diff --git a/docs/source/conf.py b/docs/source/conf.py
index 20d4e1be..12d27a66 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -25,9 +25,9 @@
 author = "The Inseq Team"
 
 # The short X.Y version
-version = "0.5"
+version = "0.6"
 # The full version, including alpha/beta/rc tags
-release = "0.5.0"
+release = "0.6.0.dev0"
 
 
 # Prefix link to point to master, comment this during version release and uncomment below line
diff --git a/examples/inseq_tutorial.ipynb b/examples/inseq_tutorial.ipynb
index 3e54fd0c..cf38b86b 100644
--- a/examples/inseq_tutorial.ipynb
+++ b/examples/inseq_tutorial.ipynb
@@ -16,9 +16,7 @@
    "source": [
     "%%capture\n",
     "# Run in Colab to install local packages\n",
-    "%pip install bitsandbytes accelerate\n",
-    "%pip install git+https://github.com/huggingface/transformers.git\n",
-    "%pip install git+https://github.com/inseq-team/inseq.git"
+    "%pip install bitsandbytes accelerate transformers inseq"
    ]
   },
   {
@@ -28,7 +26,7 @@
    "source": [
     "This tutorial showcases how to use the [inseq](https://github.com/inseq-team/inseq) library for various interpretability analyses, with a focus on advanced use cases such as aggregation and contrastive attribution. The tutorial was adapted from the [LCL'23 tutorial on Interpretability for NLP](https://github.com/gsarti/lcl23-xnlm-lab) with the goal of updating it whenever new functionalities or breaking changes are introduced.\n",
     "\n",
-    "⚠️ **IMPORTANT** ⚠️ : `inseq` is a very new library and under active development. Current results were obtained using the latest development versions on June 30, 2023. If you find any issue, or you are not able to reproduce the results shown here, we'd love if you could open an issue on [the inseq Github repository](https://github.com/inseq-team/inseq) so that we could update the tutorial accordingly! 🙂\n",
+    "⚠️ **IMPORTANT** ⚠️ : `inseq` is a very new library and under active development. Current results were obtained using the latest inseq release. If you find any issue, or you are not able to reproduce the results shown here, we'd love if you could open an issue on [the inseq Github repository](https://github.com/inseq-team/inseq) so that we could update the tutorial accordingly! 🙂\n",
     "\n",
     "# Introduction: Feature Attribution for NLP\n",
     "\n",
@@ -86,7 +84,7 @@
     "\n",
     "[Inseq](https://github.com/inseq-team/inseq) ([Sarti et al., 2023](https://arxiv.org/abs/2302.13942)) is a toolkit based on the [🤗 Transformers](https//github.com/huggingface/transformers) and [Captum](https://captum.ai/docs/introduction) libraries for intepreting language generation models using feature attribution methods. Inseq allows you to analyze the behavior of a language generation model by computing the importance of each input token for each token in the generated output using the various categories of attribution methods like those described in the previous section (use `inseq.list_feature_attribution_methods()` to list all available methods). You can refer to the [Inseq paper](https://arxiv.org/abs/2302.13942) for an overview of the tool and some usage examples.\n",
     "\n",
-    "The current version of the library (v0.5.0, December 2023) supports all [`AutoModelForSeq2SeqLM`](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForSeq2SeqLM) (among others, [T5](https://huggingface.co/docs/transformers/model_doc/t5), [Bart](https://huggingface.co/docs/transformers/model_doc/bart) and all >1000 [MarianNMT](https://huggingface.co/docs/transformers/model_doc/marian) MT models) and [AutoModelForCausalLM](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForCausalLM) (among others, [GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2), [Bloom](https://huggingface.co/docs/transformers/model_doc/bloom) and [LLaMa](https://huggingface.co/docs/transformers/model_doc/llama)), including advanced tools for custom attribution targets and post-processing of attribution matrices.\n",
+    "Inseq supports all [`AutoModelForSeq2SeqLM`](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForSeq2SeqLM) (among others, [T5](https://huggingface.co/docs/transformers/model_doc/t5), [Bart](https://huggingface.co/docs/transformers/model_doc/bart) and all >1000 [MarianNMT](https://huggingface.co/docs/transformers/model_doc/marian) MT models) and [AutoModelForCausalLM](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForCausalLM) (among others, [GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2), [Bloom](https://huggingface.co/docs/transformers/model_doc/bloom) and [LLaMa](https://huggingface.co/docs/transformers/model_doc/llama)), including advanced tools for custom attribution targets and post-processing of attribution matrices.\n",
     "\n",
     "The following code is a \"Hello world\" equivalent in Inseq: in three lines of code, an English-to-Italian machine translation model is loaded alongside an attribution method, attribution is performed, and results are visualized:"
    ]
diff --git a/inseq/attr/attribution_decorators.py b/inseq/attr/attribution_decorators.py
index 56b565dc..60c58d12 100644
--- a/inseq/attr/attribution_decorators.py
+++ b/inseq/attr/attribution_decorators.py
@@ -14,8 +14,9 @@
 """Decorators for attribution methods."""
 
 import logging
+from collections.abc import Sequence
 from functools import wraps
-from typing import Any, Callable, List, Optional, Sequence
+from typing import Any, Callable, Optional
 
 from ..data.data_utils import TensorWrapper
 
@@ -55,7 +56,7 @@ def batched(f: Callable[..., Any]) -> Callable[..., Any]:
 
     @wraps(f)
     def batched_wrapper(self, *args, batch_size: Optional[int] = None, **kwargs):
-        def get_batched(bs: Optional[int], seq: Sequence[Any]) -> List[List[Any]]:
+        def get_batched(bs: Optional[int], seq: Sequence[Any]) -> list[list[Any]]:
             if isinstance(seq, str):
                 seq = [seq]
             if isinstance(seq, list):
diff --git a/inseq/attr/feat/attribution_utils.py b/inseq/attr/feat/attribution_utils.py
index 4dddc1f6..8da4f899 100644
--- a/inseq/attr/feat/attribution_utils.py
+++ b/inseq/attr/feat/attribution_utils.py
@@ -1,8 +1,6 @@
 import logging
 import math
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
-
-import torch
+from typing import TYPE_CHECKING, Any, Callable, Optional, Union
 
 from ...utils import extract_signature_args, get_aligned_idx
 from ...utils.typing import (
@@ -10,6 +8,7 @@
     OneOrMoreIdSequences,
     OneOrMoreTokenSequences,
     SingleScorePerStepTensor,
+    StepAttributionTensor,
     TextInput,
     TokenWithId,
 )
@@ -51,7 +50,7 @@ def check_attribute_positions(
     max_length: int,
     attr_pos_start: Optional[int] = None,
     attr_pos_end: Optional[int] = None,
-) -> Tuple[int, int]:
+) -> tuple[int, int]:
     r"""Checks whether the combination of start/end positions for attribution is valid.
 
     Args:
@@ -90,8 +89,8 @@ def join_token_ids(
     tokens: OneOrMoreTokenSequences,
     ids: OneOrMoreIdSequences,
     contrast_tokens: Optional[OneOrMoreTokenSequences] = None,
-    contrast_targets_alignments: Optional[List[List[Tuple[int, int]]]] = None,
-) -> List[TokenWithId]:
+    contrast_targets_alignments: Optional[list[list[tuple[int, int]]]] = None,
+) -> list[TokenWithId]:
     """Joins tokens and ids into a list of TokenWithId objects."""
     if contrast_tokens is None:
         contrast_tokens = tokens
@@ -116,10 +115,10 @@ def join_token_ids(
 def extract_args(
     attribution_method: "FeatureAttribution",
     attributed_fn: Callable[..., SingleScorePerStepTensor],
-    step_scores: List[str],
-    default_args: List[str],
+    step_scores: list[str],
+    default_args: list[str],
     **kwargs,
-) -> Tuple[Dict[str, Any], Dict[str, Any], Dict[str, Any]]:
+) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]:
     attribution_args = kwargs.pop("attribution_args", {})
     attributed_fn_args = kwargs.pop("attributed_fn_args", {})
     step_scores_args = kwargs.pop("step_scores_args", {})
@@ -143,17 +142,13 @@ def extract_args(
 
 
 def get_source_target_attributions(
-    attr: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
+    attr: Union[StepAttributionTensor, tuple[StepAttributionTensor, StepAttributionTensor]],
     is_encoder_decoder: bool,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    if is_encoder_decoder:
-        if isinstance(attr, tuple) and len(attr) > 1:
-            return attr[0], attr[1]
-        elif isinstance(attr, tuple) and len(attr) == 1:
-            return attr[0], None
+) -> tuple[Optional[StepAttributionTensor], Optional[StepAttributionTensor]]:
+    if isinstance(attr, tuple):
+        if is_encoder_decoder:
+            return (attr[0], attr[1]) if len(attr) > 1 else (attr[0], None)
         else:
-            return attr, None
-    elif isinstance(attr, tuple):
-        return None, attr[0]
+            return (None, attr[0])
     else:
-        return None, attr
+        return (attr, None) if is_encoder_decoder else (None, attr)
diff --git a/inseq/attr/feat/feature_attribution.py b/inseq/attr/feat/feature_attribution.py
index 1bce38e6..250cd700 100644
--- a/inseq/attr/feat/feature_attribution.py
+++ b/inseq/attr/feat/feature_attribution.py
@@ -18,7 +18,7 @@
 """
 import logging
 from datetime import datetime
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Callable, Optional, Union
 
 import torch
 from jaxtyping import Int
@@ -173,12 +173,12 @@ def prepare_and_attribute(
         pretty_progress: bool = True,
         output_step_attributions: bool = False,
         attribute_target: bool = False,
-        step_scores: List[str] = [],
+        step_scores: list[str] = [],
         include_eos_baseline: bool = False,
         attributed_fn: Union[str, Callable[..., SingleScorePerStepTensor], None] = None,
-        attribution_args: Dict[str, Any] = {},
-        attributed_fn_args: Dict[str, Any] = {},
-        step_scores_args: Dict[str, Any] = {},
+        attribution_args: dict[str, Any] = {},
+        attributed_fn_args: dict[str, Any] = {},
+        step_scores_args: dict[str, Any] = {},
     ) -> FeatureAttributionOutput:
         r"""Prepares inputs and performs attribution.
 
@@ -276,11 +276,11 @@ def format_contrastive_targets(
         self,
         target_sequences: TextSequences,
         target_tokens: OneOrMoreTokenSequences,
-        attributed_fn_args: Dict[str, Any],
-        step_scores_args: Dict[str, Any],
+        attributed_fn_args: dict[str, Any],
+        step_scores_args: dict[str, Any],
         attr_pos_start: int,
         attr_pos_end: int,
-    ) -> Tuple[Optional[DecoderOnlyBatch], Optional[List[List[Tuple[int, int]]]], Dict[str, Any], Dict[str, Any]]:
+    ) -> tuple[Optional[DecoderOnlyBatch], Optional[list[list[tuple[int, int]]]], dict[str, Any], dict[str, Any]]:
         contrast_batch, contrast_targets_alignments = None, None
         contrast_targets = attributed_fn_args.get("contrast_targets", None)
         if contrast_targets is None:
@@ -327,10 +327,10 @@ def attribute(
         pretty_progress: bool = True,
         output_step_attributions: bool = False,
         attribute_target: bool = False,
-        step_scores: List[str] = [],
-        attribution_args: Dict[str, Any] = {},
-        attributed_fn_args: Dict[str, Any] = {},
-        step_scores_args: Dict[str, Any] = {},
+        step_scores: list[str] = [],
+        attribution_args: dict[str, Any] = {},
+        attributed_fn_args: dict[str, Any] = {},
+        step_scores_args: dict[str, Any] = {},
     ) -> FeatureAttributionOutput:
         r"""Performs the feature attribution procedure using the specified attribution method.
 
@@ -501,10 +501,10 @@ def filtered_attribute_step(
         attributed_fn: Callable[..., SingleScorePerStepTensor],
         target_attention_mask: Optional[Int[torch.Tensor, "batch_size 1"]] = None,
         attribute_target: bool = False,
-        step_scores: List[str] = [],
-        attribution_args: Dict[str, Any] = {},
-        attributed_fn_args: Dict[str, Any] = {},
-        step_scores_args: Dict[str, Any] = {},
+        step_scores: list[str] = [],
+        attribution_args: dict[str, Any] = {},
+        attributed_fn_args: dict[str, Any] = {},
+        step_scores_args: dict[str, Any] = {},
     ) -> FeatureAttributionStepOutput:
         r"""Performs a single attribution step for all the sequences in the batch that
         still have valid target_ids, as identified by the target_attention_mask.
@@ -581,7 +581,7 @@ def filtered_attribute_step(
             attribution_args,
         )
         # Format step scores arguments and calculate step scores
-        for step_score in step_scores:
+        for score in step_scores:
             step_fn_args = self.attribution_model.formatter.format_step_function_args(
                 attribution_model=self.attribution_model,
                 forward_output=output,
@@ -589,23 +589,23 @@ def filtered_attribute_step(
                 is_attributed_fn=False,
                 batch=batch,
             )
-            step_fn_extra_args = get_step_scores_args([step_score], step_scores_args)
-            step_output.step_scores[step_score] = get_step_scores(step_score, step_fn_args, step_fn_extra_args)
+            step_fn_extra_args = get_step_scores_args([score], step_scores_args)
+            step_output.step_scores[score] = get_step_scores(score, step_fn_args, step_fn_extra_args).to("cpu")
         # Reinsert finished sentences
         if target_attention_mask is not None and is_filtered:
             step_output.remap_from_filtered(target_attention_mask, orig_batch)
         step_output = step_output.detach().to("cpu")
         return step_output
 
-    def get_attribution_args(self, **kwargs) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+    def get_attribution_args(self, **kwargs) -> tuple[dict[str, Any], dict[str, Any]]:
         if hasattr(self, "method") and hasattr(self.method, "attribute"):
             return extract_signature_args(kwargs, self.method.attribute, self.ignore_extra_args, return_remaining=True)
         return {}, {}
 
     def attribute_step(
         self,
-        attribute_fn_main_args: Dict[str, Any],
-        attribution_args: Dict[str, Any] = {},
+        attribute_fn_main_args: dict[str, Any],
+        attribution_args: dict[str, Any] = {},
     ) -> FeatureAttributionStepOutput:
         r"""Performs a single attribution step for the specified attribution arguments.
 
@@ -663,7 +663,7 @@ class DummyAttribution(FeatureAttribution):
     method_name = "dummy"
 
     def attribute_step(
-        self, attribute_fn_main_args: Dict[str, Any], attribution_args: Dict[str, Any] = {}
+        self, attribute_fn_main_args: dict[str, Any], attribution_args: dict[str, Any] = {}
     ) -> FeatureAttributionStepOutput:
         return FeatureAttributionStepOutput(
             source_attributions=None,
diff --git a/inseq/attr/feat/gradient_attribution.py b/inseq/attr/feat/gradient_attribution.py
index b86167e5..3eefe63e 100644
--- a/inseq/attr/feat/gradient_attribution.py
+++ b/inseq/attr/feat/gradient_attribution.py
@@ -14,7 +14,7 @@
 """Gradient-based feature attribution methods."""
 
 import logging
-from typing import Any, Dict
+from typing import Any
 
 from captum.attr import (
     DeepLift,
@@ -65,8 +65,8 @@ def unhook(self, **kwargs):
 
     def attribute_step(
         self,
-        attribute_fn_main_args: Dict[str, Any],
-        attribution_args: Dict[str, Any] = {},
+        attribute_fn_main_args: dict[str, Any],
+        attribution_args: dict[str, Any] = {},
     ) -> GranularFeatureAttributionStepOutput:
         r"""Performs a single attribution step for the specified attribution arguments.
 
@@ -95,9 +95,9 @@ def attribute_step(
             attr, self.attribution_model.is_encoder_decoder
         )
         return GranularFeatureAttributionStepOutput(
-            source_attributions=source_attributions,
-            target_attributions=target_attributions,
-            step_scores={"deltas": deltas} if deltas is not None else None,
+            source_attributions=source_attributions.to("cpu") if source_attributions is not None else None,
+            target_attributions=target_attributions.to("cpu") if target_attributions is not None else None,
+            step_scores={"deltas": deltas.to("cpu")} if deltas is not None else None,
         )
 
 
diff --git a/inseq/attr/feat/internals_attribution.py b/inseq/attr/feat/internals_attribution.py
index e408a97a..9c6e8923 100644
--- a/inseq/attr/feat/internals_attribution.py
+++ b/inseq/attr/feat/internals_attribution.py
@@ -14,7 +14,7 @@
 """Attention-based feature attribution methods."""
 
 import logging
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 
 from captum._utils.typing import TensorOrTupleOfTensorsGeneric
 from captum.attr._utils.attribution import Attribution
@@ -76,7 +76,7 @@ def attribute(
             """
             # We adopt the format [batch_size, sequence_length, num_layers, num_heads]
             # for consistency with other multi-unit methods (e.g. gradient attribution)
-            decoder_self_attentions = decoder_self_attentions[..., -1, :].clone().permute(0, 3, 1, 2)
+            decoder_self_attentions = decoder_self_attentions[..., -1, :].to("cpu").clone().permute(0, 3, 1, 2)
             if self.forward_func.is_encoder_decoder:
                 sequence_scores = {}
                 if len(inputs) > 1:
@@ -84,9 +84,11 @@ def attribute(
                 else:
                     target_attributions = None
                     sequence_scores["decoder_self_attentions"] = decoder_self_attentions
-                sequence_scores["encoder_self_attentions"] = encoder_self_attentions.clone().permute(0, 3, 4, 1, 2)
+                sequence_scores["encoder_self_attentions"] = (
+                    encoder_self_attentions.to("cpu").clone().permute(0, 3, 4, 1, 2)
+                )
                 return MultiDimensionalFeatureAttributionStepOutput(
-                    source_attributions=cross_attentions[..., -1, :].clone().permute(0, 3, 1, 2),
+                    source_attributions=cross_attentions[..., -1, :].to("cpu").clone().permute(0, 3, 1, 2),
                     target_attributions=target_attributions,
                     sequence_scores=sequence_scores,
                     _num_dimensions=2,  # num_layers, num_heads
@@ -108,7 +110,7 @@ def __init__(self, attribution_model, **kwargs):
 
     def attribute_step(
         self,
-        attribute_fn_main_args: Dict[str, Any],
-        attribution_args: Dict[str, Any],
+        attribute_fn_main_args: dict[str, Any],
+        attribution_args: dict[str, Any],
     ) -> MultiDimensionalFeatureAttributionStepOutput:
         return self.method.attribute(**attribute_fn_main_args, **attribution_args)
diff --git a/inseq/attr/feat/ops/discretized_integrated_gradients.py b/inseq/attr/feat/ops/discretized_integrated_gradients.py
index 73b06bf7..c3ad4908 100644
--- a/inseq/attr/feat/ops/discretized_integrated_gradients.py
+++ b/inseq/attr/feat/ops/discretized_integrated_gradients.py
@@ -17,7 +17,7 @@
 # OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
 from pathlib import Path
-from typing import Any, Callable, List, Tuple, Union
+from typing import Any, Callable, Union
 
 import torch
 from captum._utils.common import (
@@ -51,7 +51,7 @@ def load_monotonic_path_builder(
         self,
         model_name: str,
         vocabulary_embeddings: VocabularyEmbeddingsTensor,
-        special_tokens: List[int],
+        special_tokens: list[int],
         cache_dir: Path = INSEQ_ARTIFACTS_CACHE / "dig_knn",
         embedding_scaling: int = 1,
         **kwargs,
@@ -67,7 +67,7 @@ def load_monotonic_path_builder(
         )
 
