diff --git a/model2vec/distill/distillation.py b/model2vec/distill/distillation.py
index fc1d0ae..be4541c 100644
--- a/model2vec/distill/distillation.py
+++ b/model2vec/distill/distillation.py
@@ -3,10 +3,10 @@
 import logging
 import os
 import re
-from typing import Optional, cast
+from typing import cast
 
 import numpy as np
-from huggingface_hub.hf_api import model_info
+from huggingface_hub import model_info
 from transformers import AutoModel, AutoTokenizer
 from transformers.modeling_utils import PreTrainedModel
 from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
@@ -16,6 +16,7 @@
 from model2vec.model import StaticModel
 from model2vec.quantization import DType, quantize_embeddings
 from model2vec.tokenizer import clean_and_create_vocabulary, replace_vocabulary, turn_tokens_into_ids
+from model2vec.tokenizer.tokenizer import _patch_tokenizer
 from model2vec.vocabulary_quantization import quantize_vocabulary
 
 logger = logging.getLogger(__name__)
@@ -27,11 +28,10 @@ def distill_from_model(
     vocabulary: list[str] | None = None,
     device: str | None = None,
     pca_dims: PCADimType = 256,
-    apply_zipf: bool | None = None,
     sif_coefficient: float | None = 1e-4,
     token_remove_pattern: str | None = r"\[unused\d+\]",
     quantize_to: DType | str = DType.Float16,
-    use_subword: bool | None = None,
+    lower_case: bool = True,
     vocabulary_quantization: int | None = None,
 ) -> StaticModel:
     """
@@ -50,26 +50,21 @@ def distill_from_model(
     :param pca_dims: The number of components to use for PCA.
         If this is None, we don't apply PCA.
         If this is 'auto', we don't reduce dimensionality, but still apply PCA.
-    :param apply_zipf: DEPRECATED: This parameter used to control whether Zipf is applied.
-        Zipf weighting is now controlled by the sif_coefficient parameter. If this is set to None, no weighting is applied.
     :param sif_coefficient: The SIF coefficient to use. If this is None, no weighting is applied.
         Should be a value > 0 and < 1.0. A value of 1e-4 is a good default.
     :param token_remove_pattern: If this is set to a string, we compile this into a regex. Any tokens that conform to this regex pattern will be removed from the vocabulary.
         If the pattern is so general that it removes all tokens, we throw an error. If the pattern can't be compiled into a valid regex, we also throw an error.
     :param quantize_to: The data type to quantize to. Can be any of the DType enum members or their string equivalents.
-    :param use_subword: DEPRECATED: If this is not set to None, we show a warning. It doesn't do anything.
+    :param lower_case: If this is set, all tokens in the model vocabulary will be converted to lowercase, and
+        a lowercase normalizer will be inserted. This almost always improves performance.
     :param vocabulary_quantization: The number of clusters to use for vocabulary quantization. If this is None, no quantization is performed.
     :return: A StaticModel
     :raises: ValueError if the vocabulary is empty after preprocessing.
 
     """
-    if use_subword is not None:
-        logger.warning(
-            "The `use_subword` parameter is deprecated and will be removed in the next release. It doesn't do anything."
-        )
     quantize_to = DType(quantize_to)
     backend_tokenizer = tokenizer.backend_tokenizer
-    sif_coefficient, token_remove_regex = _validate_parameters(apply_zipf, sif_coefficient, token_remove_pattern)
+    sif_coefficient, token_remove_regex = _validate_parameters(sif_coefficient, token_remove_pattern)
 
     if vocabulary is None:
         vocabulary = []
@@ -78,44 +73,40 @@ def distill_from_model(
 
     n_tokens_before = len(vocabulary)
     # Clean the vocabulary by removing duplicate tokens and tokens that are in the internal vocabulary.
-    all_tokens, backend_tokenizer = clean_and_create_vocabulary(
-        tokenizer, vocabulary, token_remove_regex=token_remove_regex
+    tokens, backend_tokenizer = clean_and_create_vocabulary(
+        tokenizer, vocabulary, token_remove_regex=token_remove_regex, lower_case=lower_case
     )
-    n_tokens_after = len([token for token in all_tokens if not token.is_internal])
+    n_tokens_after = len([token for token in tokens if not token.is_internal])
     if n_tokens_before:
         logger.info(
             f"Adding {n_tokens_after} tokens to the vocabulary. Removed {n_tokens_before - n_tokens_after} tokens during preprocessing."
         )
 
-    if not all_tokens:
+    if not tokens:
         raise ValueError("The vocabulary is empty after preprocessing. Please check your token_remove_pattern.")
 
-    unk_token = cast(Optional[str], tokenizer.special_tokens_map.get("unk_token"))
-    pad_token = cast(Optional[str], tokenizer.special_tokens_map.get("pad_token"))
-
-    # Weird if to satsify mypy
-    if pad_token is None:
-        if unk_token is not None:
-            pad_token = unk_token
-            logger.warning(
-                "The pad token is not set. Setting it to the unk token. This is a workaround for models that don't have a pad token."
-            )
-        else:
-            pad_token = unk_token or all_tokens[0].form
-            logger.warning(
-                "The pad token is not set. Setting it to the first token in the vocabulary. This is a workaround for models that don't have a pad token."
-            )
-
     # Replace the vocabulary in the tokenizer with the new vocabulary.
-    backend_tokenizer = replace_vocabulary(backend_tokenizer, all_tokens, unk_token=unk_token, pad_token=pad_token)
-    logger.info(f"Creating embeddings for {len(all_tokens)} tokens")
+    backend_tokenizer = replace_vocabulary(backend_tokenizer, tokens)
+
+    logger.info(f"Creating embeddings for {len(tokens)} tokens")
     # Convert tokens to IDs
-    token_ids = turn_tokens_into_ids(all_tokens, tokenizer, unk_token)
+    m = _patch_tokenizer(tokenizer=tokenizer, lower_case=False)
+    bb = m.to_tokenizer()
+
+    token_ids = turn_tokens_into_ids(tokens, bb)
 
     # Create the embeddings
-    embeddings = create_embeddings(
-        tokenized=token_ids, model=model, device=device, pad_token_id=tokenizer.get_vocab()[pad_token]
-    )
+    pad_token = cast(str | None, tokenizer.special_tokens_map.get("pad_token", None))
+    vocab = tokenizer.get_vocab()
+    if pad_token is None:
+        sep_token = cast(str | None, tokenizer.special_tokens_map.get("sep_token", None))
+        if sep_token is None:
+            pad_token_id = 0
+        else:
+            pad_token_id = vocab[sep_token]
+    else:
+        pad_token_id = vocab[pad_token]
+    embeddings = create_embeddings(tokenized=token_ids, model=model, device=device, pad_token_id=pad_token_id)
 
     if vocabulary_quantization is not None:
         _, weights = post_process_embeddings(np.asarray(embeddings), None, sif_coefficient=sif_coefficient)
@@ -137,7 +128,6 @@ def distill_from_model(
         "architectures": ["StaticModel"],
         "tokenizer_name": model_name,
         "apply_pca": pca_dims,
-        "apply_zipf": apply_zipf,
         "sif_coefficient": sif_coefficient,
         "hidden_dim": embeddings.shape[1],
         "seq_length": 1000000,  # Set this to a high value since we don't have a sequence length limit.
@@ -171,15 +161,12 @@ def distill_from_model(
 
 
 def _validate_parameters(
-    apply_zipf: bool | None,
     sif_coefficient: float | None,
     token_remove_pattern: str | None,
 ) -> tuple[float | None, re.Pattern | None]:
     """
     Validate the parameters passed to the distillation function.
 
