cat", "dog"])
+    assert result.shape == (2, 2)
+
+
+def test_convert_to_pipeline(mock_trained_pipeline: StaticModelForClassification) -> None:
+    """Convert a model to a pipeline."""
+    mock_trained_pipeline.eval()
+    pipeline = mock_trained_pipeline.to_pipeline()
+    encoded_pipeline = pipeline.model.encode(["dog cat", "dog"])
+    encoded_model = mock_trained_pipeline(mock_trained_pipeline.tokenize(["dog cat", "dog"]))[1].detach().numpy()
+    assert np.allclose(encoded_pipeline, encoded_model)
+    a = pipeline.predict(["dog cat", "dog"]).tolist()
+    b = mock_trained_pipeline.predict(["dog cat", "dog"]).tolist()
+    assert a == b
+    p1 = pipeline.predict_proba(["dog cat", "dog"])
+    p2 = mock_trained_pipeline.predict_proba(["dog cat", "dog"])
+    assert np.allclose(p1, p2)
+
+
+def test_train_test_split() -> None:
+    """Test the train test split function."""
+    a, b, c, d = StaticModelForClassification._train_test_split(["0", "1", "2", "3"], ["1", "1", "0", "0"], 0.5)
+    assert len(a) == 2
+    assert len(b) == 2
+    assert len(c) == len(a)
+    assert len(d) == len(b)
diff --git a/tutorials/README.md b/tutorials/README.md
index 6d9c680..2874196 100644
--- a/tutorials/README.md
+++ b/tutorials/README.md
@@ -13,3 +13,4 @@ This is a list of all our tutorials. They are all self-contained ipython noteboo
 |--------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------|
 | **Recipe search** 🍝 | Learn how to do lightning-fast semantic search by distilling a small model. Compare a really tiny model to a larger with one with a better vocabulary. Learn what Fattoush is (delicious). | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/minishlab/model2vec/blob/master/tutorials/recipe_search.ipynb)     |
 | **Semantic chunking** 🧩 | Learn how to chunk your text into meaningful segments with [Chonkie](https://github.com/bhavnicksm/chonkie) at lightning-speed. Efficiently query your chunks with [Vicinity](https://github.com/MinishLab/vicinity). | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/minishlab/model2vec/blob/master/tutorials/semantic_chunking.ipynb) |
+| **Training a classifier** 🧩 | Learn how to train a classifier using model2vec. Lightning fast, great performance, especially on small datasets | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/minishlab/model2vec/blob/master/tutorials/train_classifier.ipynb) |
diff --git a/tutorials/train_classifier.ipynb b/tutorials/train_classifier.ipynb
new file mode 100644
index 0000000..a4fddc5
--- /dev/null
+++ b/tutorials/train_classifier.ipynb
@@ -0,0 +1,248 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Training a classifier using model2vec\n",
+    "\n",
+    "Model2Vec supports built-in classifier training with an easy, scikit-learn-based syntax. Just give the model your data in `.fit`, and you'll have a trained model!\n",
+    "\n",
+    "How it works:\n",
+    "* We load a base `StaticModel` using as a torch module. By default we use [potion-base-8m](https://huggingface.co/minishlab/potion-base-8M).\n",
+    "* We add a one-layer MLP with 512 hidden units and `ReLU` activation as a head.\n",
+    "* We train the model using cross-entropy, using [`pytorch-lightning`](https://lightning.ai/docs/pytorch/stable/) as a training framework.\n",
+    "\n",
+    "After training, you can export the model using regular torch tools, such as `torch.save` and `torch.load`, or you can export the model to a `scikit-learn` pipeline. The latter option leads to a really small footprint during inference, as there is no longer a need to use `torch`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Install the necessary libraries\n",
+    "!uv pip install \"model2vec[train,inference]\"\n",
+    "!uv pip install \"datasets\"\n",
+    "!uv pip install \"scikit-learn\"\n",
+    "\n",
+    "# Import the necessary libraries\n",
+    "from model2vec.train import StaticModelForClassification\n",
+    "from model2vec.inference import StaticModelPipeline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To demonstrate how to train a model, we'll be using the `subjectivity` dataset, which contains short utterances and whether they are subjective or objective."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "dataset = load_dataset(\"setfit/20_newsgroups\")\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at the first five training samples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First 5 training samples:\n",
+    "for record in dataset[\"train\"].to_list()[:5]:\n",
+    "    print(f\"TEXT: {record['text']} LABEL: {record['label_text']}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the staticmodel\n",