     @staticmethod
-    def get_inputs_baselines(scaled_features_tpl: Tuple[Tensor, ...], n_steps: int) -> Tuple[Tensor, ...]:
+    def get_inputs_baselines(scaled_features_tpl: tuple[Tensor, ...], n_steps: int) -> tuple[Tensor, ...]:
         # Baseline and inputs are reversed in the path builder
         # For every element in the batch, the first embedding of the sub-tensor
         # of shape (n_steps x embedding_dim) is the baseline, the last is the input.
@@ -96,7 +96,7 @@ def attribute(  # type: ignore
         method: str = "greedy",
         internal_batch_size: Union[None, int] = None,
         return_convergence_delta: bool = False,
-    ) -> Union[TensorOrTupleOfTensorsGeneric, Tuple[TensorOrTupleOfTensorsGeneric, Tensor]]:
+    ) -> Union[TensorOrTupleOfTensorsGeneric, tuple[TensorOrTupleOfTensorsGeneric, Tensor]]:
         n_examples = inputs[0].shape[0]
         # Keeps track whether original input is a tuple or not before
         # converting it into a tuple.
@@ -146,11 +146,11 @@ def attribute(  # type: ignore
 
     def _attribute(
         self,
-        scaled_features_tpl: Tuple[Tensor, ...],
+        scaled_features_tpl: tuple[Tensor, ...],
         target: TargetType = None,
         additional_forward_args: Any = None,
         n_steps: int = 50,
-    ) -> Tuple[Tensor, ...]:
+    ) -> tuple[Tensor, ...]:
         additional_forward_args = _format_additional_forward_args(additional_forward_args)
         input_additional_args = (
             _expand_additional_forward_args(additional_forward_args, n_steps)
diff --git a/inseq/attr/feat/ops/monotonic_path_builder.py b/inseq/attr/feat/ops/monotonic_path_builder.py
index 7b4b468d..d45fec4b 100644
--- a/inseq/attr/feat/ops/monotonic_path_builder.py
+++ b/inseq/attr/feat/ops/monotonic_path_builder.py
@@ -23,7 +23,7 @@
 from enum import Enum
 from itertools import islice
 from pathlib import Path
-from typing import Any, List, Optional, Tuple, Union
+from typing import Any, Optional, Union
 
 import torch
 from jaxtyping import Float, Int
@@ -56,7 +56,7 @@ class UnknownPathBuildingStrategy(Exception):
     def __init__(
         self,
         strategy: str,
-        *args: Tuple[Any],
+        *args: tuple[Any],
     ) -> None:
         """Initialize the exception."""
         super().__init__(
@@ -75,7 +75,7 @@ def __init__(
         self,
         vocabulary_embeddings: VocabularyEmbeddingsTensor,
         knn_graph: "csr_matrix",
-        special_tokens: List[int] = [],
+        special_tokens: list[int] = [],
     ) -> None:
         """Initialize the monotonic path builder."""
         self.vocabulary_embeddings = vocabulary_embeddings
@@ -112,7 +112,7 @@ def load(
         overwrite_cache: bool = False,
         cache_dir: Path = INSEQ_ARTIFACTS_CACHE / "path_knn",
         vocabulary_embeddings: Optional[VocabularyEmbeddingsTensor] = None,
-        special_tokens: List[int] = [],
+        special_tokens: list[int] = [],
         embedding_scaling: int = 1,
     ) -> "MonotonicPathBuilder":
         """Load a cached monotonic path builder from a model name, or compute it if it does not exist."""
@@ -188,7 +188,7 @@ def find_path(
         baseline_idx: int,
         n_steps: Optional[int] = 30,
         strategy: Optional[str] = "greedy",
-    ) -> List[int]:
+    ) -> list[int]:
         """Find a monotonic path from a word to a baseline."""
         # if word_idx is a special token copy it and return
         if word_idx in self.special_tokens:
@@ -207,7 +207,7 @@ def find_path(
         return word_path
 
     def build_monotonic_path_embedding(
-        self, word_path: List[int], baseline_idx: int, n_steps: int = 30
+        self, word_path: list[int], baseline_idx: int, n_steps: int = 30
     ) -> Float[torch.Tensor, "n_steps embed_size"]:
         """Build a monotonic path embedding from a word path."""
         baseline_vec = self.vocabulary_embeddings[baseline_idx]
@@ -231,7 +231,7 @@ def get_closest_word(
         self,
         word_idx: int,
         baseline_idx: int,
-        word_path: List[int],
+        word_path: list[int],
         strategy: str = "greedy",
         n_steps: int = 30,
     ) -> int:
diff --git a/inseq/attr/feat/ops/sequential_integrated_gradients.py b/inseq/attr/feat/ops/sequential_integrated_gradients.py
index b89074d1..e63a27f2 100644
--- a/inseq/attr/feat/ops/sequential_integrated_gradients.py
+++ b/inseq/attr/feat/ops/sequential_integrated_gradients.py
@@ -17,7 +17,7 @@
 # OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
 import typing
-from typing import Any, Callable, List, Tuple, Union
+from typing import Any, Callable, Union
 
 import torch
 from captum._utils.common import (
@@ -138,7 +138,7 @@ def attribute(
         internal_batch_size: Union[None, int] = None,
         *,
         return_convergence_delta: Literal[True],
-    ) -> Tuple[TensorOrTupleOfTensorsGeneric, Tensor]:
+    ) -> tuple[TensorOrTupleOfTensorsGeneric, Tensor]:
         ...
 
     def attribute(  # type: ignore
@@ -153,7 +153,7 @@ def attribute(  # type: ignore
         return_convergence_delta: bool = False,
     ) -> Union[
         TensorOrTupleOfTensorsGeneric,
-        Tuple[TensorOrTupleOfTensorsGeneric, Tensor],
+        tuple[TensorOrTupleOfTensorsGeneric, Tensor],
     ]:
         r"""
         This method attributes the output of the model with given target index
@@ -367,15 +367,15 @@ def attribute(  # type: ignore
 
     def _attribute(
         self,
-        inputs: Tuple[Tensor, ...],
-        baselines: Tuple[Union[Tensor, int, float], ...],
+        inputs: tuple[Tensor, ...],
+        baselines: tuple[Union[Tensor, int, float], ...],
         target: TargetType = None,
         additional_forward_args: Any = None,
         n_steps: int = 50,
         method: str = "gausslegendre",
         idx: int = None,
-        step_sizes_and_alphas: Union[None, Tuple[List[float], List[float]]] = None,
-    ) -> Tuple[Tensor, ...]:
+        step_sizes_and_alphas: Union[None, tuple[list[float], list[float]]] = None,
+    ) -> tuple[Tensor, ...]:
         if step_sizes_and_alphas is None:
             # retrieve step size and scaling factor for specified
             # approximation method
diff --git a/inseq/attr/feat/perturbation_attribution.py b/inseq/attr/feat/perturbation_attribution.py
index 25fb791f..fbebb780 100644
--- a/inseq/attr/feat/perturbation_attribution.py
+++ b/inseq/attr/feat/perturbation_attribution.py
@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict
+from typing import Any
 
 from captum.attr import Occlusion
 
@@ -44,8 +44,8 @@ def __init__(self, attribution_model):
 
     def attribute_step(
         self,
-        attribute_fn_main_args: Dict[str, Any],
-        attribution_args: Dict[str, Any] = {},
+        attribute_fn_main_args: dict[str, Any],
+        attribution_args: dict[str, Any] = {},
     ) -> CoarseFeatureAttributionStepOutput:
         r"""Sliding window shapes is defined as a tuple.
         First entry is between 1 and length of input.
@@ -77,8 +77,8 @@ def attribute_step(
             target_attributions = target_attributions[:, :, 0].abs()
 
         return CoarseFeatureAttributionStepOutput(
-            source_attributions=source_attributions,
-            target_attributions=target_attributions,
+            source_attributions=source_attributions.to("cpu") if source_attributions is not None else None,
+            target_attributions=target_attributions.to("cpu") if target_attributions is not None else None,
         )
 
 
@@ -100,8 +100,8 @@ def __init__(self, attribution_model, **kwargs):
 
     def attribute_step(
         self,
-        attribute_fn_main_args: Dict[str, Any],
-        attribution_args: Dict[str, Any] = {},
+        attribute_fn_main_args: dict[str, Any],
+        attribution_args: dict[str, Any] = {},
     ) -> GranularFeatureAttributionStepOutput:
         if len(attribute_fn_main_args["inputs"]) > 1:
             # Captum's `_evaluate_batch` function for LIME does not account for multiple inputs when encoder-decoder
diff --git a/inseq/attr/step_functions.py b/inseq/attr/step_functions.py
index 6034f094..d9cd6092 100644
--- a/inseq/attr/step_functions.py
+++ b/inseq/attr/step_functions.py
@@ -1,7 +1,7 @@
 import logging
 from dataclasses import dataclass
 from inspect import signature
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Protocol, Tuple, Union
+from typing import TYPE_CHECKING, Any, Optional, Protocol, Union
 
 import torch
 import torch.nn.functional as F
@@ -130,7 +130,7 @@ def contrast_logits_fn(
     args: StepFunctionArgs,
     contrast_sources: Optional[FeatureAttributionInput] = None,
     contrast_targets: Optional[FeatureAttributionInput] = None,
-    contrast_targets_alignments: Optional[List[List[Tuple[int, int]]]] = None,
+    contrast_targets_alignments: Optional[list[list[tuple[int, int]]]] = None,
     contrast_force_inputs: bool = False,
 ):
     """Returns the logit of a generation target given contrastive context or target prediction alternative.
@@ -153,7 +153,7 @@ def contrast_prob_fn(
     args: StepFunctionArgs,
     contrast_sources: Optional[FeatureAttributionInput] = None,
     contrast_targets: Optional[FeatureAttributionInput] = None,
-    contrast_targets_alignments: Optional[List[List[Tuple[int, int]]]] = None,
+    contrast_targets_alignments: Optional[list[list[tuple[int, int]]]] = None,
     logprob: bool = False,
     contrast_force_inputs: bool = False,
 ):
@@ -177,7 +177,7 @@ def pcxmi_fn(
     args: StepFunctionArgs,
     contrast_sources: Optional[FeatureAttributionInput] = None,
     contrast_targets: Optional[FeatureAttributionInput] = None,
-    contrast_targets_alignments: Optional[List[List[Tuple[int, int]]]] = None,
+    contrast_targets_alignments: Optional[list[list[tuple[int, int]]]] = None,
     contrast_force_inputs: bool = False,
 ) -> SingleScorePerStepTensor:
     """Compute the pointwise conditional cross-mutual information (P-CXMI) of target ids given original and contrastive
@@ -201,7 +201,7 @@ def kl_divergence_fn(
     args: StepFunctionArgs,
     contrast_sources: Optional[FeatureAttributionInput] = None,
     contrast_targets: Optional[FeatureAttributionInput] = None,
-    contrast_targets_alignments: Optional[List[List[Tuple[int, int]]]] = None,
+    contrast_targets_alignments: Optional[list[list[tuple[int, int]]]] = None,
     top_k: int = 0,
     top_p: float = 1.0,
     min_tokens_to_keep: int = 1,
@@ -260,7 +260,7 @@ def contrast_prob_diff_fn(
     args: StepFunctionArgs,
     contrast_sources: Optional[FeatureAttributionInput] = None,
     contrast_targets: Optional[FeatureAttributionInput] = None,
-    contrast_targets_alignments: Optional[List[List[Tuple[int, int]]]] = None,
+    contrast_targets_alignments: Optional[list[list[tuple[int, int]]]] = None,
     logprob: bool = False,
     contrast_force_inputs: bool = False,
 ):
@@ -288,7 +288,7 @@ def contrast_logits_diff_fn(
     args: StepFunctionArgs,
     contrast_sources: Optional[FeatureAttributionInput] = None,
     contrast_targets: Optional[FeatureAttributionInput] = None,
-    contrast_targets_alignments: Optional[List[List[Tuple[int, int]]]] = None,
+    contrast_targets_alignments: Optional[list[list[tuple[int, int]]]] = None,
     contrast_force_inputs: bool = False,
 ):
     """Equivalent to ``contrast_prob_diff_fn`` but for logits. The original target function used in
@@ -310,7 +310,7 @@ def in_context_pvi_fn(
     args: StepFunctionArgs,
     contrast_sources: Optional[FeatureAttributionInput] = None,
     contrast_targets: Optional[FeatureAttributionInput] = None,
-    contrast_targets_alignments: Optional[List[List[Tuple[int, int]]]] = None,
+    contrast_targets_alignments: Optional[list[list[tuple[int, int]]]] = None,
     contrast_force_inputs: bool = False,
 ):
     """Returns the in-context pointwise V-usable information as defined by `Lu et al. (2023)
@@ -419,15 +419,15 @@ def get_step_function(score_identifier: str) -> StepFunction:
 def get_step_scores(
     score_identifier: str,
     step_fn_args: StepFunctionArgs,
-    step_fn_extra_args: Dict[str, Any] = {},
+    step_fn_extra_args: dict[str, Any] = {},
 ) -> SingleScorePerStepTensor:
     """Returns step scores for the target tokens in the batch."""
     return get_step_function(score_identifier)(step_fn_args, **step_fn_extra_args)
 
 
 def get_step_scores_args(
-    score_identifiers: List[str], kwargs: Dict[str, Any], default_args: Optional[Dict[str, Any]] = None
-) -> Dict[str, Any]:
+    score_identifiers: list[str], kwargs: dict[str, Any], default_args: Optional[dict[str, Any]] = None
+) -> dict[str, Any]:
     step_scores_args = {}
     for step_fn_id in score_identifiers:
         step_fn = get_step_function(step_fn_id)
@@ -442,7 +442,7 @@ def get_step_scores_args(
     return step_scores_args
 
 
-def list_step_functions() -> List[str]:
+def list_step_functions() -> list[str]:
     """Lists identifiers for all available step scores. One or more step scores identifiers can be passed to the
     :meth:`~inseq.models.AttributionModel.attribute` method either to compute scores while attributing (``step_scores``
     parameter), or as target function for the attribution, if supported by the attribution method (``attributed_fn``
@@ -454,7 +454,7 @@ def list_step_functions() -> List[str]:
 def register_step_function(
     fn: StepFunction,
     identifier: str,
-    aggregate_map: Optional[Dict[str, str]] = None,
+    aggregate_map: Optional[dict[str, str]] = None,
     overwrite: bool = False,
 ) -> None:
     """Registers a function to be used to compute step scores and store them in the
diff --git a/inseq/commands/attribute/attribute.py b/inseq/commands/attribute/attribute.py
index 55607abc..4869b846 100644
--- a/inseq/commands/attribute/attribute.py
+++ b/inseq/commands/attribute/attribute.py
@@ -1,5 +1,5 @@
 import logging
-from typing import List, Optional
+from typing import Optional
 
 from ... import FeatureAttributionOutput, load_model
 from ..base import BaseCLICommand
@@ -8,8 +8,8 @@
 
 def aggregate_attribution_scores(
     out: FeatureAttributionOutput,
-    selectors: Optional[List[int]] = None,
-    aggregators: Optional[List[str]] = None,
+    selectors: Optional[list[int]] = None,
+    aggregators: Optional[list[str]] = None,
     normalize_attributions: bool = False,
 ) -> FeatureAttributionOutput:
     if selectors is not None and aggregators is not None:
diff --git a/inseq/commands/attribute/attribute_args.py b/inseq/commands/attribute/attribute_args.py
index be6540d1..ee707b5f 100644
--- a/inseq/commands/attribute/attribute_args.py
+++ b/inseq/commands/attribute/attribute_args.py
@@ -1,5 +1,5 @@
 from dataclasses import dataclass
-from typing import List, Optional
+from typing import Optional
 
 from ... import (
     list_aggregation_functions,
@@ -38,7 +38,7 @@ class AttributeBaseArgs:
             " specified using the ``attribution_kwargs`` argument."
         ),
     )
-    attribution_selectors: Optional[List[int]] = cli_arg(
+    attribution_selectors: Optional[list[int]] = cli_arg(
         default=None,
         help=(
             "The indices of the attribution scores to be used for the attribution aggregation. If specified, the"
@@ -46,7 +46,7 @@ class AttributeBaseArgs:
             " If not specified, the aggregation function is applied to all the scores."
         ),
     )
-    attribution_aggregators: List[str] = cli_arg(
+    attribution_aggregators: list[str] = cli_arg(
         default=None,
         help=(
             "The aggregators used to aggregate the attribution scores for each context. The outcome should"
@@ -94,7 +94,7 @@ class AttributeExtendedArgs(AttributeBaseArgs):
             " since for decoder-only ones it is sufficient to add prefix to input string. Default: False."
         ),
     )
-    step_scores: List[str] = cli_arg(
+    step_scores: list[str] = cli_arg(
         default_factory=list,
         help="Adds the specified step scores to the attribution output.",
         choices=list_step_functions(),
@@ -143,8 +143,8 @@ class AttributeExtendedArgs(AttributeBaseArgs):
 @command_args_docstring
 @dataclass
 class AttributeWithInputsArgs(AttributeExtendedArgs):
-    input_texts: List[str] = cli_arg(default=None, aliases=["-i"], help="One or more input texts used for generation.")
-    generated_texts: Optional[List[str]] = cli_arg(
+    input_texts: list[str] = cli_arg(default=None, aliases=["-i"], help="One or more input texts used for generation.")
+    generated_texts: Optional[list[str]] = cli_arg(
         default=None, aliases=["-g"], help="If specified, constrains the decoding procedure to the specified outputs."
     )
 
diff --git a/inseq/commands/attribute_context/attribute_context_args.py b/inseq/commands/attribute_context/attribute_context_args.py
index f102b6e6..b2f10d33 100644
--- a/inseq/commands/attribute_context/attribute_context_args.py
+++ b/inseq/commands/attribute_context/attribute_context_args.py
@@ -1,6 +1,6 @@
 import logging
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 from ... import list_step_functions
 from ...attr.step_functions import is_contrastive_step_function
@@ -144,7 +144,7 @@ class AttributeContextMethodArgs(AttributeBaseArgs):
             " prompted to manually specify which part of the generated text corresponds to the output context."
         ),
     )
-    special_tokens_to_keep: List[str] = cli_arg(
+    special_tokens_to_keep: list[str] = cli_arg(
         default_factory=list,
         help="Special tokens to preserve in the generated string, e.g. ``<brk>`` separator between context and current.",
     )
@@ -217,5 +217,5 @@ class AttributeContextArgs(AttributeContextInputArgs, AttributeContextMethodArgs
     def __repr__(self):
         return f"{self.__class__.__name__}({pretty_dict(self.__dict__)})"
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         return dict(self.__dict__.items())
diff --git a/inseq/commands/attribute_context/attribute_context_helpers.py b/inseq/commands/attribute_context/attribute_context_helpers.py
index 713b58e5..7736c9f9 100644
--- a/inseq/commands/attribute_context/attribute_context_helpers.py
+++ b/inseq/commands/attribute_context/attribute_context_helpers.py
@@ -1,7 +1,7 @@
 import logging
 import re
 from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Optional
 
 from rich import print as rprint
 from rich.prompt import Confirm, Prompt
@@ -25,13 +25,13 @@ class CCIOutput:
     cti_score: float
     contextual_prefix: str
     contextless_prefix: str
-    input_context_scores: Optional[List[float]] = None
-    output_context_scores: Optional[List[float]] = None
+    input_context_scores: Optional[list[float]] = None
+    output_context_scores: Optional[list[float]] = None
 
     def __repr__(self):
         return f"{self.__class__.__name__}({pretty_dict(self.__dict__)})"
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         return dict(self.__dict__.items())
 
 
@@ -40,19 +40,19 @@ class AttributeContextOutput:
     """Output of the overall context attribution process."""
 
     input_context: Optional[str] = None
-    input_context_tokens: Optional[List[str]] = None
+    input_context_tokens: Optional[list[str]] = None
     output_context: Optional[str] = None
-    output_context_tokens: Optional[List[str]] = None
+    output_context_tokens: Optional[list[str]] = None
     output_current: Optional[str] = None
-    output_current_tokens: Optional[List[str]] = None
-    cti_scores: Optional[List[float]] = None
-    cci_scores: List[CCIOutput] = field(default_factory=list)
+    output_current_tokens: Optional[list[str]] = None
+    cti_scores: Optional[list[float]] = None
+    cci_scores: list[CCIOutput] = field(default_factory=list)
     info: Optional[AttributeContextArgs] = None
 
     def __repr__(self):
         return f"{self.__class__.__name__}({pretty_dict(self.__dict__)})"
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         out_dict = {k: v for k, v in self.__dict__.items() if k not in ["cci_scores", "info"]}
         out_dict["cci_scores"] = [cci_out.to_dict() for cci_out in self.cci_scores]
         if self.info:
@@ -70,10 +70,10 @@ def format_template(template: str, current: str, context: Optional[str] = None)
 def get_filtered_tokens(
     text: str,
     model: HuggingfaceModel,
-    special_tokens_to_keep: List[str],
+    special_tokens_to_keep: list[str],
     replace_special_characters: bool = False,
     is_target: bool = False,
-) -> List[str]:
+) -> list[str]:
     """Tokenize text and filter out special tokens, keeping only those in ``special_tokens_to_keep``."""
     as_targets = is_target and model.is_encoder_decoder
     return [
@@ -86,7 +86,7 @@ def get_filtered_tokens(
 def generate_with_special_tokens(
     model: HuggingfaceModel,
     model_input: str,
-    special_tokens_to_keep: List[str] = [],
+    special_tokens_to_keep: list[str] = [],
     **generation_kwargs,
 ) -> str:
     """Generate text preserving special tokens in ``special_tokens_to_keep``."""
@@ -99,8 +99,8 @@ def generate_with_special_tokens(
 def generate_model_output(
     model: HuggingfaceModel,
     model_input: str,
-    generation_kwargs: Dict[str, Any],
-    special_tokens_to_keep: List[str],
+    generation_kwargs: dict[str, Any],
+    special_tokens_to_keep: list[str],
     output_template: str,
     prefix: str,
     suffix: str,
@@ -177,9 +177,9 @@ def prepare_outputs(
     output_current_text: Optional[str],
     output_template: str,
     align_output_context_auto: bool = False,
-    generation_kwargs: Dict[str, Any] = {},
-    special_tokens_to_keep: List[str] = [],
-) -> Tuple[Optional[str], str]:
+    generation_kwargs: dict[str, Any] = {},
+    special_tokens_to_keep: list[str] = [],
+) -> tuple[Optional[str], str]:
     """Handle model outputs and prepare them for attribution.
     This procedure is valid both for encoder-decoder and decoder-only models.
 