-    :param apply_zipf: DEPRECATED: This parameter used to control whether Zipf is applied.
-        Zipf weighting is now controlled by the sif_coefficient parameter. If this is set to None, no weighting is applied.
     :param sif_coefficient: The SIF coefficient to use. If this is None, no weighting is applied.
         Should be a value >= 0 and < 1.0. A value of 1e-4 is a good default.
     :param token_remove_pattern: If this is set to a string, we compile this into a regex. Any tokens that conform to this regex pattern will be removed from the vocabulary.
@@ -187,19 +174,6 @@ def _validate_parameters(
     :raises: ValueError if the regex can't be compiled.
 
     """
-    if apply_zipf is not None:
-        logger.warning(
-            "The `apply_zipf` parameter is deprecated and will be removed in the next release. "
-            "Zipf weighting is applied based on the sif_coefficient parameter. If this is set to None, "
-            "no weighting is applied."
-        )
-        if apply_zipf and sif_coefficient is None:
-            logger.warning("You set apply_zipf to True, but sif_coefficient is None. Setting sif_coefficient to 1e-4.")
-            sif_coefficient = 1e-4
-        elif not apply_zipf:
-            logger.warning("Because you set apply_zipf to False, we ignore the sif_coefficient parameter.")
-            sif_coefficient = None
-
     if sif_coefficient is not None:
         if not 0 < sif_coefficient < 1.0:
             raise ValueError("SIF coefficient must be a value > 0 and < 1.0.")
@@ -219,12 +193,11 @@ def distill(
     vocabulary: list[str] | None = None,
     device: str | None = None,
     pca_dims: PCADimType = 256,
-    apply_zipf: bool | None = None,
     sif_coefficient: float | None = 1e-4,
     token_remove_pattern: str | None = r"\[unused\d+\]",
     trust_remote_code: bool = False,
     quantize_to: DType | str = DType.Float16,
-    use_subword: bool | None = None,
+    lower_case: bool = True,
     vocabulary_quantization: int | None = None,
 ) -> StaticModel:
     """
@@ -242,14 +215,13 @@ def distill(
     :param pca_dims: The number of components to use for PCA.
         If this is None, we don't apply PCA.
         If this is 'auto', we don't reduce dimenionality, but still apply PCA.
-    :param apply_zipf: DEPRECATED: This parameter used to control whether Zipf is applied.
-        Zipf weighting is now controlled by the sif_coefficient parameter. If this is set to None, no weighting is applied.
     :param sif_coefficient: The SIF coefficient to use. If this is None, no weighting is applied.
         Should be a value >= 0 and < 1.0. A value of 1e-4 is a good default.
     :param token_remove_pattern: If this is set to a string, we compile this into a regex. Any tokens that conform to this regex pattern will be removed from the vocabulary.
     :param trust_remote_code: Whether to trust the remote code. If this is False, we will only load components coming from `transformers`. If this is True, we will load all components.
     :param quantize_to: The data type to quantize to. Can be any of the DType enum members or their string equivalents.
-    :param use_subword: DEPRECATED: If this is not set to None, we show a warning. It doesn't do anything.
+    :param lower_case: If this is set, all tokens in the model vocabulary will be converted to lowercase, and
+        a lowercase normalizer will be inserted. This almost always improves performance.
     :param vocabulary_quantization: The number of clusters to use for vocabulary quantization. If this is None, no quantization is performed.
     :return: A StaticModel
 
@@ -266,10 +238,9 @@ def distill(
         vocabulary=vocabulary,
         device=device,
         pca_dims=pca_dims,
-        apply_zipf=apply_zipf,
         token_remove_pattern=token_remove_pattern,
         sif_coefficient=sif_coefficient,
         quantize_to=quantize_to,
-        use_subword=use_subword,
+        lower_case=lower_case,
         vocabulary_quantization=vocabulary_quantization,
     )
diff --git a/model2vec/distill/inference.py b/model2vec/distill/inference.py
index aabd98d..ba9af95 100644
--- a/model2vec/distill/inference.py
+++ b/model2vec/distill/inference.py
@@ -98,7 +98,8 @@ def _encode_mean_using_model(model: PreTrainedModel, encodings: dict[str, torch.
     """
     encodings = {k: v.to(model.device) for k, v in encodings.items()}
     encoded: BaseModelOutputWithPoolingAndCrossAttentions = model(**encodings)
-    out: torch.Tensor = encoded.last_hidden_state.cpu()  # type: ignore  # False positive
+    assert encoded.last_hidden_state is not None
+    out: torch.Tensor = encoded.last_hidden_state.cpu()
     # NOTE: If the dtype is bfloat 16, we convert to float32,
     # because numpy does not suport bfloat16
     # See here: https://github.com/numpy/numpy/issues/19808
diff --git a/model2vec/tokenizer/__init__.py b/model2vec/tokenizer/__init__.py
index 4cfe498..91a66a9 100644
--- a/model2vec/tokenizer/__init__.py
+++ b/model2vec/tokenizer/__init__.py
@@ -4,9 +4,8 @@
 
 from model2vec.tokenizer.tokenizer import (
     clean_and_create_vocabulary,
-    create_tokenizer,
     replace_vocabulary,
     turn_tokens_into_ids,
 )
 
-__all__ = ["clean_and_create_vocabulary", "create_tokenizer", "turn_tokens_into_ids", "replace_vocabulary"]
+__all__ = ["clean_and_create_vocabulary", "turn_tokens_into_ids", "replace_vocabulary"]
diff --git a/model2vec/tokenizer/datamodels.py b/model2vec/tokenizer/datamodels.py
index 6aa5ecf..1000e65 100644
--- a/model2vec/tokenizer/datamodels.py
+++ b/model2vec/tokenizer/datamodels.py
@@ -5,10 +5,13 @@
 class Token:
     """A class to represent a token."""
 