+    "model = StaticModelForClassification.from_pretrained()\n",
+    "# Optional arguments:\n",
+    "# model_name: the name of the base model (defaults to potion-base-8m)\n",
+    "# n_layers: the number of layers in the MLP (defaults to 1)\n",
+    "# hidden_dim: the number of hidden units (defaults to 512)\n",
+    "print(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's train the model on a subset of examples. We pick the first 1000 examples to train on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "# Fit the model on the first 1000 records\n",
+    "subset = dataset[\"train\"].select(range(1000))\n",
+    "s = time.time()\n",
+    "model = model.fit(subset[\"text\"], subset[\"label_text\"])\n",
+    "print(f\"training took {time.time() - s} seconds\")\n",
+    "# Fit takes many many arguments, check them out!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have trained a classifier in five seconds. Nice!\n",
+    "\n",
+    "Let's take a look at how good it is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "predictions = model.predict(dataset[\"test\"][\"text\"])\n",
+    "\n",
+    "print(classification_report(dataset[\"test\"][\"label_text\"], predictions))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our model scores 0.55 accuracy. But what does this mean? Let's compare it to a `tf-idf` pipeline from `scikit-learn`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "sklearn_pipeline = make_pipeline(TfidfVectorizer(), LogisticRegression())\n",
+    "sklearn_pipeline.fit(subset[\"text\"], subset[\"label_text\"])\n",
+    "predictions = sklearn_pipeline.predict(dataset[\"test\"][\"text\"])\n",
+    "\n",
+    "print(classification_report(dataset[\"test\"][\"label_text\"], predictions))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pretty good! We outperform the tf-idf pipeline by a wide margin.\n",
+    "\n",
+    "We can now export the model to scikit-learn, and push it to the hub. But first, let's verify whether the predictions of this model and the original model match."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline = model.to_pipeline()\n",
+    "\n",
+    "predictions = pipeline.predict(dataset[\"test\"][\"text\"])\n",
+    "\n",
+    "print(classification_report(dataset[\"test\"][\"label_text\"], predictions))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ok, so let's save the model locally, or push it to the hub!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline.save_pretrained(\"my_cool_model\")\n",
+    "# Fill in your own org\n",
+    "# pipeline.push_to_hub(\"my_org/my_model\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This saves a model to a local folder. The model can then be loaded as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_model = StaticModelPipeline.from_pretrained(\"my_cool_model\")\n",
+    "# Or from the hub\n",
+    "# model = StaticModelPipeline.from_pretrained(\"my_org/my_model\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One reason to work like this is that the `StaticModelPipeline` does not require torch to be installed at all, leading to really fast cold start predictions, smaller images, and a lot less hassle overall.\n",
+    "\n",
+    "And that's it! Super fast, super small, super good classifiers."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/uv.lock b/uv.lock
index 516d768..3d05a81 100644
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+    { name = "packaging" },
+    { name = "pytorch-lightning" },
+    { name = "pyyaml" },
+    { name = "torch" },
+    { name = "torchmetrics" },
+    { name = "tqdm" },
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+version = "0.11.9"
+source = { registry = "https://pypi.org/simple" }
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+version = "0.3.7"
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     { name = "transformers" },
 ]
+inference = [
+    { name = "scikit-learn" },
+    { name = "skops" },
+]
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+    { name = "scikit-learn" },
+    { name = "skops" },
+    { name = "torch" },
+]
 
 [package.metadata]
 requires-dist = [
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     { name = "jinja2" },
     { name = "joblib" },
+    { name = "lightning", marker = "extra == 'train'" },
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+    { name = "scikit-learn", marker = "extra == 'inference'" },
+    { name = "scikit-learn", marker = "extra == 'train'" },
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     { name = "torch", marker = "extra == 'onnx'" },
+    { name = "torch", marker = "extra == 'train'" },
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