@@ -282,11 +282,11 @@ def prepare_outputs(
 
 
 def filter_rank_tokens(
-    tokens: List[str],
-    scores: List[float],
+    tokens: list[str],
+    scores: list[float],
     std_threshold: Optional[float] = None,
     topk: Optional[int] = None,
-) -> Tuple[List[Tuple[int, float, str]], float]:
+) -> tuple[list[tuple[int, float, str]], float]:
     indices = list(range(0, len(scores)))
     token_score_tuples = sorted(zip(indices, scores, tokens), key=lambda x: abs(x[1]), reverse=True)
     if std_threshold:
@@ -300,11 +300,11 @@ def filter_rank_tokens(
 def get_contextless_prefix(
     model: HuggingfaceModel,
     input_current_text: str,
-    output_current_tokens: List[str],
+    output_current_tokens: list[str],
     cti_idx: int,
-    special_tokens_to_keep: List[str] = [],
-    generation_kwargs: Dict[str, Any] = {},
-) -> Tuple[str, str]:
+    special_tokens_to_keep: list[str] = [],
+    generation_kwargs: dict[str, Any] = {},
+) -> tuple[str, str]:
     """Generate the contextless prefix for the current token identified as context-sensitive."""
     output_current_prefix_tokens = output_current_tokens[:cti_idx]
     output_current_prefix = model.convert_tokens_to_string(output_current_prefix_tokens, skip_special_tokens=False)
@@ -333,15 +333,15 @@ def get_source_target_cci_scores(
     model: HuggingfaceModel,
     cci_attrib_out: FeatureAttributionSequenceOutput,
     input_template: str,
-    input_context_tokens: List[str],
-    input_full_tokens: List[str],
+    input_context_tokens: list[str],
+    input_full_tokens: list[str],
     output_template: str,
-    output_context_tokens: List[str],
+    output_context_tokens: list[str],
     has_input_context: bool,
     has_output_context: bool,
     model_has_lang_tag: bool,
-    special_tokens_to_keep: List[str] = [],
-) -> Tuple[Optional[List[float]], Optional[List[float]]]:
+    special_tokens_to_keep: list[str] = [],
+) -> tuple[Optional[list[float]], Optional[list[float]]]:
     """Extract attribution scores for the input and output contexts."""
     input_scores, output_scores = None, None
     if has_input_context:
diff --git a/inseq/commands/attribute_context/attribute_context_viz_helpers.py b/inseq/commands/attribute_context/attribute_context_viz_helpers.py
index 841af392..f225bd25 100644
--- a/inseq/commands/attribute_context/attribute_context_viz_helpers.py
+++ b/inseq/commands/attribute_context/attribute_context_viz_helpers.py
@@ -1,4 +1,4 @@
-from typing import List, Literal, Optional
+from typing import Literal, Optional
 
 from rich.console import Console
 
@@ -45,10 +45,10 @@ def get_formatted_attribute_context_results(
     def format_context_comment(
         model: HuggingfaceModel,
         has_other_context: bool,
-        special_tokens_to_keep: List[str],
+        special_tokens_to_keep: list[str],
         context: str,
-        context_scores: List[float],
-        other_context_scores: Optional[List[float]] = None,
+        context_scores: list[float],
+        other_context_scores: Optional[list[float]] = None,
         is_target: bool = False,
         context_type: Literal["Input", "Output"] = "Input",
     ) -> str:
diff --git a/inseq/commands/attribute_dataset/attribute_dataset.py b/inseq/commands/attribute_dataset/attribute_dataset.py
index 6d633926..c81f65a7 100644
--- a/inseq/commands/attribute_dataset/attribute_dataset.py
+++ b/inseq/commands/attribute_dataset/attribute_dataset.py
@@ -1,4 +1,4 @@
-from typing import List, Optional, Tuple
+from typing import Optional
 
 from ...utils import is_datasets_available
 from ..attribute import AttributeExtendedArgs
@@ -10,7 +10,7 @@
     from datasets import load_dataset
 
 
-def load_fields_from_dataset(dataset_args: LoadDatasetArgs) -> Tuple[List[str], Optional[List[str]]]:
+def load_fields_from_dataset(dataset_args: LoadDatasetArgs) -> tuple[list[str], Optional[list[str]]]:
     if not is_datasets_available():
         raise ImportError("The datasets library needs to be installed to use the attribute-dataset client.")
     dataset = load_dataset(
@@ -40,7 +40,7 @@ class AttributeDatasetCommand(BaseCLICommand):
     _help = "Perform feature attribution on a full dataset and save the results to a file"
     _dataclasses = AttributeExtendedArgs, LoadDatasetArgs
 
-    def run(args: Tuple[AttributeExtendedArgs, LoadDatasetArgs]):
+    def run(args: tuple[AttributeExtendedArgs, LoadDatasetArgs]):
         attribute_args, dataset_args = args
         input_texts, generated_texts = load_fields_from_dataset(dataset_args)
         attribute(input_texts, generated_texts, attribute_args)
diff --git a/inseq/commands/attribute_dataset/attribute_dataset_args.py b/inseq/commands/attribute_dataset/attribute_dataset_args.py
index 3f02ee19..3910d997 100644
--- a/inseq/commands/attribute_dataset/attribute_dataset_args.py
+++ b/inseq/commands/attribute_dataset/attribute_dataset_args.py
@@ -1,5 +1,5 @@
 from dataclasses import dataclass
-from typing import List, Optional
+from typing import Optional
 
 from ...utils import cli_arg
 from ..commands_utils import command_args_docstring
@@ -26,7 +26,7 @@ class LoadDatasetArgs:
     dataset_dir: Optional[str] = cli_arg(
         default=None, aliases=["--dir"], help="Path to the directory containing the data files."
     )
-    dataset_files: Optional[List[str]] = cli_arg(default=None, aliases=["--files"], help="Path to the dataset files.")
+    dataset_files: Optional[list[str]] = cli_arg(default=None, aliases=["--files"], help="Path to the dataset files.")
     dataset_split: Optional[str] = cli_arg(default="train", aliases=["--split"], help="Dataset split.")
     dataset_revision: Optional[str] = cli_arg(
         default=None, aliases=["--revision"], help="The Huggingface dataset revision."
diff --git a/inseq/commands/base.py b/inseq/commands/base.py
index adbe29cf..8f412d39 100644
--- a/inseq/commands/base.py
+++ b/inseq/commands/base.py
@@ -1,7 +1,8 @@
 import dataclasses
 from abc import ABC, abstractstaticmethod
 from argparse import Namespace
-from typing import Any, Iterable, NewType, Union
+from collections.abc import Iterable
+from typing import Any, NewType, Union
 
 from ..utils import InseqArgumentParser
 
diff --git a/inseq/commands/cli.py b/inseq/commands/cli.py
index b4e72257..f7fb0d2a 100644
--- a/inseq/commands/cli.py
+++ b/inseq/commands/cli.py
@@ -1,6 +1,5 @@
 """Adapted from https://github.com/huggingface/transformers/blob/main/src/transformers/commands/transformers_cli.py."""
 import sys
-from typing import List
 
 from ..utils import InseqArgumentParser
 from .attribute import AttributeCommand
@@ -8,7 +7,7 @@
 from .attribute_dataset import AttributeDatasetCommand
 from .base import BaseCLICommand
 
-COMMANDS: List[BaseCLICommand] = [AttributeCommand, AttributeDatasetCommand, AttributeContextCommand]
+COMMANDS: list[BaseCLICommand] = [AttributeCommand, AttributeDatasetCommand, AttributeContextCommand]
 
 
 def main():
diff --git a/inseq/data/aggregation_functions.py b/inseq/data/aggregation_functions.py
index cd511ad6..ac35dd84 100644
--- a/inseq/data/aggregation_functions.py
+++ b/inseq/data/aggregation_functions.py
@@ -14,7 +14,7 @@
 
 import logging
 from abc import abstractmethod
-from typing import List, Tuple, Union
+from typing import Union
 
 import torch
 from torch.linalg import vector_norm
@@ -37,7 +37,7 @@ def __init__(self):
     @abstractmethod
     def __call__(
         self,
-        scores: Union[torch.Tensor, Tuple[torch.Tensor, ...]],
+        scores: Union[torch.Tensor, tuple[torch.Tensor, ...]],
         dim: int,
         **kwargs,
     ) -> ScoreTensor:
@@ -112,6 +112,6 @@ def __call__(self, scores: torch.Tensor, dim: int, vnorm_ord: int = 2) -> ScoreT
 }
 
 
-def list_aggregation_functions() -> List[str]:
+def list_aggregation_functions() -> list[str]:
     """Lists identifiers for all available aggregation functions."""
     return available_classes(AggregationFunction)
diff --git a/inseq/data/aggregator.py b/inseq/data/aggregator.py
index d478fd09..c77c2e09 100644
--- a/inseq/data/aggregator.py
+++ b/inseq/data/aggregator.py
@@ -1,6 +1,7 @@
 import logging
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Callable, List, Optional, Sequence, Tuple, Type, TypeVar, Union
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Callable, Optional, TypeVar, Union
 
 import torch
 
@@ -125,7 +126,7 @@ def end_aggregation_hook(cls, tensors: TensorWrapper, **kwargs):
 def _get_aggregators_from_id(
     aggregator: str,
     aggregate_fn: Optional[str] = None,
-) -> Tuple[Type[Aggregator], Optional[AggregationFunction]]:
+) -> tuple[type[Aggregator], Optional[AggregationFunction]]:
     if aggregator in available_classes(Aggregator):
         aggregator = Aggregator.available_classes()[aggregator]
     elif aggregator in available_classes(AggregationFunction):
@@ -155,11 +156,11 @@ def _get_aggregators_from_id(
 class AggregatorPipeline:
     def __init__(
         self,
-        aggregators: List[Union[str, Type[Aggregator]]],
-        aggregate_fn: Optional[List[Union[str, Callable]]] = None,
+        aggregators: list[Union[str, type[Aggregator]]],
+        aggregate_fn: Optional[list[Union[str, Callable]]] = None,
     ):
-        self.aggregators: List[Type[Aggregator]] = []
-        self.aggregate_fn: List[Callable] = []
+        self.aggregators: list[type[Aggregator]] = []
+        self.aggregate_fn: list[Callable] = []
         if aggregate_fn is not None:
             if len(aggregate_fn) != len(aggregators):
                 raise ValueError(
@@ -196,16 +197,16 @@ def aggregate(
         return tensors
 
 
-AggregatorInput = Union[AggregatorPipeline, Type[Aggregator], str, Sequence[Union[str, Type[Aggregator]]], None]
+AggregatorInput = Union[AggregatorPipeline, type[Aggregator], str, Sequence[Union[str, type[Aggregator]]], None]
 
 
-def list_aggregators() -> List[str]:
+def list_aggregators() -> list[str]:
     """Lists identifiers for all available aggregators."""
     return available_classes(Aggregator)
 
 
 class AggregableMixin(ABC):
-    _aggregator: Union[AggregatorPipeline, Type[Aggregator]]
+    _aggregator: Union[AggregatorPipeline, type[Aggregator]]
 
     def aggregate(
         self: AggregableMixinClass,
@@ -304,7 +305,7 @@ def _process_attribution_scores(
         cls,
         attr: "FeatureAttributionSequenceOutput",
         aggregate_fn: AggregationFunction,
-        select_idx: Union[int, Tuple[int, int], List[int], None] = None,
+        select_idx: Union[int, tuple[int, int], list[int], None] = None,
         normalize: bool = True,
         **kwargs,
     ):
@@ -365,7 +366,7 @@ def aggregate_source_attributions(
         cls,
         attr: "FeatureAttributionSequenceOutput",
         aggregate_fn: AggregationFunction,
-        select_idx: Union[int, Tuple[int, int], List[int], None] = None,
+        select_idx: Union[int, tuple[int, int], list[int], None] = None,
         normalize: bool = True,
         **kwargs,
     ):
@@ -379,7 +380,7 @@ def aggregate_target_attributions(
         cls,
         attr: "FeatureAttributionSequenceOutput",
         aggregate_fn: AggregationFunction,
-        select_idx: Union[int, Tuple[int, int], List[int], None] = None,
+        select_idx: Union[int, tuple[int, int], list[int], None] = None,
         normalize: bool = True,
         **kwargs,
     ):
@@ -397,7 +398,7 @@ def aggregate_sequence_scores(
         cls,
         attr: "FeatureAttributionSequenceOutput",
         aggregate_fn: AggregationFunction,
-        select_idx: Union[int, Tuple[int, int], List[int], None] = None,
+        select_idx: Union[int, tuple[int, int], list[int], None] = None,
         **kwargs,
     ):
         if aggregate_fn.takes_sequence_scores:
@@ -438,7 +439,7 @@ def is_compatible(attr: "FeatureAttributionSequenceOutput"):
     def _filter_scores(
         scores: torch.Tensor,
         dim: int = -1,
-        indices: Union[int, Tuple[int, int], List[int], None] = None,
+        indices: Union[int, tuple[int, int], list[int], None] = None,
     ) -> torch.Tensor:
         n_units = scores.shape[dim]
 
@@ -481,11 +482,11 @@ def _filter_scores(
 
     @staticmethod
     def _aggregate_scores(
-        scores: Union[torch.Tensor, Tuple[torch.Tensor, ...]],
+        scores: Union[torch.Tensor, tuple[torch.Tensor, ...]],
         aggregate_fn: AggregationFunction,
         dim: int = -1,
         **kwargs,
-    ) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]:
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
         if isinstance(scores, tuple) and aggregate_fn.takes_single_tensor:
             return tuple(aggregate_fn(score, dim=dim, **kwargs) for score in scores)
         return aggregate_fn(scores, dim=dim, **kwargs)
@@ -546,7 +547,7 @@ def aggregate(
         return super().aggregate(attr, source_spans=source_spans, target_spans=target_spans, **kwargs)
 
     @staticmethod
-    def format_spans(spans) -> List[Tuple[int, int]]:
+    def format_spans(spans) -> list[tuple[int, int]]:
         if not spans:
             return spans
         return [spans] if isinstance(spans[0], int) else spans
@@ -579,7 +580,7 @@ def _aggregate_sequential_scores(scores, x_spans, y_spans, aggregate_fn):
         return scores_aggregated_x
 
     @staticmethod
-    def _relativize_target_spans(spans: List[Tuple[int, int]], start: int):
+    def _relativize_target_spans(spans: list[tuple[int, int]], start: int):
         if start != 0 and spans:
             # Remove target spans referring to the unattributed prefix, rescale remaining spans to relative idxs
             # of the generated sequences and set 0 if the span starts before the generation begins.
@@ -707,7 +708,7 @@ def aggregate(
         attr: "FeatureAttributionSequenceOutput",
         aggregate_source: bool = True,
         aggregate_target: bool = True,
-        special_chars: Union[str, Tuple[str, ...]] = "▁",
+        special_chars: Union[str, tuple[str, ...]] = "▁",
         is_suffix_symbol: bool = False,
         **kwargs,
     ):
@@ -720,7 +721,7 @@ def aggregate(
         return super().aggregate(attr, source_spans=source_spans, target_spans=target_spans, **kwargs)
 
     @staticmethod
-    def get_spans(tokens: List[TokenWithId], special_chars: Union[str, Tuple[str, ...]], is_suffix_symbol: bool):
+    def get_spans(tokens: list[TokenWithId], special_chars: Union[str, tuple[str, ...]], is_suffix_symbol: bool):
         spans = []
         last_prefix_idx = 0
         has_special_chars = any(sym in token.token for token in tokens for sym in special_chars)
diff --git a/inseq/data/attribution.py b/inseq/data/attribution.py
index 50e60c9c..b2756476 100644
--- a/inseq/data/attribution.py
+++ b/inseq/data/attribution.py
@@ -3,7 +3,7 @@
 from dataclasses import dataclass, field
 from os import PathLike
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Type, Union
+from typing import TYPE_CHECKING, Any, Callable, Optional, Union
 
 import torch
 
@@ -72,7 +72,7 @@ def get_batch_from_inputs(
     return batch
 
 
-def merge_attributions(attributions: List["FeatureAttributionOutput"]) -> "FeatureAttributionOutput":
+def merge_attributions(attributions: list["FeatureAttributionOutput"]) -> "FeatureAttributionOutput":
     """Merges multiple :class:`~inseq.data.FeatureAttributionOutput` objects into a single one.
 
     Merging is allowed only if the two outputs match on the fields specified in ``_merge_match_info_fields``.
@@ -136,16 +136,16 @@ class FeatureAttributionSequenceOutput(TensorWrapper, AggregableMixin):
             scores produced alongside attributions (n per generation step, as for attributions).
     """
 
-    source: List[TokenWithId]
-    target: List[TokenWithId]
+    source: list[TokenWithId]
+    target: list[TokenWithId]
     source_attributions: Optional[SequenceAttributionTensor] = None
     target_attributions: Optional[SequenceAttributionTensor] = None
-    step_scores: Optional[Dict[str, SingleScoresPerSequenceTensor]] = None
-    sequence_scores: Optional[Dict[str, MultipleScoresPerSequenceTensor]] = None
+    step_scores: Optional[dict[str, SingleScoresPerSequenceTensor]] = None
+    sequence_scores: Optional[dict[str, MultipleScoresPerSequenceTensor]] = None
     attr_pos_start: int = 0
     attr_pos_end: Optional[int] = None
-    _aggregator: Union[str, List[str], None] = None
-    _dict_aggregate_fn: Optional[Dict[str, str]] = None
+    _aggregator: Union[str, list[str], None] = None
+    _dict_aggregate_fn: Optional[dict[str, str]] = None
 
     def __post_init__(self):
         if self._dict_aggregate_fn is None:
@@ -177,12 +177,12 @@ def get_remove_pad_fn(attr: "FeatureAttributionStepOutput", name: str) -> Callab
     @classmethod
     def from_step_attributions(
         cls,
-        attributions: List["FeatureAttributionStepOutput"],
-        tokenized_target_sentences: Optional[List[List[TokenWithId]]] = None,
+        attributions: list["FeatureAttributionStepOutput"],
+        tokenized_target_sentences: Optional[list[list[TokenWithId]]] = None,
         pad_id: Optional[Any] = None,
         has_bos_token: bool = True,
         attr_pos_end: Optional[int] = None,
-    ) -> List["FeatureAttributionSequenceOutput"]:
+    ) -> list["FeatureAttributionSequenceOutput"]:
         """Converts a list of :class:`~inseq.data.attribution.FeatureAttributionStepOutput` objects containing multiple
         examples outputs per step into a list of :class:`~inseq.data.attribution.FeatureAttributionSequenceOutput` with
         every object containing all step outputs for an individual example.
@@ -213,7 +213,7 @@ def from_step_attributions(
             drop_padding(tokenized_target_sentences[seq_id], pad_id) for seq_id in range(num_sequences)
         ]
         if attr_pos_end is None:
-            attr_pos_end = max([len(t) for t in tokenized_target_sentences])
+            attr_pos_end = max(len(t) for t in tokenized_target_sentences)
         pos_start = [
             min(len(tokenized_target_sentences[seq_id]), attr_pos_end) - len(targets[seq_id])
             for seq_id in range(num_sequences)
@@ -237,7 +237,9 @@ def from_step_attributions(
                 ]
                 seq_attributions[seq_id].source_attributions = filtered_source_attribution
         if attr.target_attributions is not None:
-            target_attributions = get_sequences_from_batched_steps([att.target_attributions for att in attributions])
+            target_attributions = get_sequences_from_batched_steps(
+                [att.target_attributions for att in attributions], padding_dims=[1]
+            )
             for seq_id in range(num_sequences):
                 if has_bos_token:
                     target_attributions[seq_id] = target_attributions[seq_id][1:, ...]
@@ -254,7 +256,7 @@ def from_step_attributions(
             step_scores = [{} for _ in range(num_sequences)]
             for step_score_name in attr.step_scores.keys():
                 out_step_scores = get_sequences_from_batched_steps(
-                    [att.step_scores[step_score_name] for att in attributions]
+                    [att.step_scores[step_score_name] for att in attributions], stack_dim=1
                 )
                 for seq_id in range(num_sequences):
                     step_scores[seq_id][step_score_name] = out_step_scores[seq_id][: len(targets[seq_id])]
@@ -272,7 +274,7 @@ def from_step_attributions(
                     out_seq_scores = [attr.sequence_scores[seq_score_name][i, ...] for i in range(num_sequences)]
                 else:
                     out_seq_scores = get_sequences_from_batched_steps(
-                        [att.sequence_scores[seq_score_name] for att in attributions]
+                        [att.sequence_scores[seq_score_name] for att in attributions], padding_dims=[2], stack_dim=3
                     )
                 for seq_id in range(num_sequences):
                     seq_scores[seq_id][seq_score_name] = remove_pad_fn(out_seq_scores, sources, targets, seq_id)
@@ -286,7 +288,7 @@ def show(
         max_val: Optional[int] = None,
         display: bool = True,
         return_html: Optional[bool] = False,
-        aggregator: Union[AggregatorPipeline, Type[Aggregator]] = None,
+        aggregator: Union[AggregatorPipeline, type[Aggregator]] = None,
         do_aggregation: bool = True,
         **kwargs,
     ) -> Optional[str]:
@@ -365,10 +367,10 @@ def weight_attributions(self, step_fn_id: str):
 
     def get_scores_dicts(
         self,
-        aggregator: Union[AggregatorPipeline, Type[Aggregator]] = None,
+        aggregator: Union[AggregatorPipeline, type[Aggregator]] = None,
         do_aggregation: bool = True,
         **kwargs,
-    ) -> Dict[str, Dict[str, Dict[str, float]]]:
+    ) -> dict[str, dict[str, dict[str, float]]]:
         # If no aggregator is specified, the default aggregator for the class is used
         aggr = self.aggregate(aggregator, **kwargs) if do_aggregation else self
         return_dict = {"source_attributions": {}, "target_attributions": {}, "step_scores": {}}
@@ -401,13 +403,13 @@ class FeatureAttributionStepOutput(TensorWrapper):
     """Output of a single step of feature attribution, plus extra information related to what was attributed."""
 