+    # The surface form: used for featurizing
     form: str
-    # The normalized and pretokenized form of the token
+    # The normalized form: preprocessed by the new tokenizer
     normalized_form: str
     # Whether the word is a continuing subword.
     is_subword: bool
     # Whether the token is internal to the model.
     is_internal: bool
+    # Whether the token is a multiword token
+    is_multiword: bool = False
diff --git a/model2vec/tokenizer/model.py b/model2vec/tokenizer/model.py
deleted file mode 100644
index 12dd388..0000000
--- a/model2vec/tokenizer/model.py
+++ /dev/null
@@ -1,43 +0,0 @@
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-
-
-def process_tokenizer(
-    tokenizer_json: dict[str, Any], pre_tokenized_tokens: list[str], unk_token: str | None
-) -> dict[str, Any]:
-    """Process the WordPiece tokenizer JSON."""
-    if tokenizer_json["model"]["type"] == "Unigram":
-        return _process_unigram(tokenizer_json, pre_tokenized_tokens, unk_token)
-    tokenizer_json["model"]["type"] = "Unigram"
-    tokenizer_json["model"]["unk_id"] = pre_tokenized_tokens.index(unk_token) if unk_token else None
-
-    token_weights = np.asarray([_calculate_token_weight_for_unigram(token) for token in pre_tokenized_tokens])
-    proba = (token_weights / np.sum(token_weights)).tolist()
-    tokenizer_json["model"]["vocab"] = [(token, np.log(p)) for token, p in zip(pre_tokenized_tokens, proba)]
-
-    return tokenizer_json
-
-
-def _process_unigram(
-    tokenizer_json: dict[str, Any], pre_tokenized_tokens: list[str], unk_token: str | None
-) -> dict[str, Any]:
-    """Process the Unigram tokenizer JSON."""
-    current_probas = dict(tokenizer_json["model"]["vocab"])
-    avg_proba = sum(current_probas.values()) / len(current_probas)
-    new_probas = [[word, current_probas.get(word, avg_proba)] for word in pre_tokenized_tokens]
-    tokenizer_json["model"]["vocab"] = new_probas
-
-    tokens, _ = zip(*tokenizer_json["model"]["vocab"])
-    if unk_token is not None:
-        tokenizer_json["model"]["unk_id"] = list(tokens).index(unk_token)
-
-    return tokenizer_json
-
-
-def _calculate_token_weight_for_unigram(token: str) -> float:
-    """Calculate the token weight for Unigram."""
-    # Always prefer longer tokens.
-    return len(token) + token.count("▁") + token.count("Ġ")
diff --git a/model2vec/tokenizer/normalizer.py b/model2vec/tokenizer/normalizer.py
deleted file mode 100644
index 15cb11e..0000000
--- a/model2vec/tokenizer/normalizer.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from string import punctuation
-
-from tokenizers import Regex, Tokenizer
-from tokenizers.normalizers import Replace, Sequence, Strip
-
-
-def replace_normalizer(
-    tokenizer: Tokenizer,
-) -> Tokenizer:
-    """
-    Replace the normalizer for the tokenizer.
-
-    The new normalizer will replace punctuation with a space before and after the punctuation.
-    It will also replace multiple spaces with a single space and strip the right side of the string.
-    If the tokenizer already has a normalizer, it will be added to the new normalizer.
-    If the tokenizer does not have a normalizer, a new normalizer will be created.
-
-    :param tokenizer: The tokenizer to change.
-    :return: The tokenizer with a replaced normalizer.
-    """
-    spaces_punctuation = tokenizer.encode("a, ,", add_special_tokens=False).tokens
-    if len(spaces_punctuation) != 3:
-        add_space = False
-    else:
-        _, first_comma, second_comma = spaces_punctuation
-        add_space = first_comma == second_comma == ","
-
-    normalizer = tokenizer.normalizer
-    new_normalizers = []
-    for char in punctuation:
-        replacement = f" {char} " if add_space else f"{char} "
-        new_normalizers.append(Replace(char, replacement))
-
-    new_normalizers.append(Replace(Regex(r"\s+"), " "))
-    new_normalizers.append(Strip(right=True))
-    if normalizer is None:
-        normalizer = Sequence(new_normalizers)  # type: ignore
-    else:
-        normalizer = Sequence([normalizer] + new_normalizers)  # type: ignore
-    tokenizer.normalizer = normalizer  # type: ignore
-
-    return tokenizer
diff --git a/model2vec/tokenizer/pretokenizer.py b/model2vec/tokenizer/pretokenizer.py
deleted file mode 100644
index ac7ee42..0000000
--- a/model2vec/tokenizer/pretokenizer.py
+++ /dev/null
@@ -1,57 +0,0 @@
-from __future__ import annotations
-
-import json
-from typing import Any
-
-from tokenizers import Tokenizer
-
-_FORBIDDEN_PRETOKENIZERS = (
-    "WhiteSpace",
-    "WhitespaceSplit",
-    "BertPreTokenizer",
-    "CharDelimiterSplit",
-    "Punctuation",
-    "Split",
-    "UnicodeScripts",
-)
-_BASIC_METASPACE = {"type": "Metaspace", "replacement": "▁", "prepend_scheme": "always", "split": False}
-
-
-def _fix_single_pretokenizer(pre_tokenizer: dict[str, Any]) -> dict[str, Any] | None:
-    """Fixes a single pretokenizer to allow multiword units."""
-    if pre_tokenizer["type"] in _FORBIDDEN_PRETOKENIZERS:
-        return None
-    if pre_tokenizer["type"] == "ByteLevel":
-        pre_tokenizer["add_prefix_space"] = True
-        pre_tokenizer["use_regex"] = False
-    if pre_tokenizer["type"] == "Metaspace":
-        pre_tokenizer["split"] = False
-        pre_tokenizer["prepend_scheme"] = "always"
-
-    return pre_tokenizer
-
-
-def replace_pretokenizer(tokenizer: Tokenizer) -> Tokenizer:
-    """Fixes a single pretokenizer to allow multiword units."""
-    tokenizer_json = json.loads(tokenizer.to_str())
-    pre_tokenizer_json = tokenizer_json.get("pre_tokenizer", None)
-
-    if pre_tokenizer_json is None:
-        pre_tokenizer_json = _BASIC_METASPACE
-
-    elif pre_tokenizer_json["type"] == "Sequence":
-        new_pretokenizers = []
-        for single_pretokenizer in pre_tokenizer_json["pretokenizers"]:
-            new_pretokenizer = _fix_single_pretokenizer(single_pretokenizer)
-            if new_pretokenizer is not None:
-                new_pretokenizers.append(new_pretokenizer)
-
-        if new_pretokenizers:
-            pre_tokenizer_json["pretokenizers"] = new_pretokenizers
-        else:
-            pre_tokenizer_json = _BASIC_METASPACE
-
-    pre_tokenizer_json = _fix_single_pretokenizer(pre_tokenizer_json) or _BASIC_METASPACE
-    tokenizer_json["pre_tokenizer"] = pre_tokenizer_json
-
-    return tokenizer.from_str(json.dumps(tokenizer_json))
diff --git a/model2vec/tokenizer/tokenizer.py b/model2vec/tokenizer/tokenizer.py
index 3149a80..db2fdd6 100644
--- a/model2vec/tokenizer/tokenizer.py
+++ b/model2vec/tokenizer/tokenizer.py
@@ -1,10 +1,13 @@
 from __future__ import annotations
 
-import json
 import logging
 import re
-from typing import Any, Optional, cast
+from typing import cast
 
+from skeletoken import TokenizerModel
+from skeletoken.addedtoken import AddedToken, AddedTokens
+from skeletoken.models import WordPiece
+from skeletoken.pretokenizers import ByteLevelPreTokenizer, PreTokenizerSequence
 from tokenizers import Tokenizer
 from tokenizers.normalizers import Normalizer
 from tokenizers.pre_tokenizers import (
@@ -13,115 +16,83 @@
 from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
 
 from model2vec.tokenizer.datamodels import Token
-from model2vec.tokenizer.model import process_tokenizer
-from model2vec.tokenizer.normalizer import replace_normalizer
-from model2vec.tokenizer.pretokenizer import replace_pretokenizer
 
 logger = logging.getLogger(__name__)
 
 
-_DEFAULT_POST_PROCESSOR_TEMPLATE = {
-    "type": "TemplateProcessing",
-    "single": [{"Sequence": {"id": "A", "type_id": 0}}],
-    "pair": [{"Sequence": {"id": "A", "type_id": 0}}, {"Sequence": {"id": "B", "type_id": 0}}],
-    "special_tokens": {},
-}
-
-
 def _remap_added_tokens(
-    special_tokens: list[dict[str, Any]],
+    added_tokens: list[AddedToken],
     vocabulary: list[str],
-) -> list[dict[str, Any]]:
+) -> list[AddedToken]:
     """
-    Remap special tokens in the tokenizer.
+    Remap added tokens in the tokenizer.
 
-    This function updates the special tokens in the tokenizer based on a mapping provided.
-    It also ensures that the special tokens are present in the vocabulary.
+    This function updates the added tokens in the tokenizer based on a mapping provided.
+    It also ensures that the added tokens are present in the vocabulary.
 