     source_attributions: Optional[StepAttributionTensor] = None
-    step_scores: Optional[Dict[str, SingleScorePerStepTensor]] = None
+    step_scores: Optional[dict[str, SingleScorePerStepTensor]] = None
     target_attributions: Optional[StepAttributionTensor] = None
-    sequence_scores: Optional[Dict[str, MultipleScoresPerStepTensor]] = None
+    sequence_scores: Optional[dict[str, MultipleScoresPerStepTensor]] = None
     source: Optional[OneOrMoreTokenWithIdSequences] = None
     prefix: Optional[OneOrMoreTokenWithIdSequences] = None
     target: Optional[OneOrMoreTokenWithIdSequences] = None
-    _sequence_cls: Type["FeatureAttributionSequenceOutput"] = FeatureAttributionSequenceOutput
+    _sequence_cls: type["FeatureAttributionSequenceOutput"] = FeatureAttributionSequenceOutput
 
     def __post_init__(self):
         self.to(torch.float32)
@@ -446,8 +448,26 @@ def remap_from_filtered(
                 ).squeeze(-1)
         if self.sequence_scores is not None:
             for score_name, score_tensor in self.sequence_scores.items():
+                if score_name.startswith("decoder"):
+                    original_shape = (
+                        len(batch.target_tokens),
+                        self.target_attributions.shape[1],
+                        *self.target_attributions.shape[1:],
+                    )
+                elif score_name.startswith("encoder"):
+                    original_shape = (
+                        len(batch.sources.input_tokens),
+                        self.source_attributions.shape[1],
+                        *self.source_attributions.shape[1:],
+                    )
+                else:  # default case: cross-attention
+                    original_shape = (
+                        len(batch.sources.input_tokens),
+                        self.target_attributions.shape[1],
+                        *self.source_attributions.shape[1:],
+                    )
                 self.sequence_scores[score_name] = remap_from_filtered(
-                    original_shape=(len(batch.target_tokens), *self.source_attributions.shape[1:]),
+                    original_shape=original_shape,
                     mask=target_attention_mask,
                     filtered=score_tensor,
                 )
@@ -481,9 +501,9 @@ class FeatureAttributionOutput:
         "tokenizer_name",
     ]
 
-    sequence_attributions: List[FeatureAttributionSequenceOutput]
-    step_attributions: Optional[List[FeatureAttributionStepOutput]] = None
-    info: Dict[str, Any] = field(default_factory=dict)
+    sequence_attributions: list[FeatureAttributionSequenceOutput]
+    step_attributions: Optional[list[FeatureAttributionStepOutput]] = None
+    info: dict[str, Any] = field(default_factory=dict)
 
     def __str__(self):
         return f"{self.__class__.__name__}({pretty_dict(self.__dict__)})"
@@ -602,7 +622,7 @@ def load(
 
     def aggregate(
         self,
-        aggregator: Union[AggregatorPipeline, Type[Aggregator]] = None,
+        aggregator: Union[AggregatorPipeline, type[Aggregator]] = None,
         **kwargs,
     ) -> "FeatureAttributionOutput":
         """Aggregate the sequence attributions using one or more aggregators.
@@ -626,7 +646,7 @@ def show(
         max_val: Optional[int] = None,
         display: bool = True,
         return_html: Optional[bool] = False,
-        aggregator: Union[AggregatorPipeline, Type[Aggregator]] = None,
+        aggregator: Union[AggregatorPipeline, type[Aggregator]] = None,
         do_aggregation: bool = True,
         **kwargs,
     ) -> Optional[str]:
@@ -661,8 +681,8 @@ def weight_attributions(self, step_score_id: str):
             self.sequence_attributions[i] = attr.weight_attributions(step_score_id)
 
     def get_scores_dicts(
-        self, aggregator: Union[AggregatorPipeline, Type[Aggregator]] = None, do_aggregation: bool = True, **kwargs
-    ) -> List[Dict[str, Dict[str, Dict[str, float]]]]:
+        self, aggregator: Union[AggregatorPipeline, type[Aggregator]] = None, do_aggregation: bool = True, **kwargs
+    ) -> list[dict[str, dict[str, dict[str, float]]]]:
         """Get all computed scores (attributions and step scores) for all sequences as a list of dictionaries.
 
         Returns:
@@ -706,7 +726,7 @@ def __post_init__(self):
 class GranularFeatureAttributionStepOutput(FeatureAttributionStepOutput):
     """Raw output of a single step of gradient feature attribution."""
 
-    _sequence_cls: Type["FeatureAttributionSequenceOutput"] = GranularFeatureAttributionSequenceOutput
+    _sequence_cls: type["FeatureAttributionSequenceOutput"] = GranularFeatureAttributionSequenceOutput
 
 
 @dataclass(eq=False, repr=False)
@@ -726,7 +746,7 @@ def __post_init__(self):
 class CoarseFeatureAttributionStepOutput(FeatureAttributionStepOutput):
     """Raw output of a single step of coarse-grained feature attribution."""
 
-    _sequence_cls: Type["FeatureAttributionSequenceOutput"] = CoarseFeatureAttributionSequenceOutput
+    _sequence_cls: type["FeatureAttributionSequenceOutput"] = CoarseFeatureAttributionSequenceOutput
 
 
 @dataclass(eq=False, repr=False)
@@ -750,7 +770,7 @@ class MultiDimensionalFeatureAttributionStepOutput(FeatureAttributionStepOutput)
     """Raw output of a single step of multi-dimensional feature attribution."""
 
     _num_dimensions: int = 2
-    _sequence_cls: Type["FeatureAttributionSequenceOutput"] = MultiDimensionalFeatureAttributionSequenceOutput
+    _sequence_cls: type["FeatureAttributionSequenceOutput"] = MultiDimensionalFeatureAttributionSequenceOutput
 
     def get_sequence_cls(self, **kwargs):
         return MultiDimensionalFeatureAttributionSequenceOutput(_num_dimensions=self._num_dimensions, **kwargs)
diff --git a/inseq/data/batch.py b/inseq/data/batch.py
index c72b95c8..65c49a9e 100644
--- a/inseq/data/batch.py
+++ b/inseq/data/batch.py
@@ -1,5 +1,5 @@
 from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
+from typing import Optional, Union
 
 from ..utils import get_aligned_idx
 from ..utils.typing import EmbeddingsTensor, ExpandedTargetIdsTensor, IdsTensor, OneOrMoreTokenSequences
@@ -95,7 +95,7 @@ def input_ids(self, value: IdsTensor):
         self.encoding.input_ids = value
 
     @input_tokens.setter
-    def input_tokens(self, value: List[List[str]]):
+    def input_tokens(self, value: list[list[str]]):
         self.encoding.input_tokens = value
 
     @attention_mask.setter
@@ -169,7 +169,7 @@ def target_mask(self) -> IdsTensor:
 
     def get_step_target(
         self, step: int, with_attention: bool = False
-    ) -> Union[ExpandedTargetIdsTensor, Tuple[ExpandedTargetIdsTensor, ExpandedTargetIdsTensor]]:
+    ) -> Union[ExpandedTargetIdsTensor, tuple[ExpandedTargetIdsTensor, ExpandedTargetIdsTensor]]:
         tgt = self.targets.input_ids[:, step]
         if with_attention:
             return tgt, self.targets.attention_mask[:, step]
@@ -218,7 +218,7 @@ def target_mask(self) -> IdsTensor:
 
     def get_step_target(
         self, step: int, with_attention: bool = False
-    ) -> Union[ExpandedTargetIdsTensor, Tuple[ExpandedTargetIdsTensor, ExpandedTargetIdsTensor]]:
+    ) -> Union[ExpandedTargetIdsTensor, tuple[ExpandedTargetIdsTensor, ExpandedTargetIdsTensor]]:
         tgt = self.input_ids[:, step]
         if with_attention:
             return tgt, self.attention_mask[:, step]
@@ -233,8 +233,8 @@ def from_batch(self, batch: Batch) -> "DecoderOnlyBatch":
 
 
 def slice_batch_from_position(
-    batch: DecoderOnlyBatch, curr_idx: int, alignments: Optional[List[Tuple[int, int]]] = None
-) -> Tuple[DecoderOnlyBatch, IdsTensor]:
+    batch: DecoderOnlyBatch, curr_idx: int, alignments: Optional[list[tuple[int, int]]] = None
+) -> tuple[DecoderOnlyBatch, IdsTensor]:
     if len(alignments) > 0 and isinstance(alignments[0], list):
         alignments = alignments[0]
     truncate_idx = get_aligned_idx(curr_idx, alignments)
diff --git a/inseq/data/data_utils.py b/inseq/data/data_utils.py
index cc2b2bd6..b907d627 100644
--- a/inseq/data/data_utils.py
+++ b/inseq/data/data_utils.py
@@ -1,6 +1,6 @@
 from copy import deepcopy
 from dataclasses import dataclass, fields
-from typing import Any, Dict, TypeVar
+from typing import Any, TypeVar
 
 import numpy as np
 import torch
@@ -175,7 +175,7 @@ def clone(self: TensorClass) -> TensorClass:
                 out_params[field.name] = None
         return self.__class__(**out_params)
 
-    def to_dict(self: TensorClass) -> Dict[str, Any]:
+    def to_dict(self: TensorClass) -> dict[str, Any]:
         return {k: v for k, v in self.__dict__.items() if not k.startswith("_")}
 
     def __str__(self):
diff --git a/inseq/data/viz.py b/inseq/data/viz.py
index 59634420..fef73b58 100644
--- a/inseq/data/viz.py
+++ b/inseq/data/viz.py
@@ -18,7 +18,7 @@
 
 import random
 import string
-from typing import Dict, List, Literal, Optional, Tuple, Union
+from typing import Literal, Optional, Union
 
 import numpy as np
 from matplotlib.colors import Colormap
@@ -128,13 +128,13 @@ def show_attributions(
 
 
 def get_attribution_colors(
-    attributions: List[FeatureAttributionSequenceOutput],
+    attributions: list[FeatureAttributionSequenceOutput],
     min_val: Optional[int] = None,
     max_val: Optional[int] = None,
     cmap: Union[str, Colormap, None] = None,
     return_alpha: bool = True,
     return_strings: bool = True,
-) -> List[List[List[Union[str, Tuple[float, float, float]]]]]:
+) -> list[list[list[Union[str, tuple[float, float, float]]]]]:
     """A list (one element = one sentence) of lists (one element = attributions for one token)
     of lists (one element = one attribution) of colors. Colors are either strings or RGB(A) tuples.
     """
@@ -197,12 +197,12 @@ def get_heatmap_type(
 
 def get_saliency_heatmap_html(
     scores: Union[np.ndarray, None],
-    column_labels: List[str],
-    row_labels: List[str],
-    input_colors: List[List[str]],
-    step_scores: Optional[Dict[str, np.ndarray]] = None,
+    column_labels: list[str],
+    row_labels: list[str],
+    input_colors: list[list[str]],
+    step_scores: Optional[dict[str, np.ndarray]] = None,
     label: str = "",
-    step_scores_threshold: Union[float, Dict[str, float]] = 0.5,
+    step_scores_threshold: Union[float, dict[str, float]] = 0.5,
 ):
     # unique ID added to HTML elements and function to avoid collision of differnent instances
     uuid = "".join(random.choices(string.ascii_lowercase, k=20))
@@ -246,12 +246,12 @@ def get_saliency_heatmap_html(
 
 def get_saliency_heatmap_rich(
     scores: Union[np.ndarray, None],
-    column_labels: List[str],
-    row_labels: List[str],
-    input_colors: List[List[str]],
-    step_scores: Optional[Dict[str, np.ndarray]] = None,
+    column_labels: list[str],
+    row_labels: list[str],
+    input_colors: list[list[str]],
+    step_scores: Optional[dict[str, np.ndarray]] = None,
     label: str = "",
-    step_scores_threshold: Union[float, Dict[str, float]] = 0.5,
+    step_scores_threshold: Union[float, dict[str, float]] = 0.5,
 ):
     columns = [Column(header="", justify="right", overflow="fold")]
     for column_label in column_labels:
@@ -294,13 +294,13 @@ def get_saliency_heatmap_rich(
 
 def get_progress_bar(
     sequences: TextSequences,
-    target_lengths: List[int],
+    target_lengths: list[int],
     method_name: str,
     show: bool,
     pretty: bool,
     attr_pos_start: int,
     attr_pos_end: int,
-) -> Union[tqdm, Tuple[Progress, Live], None]:
+) -> Union[tqdm, tuple[Progress, Live], None]:
     if not show:
         return None
     elif show and not pretty:
@@ -348,12 +348,12 @@ def get_progress_bar(
 
 
 def update_progress_bar(
-    pbar: Union[tqdm, Tuple[Progress, Live], None],
-    skipped_prefixes: Optional[List[str]] = None,
-    attributed_sentences: Optional[List[str]] = None,
-    unattributed_suffixes: Optional[List[str]] = None,
-    skipped_suffixes: Optional[List[str]] = None,
-    whitespace_indexes: List[List[int]] = None,
+    pbar: Union[tqdm, tuple[Progress, Live], None],
+    skipped_prefixes: Optional[list[str]] = None,
+    attributed_sentences: Optional[list[str]] = None,
+    unattributed_suffixes: Optional[list[str]] = None,
+    skipped_suffixes: Optional[list[str]] = None,
+    whitespace_indexes: list[list[int]] = None,
     show: bool = False,
     pretty: bool = False,
 ) -> None:
@@ -378,7 +378,7 @@ def update_progress_bar(
                 pbar[0].update(job.id, description=formatted_desc, refresh=True)
 
 
-def close_progress_bar(pbar: Union[tqdm, Tuple[Progress, Live], None], show: bool, pretty: bool) -> None:
+def close_progress_bar(pbar: Union[tqdm, tuple[Progress, Live], None], show: bool, pretty: bool) -> None:
     if not show:
         return
     elif show and not pretty:
diff --git a/inseq/models/__init__.py b/inseq/models/__init__.py
index 73896b8b..5959d5f9 100644
--- a/inseq/models/__init__.py
+++ b/inseq/models/__init__.py
@@ -1,5 +1,5 @@
 import logging
-from typing import List, Optional, Union
+from typing import Optional, Union
 
 from rich.status import Status
 
@@ -47,7 +47,7 @@ def load_model(
         return FRAMEWORKS_MAP[framework].load(model, attribution_method, **kwargs)
 
 
-def list_supported_frameworks() -> List[str]:
+def list_supported_frameworks() -> list[str]:
     """Lists identifiers for all available frameworks. These can be used to load models with the `framework` argument
     in the :meth:`~inseq.load_model` function.
     """
diff --git a/inseq/models/attribution_model.py b/inseq/models/attribution_model.py
index e95e168b..c74e82c8 100644
--- a/inseq/models/attribution_model.py
+++ b/inseq/models/attribution_model.py
@@ -1,7 +1,7 @@
 import logging
 from abc import ABC, abstractmethod
 from functools import wraps
-from typing import Any, Callable, Dict, List, Optional, Protocol, Tuple, TypeVar, Union
+from typing import Any, Callable, Optional, Protocol, TypeVar, Union
 
 import torch
 
@@ -56,7 +56,7 @@ def __call__(
         target_ids: ExpandedTargetIdsTensor,
         attributed_fn: Callable[..., SingleScorePerStepTensor],
         use_embeddings: bool,
-        attributed_fn_argnames: Optional[List[str]],
+        attributed_fn_argnames: Optional[list[str]],
         *args,
     ) -> CustomForwardOutput:
         ...
@@ -79,11 +79,11 @@ def format_attribution_args(
         target_ids: TargetIdsTensor,
         attributed_fn: Callable[..., SingleScorePerStepTensor],
         attribute_target: bool = False,
-        attributed_fn_args: Dict[str, Any] = {},
+        attributed_fn_args: dict[str, Any] = {},
         attribute_batch_ids: bool = False,
         forward_batch_embeds: bool = True,
         use_baselines: bool = False,
-    ) -> Tuple[Dict[str, Any], Tuple[Union[IdsTensor, EmbeddingsTensor, None], ...]]:
+    ) -> tuple[dict[str, Any], tuple[Union[IdsTensor, EmbeddingsTensor, None], ...]]:
         raise NotImplementedError()
 
     @staticmethod
@@ -95,7 +95,7 @@ def enrich_step_output(
         target_tokens: OneOrMoreTokenSequences,
         target_ids: TargetIdsTensor,
         contrast_batch: Optional[DecoderOnlyBatch] = None,
-        contrast_targets_alignments: Optional[List[List[Tuple[int, int]]]] = None,
+        contrast_targets_alignments: Optional[list[list[tuple[int, int]]]] = None,
     ) -> FeatureAttributionStepOutput:
         r"""Enriches the attribution output with token information, producing the finished
         :class:`~inseq.data.FeatureAttributionStepOutput` object.
@@ -146,20 +146,20 @@ def get_text_sequences(
 
     @staticmethod
     @abstractmethod
-    def get_step_function_reserved_args() -> List[str]:
+    def get_step_function_reserved_args() -> list[str]:
         raise NotImplementedError()
 
     @staticmethod
     def format_contrast_targets_alignments(
-        contrast_targets_alignments: Union[List[Tuple[int, int]], List[List[Tuple[int, int]]], str],
-        target_sequences: List[str],
-        target_tokens: List[List[str]],
-        contrast_sequences: List[str],
-        contrast_tokens: List[List[str]],
-        special_tokens: List[str] = [],
+        contrast_targets_alignments: Union[list[tuple[int, int]], list[list[tuple[int, int]]], str],
+        target_sequences: list[str],
+        target_tokens: list[list[str]],
+        contrast_sequences: list[str],
+        contrast_tokens: list[list[str]],
+        special_tokens: list[str] = [],
         start_pos: int = 0,
         end_pos: Optional[int] = None,
-    ) -> Tuple[DecoderOnlyBatch, Optional[List[List[Tuple[int, int]]]]]:
+    ) -> tuple[DecoderOnlyBatch, Optional[list[list[tuple[int, int]]]]]:
         # Ensure that the contrast_targets_alignments are in the correct format (list of lists of idxs pairs)
         if contrast_targets_alignments:
             if isinstance(contrast_targets_alignments, list) and len(contrast_targets_alignments) > 0:
@@ -256,7 +256,7 @@ def set_attributed_fn(self, fn: str):
         self._default_attributed_fn_id = fn
 
     @property
-    def info(self) -> Dict[Optional[str], Optional[str]]:
+    def info(self) -> dict[Optional[str], Optional[str]]:
         return {
             "model_name": self.model_name,
             "model_class": self.model.__class__.__name__ if self.model is not None else None,
@@ -309,13 +309,13 @@ def attribute(
         pretty_progress: bool = True,
         output_step_attributions: bool = False,
         attribute_target: bool = False,
-        step_scores: List[str] = [],
+        step_scores: list[str] = [],
         include_eos_baseline: bool = False,
         attributed_fn: Union[str, Callable[..., SingleScorePerStepTensor], None] = None,
         device: Optional[str] = None,
         batch_size: Optional[int] = None,
         generate_from_target_prefix: bool = False,
-        generation_args: Dict[str, Any] = {},
+        generation_args: dict[str, Any] = {},
         **kwargs,
     ) -> FeatureAttributionOutput:
         """Perform sequential attribution of input texts for every token in generated texts using the specified method.
@@ -483,8 +483,8 @@ def get_token_with_ids(
         self,
         batch: Union[EncoderDecoderBatch, DecoderOnlyBatch],
         contrast_target_tokens: Optional[OneOrMoreTokenSequences] = None,
-        contrast_targets_alignments: Optional[List[List[Tuple[int, int]]]] = None,
-    ) -> List[List[TokenWithId]]:
+        contrast_targets_alignments: Optional[list[list[tuple[int, int]]]] = None,
+    ) -> list[list[TokenWithId]]:
         if contrast_target_tokens is not None:
             return join_token_ids(
                 batch.target_tokens,
@@ -503,7 +503,7 @@ def generate(
         encodings: Union[TextInput, BatchEncoding],
         return_generation_output: Optional[bool] = False,
         **kwargs,
-    ) -> Union[List[str], Tuple[List[str], Any]]:
+    ) -> Union[list[str], tuple[list[str], Any]]:
         pass
 
     @staticmethod
@@ -522,7 +522,7 @@ def encode(
         pass
 
     @abstractmethod
-    def decode(self, ids: IdsTensor, skip_special_tokens: bool = True) -> List[str]:
+    def decode(self, ids: IdsTensor, skip_special_tokens: bool = True) -> list[str]:
         pass
 
     @abstractmethod
@@ -538,7 +538,7 @@ def convert_ids_to_tokens(
     @abstractmethod
     def convert_tokens_to_ids(
         self,
-        tokens: Union[List[str], List[List[str]]],
+        tokens: Union[list[str], list[list[str]]],
     ) -> OneOrMoreIdSequences:
         pass
 
@@ -571,12 +571,12 @@ def clean_tokens(
 
     @property
     @abstractmethod
-    def special_tokens(self) -> List[str]:
+    def special_tokens(self) -> list[str]:
         pass
 
     @property
     @abstractmethod
-    def special_tokens_ids(self) -> List[int]:
+    def special_tokens_ids(self) -> list[int]:
         pass
 
     @property
@@ -626,12 +626,12 @@ def get_decoder(self) -> torch.nn.Module:
 
     @staticmethod
     @abstractmethod
-    def get_attentions_dict(output: ModelOutput) -> Dict[str, torch.Tensor]:
+    def get_attentions_dict(output: ModelOutput) -> dict[str, torch.Tensor]:
         pass
 
     @staticmethod
     @abstractmethod
-    def get_hidden_states_dict(output: ModelOutput) -> Dict[str, torch.Tensor]:
+    def get_hidden_states_dict(output: ModelOutput) -> dict[str, torch.Tensor]:
         pass
 
     # Model forward
@@ -642,7 +642,7 @@ def _forward(
         target_ids: ExpandedTargetIdsTensor,
         attributed_fn: Callable[..., SingleScorePerStepTensor],
         use_embeddings: bool = True,
-        attributed_fn_argnames: Optional[List[str]] = None,
+        attributed_fn_argnames: Optional[list[str]] = None,
         *args,
         **kwargs,
     ) -> LogitsTensor:
diff --git a/inseq/models/decoder_only.py b/inseq/models/decoder_only.py
index b5586ef3..f4355e42 100644
--- a/inseq/models/decoder_only.py
+++ b/inseq/models/decoder_only.py
@@ -1,6 +1,6 @@
 import logging
 from functools import wraps
-from typing import Any, Callable, Dict, List, Optional, Tuple, TypeVar, Union
+from typing import Any, Callable, Optional, TypeVar, Union
 
 import torch
 
@@ -54,11 +54,11 @@ def format_attribution_args(
         target_ids: TargetIdsTensor,
         attributed_fn: Callable[..., SingleScorePerStepTensor],
         attribute_target: bool = False,  # Needed for compatibility with EncoderDecoderAttributionModel
-        attributed_fn_args: Dict[str, Any] = {},
+        attributed_fn_args: dict[str, Any] = {},
         attribute_batch_ids: bool = False,
         forward_batch_embeds: bool = True,
         use_baselines: bool = False,
-    ) -> Tuple[Dict[str, Any], Tuple[Union[IdsTensor, EmbeddingsTensor, None], ...]]:
+    ) -> tuple[dict[str, Any], tuple[Union[IdsTensor, EmbeddingsTensor, None], ...]]:
         if attribute_batch_ids:
             inputs = (batch.input_ids,)
             baselines = (batch.baseline_ids,)
@@ -96,7 +96,7 @@ def enrich_step_output(
         target_tokens: OneOrMoreTokenSequences,
         target_ids: TargetIdsTensor,
         contrast_batch: Optional[DecoderOnlyBatch] = None,
-        contrast_targets_alignments: Optional[List[List[Tuple[int, int]]]] = None,
+        contrast_targets_alignments: Optional[list[list[tuple[int, int]]]] = None,
     ) -> FeatureAttributionStepOutput:
         r"""Enriches the attribution output with token information, producing the finished
         :class:`~inseq.data.FeatureAttributionStepOutput` object.
@@ -175,7 +175,7 @@ def formatted_forward_input_wrapper(
             attributed_fn: Callable[..., SingleScorePerStepTensor],
             attention_mask: Optional[IdsTensor] = None,
             use_embeddings: bool = True,
-            attributed_fn_argnames: Optional[List[str]] = None,
+            attributed_fn_argnames: Optional[list[str]] = None,
             *args,
             **kwargs,
         ) -> CustomForwardOutput:
@@ -198,7 +198,7 @@ def get_text_sequences(attribution_model: "DecoderOnlyAttributionModel", batch:
         )
 
     @staticmethod
-    def get_step_function_reserved_args() -> List[str]:
+    def get_step_function_reserved_args() -> list[str]:
         return [f.name for f in StepFunctionDecoderOnlyArgs.__dataclass_fields__.values()]
 
 
diff --git a/inseq/models/encoder_decoder.py b/inseq/models/encoder_decoder.py
index c3058fc8..39fd7c65 100644
--- a/inseq/models/encoder_decoder.py
+++ b/inseq/models/encoder_decoder.py
@@ -1,6 +1,6 @@
 import logging
 from functools import wraps
-from typing import Any, Callable, Dict, List, Optional, Tuple, TypeVar, Union
+from typing import Any, Callable, Optional, TypeVar, Union
 
 from ..attr.feat import join_token_ids
 from ..attr.step_functions import StepFunctionEncoderDecoderArgs
@@ -36,7 +36,7 @@
 class EncoderDecoderInputFormatter(InputFormatter):
     def prepare_inputs_for_attribution(
         attribution_model: "EncoderDecoderAttributionModel",
-        inputs: Tuple[FeatureAttributionInput, FeatureAttributionInput],
+        inputs: tuple[FeatureAttributionInput, FeatureAttributionInput],
         include_eos_baseline: bool = False,
     ) -> EncoderDecoderBatch:
         r"""Prepares sources and target to produce an :class:`~inseq.data.EncoderDecoderBatch`.
@@ -82,11 +82,11 @@ def format_attribution_args(
         target_ids: TargetIdsTensor,
         attributed_fn: Callable[..., SingleScorePerStepTensor],
         attribute_target: bool = False,
-        attributed_fn_args: Dict[str, Any] = {},
+        attributed_fn_args: dict[str, Any] = {},
         attribute_batch_ids: bool = False,
         forward_batch_embeds: bool = True,
         use_baselines: bool = False,
-    ) -> Tuple[Dict[str, Any], Tuple[Union[IdsTensor, EmbeddingsTensor, None], ...]]:
+    ) -> tuple[dict[str, Any], tuple[Union[IdsTensor, EmbeddingsTensor, None], ...]]:
         if attribute_batch_ids:
             inputs = (batch.sources.input_ids,)
             baselines = (batch.sources.baseline_ids,)
@@ -137,7 +137,7 @@ def enrich_step_output(
         target_tokens: OneOrMoreTokenSequences,
         target_ids: TargetIdsTensor,
         contrast_batch: Optional[DecoderOnlyBatch] = None,
-        contrast_targets_alignments: Optional[List[List[Tuple[int, int]]]] = None,
+        contrast_targets_alignments: Optional[list[list[tuple[int, int]]]] = None,
     ) -> FeatureAttributionStepOutput:
         r"""Enriches the attribution output with token information, producing the finished
         :class:`~inseq.data.FeatureAttributionStepOutput` object.
@@ -232,7 +232,7 @@ def formatted_forward_input_wrapper(
             encoder_attention_mask: Optional[IdsTensor] = None,
             decoder_attention_mask: Optional[IdsTensor] = None,
             use_embeddings: bool = True,
-            attributed_fn_argnames: Optional[List[str]] = None,
+            attributed_fn_argnames: Optional[list[str]] = None,
             *args,
             **kwargs,
         ) -> CustomForwardOutput:
@@ -260,7 +260,7 @@ def get_text_sequences(
         )
 
     @staticmethod
-    def get_step_function_reserved_args() -> List[str]:
+    def get_step_function_reserved_args() -> list[str]:
         return [f.name for f in StepFunctionEncoderDecoderArgs.__dataclass_fields__.values()]
 
 
diff --git a/inseq/models/huggingface_model.py b/inseq/models/huggingface_model.py
index 23ab3a9c..930507c9 100644
--- a/inseq/models/huggingface_model.py
+++ b/inseq/models/huggingface_model.py
@@ -1,7 +1,7 @@
 """HuggingFace Seq2seq model."""
 import logging
 from abc import abstractmethod
-from typing import Any, Dict, List, NoReturn, Optional, Tuple, Union
+from typing import Any, NoReturn, Optional, Union
 
 import torch
 from torch import long
@@ -69,8 +69,8 @@ def __init__(
         attribution_method: Optional[str] = None,
         tokenizer: Union[str, PreTrainedTokenizerBase, None] = None,
         device: Optional[str] = None,
-        model_kwargs: Optional[Dict[str, Any]] = {},
-        tokenizer_kwargs: Optional[Dict[str, Any]] = {},
+        model_kwargs: Optional[dict[str, Any]] = {},
+        tokenizer_kwargs: Optional[dict[str, Any]] = {},
         **kwargs,
     ) -> None:
         """AttributionModel subclass for Huggingface-compatible models.
@@ -137,8 +137,8 @@ def load(
         attribution_method: Optional[str] = None,
         tokenizer: Union[str, PreTrainedTokenizerBase, None] = None,
         device: str = None,
-        model_kwargs: Optional[Dict[str, Any]] = {},
-        tokenizer_kwargs: Optional[Dict[str, Any]] = {},
+        model_kwargs: Optional[dict[str, Any]] = {},
+        tokenizer_kwargs: Optional[dict[str, Any]] = {},
         **kwargs,
     ) -> "HuggingfaceModel":
         """Loads a HuggingFace model and tokenizer and wraps them in the appropriate AttributionModel."""
@@ -179,8 +179,8 @@ def configure_embeddings_scale(self) -> None:
         pass
 
     @property
-    def info(self) -> Dict[str, str]:
-        dic_info: Dict[str, str] = super().info
+    def info(self) -> dict[str, str]:
+        dic_info: dict[str, str] = super().info
         extra_info = {
             "tokenizer_name": self.tokenizer_name,
             "tokenizer_class": self.tokenizer.__class__.__name__,
@@ -196,7 +196,7 @@ def generate(
         return_generation_output: bool = False,
         skip_special_tokens: bool = True,
         **kwargs,
-    ) -> Union[List[str], Tuple[List[str], ModelOutput]]:
+    ) -> Union[list[str], tuple[list[str], ModelOutput]]:
         """Wrapper of model.generate to handle tokenization and decoding.
 
         Args:
@@ -284,9 +284,9 @@ def encode(
 
     def decode(
         self,
-        ids: Union[List[int], List[List[int]], IdsTensor],
+        ids: Union[list[int], list[list[int]], IdsTensor],
         skip_special_tokens: bool = True,
-    ) -> List[str]:
+    ) -> list[str]:
         return self.tokenizer.batch_decode(
             ids,
             skip_special_tokens=skip_special_tokens,
@@ -386,11 +386,11 @@ def clean_tokens(
         return [self.clean_tokens(token_seq, skip_special_tokens, as_targets) for token_seq in tokens]
 
     @property
-    def special_tokens(self) -> List[str]:
+    def special_tokens(self) -> list[str]:
         return self.tokenizer.all_special_tokens
 
     @property
-    def special_tokens_ids(self) -> List[int]:
+    def special_tokens_ids(self) -> list[int]:
         return self.tokenizer.all_special_ids
 
     @property
@@ -429,7 +429,7 @@ def get_decoder(self) -> torch.nn.Module:
     @staticmethod
     def get_attentions_dict(
         output: Seq2SeqLMOutput,
-    ) -> Dict[str, MultiLayerMultiUnitScoreTensor]:
+    ) -> dict[str, MultiLayerMultiUnitScoreTensor]:
         if output.encoder_attentions is None or output.decoder_attentions is None:
             raise ValueError("Model does not support attribution relying on attention outputs.")
         return {
@@ -439,7 +439,7 @@ def get_attentions_dict(
         }
 
     @staticmethod
-    def get_hidden_states_dict(output: Seq2SeqLMOutput) -> Dict[str, MultiLayerEmbeddingsTensor]:
+    def get_hidden_states_dict(output: Seq2SeqLMOutput) -> dict[str, MultiLayerEmbeddingsTensor]:
         return {
             "encoder_hidden_states": torch.stack(output.encoder_hidden_states, dim=1),
             "decoder_hidden_states": torch.stack(output.decoder_hidden_states, dim=1),
@@ -463,8 +463,8 @@ def __init__(
         attribution_method: Optional[str] = None,
         tokenizer: Union[str, PreTrainedTokenizerBase, None] = None,
         device: str = None,
-        model_kwargs: Optional[Dict[str, Any]] = {},
-        tokenizer_kwargs: Optional[Dict[str, Any]] = {},
+        model_kwargs: Optional[dict[str, Any]] = {},
+        tokenizer_kwargs: Optional[dict[str, Any]] = {},
         **kwargs,
     ) -> NoReturn:
         super().__init__(model, attribution_method, tokenizer, device, model_kwargs, tokenizer_kwargs, **kwargs)
@@ -479,7 +479,7 @@ def configure_embeddings_scale(self):
             self.embed_scale = self.model.embed_scale
 
     @staticmethod
-    def get_attentions_dict(output: CausalLMOutput) -> Dict[str, MultiLayerMultiUnitScoreTensor]:
+    def get_attentions_dict(output: CausalLMOutput) -> dict[str, MultiLayerMultiUnitScoreTensor]:
         if output.attentions is None:
             raise ValueError("Model does not support attribution relying on attention outputs.")
         return {
@@ -487,7 +487,7 @@ def get_attentions_dict(output: CausalLMOutput) -> Dict[str, MultiLayerMultiUnit
         }
 
     @staticmethod
-    def get_hidden_states_dict(output: CausalLMOutput) -> Dict[str, MultiLayerEmbeddingsTensor]:
+    def get_hidden_states_dict(output: CausalLMOutput) -> dict[str, MultiLayerEmbeddingsTensor]:
         return {
             "decoder_hidden_states": torch.stack(output.hidden_states, dim=1),
         }
diff --git a/inseq/utils/alignment_utils.py b/inseq/utils/alignment_utils.py
index 1b3e256b..c465f1e3 100644
--- a/inseq/utils/alignment_utils.py
+++ b/inseq/utils/alignment_utils.py
@@ -4,7 +4,7 @@
 from enum import Enum
 from functools import lru_cache
 from itertools import chain
-from typing import List, Optional, Tuple, Union
+from typing import Optional, Union
 
 import torch
 from transformers import AutoModel, AutoTokenizer, PreTrainedModel, PreTrainedTokenizerBase
@@ -18,12 +18,12 @@
 
 @dataclass
 class AlignedSequences:
-    source_tokens: List[str]
-    target_tokens: List[str]
-    alignments: List[Tuple[int, int]]
+    source_tokens: list[str]
+    target_tokens: list[str]
+    alignments: list[tuple[int, int]]
 
     @property
-    def aligned_tokens(self) -> List[Tuple[str, str]]:
+    def aligned_tokens(self) -> list[tuple[str, str]]:
         return [(self.source_tokens[a_idx], self.target_tokens[b_idx]) for a_idx, b_idx in self.alignments]
 
     def reverse(self) -> "AlignedSequences":
@@ -51,7 +51,7 @@ def get_aligner_tokenizer() -> PreTrainedTokenizerBase:
     return AutoTokenizer.from_pretrained(ALIGN_MODEL_ID)
 
 
-def _preprocess_sequence_for_alignment(tokenized_seq: List[str]) -> Tuple[torch.Tensor, List[List[int]]]:
+def _preprocess_sequence_for_alignment(tokenized_seq: list[str]) -> tuple[torch.Tensor, list[list[int]]]:
     aligner_tokenizer = get_aligner_tokenizer()
     idxs = [aligner_tokenizer.convert_tokens_to_ids(x) for x in tokenized_seq]
     idxs = aligner_tokenizer.prepare_for_model(
@@ -67,8 +67,8 @@ def _preprocess_sequence_for_alignment(tokenized_seq: List[str]) -> Tuple[torch.
 
 
 def _get_aligner_subword_aligns(
-    src: List[str],
-    tgt: List[str],
+    src: list[str],
+    tgt: list[str],
     align_layer: int,
     score_threshold: float,
 ) -> torch.Tensor:
@@ -91,8 +91,8 @@ def _get_aligner_subword_aligns(
 
 
 def compute_word_aligns(
-    src: Union[str, List[str]],
-    tgt: Union[str, List[str]],
+    src: Union[str, list[str]],
+    tgt: Union[str, list[str]],
     split_pattern: str = r"\s+|\b",
     align_layer: int = 8,
     score_threshold: float = 1e-3,
@@ -116,8 +116,8 @@ def compute_word_aligns(
 
 
 def align_tokenizations(
-    tok_a: List[str],
-    tok_b: List[str],
+    tok_a: list[str],
+    tok_b: list[str],
 ) -> AlignedSequences:
     """Align tokens from a sentence tokenized by different tokenizers.
 
@@ -193,8 +193,8 @@ def propagate_alignments(aligns_a_b: AlignedSequences, aligns_b_c: AlignedSequen
 
 
 def add_alignment_extra_positions(
-    alignments: List[Tuple[int, int]], extra_positions: List[Tuple[int, int]]
-) -> List[Tuple[int, int]]:
+    alignments: list[tuple[int, int]], extra_positions: list[tuple[int, int]]
+) -> list[tuple[int, int]]:
     for x_idx_a, x_idx_b in extra_positions:
         for pos, (idx_a, idx_b) in enumerate(alignments):
             a_val, b_val = idx_a, idx_b
@@ -208,10 +208,10 @@ def add_alignment_extra_positions(
 
 def auto_align_sequences(
     a_sequence: Optional[str] = None,
-    a_tokens: Optional[List[str]] = None,
+    a_tokens: Optional[list[str]] = None,
     b_sequence: Optional[str] = None,
-    b_tokens: Optional[List[str]] = None,
-    filter_special_tokens: List[str] = [],
+    b_tokens: Optional[list[str]] = None,
+    filter_special_tokens: list[str] = [],
     split_pattern: str = r"\s+|\b",
 ) -> AlignedSequences:
     if not a_sequence or not b_sequence or not a_tokens or not b_tokens:
@@ -274,16 +274,16 @@ def auto_align_sequences(
 
 
 def get_adjusted_alignments(
-    alignments: Union[List[Tuple[int, int]], str],
+    alignments: Union[list[tuple[int, int]], str],
     target_sequence: Optional[str] = None,
-    target_tokens: Optional[List[str]] = None,
+    target_tokens: Optional[list[str]] = None,
     contrast_sequence: Optional[str] = None,
-    contrast_tokens: Optional[List[str]] = None,
+    contrast_tokens: Optional[list[str]] = None,
     fill_missing: bool = False,
-    special_tokens: List[str] = [],
+    special_tokens: list[str] = [],
     start_pos: int = 0,
     end_pos: Optional[int] = None,
-) -> List[Tuple[int, int]]:
+) -> list[tuple[int, int]]:
     is_auto_aligned = False
     if fill_missing and not target_tokens:
         raise ValueError("Missing target tokens. Please provide target tokens to fill missing alignments.")
@@ -357,7 +357,7 @@ def get_adjusted_alignments(
     return filter_aligns
 
 
-def get_aligned_idx(a_idx: int, alignments: List[Tuple[int, int]]) -> int:
+def get_aligned_idx(a_idx: int, alignments: list[tuple[int, int]]) -> int:
     if alignments:
         # Find all alignment pairs for the current original target
         aligned_idxs = [t_idx for s_idx, t_idx in alignments if s_idx == a_idx]
diff --git a/inseq/utils/argparse.py b/inseq/utils/argparse.py
index f074274e..f8e27d98 100644
--- a/inseq/utils/argparse.py
+++ b/inseq/utils/argparse.py
@@ -17,11 +17,12 @@
 import sys
 import types
 from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
+from collections.abc import Iterable
 from copy import copy
 from enum import Enum
 from inspect import isclass
 from pathlib import Path
-from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
+from typing import Any, Callable, Literal, NewType, Optional, Union, get_type_hints
 
 import yaml
 
@@ -60,11 +61,11 @@ def make_choice_type_function(choices: list) -> Callable[[str], Any]:
 
 def cli_arg(
     *,
-    aliases: Union[str, List[str]] = None,
+    aliases: Union[str, list[str]] = None,
     help: str = None,
     default: Any = dataclasses.MISSING,
     default_factory: Callable[[], Any] = dataclasses.MISSING,
-    choices: List[Any] = None,
+    choices: list[Any] = None,
     metadata: dict = None,
     **kwargs,
 ) -> dataclasses.Field:
@@ -242,7 +243,7 @@ def _add_dataclass_arguments(self, dtype: DataClassType):
             parser = self
 
         try:
-            type_hints: Dict[str, type] = get_type_hints(dtype)
+            type_hints: dict[str, type] = get_type_hints(dtype)
         except NameError as ex:
             raise RuntimeError(
                 f"Type resolution failed for {dtype}. Try declaring the class in global scope or "
@@ -275,7 +276,7 @@ def parse_args_into_dataclasses(
         look_for_args_file=True,
         args_filename=None,
         args_file_flag=None,
-    ) -> Tuple[DataClass, ...]:
+    ) -> tuple[DataClass, ...]:
         """
         Parse command-line args into instances of the specified dataclass types.
 