-    :param special_tokens: The special tokens to remap.
+    :param added_tokens: The added tokens to remap.
     :param vocabulary: The vocabulary as a list of tokens.
-    :return: The updated special tokens.
+    :return: The updated added tokens.
     """
-    # Deepcopy
-    special_tokens = [{**x} for x in special_tokens]
-    for token in special_tokens:
-        token["id"] = vocabulary.index(token["content"])
+    for token in added_tokens:
+        token.id = vocabulary.index(token.content)
 
-    return special_tokens
+    return added_tokens
 
 
-def replace_vocabulary(
-    tokenizer: Tokenizer, new_vocabulary: list[Token], unk_token: str | None, pad_token: str | None
-) -> Tokenizer:
+def replace_vocabulary(tokenizer: Tokenizer, new_vocabulary: list[Token]) -> Tokenizer:
     """Replace the vocabulary of a tokenizer with a new one."""
-    tokenizer_json: dict[str, Any] = json.loads(tokenizer.to_str())
-    added_tokens: list[dict[str, Any]] = tokenizer_json["added_tokens"]
-
-    pre_tokenized_tokens = [x.normalized_form for x in new_vocabulary]
+    tokenizer_model = TokenizerModel.from_tokenizer(tokenizer)
 
-    # We need to remove the added tokens but keep [UNK] and [PAD] tokens.
-    added_tokens = _rename_added_token(unk_token, "[UNK]", added_tokens, pre_tokenized_tokens)
-    added_tokens = _rename_added_token(pad_token, "[PAD]", added_tokens, pre_tokenized_tokens)
+    tokens = [token.normalized_form for token in new_vocabulary]
 
-    # Remove old added tokens from added tokens
-    tokenizer_json["added_tokens"] = [x for x in added_tokens if x["content"] in {"[UNK]", "[PAD]"}]
-    tokenizer_json = process_tokenizer(
-        tokenizer_json, pre_tokenized_tokens, "[UNK]" if "[UNK]" in pre_tokenized_tokens else None
-    )
+    assert isinstance(tokenizer_model.model, WordPiece)
+    tokenizer_model.model.vocab.replace_vocabulary(tokens)
 
-    # Remap special tokens
-    tokenizer_json["added_tokens"] = _remap_added_tokens(
-        special_tokens=tokenizer_json["added_tokens"],
-        vocabulary=pre_tokenized_tokens,
-    )
-    tokenizer_json["post_processor"] = _DEFAULT_POST_PROCESSOR_TEMPLATE
-
-    return Tokenizer.from_str(json.dumps(tokenizer_json))
-
-
-def _rename_added_token(
-    form: str | None, new_form: str, added_tokens: list[dict[str, Any]], vocabulary: list[str]
-) -> list[dict[str, Any]]:
-    """Rename added tokens in the tokenizer."""
-    if form is None:
-        return added_tokens
+    new_added_tokens = []
+    for added_token in tokenizer_model.added_tokens.root:
+        if added_token.content not in {tokenizer_model.unk_token, tokenizer_model.pad_token}:
+            continue
+        new_added_tokens.append(added_token)
+    for token in new_vocabulary:
+        if token.is_multiword and token.form not in {tokenizer_model.unk_token, tokenizer_model.pad_token}:
+            tokenizer_model.add_addedtoken(token.form, normalized=True, single_word=False)
 
-    idx = vocabulary.index(form)
-    added_token = [x for x in added_tokens if x["content"] == form]
-    if added_token:
-        added_token[0]["id"] = idx
-        added_token[0]["content"] = new_form
-        vocabulary[idx] = new_form
+    pre_tokenized_tokens = [x.normalized_form for x in new_vocabulary]
+    tokenizer_model.added_tokens = AddedTokens(_remap_added_tokens(new_added_tokens, pre_tokenized_tokens))
+    # Set post processor to None because we don't care about it
+    tokenizer_model.post_processor = None
+    # We need to re-set the pad and unk tokens to put the correct indices.
+    tokenizer_model.unk_token = tokenizer_model.unk_token
+    tokenizer_model.pad_token = tokenizer_model.pad_token
 
-    return added_tokens
+    return tokenizer_model.to_tokenizer()
 
 
 def clean_and_create_vocabulary(
     tokenizer: PreTrainedTokenizerFast,
-    vocabulary: list[str],
+    new_vocabulary: list[str],
     token_remove_regex: re.Pattern | None,
+    lower_case: bool = True,
 ) -> tuple[list[Token], Tokenizer]:
     """Cleans a vocabulary by removing duplicates and tokens that were already in the vocabulary."""
     seen_tokens = set()
-    post_normalize_seen_tokens = set()
     n_empty = 0
     n_duplicates = 0
 
-    backend_tokenizer = tokenizer.backend_tokenizer
+    tokenizer_model = _patch_tokenizer(tokenizer, lower_case=lower_case)
 
     # Make a base list of tokens.
     internal_vocab: dict[str, int] = tokenizer.get_vocab()
     internal_tokens: list[str] = [k for k, _ in sorted(internal_vocab.items(), key=lambda x: x[1])]
 
-    cleaned_vocabulary = _process_internal_tokens(tokenizer, backend_tokenizer, internal_tokens, token_remove_regex)
-    # Copy the backend tokenizer to avoid modifying the original.
-    backend_tokenizer = backend_tokenizer.from_str(backend_tokenizer.to_str())
-    backend_tokenizer = replace_normalizer(backend_tokenizer)
-
+    # These need to be processed with the new backend tokenizer.
+    cleaned_vocabulary = _process_internal_tokens(tokenizer_model, internal_tokens, token_remove_regex)
     internal_tokens_set = {token.form for token in cleaned_vocabulary}
 
+    backend_tokenizer = tokenizer_model.to_tokenizer()
     normalizer: Normalizer | None = backend_tokenizer.normalizer
-    for token in vocabulary:
+    for token in new_vocabulary:
         if normalizer is not None:
             token = cast(str, normalizer.normalize_str(token))
 
@@ -131,41 +102,39 @@ def clean_and_create_vocabulary(
 
         pre_tokenizer: PreTokenizer | None = backend_tokenizer.pre_tokenizer
         normalized_token = token
+        is_multiword = False
         if pre_tokenizer is not None:
-            normalized_token = _normalize_vocabulary_token(
-                token=token,
-                pre_tokenizer=pre_tokenizer,
-            )
+            tokens = pre_tokenizer.pre_tokenize_str(token)
+            is_multiword = len(tokens) > 1
+            if not tokens:
+                # If no tokens are found, we have an empty string
+                normalized_token = ""
+            elif not is_multiword:
+                # If it's not a multiword, take the first token.
+                normalized_token = tokens[0][0]
+            else:
+                # This means it is a multiword.
+                # Multiwords are found verbatim, so there's no need to change the normalized form.
+                normalized_token = normalized_token
 
         # We need to check whether the pretokenized token is in the vocabulary.
         # But we need to return the original token, because that will be tokenized
         # again by the tokenizer during featurization.
-        if normalized_token in seen_tokens or normalized_token in internal_tokens_set:
+        if token in seen_tokens or token in internal_tokens_set:
             n_duplicates += 1
             continue
 
         # Add the possibly pretokenized token to seen
-        seen_tokens.add(normalized_token)
-
-        # After checking the token exists, we need to normalize it into the token
-        # it will become. For byte tokens, this means we don't do anything. For
-        # other types of tokens, we will insert a metaspace.
-        # In the case of multiword tokens, we replace any spaces with the metaspace
-        # or byte prefix token.
-        if not normalized_token.startswith(("▁", "Ġ")):
-            normalized_token = normalized_token.replace(" ", "▁")
-            normalized_token = f"▁{normalized_token}"
-        else:
-            normalized_token = normalized_token.replace(" ", normalized_token[0])
-
-        if normalized_token in post_normalize_seen_tokens:
-            n_duplicates += 1
-            continue
-
-        post_normalize_seen_tokens.add(normalized_token)
+        seen_tokens.add(token)
         # Add the original string to the vocabulary.
         cleaned_vocabulary.append(
-            Token(form=token, normalized_form=normalized_token, is_subword=False, is_internal=False)
+            Token(
+                form=token,
+                normalized_form=normalized_token,
+                is_subword=False,
+                is_internal=False,
+                is_multiword=is_multiword,
+            )
         )
 
     if n_duplicates:
@@ -173,50 +142,28 @@ def clean_and_create_vocabulary(
     if n_empty:
         logger.warning(f"Removed {n_empty} empty tokens.")
 