@@ -354,7 +355,7 @@ def parse_args_into_dataclasses(
 
             return (*outputs,)
 
-    def parse_dict(self, args: Dict[str, Any], allow_extra_keys: bool = False) -> Tuple[DataClass, ...]:
+    def parse_dict(self, args: dict[str, Any], allow_extra_keys: bool = False) -> tuple[DataClass, ...]:
         """
         Alternative helper method that does not use `argparse` at all, instead uses a dict and populating the dataclass
         types.
@@ -382,7 +383,7 @@ def parse_dict(self, args: Dict[str, Any], allow_extra_keys: bool = False) -> Tu
             raise ValueError(f"Some keys are not used by the HfArgumentParser: {sorted(unused_keys)}")
         return tuple(outputs)
 
-    def parse_json_file(self, json_file: str, allow_extra_keys: bool = False) -> Tuple[DataClass, ...]:
+    def parse_json_file(self, json_file: str, allow_extra_keys: bool = False) -> tuple[DataClass, ...]:
         """
         Alternative helper method that does not use `argparse` at all, instead loading a json file and populating the
         dataclass types.
@@ -404,7 +405,7 @@ def parse_json_file(self, json_file: str, allow_extra_keys: bool = False) -> Tup
         outputs = self.parse_dict(data, allow_extra_keys=allow_extra_keys)
         return tuple(outputs)
 
-    def parse_yaml_file(self, yaml_file: str, allow_extra_keys: bool = False) -> Tuple[DataClass, ...]:
+    def parse_yaml_file(self, yaml_file: str, allow_extra_keys: bool = False) -> tuple[DataClass, ...]:
         """
         Alternative helper method that does not use `argparse` at all, instead loading a yaml file and populating the
         dataclass types.
diff --git a/inseq/utils/contrast_utils.py b/inseq/utils/contrast_utils.py
index e7dda871..5d8c00f4 100644
--- a/inseq/utils/contrast_utils.py
+++ b/inseq/utils/contrast_utils.py
@@ -1,7 +1,7 @@
 import logging
 import warnings
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Callable, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Callable, Optional, Union
 
 import torch
 
@@ -61,7 +61,7 @@ def _get_contrast_inputs(
     args: "StepFunctionArgs",
     contrast_sources: Optional[FeatureAttributionInput] = None,
     contrast_targets: Optional[FeatureAttributionInput] = None,
-    contrast_targets_alignments: Optional[List[List[Tuple[int, int]]]] = None,
+    contrast_targets_alignments: Optional[list[list[tuple[int, int]]]] = None,
     return_contrastive_target_ids: bool = False,
     return_contrastive_batch: bool = False,
     **forward_kwargs,
@@ -126,7 +126,7 @@ def _setup_contrast_args(
     args: "StepFunctionArgs",
     contrast_sources: Optional[FeatureAttributionInput] = None,
     contrast_targets: Optional[FeatureAttributionInput] = None,
-    contrast_targets_alignments: Optional[List[List[Tuple[int, int]]]] = None,
+    contrast_targets_alignments: Optional[list[list[tuple[int, int]]]] = None,
     contrast_force_inputs: bool = False,
 ):
     c_inputs = _get_contrast_inputs(
diff --git a/inseq/utils/errors.py b/inseq/utils/errors.py
index 905e0ae6..8189a331 100644
--- a/inseq/utils/errors.py
+++ b/inseq/utils/errors.py
@@ -1,4 +1,4 @@
-from typing import Any, Tuple
+from typing import Any
 
 from .registry import available_classes
 
@@ -21,7 +21,7 @@ def __init__(
         self,
         method_name: str,
         msg: str = UNKNOWN_ATTRIBUTION_METHOD_MSG,
-        *args: Tuple[Any],
+        *args: tuple[Any],
     ) -> None:
         from inseq.attr import FeatureAttribution
 
@@ -41,7 +41,7 @@ class MissingAttributionMethodError(Exception):
         "or pass it to the attribute method.\nAvailable methods: {available_methods}"
     )
 
-    def __init__(self, msg: str = MISSING_ATTRIBUTION_METHOD_MSG, *args: Tuple[Any]) -> None:
+    def __init__(self, msg: str = MISSING_ATTRIBUTION_METHOD_MSG, *args: tuple[Any]) -> None:
         from inseq.attr import FeatureAttribution
 
         msg = msg.format(available_methods=", ".join(available_classes(FeatureAttribution)))
diff --git a/inseq/utils/misc.py b/inseq/utils/misc.py
index 29b1bcbf..e09e5df7 100644
--- a/inseq/utils/misc.py
+++ b/inseq/utils/misc.py
@@ -5,6 +5,7 @@
 import math
 from base64 import standard_b64decode, standard_b64encode
 from collections import OrderedDict
+from collections.abc import Sequence
 from contextlib import contextmanager
 from functools import wraps
 from importlib import import_module
@@ -12,7 +13,7 @@
 from itertools import dropwhile
 from numbers import Number
 from os import PathLike, fsync
-from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
+from typing import Any, Callable, Optional, Union
 
 from numpy import asarray, frombuffer
 from torch import Tensor
@@ -85,7 +86,7 @@ def pretty_tensor(t: Optional[Tensor] = None, lpad: int = 8) -> str:
         return f"{t.dtype} tensor of shape {list(t.shape)} on {t.device}: {out_list}"
 
 
-def pretty_dict(d: Dict[str, Any], lpad: int = 4) -> str:
+def pretty_dict(d: dict[str, Any], lpad: int = 4) -> str:
     if not d:
         return "{}"
     out_txt = "{\n"
@@ -110,11 +111,11 @@ def pretty_dict(d: Dict[str, Any], lpad: int = 4) -> str:
 
 
 def extract_signature_args(
-    full_args: Dict[str, Any],
+    full_args: dict[str, Any],
     func: Callable[[Any], Any],
     exclude_args: Optional[Sequence[str]] = None,
     return_remaining: bool = False,
-) -> Union[Dict[str, Any], Tuple[Dict[str, Any], Dict[str, Any]]]:
+) -> Union[dict[str, Any], tuple[dict[str, Any], dict[str, Any]]]:
     extracted_args = {
         k: v
         for k, v in full_args.items()
@@ -202,7 +203,7 @@ def isnotebook():
 def format_input_texts(
     texts: TextInput,
     ref_texts: Optional[TextInput] = None,
-) -> Tuple[List[str], List[str]]:
+) -> tuple[list[str], list[str]]:
     texts = [texts] if isinstance(texts, str) else texts
     reference_texts = [ref_texts] if isinstance(ref_texts, str) else ref_texts
     if reference_texts and texts and len(texts) != len(reference_texts):
@@ -235,7 +236,7 @@ def aggregate_token_sequence(token_sequence, spans):
     return out_sequence
 
 
-def aggregate_token_pair(tokens: List[TokenWithId], other_tokens: List[TokenWithId]):
+def aggregate_token_pair(tokens: list[TokenWithId], other_tokens: list[TokenWithId]):
     if not other_tokens:
         return tokens
     out_tokens = []
@@ -424,7 +425,7 @@ def get_cls_from_instance_type(mod, name, cls_lookup_map):
     return curr_class
 
 
-def clean_tokens(tokens: List[str], remove_tokens: List[str]) -> Tuple[List[str], List[int]]:
+def clean_tokens(tokens: list[str], remove_tokens: list[str]) -> tuple[list[str], list[int]]:
     """Removes tokens from a list of tokens and returns the cleaned list and the removed token indexes."""
     clean_tokens = []
     removed_token_idxs = []
diff --git a/inseq/utils/serialization.py b/inseq/utils/serialization.py
index 9cefe631..b45f8580 100644
--- a/inseq/utils/serialization.py
+++ b/inseq/utils/serialization.py
@@ -33,7 +33,7 @@
 from collections import OrderedDict
 from json import JSONEncoder
 from os import PathLike
-from typing import Any, Callable, Dict, List, Optional, TypeVar, Union
+from typing import Any, Callable, Optional, TypeVar, Union
 
 from numpy import generic, ndarray
 
@@ -96,7 +96,7 @@ def ndarray_encode(
     ndarray_compact: Optional[bool] = None,
     compression: bool = False,
     **kwargs,
-) -> Dict[str, Any]:
+) -> dict[str, Any]:
     """Encodes numpy ``ndarray`` as lists with meta data.
     Encodes numpy scalar types as Python equivalents. Special encoding is not possible,
     because float64 is a subclass of primitives, which never reach the encoder.
@@ -135,7 +135,7 @@ def ndarray_encode(
 class AttributionSerializer(JSONEncoder):
     def __init__(
         self,
-        encoders: Optional[List[Callable]] = None,
+        encoders: Optional[list[Callable]] = None,
         use_primitives: bool = True,
         ndarray_compact: Optional[bool] = None,
         compression: bool = False,
@@ -174,7 +174,7 @@ def default(self, obj: EncodableObject, *args, **kwargs):
 def json_advanced_dumps(
     obj: EncodableObject,
     sort_keys: bool = True,
-    encoders: List[Callable] = ENCODE_HOOKS,
+    encoders: list[Callable] = ENCODE_HOOKS,
     use_primitives: bool = True,
     allow_nan: bool = True,
     ndarray_compact: Optional[bool] = None,
@@ -221,7 +221,7 @@ def json_advanced_dumps(
 def json_advanced_dump(
     obj: EncodableObject,
     sort_keys: bool = True,
-    encoders: List[Callable] = ENCODE_HOOKS,
+    encoders: list[Callable] = ENCODE_HOOKS,
     use_primitives: bool = False,
     allow_nan: bool = True,
     ndarray_compact: Optional[bool] = None,
@@ -301,7 +301,7 @@ def ndarray_hook(dct: Any, **kwargs) -> DecodableObject:
         return scalar_to_numpy(data_json, nptype)
 
 
-def class_instance_hook(dct: Any, cls_lookup_map: Optional[Dict[str, type]] = None, **kwargs) -> DecodableObject:
+def class_instance_hook(dct: Any, cls_lookup_map: Optional[dict[str, type]] = None, **kwargs) -> DecodableObject:
     """This hook tries to convert json encoded by class_instance_encoder back to it's original instance.
     It only works if the environment is the same, e.g. the class is similarly importable and hasn't changed.
 
@@ -343,8 +343,8 @@ class AttributionDeserializer:
     def __init__(
         self,
         ordered: bool = False,
-        hooks: Optional[List[Callable]] = None,
-        cls_lookup_map: Optional[Dict[str, type]] = None,
+        hooks: Optional[list[Callable]] = None,
+        cls_lookup_map: Optional[dict[str, type]] = None,
     ):
         self.map_type = OrderedDict if ordered else dict
         self.hooks = hooks if hooks else []
@@ -365,8 +365,8 @@ def json_advanced_loads(
     string: str,
     ordered: bool = False,
     decompression: bool = False,
-    hooks: List[Callable] = DECODE_HOOKS,
-    cls_lookup_map: Optional[Dict[str, type]] = None,
+    hooks: list[Callable] = DECODE_HOOKS,
+    cls_lookup_map: Optional[dict[str, type]] = None,
     **jsonkwargs,
 ) -> Any:
     """Load a complex object containing classes and arrays object from a string.
@@ -405,8 +405,8 @@ def json_advanced_load(
     fp: Union[str, bytes, PathLike],
     ordered: bool = True,
     decompression: bool = False,
-    hooks: List[Callable] = DECODE_HOOKS,
-    cls_lookup_map: Optional[Dict[str, type]] = None,
+    hooks: list[Callable] = DECODE_HOOKS,
+    cls_lookup_map: Optional[dict[str, type]] = None,
     **jsonkwargs,
 ) -> Any:
     """Load a complex object containing classes and arrays from a JSON file.
diff --git a/inseq/utils/torch_utils.py b/inseq/utils/torch_utils.py
index 65ea6b30..88e807cc 100644
--- a/inseq/utils/torch_utils.py
+++ b/inseq/utils/torch_utils.py
@@ -1,5 +1,6 @@
 import logging
-from typing import TYPE_CHECKING, Callable, List, Literal, Optional, Sequence, Tuple, Union
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Callable, Literal, Optional, Union
 
 import torch
 import torch.nn.functional as F
@@ -22,7 +23,7 @@
 
 @torch.no_grad()
 def remap_from_filtered(
-    original_shape: Tuple[int, ...],
+    original_shape: tuple[int, ...],
     mask: Int[torch.Tensor, "batch_size 1"],
     filtered: Num[torch.Tensor, "filtered_batch_size"],
 ) -> Num[torch.Tensor, "batch_size"]:
@@ -35,10 +36,10 @@ def remap_from_filtered(
 
 
 def normalize(
-    attributions: Union[torch.Tensor, Tuple[torch.Tensor, ...]],
+    attributions: Union[torch.Tensor, tuple[torch.Tensor, ...]],
     norm_dim: int = 0,
     norm_ord: int = 1,
-) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]:
+) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
     multi_input = False
     if isinstance(attributions, tuple):
         orig_sizes = [a.shape[norm_dim] for a in attributions]
@@ -100,7 +101,7 @@ def filter_logits(
     top_k: int = 0,
     min_tokens_to_keep: int = 1,
     filter_strategy: Union[Literal["original"], Literal["contrast"], Literal["merged"], None] = None,
-) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
     """Applies top-k and top-p filtering to logits, and optionally to an additional set of contrastive logits."""
     if top_k > original_logits.size(-1) or top_k < 0:
         raise ValueError(f"`top_k` has to be a positive integer < {original_logits.size(-1)}, but is {top_k}")
@@ -141,7 +142,7 @@ def euclidean_distance(vec_a: torch.Tensor, vec_b: torch.Tensor) -> torch.Tensor
 
 def aggregate_contiguous(
     t: torch.Tensor,
-    spans: Sequence[Tuple[int, int]],
+    spans: Sequence[tuple[int, int]],
     aggregate_fn: Optional[Callable] = None,
     aggregate_dim: int = 0,
 ):
@@ -177,39 +178,48 @@ def aggregate_contiguous(
     return out_cat
 
 
-def get_front_padding(t: torch.Tensor, pad: int = 0, dim: int = 1) -> List[int]:
+def get_front_padding(t: torch.Tensor, pad: int = 0, dim: int = 1) -> list[int]:
     """Given a tensor of shape (batch, seq_len) of ids, return a list of length batch containing
     the number of padding tokens at the beginning of each sequence.
     """
     return (t != pad).int().argmax(dim).tolist()
 
 
-def get_sequences_from_batched_steps(bsteps: List[torch.Tensor]) -> List[torch.Tensor]:
+def get_sequences_from_batched_steps(
+    bsteps: list[torch.Tensor], padding_dims: Sequence[int] = [], stack_dim: int = 2
+) -> list[torch.Tensor]:
     """Given a sequence of batched step tensors of shape (batch_size, seq_len, ...) builds a sequence
     of tensors of shape (seq_len, ...) where each resulting tensor is the aggregation
     across batch steps for every batch element.
 
+    Source attribution shape: (batch_size, source_seq_len, ...)
+    Target attribution shape: (batch_size, target_seq_len, ...)
+    Step scores shape: (batch_size)
+    Sequence scores shape: (batch_size, source/target_seq_len, ...)
+
     Input tensors will be padded with nans up to max length in non-uniform dimensions to allow for stacking.
     """
-    dim_ranges = {dim: [bstep.shape[dim] for bstep in bsteps] for dim in range(bsteps[0].ndim)}
-    for dim, dim_range in dim_ranges.items():
-        # If dimension grows across batch steps, it will be padded
-        if max(dim_range) > min(dim_range):
-            for bstep_idx, bstep in enumerate(bsteps):
-                padded_bstep = torch.ones(
-                    *bstep.shape[:dim],
-                    max(dim_range) - bstep.shape[dim],
-                    *bstep.shape[dim + 1 :],  # noqa
-                    dtype=bstep.dtype,
-                    device=bstep.device,
-                )
-                padded_bstep = torch.cat([bstep, padded_bstep * float("nan")], dim=dim)
+    bsteps_num_dims = bsteps[0].ndim
+    if stack_dim > bsteps_num_dims:
+        raise ValueError(f"Stack dimension {stack_dim} is greater than tensor dimension {bsteps_num_dims}")
+    if not padding_dims:
+        sequences = torch.stack(bsteps, dim=stack_dim).split(1, dim=0)
+        return [seq.squeeze(0) for seq in sequences]
+    for dim in padding_dims:
+        if dim >= bsteps_num_dims:
+            raise ValueError(f"Padding dimension {dim} is greater than tensor dimension {bsteps_num_dims}")
+    padding_dims = set(padding_dims)
+    max_dims = tuple(max([bstep.shape[dim] for bstep in bsteps]) for dim in padding_dims)
+    for bstep_idx, bstep in enumerate(bsteps):
+        for curr_dim, max_dim in zip(padding_dims, max_dims):
+            bstep_dim = bstep.shape[curr_dim]
+            if bstep_dim < max_dim:
+                # Pad the end of curr_dim with nans
+                pad_shape = (0,) * ((bsteps_num_dims - curr_dim) * 2 - 1) + (max_dim - bstep_dim,)
+                padded_bstep = F.pad(bstep, pad=pad_shape, mode="constant", value=float("nan"))
                 bsteps[bstep_idx] = padded_bstep
-    dim = 2 if bsteps[0].ndim > 1 else 1
-    sequences = torch.stack(bsteps, dim=dim)
-    sequences = sequences.split(1, dim=0)
-    squeezed_sequences = [seq.squeeze(0) for seq in sequences]
-    return squeezed_sequences
+    sequences = torch.stack(bsteps, dim=stack_dim).split(1, dim=0)
+    return [seq.squeeze(0) for seq in sequences]
 
 
 def check_device(device_name: str) -> bool:
diff --git a/inseq/utils/typing.py b/inseq/utils/typing.py
index 9ffe203f..7599bbc7 100644
--- a/inseq/utils/typing.py
+++ b/inseq/utils/typing.py
@@ -1,5 +1,6 @@
+from collections.abc import Sequence
 from dataclasses import dataclass
-from typing import Optional, Sequence, Tuple, Union
+from typing import Optional, Union
 
 import torch
 from jaxtyping import Float, Float32, Int64
@@ -38,7 +39,7 @@ class TextSequences:
 OneOrMoreTokenWithIdSequences = Sequence[Sequence[TokenWithId]]
 OneOrMoreAttributionSequences = Sequence[Sequence[float]]
 
-IndexSpan = Union[Tuple[int, int], Sequence[Tuple[int, int]]]
+IndexSpan = Union[tuple[int, int], Sequence[tuple[int, int]]]
 
 IdsTensor = Int64[torch.Tensor, "batch_size seq_len"]
 TargetIdsTensor = Int64[torch.Tensor, "batch_size"]
@@ -92,7 +93,7 @@ class TextSequences:
 
 AttributionForwardInputs = Union[IdsTensor, EmbeddingsTensor]
 AttributionForwardInputsPair = Union[
-    Tuple[IdsTensor, IdsTensor],
-    Tuple[EmbeddingsTensor, EmbeddingsTensor],
+    tuple[IdsTensor, IdsTensor],
+    tuple[EmbeddingsTensor, EmbeddingsTensor],
 ]
 OneOrTwoAttributionForwardInputs = Union[AttributionForwardInputs, AttributionForwardInputsPair]
diff --git a/poetry.lock b/poetry.lock
index 28b69724..46c34493 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,13 +2,13 @@
 