-    return cleaned_vocabulary, replace_pretokenizer(backend_tokenizer)
+    return cleaned_vocabulary, backend_tokenizer
 
 
 def _process_internal_tokens(
-    tokenizer: PreTrainedTokenizerFast,
-    backend_tokenizer: Tokenizer,
+    tokenizer_model: TokenizerModel,
     internal_tokens: list[str],
     token_remove_regex: re.Pattern | None,
 ) -> list[Token]:
     """Clean internal tokens."""
-    # Get the pad and unk token from the tokenizer.
-    pad_token: str | None = tokenizer.special_tokens_map.get("pad_token")  # type: ignore[assignment]
-    unk_token: str | None = tokenizer.special_tokens_map.get("unk_token")  # type: ignore[assignment]
     # Empty set if no pad or unk token is set.
-    added_tokens_to_keep: set[str] = {x for x in (pad_token, unk_token) if x is not None}
-    added_tokens_to_remove = set(tokenizer.added_tokens_encoder) - added_tokens_to_keep
+    added_tokens_to_keep: set[str] = {
+        x for x in (tokenizer_model.pad_token, tokenizer_model.unk_token) if x is not None
+    }
+    added_tokens_to_remove = {x.content for x in tokenizer_model.added_tokens.root} - added_tokens_to_keep
     cleaned_internal_tokens: list[Token] = []
 
-    # Figure out whether token is a subword or not.
-    encoded = backend_tokenizer.encode(f" {'a' * 25}", add_special_tokens=False)
-    first_token, second_token, *_ = encoded.tokens
-    # Isolate the prefix. We can't do first_token[0] because we don't know
-    # how long the prefix is.
-    # e.g., "Ġaaaa" -> "Ġ"
-    a_index = None if "a" not in first_token else first_token.index("a")
-    word_prefix = first_token[:a_index]
-    is_byte_prefix = word_prefix == "Ġ"
-    second_token = encoded.tokens[1]
-    # The second token is the first subword token.
-    # If a tokenizer uses subwords, this token will have been prefixed.
-    # We don't know how long the prefix is.
-    a_index = None if "a" not in second_token else second_token.index("a")
-    subword_prefix = second_token[:a_index]
-
-    pre_tokenizer: PreTokenizer | None = backend_tokenizer.pre_tokenizer
-
     for token in internal_tokens:
         # Create the token objects. If this returns None, it was unsucessful for some reason.
         if token_object := _create_single_internal_token(
             token=token,
-            subword_prefix=subword_prefix,
-            word_prefix=word_prefix,
-            pre_tokenizer=pre_tokenizer,
-            is_byte_prefix=is_byte_prefix,
+            subword_prefix=tokenizer_model.subword_prefix,
+            word_prefix=tokenizer_model.word_prefix,
             token_remove_regex=token_remove_regex,
             added_tokens_to_keep=added_tokens_to_keep,
             added_tokens_to_remove=added_tokens_to_remove,
@@ -233,10 +180,8 @@ def _process_internal_tokens(
 
 def _create_single_internal_token(
     token: str,
-    subword_prefix: str,
-    word_prefix: str,
-    pre_tokenizer: PreTokenizer | None,
-    is_byte_prefix: bool,
+    subword_prefix: str | None,
+    word_prefix: str | None,
     token_remove_regex: re.Pattern | None,
     added_tokens_to_keep: set[str],
     added_tokens_to_remove: set[str],
@@ -264,135 +209,70 @@ def _create_single_internal_token(
     if word_prefix:
         is_subword = not bool(token.startswith(word_prefix))
 
-    # Byte prefixed tokenizers don't need to be checked.
-    if pre_tokenizer is not None and not is_byte_prefix:
-        # We need to check the thing without prefixes. If we have a word prefix,
-        # we need to check tokens that have are subwords. Other way around for subword
-        # prefixes.
-        if (subword_prefix and not is_subword) or (word_prefix and is_subword):
-            # If this is True, the token is unreachable, even though it is a subword token.
-            if len(pre_tokenizer.pre_tokenize_str(token)) > 1:
-                return None
-
-    # Turn a token into a normalized form for later processing.
-    normalized_form = _create_normalized_form(token, subword_prefix, word_prefix, is_byte_prefix, is_subword)
-
-    return Token(form=token, normalized_form=normalized_form, is_subword=is_subword, is_internal=True)
-
-
-def _create_normalized_form(
-    token: str, subword_prefix: str, word_prefix: str, is_byte_prefix: bool, is_subword: bool
-) -> str:
-    """Turn an internal token string into a normalized form."""
-    # We don't need to check byte prefixed strings.
-    if is_byte_prefix:
-        return token
-    # We need to check if the token is a subword or not and remove the prefix.
-    if is_subword:
-        return token.removeprefix(subword_prefix)
-    # If the token is not a subword, we need to remove the word prefix, and add metaspace.
-    return f"▁{token.removeprefix(word_prefix)}"
-
-
-def turn_tokens_into_ids(
-    tokens: list[Token], tokenizer: PreTrainedTokenizerFast, unk_token: str | None
-) -> list[list[int]]:
+    # For internal tokens, the normalized form is always the form.
+    return Token(form=token, normalized_form=token, is_subword=is_subword, is_internal=True)
+
+
+def turn_tokens_into_ids(tokens: list[Token], tokenizer: Tokenizer) -> list[list[int]]:
     """
     Convert a list of Token objects to their corresponding token ID sequences.
 
     :param tokens: List of Token objects to convert
     :param tokenizer: The tokenizer to use for converting tokens to IDs
-    :param unk_token: The string form of the unk token.
     :return: List of token IDs corresponding to the input tokens
     """
-    unk_id = None if unk_token is None else tokenizer.convert_tokens_to_ids(unk_token)
     prefix, suffix = find_eos_bos(tokenizer)
 
+    prefix_ids, suffix_ids = None, None
+    vocab = tokenizer.get_vocab()
+    if prefix is not None:
+        prefix_ids = [vocab[token] for token in prefix]
+    if suffix is not None:
+        suffix_ids = [vocab[token] for token in suffix]
+
     token_ids: list[list[int]] = []
     for token in tokens:
+        token_sequence = []
+        if prefix_ids is not None:
+            token_sequence.extend(prefix_ids)
         if token.is_internal:
-            # Careful. Any incorrect tokens will just get `[UNK]``, so this could go horribly wrong
-            # Cast because return type is wrong.
-            token_id: int = cast(int, tokenizer.convert_tokens_to_ids(token.form)) or 0
-            # Explicitly check and warn if `unk_id` appears, but don't crash.
-            if unk_id is not None and token_id == unk_id and token.form != unk_token:
-                logger.warning(f"Token {token.form} was set to unk. This is wrong.")
-            token_ids.append([*prefix, token_id, *suffix])
+            token_id = vocab[token.form]
+            token_sequence.append(token_id)
         else:
-            token_ids.append(tokenizer.encode(token.form))
+            token_sequence.extend(tokenizer.encode(token.form).ids)
+        if suffix_ids is not None:
+            token_sequence.extend(suffix_ids)
+
+        token_ids.append(token_sequence)
 
     return token_ids
 
 
-def find_eos_bos(tokenizer: PreTrainedTokenizerFast) -> tuple[list[int], list[int]]:
+def find_eos_bos(tokenizer: Tokenizer) -> tuple[list[str] | None, list[str] | None]:
     """Finds the eos and bos tokens for a tokenizer."""
-    # Little bit complicated, because not all tokenizers have eos and bos tokens.
-    encoding = tokenizer.encode("a", add_special_tokens=True)
-    if len(encoding) != 3:
-        a_encoded = tokenizer.encode("a", add_special_tokens=False)
-        if len(a_encoded) != 1:
-            raise ValueError(
-                f"Error while encoding, couldn't determine eos and bos tokens. The model tokenizes 'a' to '{a_encoded}'"
-            )
-        a_idx = encoding.index(a_encoded[0])
-        prefix, suffix = encoding[:a_idx], encoding[a_idx + 1 :]
-    else:
-        prefix, suffix = encoding[:1], encoding[2:]
-    return prefix, suffix
-
-
-def _normalize_vocabulary_token(token: str, pre_tokenizer: PreTokenizer) -> str:
-    """Normalize a token that is not in the initial token vocabulary."""
-    # Add prefix space for byte tokenizers.
-    prefixed_token = f" {token}"
-    pretokenized_tokens: tuple[str, ...]
-    pretokenized_tokens, offsets = zip(*pre_tokenizer.pre_tokenize_str(prefixed_token))
-    # The first item is always the start of the token.
-    new_token = [pretokenized_tokens[0]]
-    # Loop over the subtokens and offsets.
-    for t, (s, _) in zip(pretokenized_tokens[1:], offsets[1:]):
-        # Do not prefix the token with a space if it starts with a metaspace.
-        if t.startswith("▁"):
-            new_token.append(t)
-        # If the character before the subtoken is a space, we have a
-        # multiword token. e.g., "room for the moon", which is split into
-        # ["room", "for", "the", "moon"].
-        # If it doesn't have a space, it is part of a complex multiword token,
-        # e.g., "chat-gpt", which is split into ["chat", "-", "gpt"].
-        elif prefixed_token[s - 1] == " ":
-            new_token.append(f" {t}")
-        else:
-            new_token.append(t)
-    normalized_token = "".join(new_token)
+    model = TokenizerModel.from_tokenizer(tokenizer)
+    return model.bos, model.eos
 
-    return normalized_token
 
+def _patch_tokenizer(tokenizer: PreTrainedTokenizerFast, lower_case: bool = True) -> TokenizerModel:
+    unk_token = cast(str | None, tokenizer.special_tokens_map.get("unk_token"))
+    pad_token = cast(str | None, tokenizer.special_tokens_map.get("pad_token"))
+    tokenizer_model = TokenizerModel.from_tokenizer(tokenizer.backend_tokenizer).make_model_greedy()
 
-def create_tokenizer(
-    tokenizer: PreTrainedTokenizerFast,
-    vocabulary: list[str],
-    token_remove_regex: re.Pattern | None = None,
-) -> PreTrainedTokenizerFast:
-    """
-    Create a tokenizer by adding tokens to the vocabulary.
-
-    This function turns any tokenizer into a supertoken tokenizer. It does the following:
-    1. Turns the tokenizer model into a unigram model.
-    2. Adds a new pretokenizer, splitting on punctuation.
-    3. Adds all tokens in vocabulary to the model.
-    4. Removes any internal tokens that conform to the regex.
-
-    :param tokenizer: The tokenizer to use.
-    :param vocabulary: The vocabulary to use.
-    :param token_remove_regex: The regex to use to remove tokens from the vocabulary.
-    :return: The created tokenizer.
-    """
-    unk_token = cast(Optional[str], tokenizer.special_tokens_map.get("unk_token"))
-    pad_token = cast(Optional[str], tokenizer.special_tokens_map.get("pad_token"))
-    cleaned_vocabulary, backend_tokenizer = clean_and_create_vocabulary(tokenizer, vocabulary, token_remove_regex)
-    new_tokenizer = replace_vocabulary(backend_tokenizer, cleaned_vocabulary, unk_token, pad_token)
+    # special_tokens map is preferred, then the original token is preferred, otherwise set manually.
+    tokenizer_model.unk_token = unk_token or tokenizer_model.unk_token or "[UNK]"
+    tokenizer_model.pad_token = pad_token or tokenizer_model.pad_token or "[PAD]"
+
+    if lower_case:
+        tokenizer_model = tokenizer_model.decase_vocabulary(remove_collisions=True)
 
-    tokenizer_object = PreTrainedTokenizerFast(tokenizer_object=new_tokenizer)
-    tokenizer_object.add_special_tokens({"pad_token": "[PAD]", "unk_token": "[UNK]"})
+    pretokenizer = tokenizer_model.pre_tokenizer
+    if pretokenizer is not None:
+        if isinstance(pretokenizer, PreTokenizerSequence):
+            for pretokenizer_module in pretokenizer.pretokenizers:
+                if isinstance(pretokenizer_module, ByteLevelPreTokenizer):
+                    pretokenizer_module.add_prefix_space = True
+        if isinstance(pretokenizer, ByteLevelPreTokenizer):
+            pretokenizer.add_prefix_space = True
 
-    return tokenizer_object
+    return tokenizer_model
diff --git a/model2vec/utils.py b/model2vec/utils.py
index eba1a4e..906b4d7 100644
--- a/model2vec/utils.py
+++ b/model2vec/utils.py
@@ -1,18 +1,14 @@
 # -*- coding: utf-8 -*-
 from __future__ import annotations
 
-import json
 import logging
 import re
 from importlib import import_module
 from importlib.metadata import metadata
-from pathlib import Path
 from typing import Any, Iterator, Protocol, cast
 
 import numpy as np
-import safetensors
 from joblib import Parallel
-from tokenizers import Tokenizer
 from tqdm import tqdm
 
 logger = logging.getLogger(__name__)
@@ -78,12 +74,15 @@ def get_package_extras(package: str, extra: str) -> Iterator[str]:
             found_extra = rest[0].split("==")[-1].strip(" \"'")
             if found_extra == extra:
                 prefix, *_ = _DIVIDERS.split(name)
+                prefix = prefix.split("@")[0].strip()
                 yield prefix.strip()
 
 
 def importable(module: str, extra: str) -> None:
     """Check if a module is importable."""
     module = dict(_MODULE_MAP).get(module, module)
+    # Allows this to work with git installed modules.
+    module = module.split("@")[0].strip()
     try:
         import_module(module)
     except ImportError:
diff --git a/pyproject.toml b/pyproject.toml
index d5c895c..72b98b5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,7 +3,7 @@ name = "model2vec"
 description = "Fast State-of-the-Art Static Embeddings"
 readme = { file = "README.md", content-type = "text/markdown" }
 license = { file = "LICENSE" }
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 authors = [{ name = "Stéphan Tulkens", email = "stephantul@gmail.com"}, {name = "Thomas van Dongen", email = "thomas123@live.nl"}]
 dynamic = ["version"]
 
@@ -60,7 +60,7 @@ dev = [
     "ruff",
 ]
 
-distill = ["torch", "transformers", "scikit-learn"]
+distill = ["torch", "transformers", "scikit-learn", "skeletoken"]
 onnx = ["onnx", "torch"]
 # train also installs inference
 train = ["torch", "lightning", "scikit-learn", "skops"]
diff --git a/tests/test_distillation.py b/tests/test_distillation.py
index 592fe0f..595cae9 100644
--- a/tests/test_distillation.py
+++ b/tests/test_distillation.py
@@ -29,14 +29,14 @@
 