 [[package]]
 name = "accelerate"
-version = "0.25.0"
+version = "0.26.1"
 description = "Accelerate"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "accelerate-0.25.0-py3-none-any.whl", hash = "sha256:c7bb817eb974bba0ff3ea1ba0f24d55afb86d50e3d4fe98d6922dc69cf2ccff1"},
-    {file = "accelerate-0.25.0.tar.gz", hash = "sha256:ecf55b0ab278a1dac8539dde0d276977aff04683f07ede73eaf02478538576a1"},
+    {file = "accelerate-0.26.1-py3-none-any.whl", hash = "sha256:04df826b84ac7bad8a0a8ab90e6aeacdecb1ea5a2d744d7e94f6735c29183227"},
+    {file = "accelerate-0.26.1.tar.gz", hash = "sha256:bf63716b6bd9460d87da970cf4d833abb824ca0aa633be36b741e63a1b504f89"},
 ]
 
 [package.dependencies]
@@ -142,13 +142,13 @@ frozenlist = ">=1.1.0"
 
 [[package]]
 name = "alabaster"
-version = "0.7.13"
-description = "A configurable sidebar-enabled Sphinx theme"
+version = "0.7.16"
+description = "A light, configurable Sphinx theme"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.9"
 files = [
-    {file = "alabaster-0.7.13-py3-none-any.whl", hash = "sha256:1ee19aca801bbabb5ba3f5f258e4422dfa86f82f3e9cefb0859b283cdd7f62a3"},
-    {file = "alabaster-0.7.13.tar.gz", hash = "sha256:a27a4a084d5e690e16e01e03ad2b2e552c61a65469419b907243193de1a84ae2"},
+    {file = "alabaster-0.7.16-py3-none-any.whl", hash = "sha256:b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92"},
+    {file = "alabaster-0.7.16.tar.gz", hash = "sha256:75a8b99c28a5dad50dd7f8ccdd447a121ddb3892da9e53d1ca5cca3106d58d65"},
 ]
 
 [[package]]
@@ -193,31 +193,32 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "23.1.0"
+version = "23.2.0"
 description = "Classes Without Boilerplate"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "attrs-23.1.0-py3-none-any.whl", hash = "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04"},
-    {file = "attrs-23.1.0.tar.gz", hash = "sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015"},
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 [package.extras]
 cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
-dev = ["attrs[docs,tests]", "pre-commit"]
+dev = ["attrs[tests]", "pre-commit"]
 docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
 tests = ["attrs[tests-no-zope]", "zope-interface"]
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+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
 
 [[package]]
 name = "babel"
-version = "2.13.1"
+version = "2.14.0"
 description = "Internationalization utilities"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.extras]
@@ -225,18 +226,18 @@ dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
 [[package]]
 name = "bandit"
-version = "1.7.5"
+version = "1.7.6"
 description = "Security oriented static analyser for python code."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
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-GitPython = ">=1.0.1"
+GitPython = ">=3.1.30"
 PyYAML = ">=5.3.1"
 rich = "*"
 stevedore = ">=1.20.0"
@@ -486,35 +487,6 @@ files = [
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
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-
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -528,13 +500,13 @@ files = [
 
 [[package]]
 name = "comm"
-version = "0.2.0"
+version = "0.2.1"
 description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
 optional = true
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -608,63 +580,63 @@ test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
 
 [[package]]
 name = "coverage"
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+version = "7.4.0"
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 [package.dependencies]
@@ -701,26 +673,25 @@ files = [
 
 [[package]]
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 description = "HuggingFace community-driven open-source library of datasets"
 optional = true
 python-versions = ">=3.8.0"
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 [package.dependencies]
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 multiprocess = "*"
 numpy = ">=1.17"
 packaging = "*"
 pandas = "*"
 pyarrow = ">=8.0.0"
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 pyyaml = ">=5.1"
 requests = ">=2.19.0"
 tqdm = ">=4.62.1"
@@ -730,15 +701,15 @@ xxhash = "*"
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 audio = ["librosa", "soundfile (>=0.12.1)"]
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 metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
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 torch = ["torch"]
 vision = ["Pillow (>=6.2.1)"]
 
@@ -796,13 +767,13 @@ graph = ["objgraph (>=1.7.2)"]
 
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+version = "0.3.8"
 description = "Distribution utilities"
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 python-versions = "*"
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 ]
 
 [[package]]
@@ -881,59 +852,59 @@ typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
 name = "fonttools"
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+version = "4.47.2"
 description = "Tools to manipulate font files"
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 python-versions = ">=3.8"
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 ]
 
 [[package]]
 name = "fsspec"
-version = "2023.10.0"
+version = "2023.12.2"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2023.10.0-py3-none-any.whl", hash = "sha256:346a8f024efeb749d2a5fca7ba8854474b1ff9af7c3faaf636a4548781136529"},
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 ]
 
 [package.dependencies]
@@ -1086,30 +1073,30 @@ smmap = ">=3.0.1,<6"
 
 [[package]]
 name = "gitpython"
-version = "3.1.40"
+version = "3.1.41"
 description = "GitPython is a Python library used to interact with Git repositories"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "GitPython-3.1.40-py3-none-any.whl", hash = "sha256:cf14627d5a8049ffbf49915732e5eddbe8134c3bdb9d476e6182b676fc573f8a"},
-    {file = "GitPython-3.1.40.tar.gz", hash = "sha256:22b126e9ffb671fdd0c129796343a02bf67bf2994b35449ffc9321aa755e18a4"},
+    {file = "GitPython-3.1.41-py3-none-any.whl", hash = "sha256:c36b6634d069b3f719610175020a9aed919421c87552185b085e04fbbdb10b7c"},
+    {file = "GitPython-3.1.41.tar.gz", hash = "sha256:ed66e624884f76df22c8e16066d567aaa5a37d5b5fa19db2c6df6f7156db9048"},
 ]
 
 [package.dependencies]
 gitdb = ">=4.0.1,<5"
 
 [package.extras]
-test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-instafail", "pytest-subtests", "pytest-sugar"]
+test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "sumtypes"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.19.4"
+version = "0.20.2"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "huggingface_hub-0.19.4-py3-none-any.whl", hash = "sha256:dba013f779da16f14b606492828f3760600a1e1801432d09fe1c33e50b825bb5"},
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 ]
 
 [package.dependencies]
@@ -1122,27 +1109,26 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
-docs = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "hf-doc-builder", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)", "watchdog"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
 inference = ["aiohttp", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)"]
 quality = ["mypy (==1.5.1)", "ruff (>=0.1.3)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
 torch = ["torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
 [[package]]
 name = "identify"
-version = "2.5.32"
+version = "2.5.33"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "identify-2.5.32-py2.py3-none-any.whl", hash = "sha256:0b7656ef6cba81664b783352c73f8c24b39cf82f926f78f4550eda928e5e0545"},
-    {file = "identify-2.5.32.tar.gz", hash = "sha256:5d9979348ec1a21c768ae07e0a652924538e8bce67313a73cb0f681cf08ba407"},
+    {file = "identify-2.5.33-py2.py3-none-any.whl", hash = "sha256:d40ce5fcd762817627670da8a7d8d8e65f24342d14539c59488dc603bf662e34"},
+    {file = "identify-2.5.33.tar.gz", hash = "sha256:161558f9fe4559e1557e1bff323e8631f6a0e4837f7497767c1782832f16b62d"},
 ]
 
 [package.extras]
@@ -1172,13 +1158,13 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "7.0.0"
+version = "7.0.1"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_metadata-7.0.0-py3-none-any.whl", hash = "sha256:d97503976bb81f40a193d41ee6570868479c69d5068651eb039c40d850c59d67"},
-    {file = "importlib_metadata-7.0.0.tar.gz", hash = "sha256:7fc841f8b8332803464e5dc1c63a2e59121f46ca186c0e2e182e80bf8c1319f7"},
+    {file = "importlib_metadata-7.0.1-py3-none-any.whl", hash = "sha256:4805911c3a4ec7c3966410053e9ec6a1fecd629117df5adee56dfc9432a1081e"},
+    {file = "importlib_metadata-7.0.1.tar.gz", hash = "sha256:f238736bb06590ae52ac1fab06a3a9ef1d8dce2b7a35b5ab329371d6c8f5d2cc"},
 ]
 
 [package.dependencies]
@@ -1220,13 +1206,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.27.1"
+version = "6.28.0"
 description = "IPython Kernel for Jupyter"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.27.1-py3-none-any.whl", hash = "sha256:dab88b47f112f9f7df62236511023c9bdeef67abc73af7c652e4ce4441601686"},
-    {file = "ipykernel-6.27.1.tar.gz", hash = "sha256:7d5d594b6690654b4d299edba5e872dc17bb7396a8d0609c97cb7b8a1c605de6"},
+    {file = "ipykernel-6.28.0-py3-none-any.whl", hash = "sha256:c6e9a9c63a7f4095c0a22a79f765f079f9ec7be4f2430a898ddea889e8665661"},
+    {file = "ipykernel-6.28.0.tar.gz", hash = "sha256:69c11403d26de69df02225916f916b37ea4b9af417da0a8c827f84328d88e5f3"},
 ]
 
 [package.dependencies]
@@ -1240,7 +1226,7 @@ matplotlib-inline = ">=0.1"
 nest-asyncio = "*"
 packaging = "*"
 psutil = "*"
-pyzmq = ">=20"
+pyzmq = ">=24"
 tornado = ">=6.1"
 traitlets = ">=5.4.0"
 
@@ -1311,13 +1297,13 @@ test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
 
 [[package]]
 name = "jax"
-version = "0.4.21"
+version = "0.4.23"
 description = "Differentiate, compile, and transform Numpy code."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "jax-0.4.21-py3-none-any.whl", hash = "sha256:caa82aaf4e58741126f645f2c46822ebf526cc793fbfdb3732b805f955950b32"},
-    {file = "jax-0.4.21.tar.gz", hash = "sha256:c97fd0d2751d6e1eb15aa2052ff7cfdc129f8fafc2c14cd779720658926a587b"},
+    {file = "jax-0.4.23-py3-none-any.whl", hash = "sha256:a7a07ccd1577111e3b82378c79a8ed0f9d6613b1e98fb6bf3c0b459198f73eaa"},
+    {file = "jax-0.4.23.tar.gz", hash = "sha256:2a229a5a758d1b803891b2eaed329723f6b15b4258b14dc0ccb1498c84963685"},
 ]
 
 [package.dependencies]
@@ -1332,44 +1318,45 @@ scipy = ">=1.9"
 
 [package.extras]
 australis = ["protobuf (>=3.13,<4)"]
-ci = ["jaxlib (==0.4.20)"]
-cpu = ["jaxlib (==0.4.21)"]
-cuda = ["jaxlib (==0.4.21+cuda11.cudnn86)"]
-cuda11-cudnn86 = ["jaxlib (==0.4.21+cuda11.cudnn86)"]
-cuda11-local = ["jaxlib (==0.4.21+cuda11.cudnn86)"]
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 ]
 
 [package.dependencies]
@@ -1383,12 +1370,13 @@ cuda12-pip = ["nvidia-cublas-cu12", "nvidia-cuda-cupti-cu12", "nvidia-cuda-nvcc-
 
 [[package]]
 name = "jaxtyping"
-version = "0.2.24"
+version = "0.2.25"
 description = "Type annotations and runtime checking for shape and dtype of JAX arrays, and PyTrees."
 optional = false
 python-versions = "~=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -1417,13 +1405,13 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.2"
+version = "3.1.3"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1468,13 +1456,13 @@ test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pyt
 
 [[package]]
 name = "jupyter-core"
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 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -1610,16 +1598,6 @@ files = [
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-python-versions = "*"
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-
 [[package]]
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 version = "3.0.0"
@@ -1789,35 +1767,35 @@ files = [
 
 [[package]]
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 python-versions = ">=3.9"
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-[package.dependencies]
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-wheel = "*"
+nvidia-cublas-cu11 = "*"
+nvidia-cuda-nvrtc-cu11 = "*"
+
+[[package]]
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+python-versions = ">=3"
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+[package.dependencies]
+nvidia-cublas-cu12 = "*"
 
 [[package]]
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+version = "11.0.2.54"
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-[package.dependencies]
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 [package.dependencies]
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+
+[[package]]
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+[package.dependencies]
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+nvidia-cusparse-cu12 = "*"
+nvidia-nvjitlink-cu12 = "*"
 
 [[package]]
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+version = "11.7.5.86"
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+optional = false
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 [package.dependencies]
-setuptools = "*"
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+nvidia-nvjitlink-cu12 = "*"
 
 [[package]]
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 [[package]]
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 python-versions = ">=3"
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-[package.dependencies]
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+[[package]]
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+python-versions = ">=3"
+files = [
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 [[package]]
 name = "opt-einsum"
@@ -2228,36 +2315,36 @@ files = [
 
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 python-versions = ">=3.9"
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 [package.dependencies]
@@ -2346,70 +2433,88 @@ ptyprocess = ">=0.5"
 
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 docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
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 name = "platformdirs"
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 [[package]]
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+version = "3.0.43"
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 [package.dependencies]
 numpy = ">=1.16.6"
 
-[[package]]
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-version = "0.6"
-description = ""
-optional = true
-python-versions = ">=3.5"
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 [[package]]
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 version = "2.21"
@@ -2665,13 +2759,13 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytest"
-version = "7.4.3"
+version = "7.4.4"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
@@ -2917,99 +3011,104 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 
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 name = "regex"
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 description = "Alternative regular expression module, to replace re."
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 [[package]]
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 optional = false
 python-versions = ">=3.7"
 files = [
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 [[package]]
@@ -3441,13 +3540,13 @@ files = [
 
 [[package]]
 name = "setuptools"
-version = "69.0.2"
+version = "69.0.3"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -3609,56 +3708,50 @@ sphinx = ">=1.2"
 
 [[package]]
 name = "sphinxcontrib-applehelp"
-version = "1.0.7"
+version = "1.0.8"
 description = "sphinxcontrib-applehelp is a Sphinx extension which outputs Apple help books"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
-[package.dependencies]
-Sphinx = ">=5"
-
 [package.extras]
 lint = ["docutils-stubs", "flake8", "mypy"]
+standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
 name = "sphinxcontrib-devhelp"
-version = "1.0.5"
+version = "1.0.6"
 description = "sphinxcontrib-devhelp is a sphinx extension which outputs Devhelp documents"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
-[package.dependencies]
-Sphinx = ">=5"
-
 [package.extras]
 lint = ["docutils-stubs", "flake8", "mypy"]
+standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
 name = "sphinxcontrib-htmlhelp"
-version = "2.0.4"
+version = "2.0.5"
 description = "sphinxcontrib-htmlhelp is a sphinx extension which renders HTML help files"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
-[package.dependencies]
-Sphinx = ">=5"
-
 [package.extras]
 lint = ["docutils-stubs", "flake8", "mypy"]
+standalone = ["Sphinx (>=5)"]
 test = ["html5lib", "pytest"]
 
 [[package]]
@@ -3677,38 +3770,34 @@ test = ["flake8", "mypy", "pytest"]
 
 [[package]]
 name = "sphinxcontrib-qthelp"
-version = "1.0.6"
+version = "1.0.7"
 description = "sphinxcontrib-qthelp is a sphinx extension which outputs QtHelp documents"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
-[package.dependencies]
-Sphinx = ">=5"
-
 [package.extras]
 lint = ["docutils-stubs", "flake8", "mypy"]
+standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
 name = "sphinxcontrib-serializinghtml"
-version = "1.1.9"
+version = "1.1.10"
 description = "sphinxcontrib-serializinghtml is a sphinx extension which outputs \"serialized\" HTML files (json and pickle)"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
-[package.dependencies]
-Sphinx = ">=5"
-
 [package.extras]
 lint = ["docutils-stubs", "flake8", "mypy"]
+standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
@@ -3726,13 +3815,13 @@ sphinx = ">=1.8"
 
 [[package]]
 name = "sphinxext-opengraph"
-version = "0.9.0"
+version = "0.9.1"
 description = "Sphinx Extension to enable OGP support"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3924,57 +4013,55 @@ files = [
 
 [[package]]
 name = "torch"
-version = "2.0.0"
+version = "2.1.2"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
 python-versions = ">=3.8.0"
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 ]
 
 [package.dependencies]
 filelock = "*"
+fsspec = "*"
 jinja2 = "*"
 networkx = "*"
-nvidia-cublas-cu11 = {version = "11.10.3.66", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-cupti-cu11 = {version = "11.7.101", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-nvrtc-cu11 = {version = "11.7.99", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-runtime-cu11 = {version = "11.7.99", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cudnn-cu11 = {version = "8.5.0.96", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cufft-cu11 = {version = "10.9.0.58", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-curand-cu11 = {version = "10.2.10.91", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cusolver-cu11 = {version = "11.4.0.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cusparse-cu11 = {version = "11.7.4.91", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nccl-cu11 = {version = "2.14.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nvtx-cu11 = {version = "11.7.91", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-nvrtc-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-runtime-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cudnn-cu12 = {version = "8.9.2.26", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.18.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 sympy = "*"
-triton = {version = "2.0.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+triton = {version = "2.1.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 typing-extensions = "*"
 
 [package.extras]
+dynamo = ["jinja2"]
 opt-einsum = ["opt-einsum (>=3.3)"]
 
 [[package]]
@@ -4019,13 +4106,13 @@ telegram = ["requests"]
 
 [[package]]
 name = "traitlets"
-version = "5.14.0"
+version = "5.14.1"
 description = "Traitlets Python configuration system"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "traitlets-5.14.0-py3-none-any.whl", hash = "sha256:f14949d23829023013c47df20b4a76ccd1a85effb786dc060f34de7948361b33"},
-    {file = "traitlets-5.14.0.tar.gz", hash = "sha256:fcdaa8ac49c04dfa0ed3ee3384ef6dfdb5d6f3741502be247279407679296772"},
+    {file = "traitlets-5.14.1-py3-none-any.whl", hash = "sha256:2e5a030e6eff91737c643231bfcf04a65b0132078dad75e4936700b213652e74"},
+    {file = "traitlets-5.14.1.tar.gz", hash = "sha256:8585105b371a04b8316a43d5ce29c098575c2e477850b62b848b964f1444527e"},
 ]
 
 [package.extras]
@@ -4034,19 +4121,19 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "transformers"
-version = "4.35.2"
+version = "4.36.2"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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-    {file = "transformers-4.35.2.tar.gz", hash = "sha256:2d125e197d77b0cdb6c9201df9fa7e2101493272e448b9fba9341c695bee2f52"},
+    {file = "transformers-4.36.2-py3-none-any.whl", hash = "sha256:462066c4f74ee52516f12890dcc9ec71d1a5e97998db621668455117a54330f6"},
+    {file = "transformers-4.36.2.tar.gz", hash = "sha256:d8068e897e47793281501e547d2bbdfc5b8556409c2cb6c3d9e2ca77d4c0b4ec"},
 ]
 
 [package.dependencies]
-accelerate = {version = ">=0.20.3", optional = true, markers = "extra == \"torch\""}
+accelerate = {version = ">=0.21.0", optional = true, markers = "extra == \"torch\""}
 filelock = "*"
-huggingface-hub = ">=0.16.4,<1.0"
+huggingface-hub = ">=0.19.3,<1.0"
 numpy = ">=1.17"
 packaging = ">=20.0"
 protobuf = {version = "*", optional = true, markers = "extra == \"sentencepiece\""}
@@ -4060,30 +4147,30 @@ torch = {version = ">=1.10,<1.12.0 || >1.12.0", optional = true, markers = "extr
 tqdm = ">=4.27"
 
 [package.extras]
-accelerate = ["accelerate (>=0.20.3)"]
-agents = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch (>=1.10,!=1.12.0)"]
-all = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision"]
+accelerate = ["accelerate (>=0.21.0)"]
+agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch (>=1.10,!=1.12.0)"]
+all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision"]
 audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 codecarbon = ["codecarbon (==1.2.0)"]
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 [package.dependencies]
-cmake = "*"
 filelock = "*"
-lit = "*"
-torch = "*"
 
 [package.extras]
+build = ["cmake (>=3.18)", "lit"]
 tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
 tutorials = ["matplotlib", "pandas", "tabulate"]
 
@@ -4158,24 +4233,24 @@ test = ["mypy", "pytest", "typing-extensions"]
 