 
 @pytest.mark.parametrize(
-    "vocabulary, pca_dims, apply_zipf",
+    "vocabulary, pca_dims",
     [
-        (None, 256, True),  # Output vocab with subwords, PCA applied
-        (["wordA", "wordB"], 4, False),  # Custom vocab with subword, PCA applied
-        (None, "auto", False),  # Subword, PCA set to 'auto'
-        (None, 1024, False),  # Subword, PCA set to high number.
-        (None, None, True),  # No PCA applied
-        (None, 0.9, True),  # PCA as float applied
+        (None, 256),  # Output vocab with subwords, PCA applied
+        (["wordA", "wordB"], 4),  # Custom vocab with subword, PCA applied
+        (None, "auto"),  # Subword, PCA set to 'auto'
+        (None, 1024),  # Subword, PCA set to high number.
+        (None, None),  # No PCA applied
+        (None, 0.9),  # PCA as float applied
     ],
 )
 @patch.object(import_module("model2vec.distill.distillation"), "model_info")
@@ -48,7 +48,6 @@ def test_distill_from_model(
     mock_transformer: PreTrainedModel,
     vocabulary: list[str] | None,
     pca_dims: int | None,
-    apply_zipf: bool,
 ) -> None:
     """Test distill function with different parameters."""
     # Mock the return value of model_info to avoid calling the Hugging Face API
@@ -65,7 +64,6 @@ def test_distill_from_model(
         vocabulary=vocabulary,
         device="cpu",
         pca_dims=pca_dims,
-        apply_zipf=apply_zipf,
         token_remove_pattern=None,
     )
 
@@ -74,7 +72,6 @@ def test_distill_from_model(
         vocabulary=vocabulary,
         device="cpu",
         pca_dims=pca_dims,
-        apply_zipf=apply_zipf,
         token_remove_pattern=None,
     )
 
@@ -100,9 +97,6 @@ def test_distill_removal_pattern(
     # mock_auto_tokenizer.return_value = mock_berttokenizer
     mock_auto_model.return_value = mock_transformer
 
-    # The vocab size is 30522, but we remove 998 tokens: [CLS], [SEP], and [MASK], and all [unused] tokens.
-    expected_vocab_size = mock_berttokenizer.vocab_size - 998
-
     static_model = distill_from_model(
         model=mock_transformer,
         tokenizer=mock_berttokenizer,
@@ -111,7 +105,8 @@ def test_distill_removal_pattern(
         token_remove_pattern=None,
     )
 
-    assert len(static_model.embedding) == expected_vocab_size
+    # 3 special tokens removed.
+    assert len(static_model.embedding) == mock_berttokenizer.vocab_size - 3
 
     # No tokens removed, nonsensical pattern
     static_model = distill_from_model(
@@ -122,7 +117,20 @@ def test_distill_removal_pattern(
         token_remove_pattern="£££££££££££££££££",
     )
 
-    assert len(static_model.embedding) == expected_vocab_size
+    # 3 special tokens removed
+    assert len(static_model.embedding) == mock_berttokenizer.vocab_size - 3
+
+    # No tokens removed, nonsensical pattern
+    static_model = distill_from_model(
+        model=mock_transformer,
+        tokenizer=mock_berttokenizer,
+        vocabulary=None,
+        device="cpu",
+        token_remove_pattern=r"\[unused.+\]",
+    )
+
+    # 995 unused + 3 special tokens removed
+    assert len(static_model.embedding) == mock_berttokenizer.vocab_size - 997
 
     # Weird pattern.
     with pytest.raises(ValueError):
@@ -136,19 +144,19 @@ def test_distill_removal_pattern(
 
 
 @pytest.mark.parametrize(
-    "vocabulary, pca_dims, apply_zipf, sif_coefficient, expected_shape",
+    "vocabulary, pca_dims, sif_coefficient, expected_shape",
     [
-        (None, 256, True, None, (29524, 256)),  # Output vocab with subwords, PCA applied
-        (None, "auto", False, None, (29524, 768)),  # Subword, PCA set to 'auto'
-        (None, "auto", True, 1e-4, (29524, 768)),  # Subword, PCA set to 'auto'
-        (None, "auto", False, 1e-4, (29524, 768)),  # Subword, PCA set to 'auto'
-        (None, "auto", True, 0, None),  # Sif too low
-        (None, "auto", True, 1, None),  # Sif too high
-        (None, "auto", False, 0, (29524, 768)),  # Sif too low, but apply_zipf is False
-        (None, "auto", False, 1, (29524, 768)),  # Sif too high, but apply_zipf is False
-        (None, 1024, False, None, (29524, 768)),  # Subword, PCA set to high number.
-        (["wordA", "wordB"], 4, False, None, (29526, 4)),  # Custom vocab with subword, PCA applied
-        (None, None, True, None, (29524, 768)),  # No PCA applied
+        (None, 256, None, (29525, 256)),  # Output vocab with subwords, PCA applied
+        (None, "auto", None, (29525, 768)),  # Subword, PCA set to 'auto'
+        (None, "auto", 1e-4, (29525, 768)),  # Subword, PCA set to 'auto'
+        (None, "auto", 1e-4, (29525, 768)),  # Subword, PCA set to 'auto'
+        (None, "auto", 0, None),  # Sif too low
+        (None, "auto", 1, None),  # Sif too high
+        (None, "auto", 0, (29525, 768)),  # Sif too low, but apply_zipf is False
+        (None, "auto", 1, (29525, 768)),  # Sif too high, but apply_zipf is False
+        (None, 1024, None, (29525, 768)),  # Subword, PCA set to high number.
+        (["wordA", "wordB"], 4, None, (29527, 4)),  # Custom vocab with subword, PCA applied
+        (None, None, None, (29525, 768)),  # No PCA applied
     ],
 )
 @patch.object(import_module("model2vec.distill.distillation"), "model_info")
@@ -159,7 +167,6 @@ def test_distill(
     mock_transformer: PreTrainedModel,
     vocabulary: list[str] | None,
     pca_dims: int | None,
-    apply_zipf: bool,
     sif_coefficient: float | None,
     expected_shape: tuple[int, int],
 ) -> None:
@@ -172,19 +179,13 @@ def test_distill(
 
     model_name = "tests/data/test_tokenizer"
 
-    if (
-        apply_zipf is not None
-        and apply_zipf
-        and sif_coefficient is not None
-        and (sif_coefficient <= 0 or sif_coefficient >= 1)
-    ):
+    if sif_coefficient is not None and (sif_coefficient <= 0 or sif_coefficient >= 1):
         with pytest.raises(ValueError):
             static_model = distill(
                 model_name=model_name,
                 vocabulary=vocabulary,
                 device="cpu",
                 pca_dims=pca_dims,
-                apply_zipf=apply_zipf,
                 sif_coefficient=sif_coefficient,
             )
 
@@ -195,7 +196,6 @@ def test_distill(
             vocabulary=vocabulary,
             device="cpu",
             pca_dims=pca_dims,
-            apply_zipf=apply_zipf,
             sif_coefficient=sif_coefficient,
         )
 
diff --git a/tests/test_tokenizer.py b/tests/test_tokenizer.py
deleted file mode 100644
index e1267df..0000000
--- a/tests/test_tokenizer.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import json
-
-import pytest
-from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
-
-from model2vec.tokenizer.model import _calculate_token_weight_for_unigram, _process_unigram, process_tokenizer
-from model2vec.tokenizer.normalizer import replace_normalizer
-from model2vec.tokenizer.pretokenizer import _FORBIDDEN_PRETOKENIZERS, _fix_single_pretokenizer, replace_pretokenizer
-from model2vec.tokenizer.tokenizer import _rename_added_token, create_tokenizer
-
-
-def test_fix_single_pretokenizer() -> None:
-    """Test the _fix_single_pretokenizer function."""
-    result = _fix_single_pretokenizer({"type": "ByteLevel", "add_prefix_space": False, "use_regex": True})
-    assert result == {"type": "ByteLevel", "add_prefix_space": True, "use_regex": False}
-
-    for tokenizer_type in _FORBIDDEN_PRETOKENIZERS:
-        result = _fix_single_pretokenizer({"type": tokenizer_type})
-        assert result is None
-
-    result = _fix_single_pretokenizer(
-        {"type": "Metaspace", "split": True, "prepend_scheme": "never", "replacement": "▁"}
-    )
-    assert result == {"type": "Metaspace", "replacement": "▁", "prepend_scheme": "always", "split": False}
-
-
-def test_replace_pretokenizer(mock_berttokenizer: PreTrainedTokenizerFast) -> None:
-    """Test the replace_pretokenizer function."""
-    tokenizer = replace_pretokenizer(mock_berttokenizer.backend_tokenizer)
-    assert tokenizer.pre_tokenizer is not None
-    assert tokenizer.pre_tokenizer.__class__.__name__ == "Metaspace"
-    assert tokenizer.pre_tokenizer.replacement == "▁"
-    assert tokenizer.pre_tokenizer.prepend_scheme == "always"
-    assert not tokenizer.pre_tokenizer.split
-
-    tokenizer.pre_tokenizer = None  # type: ignore
-    tokenizer = replace_pretokenizer(tokenizer)
-    assert tokenizer.pre_tokenizer is not None
-    assert tokenizer.pre_tokenizer.__class__.__name__ == "Metaspace"
-    assert tokenizer.pre_tokenizer.replacement == "▁"
-    assert tokenizer.pre_tokenizer.prepend_scheme == "always"
-    assert tokenizer.pre_tokenizer.split is False
-
-
-def test_replace_normalizer(mock_berttokenizer: PreTrainedTokenizerFast) -> None:
-    """Test the replace_normalizer function."""
-    tokenizer = replace_normalizer(mock_berttokenizer.backend_tokenizer)
-    assert tokenizer.normalizer is not None
-    assert tokenizer.normalizer.__class__.__name__ == "Sequence"
-
-    assert tokenizer.normalizer.normalize_str("Hello, World!") == "hello , world !"
-
-    tokenizer.normalizer = None  # type: ignore
-    tokenizer = replace_normalizer(tokenizer)
-    assert tokenizer.normalizer.normalize_str("Hello, World!") == "Hello , World !"
-
-
-@pytest.mark.parametrize(
-    "word,weight",
-    [
-        ("dog", 3),
-        ("cat", 3),
-        ("▁longer▁word", 14),
-        ("▁word", 6),
-        ("▁", 2),  # Single underscore
-        ("", 0),  # Empty string
-        ("▁a" * 100, 300),  # Long word with underscores
-    ],
-)
-def test_calculate_token_weight_for_unigram(word: str, weight: int) -> None:
-    """Test the _calculate_token_weight_for_unigram function."""
-    assert _calculate_token_weight_for_unigram(word) == weight
-
-
-def test_process_tokenizer(mock_berttokenizer: PreTrainedTokenizerFast) -> None:
-    """Test the process_tokenizer function."""
-    vocab = ["dog", "cat", "longer_word", "word", "a" * 100, "[UNK]"]
-    tokenizer_json = json.loads(mock_berttokenizer.backend_tokenizer.to_str())
-    tokenizer_json = process_tokenizer(tokenizer_json=tokenizer_json, pre_tokenized_tokens=vocab, unk_token="[UNK]")
-
-    assert tokenizer_json["model"]["type"] == "Unigram"
-    assert tokenizer_json["model"]["unk_id"] == 5  # Index of "[UNK]"
-    assert len(tokenizer_json["model"]["vocab"]) == 6
-    assert all(isinstance(token, tuple) and len(token) == 2 for token in tokenizer_json["model"]["vocab"])
-    for (x, _), y in zip(tokenizer_json["model"]["vocab"], vocab):
-        assert x == y, f"Expected {y}, but got {x}"
-
-
-def test_process_unigram() -> None:
-    """Test the _process_unigram function."""
-    vocab = ["dog", "cat", "longer_word", "word", "a" * 100, "[UNK]"]
-    orig_vocab = [("dog", 0), ("cat", 0)]
-    model = {"model": {"type": "Unigram", "vocab": orig_vocab}}
-    processed_model = _process_unigram(model, vocab, "[UNK]")
-    assert processed_model["model"]["type"] == "Unigram"
-    assert processed_model["model"]["unk_id"] == 5  # Index of "[UNK]"
-    assert len(processed_model["model"]["vocab"]) == 6
-    assert all(isinstance(token, list) and len(token) == 2 for token in processed_model["model"]["vocab"])
-
-    for (x, score), y in zip(processed_model["model"]["vocab"], vocab):
-        assert x == y, f"Expected {y}, but got {x}"
-        if x in orig_vocab:
-            assert score == 0
-
-    assert process_tokenizer(model, vocab, "[UNK]") == processed_model
-
-
-def test_rename_added_token() -> None:
-    """Test the _rename_added_token function."""
-    # Invalid input
-    result = _rename_added_token(None, "a", [{"content": "a", "id": 0}], ["a"])
-    assert result == [{"content": "a", "id": 0}]
-
-    # Rename 'a' to 'c'
-    result = _rename_added_token("a", "c", [{"content": "a"}], ["a"])
-    assert result == [{"content": "c", "id": 0}]
-
-
-def test_create_tokenizer(mock_berttokenizer: PreTrainedTokenizerFast) -> None:
-    """Test the create_tokenizer function."""
-    tokenizer = create_tokenizer(tokenizer=mock_berttokenizer, vocabulary=["dog", "catssssss"], token_remove_regex=None)
-    assert tokenizer.backend_tokenizer.get_vocab_size() == 29525
-    assert tokenizer.encode("catssssss") == [29524]
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 229615f..c3c9339 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -72,7 +72,7 @@ def test_importable() -> None:
 def test_get_package_extras() -> None:
     """Test package extras."""
     extras = set(get_package_extras("model2vec", "distill"))
-    assert extras == {"torch", "transformers", "scikit-learn"}
+    assert extras == {"torch", "transformers", "scikit-learn", "skeletoken"}
 
 
 def test_get_package_extras_empty() -> None:
diff --git a/uv.lock b/uv.lock
index c47be21..e430a7b 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,11 +1,10 @@
 version = 1
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.13'",
     "python_full_version == '3.12.*'",
     "python_full_version == '3.11.*'",
-    "python_full_version == '3.10.*'",
-    "python_full_version < '3.10'",
+    "python_full_version < '3.11'",
 ]
 
 [[package]]
@@ -101,23 +100,6 @@ wheels = [
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+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081 }
+wheels = [
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+]
+
 [[package]]
 name = "asttokens"
 version = "3.0.0"
@@ -165,8 +156,7 @@ name = "black"
 version = "25.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "click", version = "8.1.8", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
-    { name = "click", version = "8.2.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+    { name = "click" },
     { name = "mypy-extensions" },
     { name = "packaging" },
     { name = "pathspec" },
@@ -192,10 +182,6 @@ wheels = [
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