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 name = "typing-extensions"
-version = "4.8.0"
+version = "4.9.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
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 name = "tzdata"
-version = "2023.3"
+version = "2023.4"
 description = "Provider of IANA time zone data"
 optional = true
 python-versions = ">=2"
 files = [
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 [[package]]
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 [[package]]
 name = "wcwidth"
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-[package.extras]
-test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
-
 [[package]]
 name = "widgetsnbextension"
 version = "4.0.9"
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+    {file = "yarl-1.9.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:206a55215e6d05dbc6c98ce598a59e6fbd0c493e2de4ea6cc2f4934d5a18d130"},
+    {file = "yarl-1.9.4-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:07574b007ee20e5c375a8fe4a0789fad26db905f9813be0f9fef5a68080de559"},
+    {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:5a2e2433eb9344a163aced6a5f6c9222c0786e5a9e9cac2c89f0b28433f56e23"},
+    {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:6ad6d10ed9b67a382b45f29ea028f92d25bc0bc1daf6c5b801b90b5aa70fb9ec"},
+    {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:6fe79f998a4052d79e1c30eeb7d6c1c1056ad33300f682465e1b4e9b5a188b78"},
+    {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:a825ec844298c791fd28ed14ed1bffc56a98d15b8c58a20e0e08c1f5f2bea1be"},
+    {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:8619d6915b3b0b34420cf9b2bb6d81ef59d984cb0fde7544e9ece32b4b3043c3"},
+    {file = "yarl-1.9.4-cp38-cp38-win32.whl", hash = "sha256:686a0c2f85f83463272ddffd4deb5e591c98aac1897d65e92319f729c320eece"},
+    {file = "yarl-1.9.4-cp38-cp38-win_amd64.whl", hash = "sha256:a00862fb23195b6b8322f7d781b0dc1d82cb3bcac346d1e38689370cc1cc398b"},
+    {file = "yarl-1.9.4-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:604f31d97fa493083ea21bd9b92c419012531c4e17ea6da0f65cacdcf5d0bd27"},
+    {file = "yarl-1.9.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:8a854227cf581330ffa2c4824d96e52ee621dd571078a252c25e3a3b3d94a1b1"},
+    {file = "yarl-1.9.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ba6f52cbc7809cd8d74604cce9c14868306ae4aa0282016b641c661f981a6e91"},
+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a6327976c7c2f4ee6816eff196e25385ccc02cb81427952414a64811037bbc8b"},
+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8397a3817d7dcdd14bb266283cd1d6fc7264a48c186b986f32e86d86d35fbac5"},
+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e0381b4ce23ff92f8170080c97678040fc5b08da85e9e292292aba67fdac6c34"},
+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:23d32a2594cb5d565d358a92e151315d1b2268bc10f4610d098f96b147370136"},
+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ddb2a5c08a4eaaba605340fdee8fc08e406c56617566d9643ad8bf6852778fc7"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:26a1dc6285e03f3cc9e839a2da83bcbf31dcb0d004c72d0730e755b33466c30e"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:18580f672e44ce1238b82f7fb87d727c4a131f3a9d33a5e0e82b793362bf18b4"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:29e0f83f37610f173eb7e7b5562dd71467993495e568e708d99e9d1944f561ec"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:1f23e4fe1e8794f74b6027d7cf19dc25f8b63af1483d91d595d4a07eca1fb26c"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:db8e58b9d79200c76956cefd14d5c90af54416ff5353c5bfd7cbe58818e26ef0"},
+    {file = "yarl-1.9.4-cp39-cp39-win32.whl", hash = "sha256:c7224cab95645c7ab53791022ae77a4509472613e839dab722a72abe5a684575"},
+    {file = "yarl-1.9.4-cp39-cp39-win_amd64.whl", hash = "sha256:824d6c50492add5da9374875ce72db7a0733b29c2394890aef23d533106e2b15"},
+    {file = "yarl-1.9.4-py3-none-any.whl", hash = "sha256:928cecb0ef9d5a7946eb6ff58417ad2fe9375762382f1bf5c55e61645f2c43ad"},
+    {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
 ]
 
 [package.dependencies]
@@ -4493,4 +4554,4 @@ sklearn = ["joblib", "scikit-learn"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "1e47d98769015c300b82c3cf5b885ba550cf5ffa1590c9112a27b326b057dcd3"
+content-hash = "a5349e31cc7c214e409d6f9ee643e20dc04a8997f7cadfe4a4bd553a6e095851"
diff --git a/pyproject.toml b/pyproject.toml
index 143c22d5..a0125b4a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "inseq"
-version = "0.5.0"
+version = "0.6.0.dev0"
 description = "Interpretability for Sequence Generation Models 🔍"
 readme = "README.md"
 authors = ["The Inseq Team"]
@@ -53,8 +53,7 @@ captum = ">=0.6.0"
 numpy = ">=1.21.6"
 jaxtyping = "^0.2.23"
 typeguard = "<=2.13.3"
-# Omitting future torch releases as they don't install nvidia dependencies: https://github.com/pytorch/pytorch/issues/100974
-torch = "2.0.0"
+torch = ">2.1.1"
 matplotlib = ">=3.5.3"
 tqdm = ">=4.64.0"
 poethepoet = ">=0.13.1"
@@ -121,7 +120,7 @@ torch-cuda = "python -m pip install torch"
 
 [tool.mypy]
 # https://mypy.readthedocs.io/en/latest/config_file.html#using-a-pyproject-toml-file
-python_version = 3.9
+python_version = "3.9"
 strict = true
 
 
@@ -171,7 +170,7 @@ targets = ["inseq"]
 skips = ["B301"]
 
 [tool.ruff]
-target-version = "py38"
+target-version = "py39"
 exclude = [
     ".git",
     ".vscode",
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 33441be7..7ff4b60d 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,13 +1,13 @@
-accelerate==0.25.0 ; python_version >= "3.9" and python_version < "3.12"
+accelerate==0.26.1 ; python_version >= "3.9" and python_version < "3.12"
 aiohttp==3.9.1 ; python_version >= "3.9" and python_version < "3.12"
 aiosignal==1.3.1 ; python_version >= "3.9" and python_version < "3.12"
-alabaster==0.7.13 ; python_version >= "3.9" and python_version < "3.12"
+alabaster==0.7.16 ; python_version >= "3.9" and python_version < "3.12"
 appnope==0.1.3 ; python_version >= "3.9" and python_version < "3.12" and platform_system == "Darwin"
 asttokens==2.4.1 ; python_version >= "3.9" and python_version < "3.12"
 async-timeout==4.0.3 ; python_version >= "3.9" and python_version < "3.11"
-attrs==23.1.0 ; python_version >= "3.9" and python_version < "3.12"
-babel==2.13.1 ; python_version >= "3.9" and python_version < "3.12"
-bandit==1.7.5 ; python_version >= "3.9" and python_version < "3.12"
+attrs==23.2.0 ; python_version >= "3.9" and python_version < "3.12"
+babel==2.14.0 ; python_version >= "3.9" and python_version < "3.12"
+bandit==1.7.6 ; python_version >= "3.9" and python_version < "3.12"
 beautifulsoup4==4.12.2 ; python_version >= "3.9" and python_version < "3.12"
 captum==0.7.0 ; python_version >= "3.9" and python_version < "3.12"
 certifi==2023.11.17 ; python_version >= "3.9" and python_version < "3.12"
@@ -15,49 +15,47 @@ cffi==1.16.0 ; python_version >= "3.9" and python_version < "3.12" and implement
 cfgv==3.4.0 ; python_version >= "3.9" and python_version < "3.12"
 charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.12"
 click==8.1.7 ; python_version >= "3.9" and python_version < "3.12"
-cmake==3.27.9 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
 colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.12" and (platform_system == "Windows" or sys_platform == "win32")
-comm==0.2.0 ; python_version >= "3.9" and python_version < "3.12"
+comm==0.2.1 ; python_version >= "3.9" and python_version < "3.12"
 contourpy==1.2.0 ; python_version >= "3.9" and python_version < "3.12"
-coverage[toml]==7.3.2 ; python_version >= "3.9" and python_version < "3.12"
+coverage[toml]==7.4.0 ; python_version >= "3.9" and python_version < "3.12"
 cycler==0.12.1 ; python_version >= "3.9" and python_version < "3.12"
 darglint==1.8.1 ; python_version >= "3.9" and python_version < "3.12"
-datasets==2.15.0 ; python_version >= "3.9" and python_version < "3.12"
+datasets==2.14.4 ; python_version >= "3.9" and python_version < "3.12"
 debugpy==1.8.0 ; python_version >= "3.9" and python_version < "3.12"
 decorator==5.1.1 ; python_version >= "3.9" and python_version < "3.12"
 dill==0.3.7 ; python_version >= "3.9" and python_version < "3.12"
-distlib==0.3.7 ; python_version >= "3.9" and python_version < "3.12"
+distlib==0.3.8 ; python_version >= "3.9" and python_version < "3.12"
 docutils==0.20.1 ; python_version >= "3.9" and python_version < "3.12"
 dparse==0.6.3 ; python_version >= "3.9" and python_version < "3.12"
 exceptiongroup==1.2.0 ; python_version >= "3.9" and python_version < "3.11"
 executing==2.0.1 ; python_version >= "3.9" and python_version < "3.12"
 filelock==3.13.1 ; python_version >= "3.9" and python_version < "3.12"
-fonttools==4.46.0 ; python_version >= "3.9" and python_version < "3.12"
-frozenlist==1.4.0 ; python_version >= "3.9" and python_version < "3.12"
-fsspec==2023.10.0 ; python_version >= "3.9" and python_version < "3.12"
-fsspec[http]==2023.10.0 ; python_version >= "3.9" and python_version < "3.12"
+fonttools==4.47.2 ; python_version >= "3.9" and python_version < "3.12"
+frozenlist==1.4.1 ; python_version >= "3.9" and python_version < "3.12"
+fsspec==2023.12.2 ; python_version >= "3.9" and python_version < "3.12"
+fsspec[http]==2023.12.2 ; python_version >= "3.9" and python_version < "3.12"
 furo==2023.9.10 ; python_version >= "3.9" and python_version < "3.12"
 gitdb==4.0.11 ; python_version >= "3.9" and python_version < "3.12"
-gitpython==3.1.40 ; python_version >= "3.9" and python_version < "3.12"
-huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.12"
-identify==2.5.32 ; python_version >= "3.9" and python_version < "3.12"
+gitpython==3.1.41 ; python_version >= "3.9" and python_version < "3.12"
+huggingface-hub==0.20.2 ; python_version >= "3.9" and python_version < "3.12"
+identify==2.5.33 ; python_version >= "3.9" and python_version < "3.12"
 idna==3.6 ; python_version >= "3.9" and python_version < "3.12"
 imagesize==1.4.1 ; python_version >= "3.9" and python_version < "3.12"
-importlib-metadata==7.0.0 ; python_version >= "3.9" and python_version < "3.10"
+importlib-metadata==7.0.1 ; python_version >= "3.9" and python_version < "3.10"
 importlib-resources==6.1.1 ; python_version >= "3.9" and python_version < "3.10"
 iniconfig==2.0.0 ; python_version >= "3.9" and python_version < "3.12"
-ipykernel==6.27.1 ; python_version >= "3.9" and python_version < "3.12"
+ipykernel==6.28.0 ; python_version >= "3.9" and python_version < "3.12"
 ipython==8.18.1 ; python_version >= "3.9" and python_version < "3.12"
 ipywidgets==8.1.1 ; python_version >= "3.9" and python_version < "3.12"
-jaxtyping==0.2.24 ; python_version >= "3.9" and python_version < "3.12"
+jaxtyping==0.2.25 ; python_version >= "3.9" and python_version < "3.12"
 jedi==0.19.1 ; python_version >= "3.9" and python_version < "3.12"
-jinja2==3.1.2 ; python_version >= "3.9" and python_version < "3.12"
+jinja2==3.1.3 ; python_version >= "3.9" and python_version < "3.12"
 joblib==1.3.2 ; python_version >= "3.9" and python_version < "3.12"
 jupyter-client==8.6.0 ; python_version >= "3.9" and python_version < "3.12"
-jupyter-core==5.5.0 ; python_version >= "3.9" and python_version < "3.12"
+jupyter-core==5.7.1 ; python_version >= "3.9" and python_version < "3.12"
 jupyterlab-widgets==3.0.9 ; python_version >= "3.9" and python_version < "3.12"
 kiwisolver==1.4.5 ; python_version >= "3.9" and python_version < "3.12"
-lit==17.0.6 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
 markdown-it-py==3.0.0 ; python_version >= "3.9" and python_version < "3.12"
 markupsafe==2.1.3 ; python_version >= "3.9" and python_version < "3.12"
 matplotlib-inline==0.1.6 ; python_version >= "3.9" and python_version < "3.12"
@@ -69,58 +67,69 @@ multiprocess==0.70.15 ; python_version >= "3.9" and python_version < "3.12"
 nest-asyncio==1.5.8 ; python_version >= "3.9" and python_version < "3.12"
 networkx==3.2.1 ; python_version >= "3.9" and python_version < "3.12"
 nodeenv==1.8.0 ; python_version >= "3.9" and python_version < "3.12"
-numpy==1.26.2 ; python_version >= "3.9" and python_version < "3.12"
-nvidia-cublas-cu11==11.10.3.66 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-cuda-cupti-cu11==11.7.101 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-cuda-nvrtc-cu11==11.7.99 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-cuda-runtime-cu11==11.7.99 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-cudnn-cu11==8.5.0.96 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-cufft-cu11==10.9.0.58 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-curand-cu11==10.2.10.91 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-cusolver-cu11==11.4.0.1 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-cusparse-cu11==11.7.4.91 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-nccl-cu11==2.14.3 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-nvtx-cu11==11.7.91 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
+numpy==1.26.3 ; python_version >= "3.9" and python_version < "3.12"
+nvidia-cublas-cu11==11.11.3.6 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-cublas-cu12==12.1.3.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-cuda-cupti-cu11==11.8.87 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-cuda-cupti-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-cuda-nvrtc-cu11==11.8.89 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-cuda-nvrtc-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-cuda-runtime-cu11==11.8.89 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-cuda-runtime-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-cudnn-cu11==8.9.6.50 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-cudnn-cu12==8.9.2.26 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-cufft-cu11==10.9.0.58 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-cufft-cu12==11.0.2.54 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-curand-cu11==10.3.0.86 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-curand-cu12==10.3.2.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-cusolver-cu11==11.4.1.48 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-cusolver-cu12==11.4.5.107 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-cusparse-cu11==11.7.5.86 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-cusparse-cu12==12.1.0.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-nccl-cu11==2.19.3 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-nccl-cu12==2.18.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-nvjitlink-cu12==12.3.101 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-nvtx-cu11==11.8.86 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-nvtx-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
 packaging==23.2 ; python_version >= "3.9" and python_version < "3.12"
-pandas==2.1.3 ; python_version >= "3.9" and python_version < "3.12"
+pandas==2.1.4 ; python_version >= "3.9" and python_version < "3.12"
 parso==0.8.3 ; python_version >= "3.9" and python_version < "3.12"
 pastel==0.2.1 ; python_version >= "3.9" and python_version < "3.12"
 pbr==6.0.0 ; python_version >= "3.9" and python_version < "3.12"
 pexpect==4.9.0 ; python_version >= "3.9" and python_version < "3.12" and sys_platform != "win32"
-pillow==10.1.0 ; python_version >= "3.9" and python_version < "3.12"
+pillow==10.2.0 ; python_version >= "3.9" and python_version < "3.12"
 platformdirs==4.1.0 ; python_version >= "3.9" and python_version < "3.12"
 pluggy==1.3.0 ; python_version >= "3.9" and python_version < "3.12"
 poethepoet==0.24.4 ; python_version >= "3.9" and python_version < "3.12"
-pre-commit==3.5.0 ; python_version >= "3.9" and python_version < "3.12"
-prompt-toolkit==3.0.41 ; python_version >= "3.9" and python_version < "3.12"
-protobuf==4.25.1 ; python_version >= "3.9" and python_version < "3.12"
-psutil==5.9.6 ; python_version >= "3.9" and python_version < "3.12"
+pre-commit==3.6.0 ; python_version >= "3.9" and python_version < "3.12"
+prompt-toolkit==3.0.43 ; python_version >= "3.9" and python_version < "3.12"
+protobuf==4.25.2 ; python_version >= "3.9" and python_version < "3.12"
+psutil==5.9.7 ; python_version >= "3.9" and python_version < "3.12"
 ptyprocess==0.7.0 ; python_version >= "3.9" and python_version < "3.12" and sys_platform != "win32"
 pure-eval==0.2.2 ; python_version >= "3.9" and python_version < "3.12"
-pyarrow-hotfix==0.6 ; python_version >= "3.9" and python_version < "3.12"
-pyarrow==14.0.1 ; python_version >= "3.9" and python_version < "3.12"
+pyarrow==14.0.2 ; python_version >= "3.9" and python_version < "3.12"
 pycparser==2.21 ; python_version >= "3.9" and python_version < "3.12" and implementation_name == "pypy"
 pygments==2.17.2 ; python_version >= "3.9" and python_version < "3.12"
 pyparsing==3.1.1 ; python_version >= "3.9" and python_version < "3.12"
 pytest-cov==4.1.0 ; python_version >= "3.9" and python_version < "3.12"
-pytest==7.4.3 ; python_version >= "3.9" and python_version < "3.12"
+pytest==7.4.4 ; python_version >= "3.9" and python_version < "3.12"
 python-dateutil==2.8.2 ; python_version >= "3.9" and python_version < "3.12"
 pytz==2023.3.post1 ; python_version >= "3.9" and python_version < "3.12"
 pywin32==306 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.9" and python_version < "3.12"
 pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.12"
 pyzmq==25.1.2 ; python_version >= "3.9" and python_version < "3.12"
-regex==2023.10.3 ; python_version >= "3.9" and python_version < "3.12"
+regex==2023.12.25 ; python_version >= "3.9" and python_version < "3.12"
 requests==2.31.0 ; python_version >= "3.9" and python_version < "3.12"
 rich==13.7.0 ; python_version >= "3.9" and python_version < "3.12"
 ruamel-yaml-clib==0.2.8 ; platform_python_implementation == "CPython" and python_version < "3.12" and python_version >= "3.9"
 ruamel-yaml==0.18.5 ; python_version >= "3.9" and python_version < "3.12"
-ruff==0.1.7 ; python_version >= "3.9" and python_version < "3.12"
+ruff==0.1.13 ; python_version >= "3.9" and python_version < "3.12"
 safetensors==0.4.1 ; python_version >= "3.9" and python_version < "3.12"
 safety==2.3.4 ; python_version >= "3.9" and python_version < "3.12"
 scikit-learn==1.3.2 ; python_version >= "3.9" and python_version < "3.12"
 scipy==1.11.4 ; python_version >= "3.9" and python_version < "3.12"
 sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.12"
-setuptools==69.0.2 ; python_version >= "3.9" and python_version < "3.12"
+setuptools==69.0.3 ; python_version >= "3.9" and python_version < "3.12"
 six==1.16.0 ; python_version >= "3.9" and python_version < "3.12"
 smmap==5.0.1 ; python_version >= "3.9" and python_version < "3.12"
 snowballstemmer==2.2.0 ; python_version >= "3.9" and python_version < "3.12"
@@ -130,34 +139,33 @@ sphinx-copybutton==0.5.2 ; python_version >= "3.9" and python_version < "3.12"
 sphinx-design==0.5.0 ; python_version >= "3.9" and python_version < "3.12"
 sphinx-gitstamp==0.4.0 ; python_version >= "3.9" and python_version < "3.12"
 sphinx==7.2.6 ; python_version >= "3.9" and python_version < "3.12"
-sphinxcontrib-applehelp==1.0.7 ; python_version >= "3.9" and python_version < "3.12"
-sphinxcontrib-devhelp==1.0.5 ; python_version >= "3.9" and python_version < "3.12"
-sphinxcontrib-htmlhelp==2.0.4 ; python_version >= "3.9" and python_version < "3.12"
+sphinxcontrib-applehelp==1.0.8 ; python_version >= "3.9" and python_version < "3.12"
+sphinxcontrib-devhelp==1.0.6 ; python_version >= "3.9" and python_version < "3.12"
+sphinxcontrib-htmlhelp==2.0.5 ; python_version >= "3.9" and python_version < "3.12"
 sphinxcontrib-jsmath==1.0.1 ; python_version >= "3.9" and python_version < "3.12"
-sphinxcontrib-qthelp==1.0.6 ; python_version >= "3.9" and python_version < "3.12"
-sphinxcontrib-serializinghtml==1.1.9 ; python_version >= "3.9" and python_version < "3.12"
+sphinxcontrib-qthelp==1.0.7 ; python_version >= "3.9" and python_version < "3.12"
+sphinxcontrib-serializinghtml==1.1.10 ; python_version >= "3.9" and python_version < "3.12"
 sphinxemoji==0.2.0 ; python_version >= "3.9" and python_version < "3.12"
-sphinxext-opengraph==0.9.0 ; python_version >= "3.9" and python_version < "3.12"
+sphinxext-opengraph==0.9.1 ; python_version >= "3.9" and python_version < "3.12"
 stack-data==0.6.3 ; python_version >= "3.9" and python_version < "3.12"
 stevedore==5.1.0 ; python_version >= "3.9" and python_version < "3.12"
 sympy==1.12 ; python_version >= "3.9" and python_version < "3.12"
 threadpoolctl==3.2.0 ; python_version >= "3.9" and python_version < "3.12"
 tokenizers==0.15.0 ; python_version >= "3.9" and python_version < "3.12"
 tomli==2.0.1 ; python_version >= "3.9" and python_version < "3.12"
-torch==2.0.0 ; python_version >= "3.9" and python_version < "3.12"
+torch==2.1.2 ; python_version >= "3.9" and python_version < "3.12"
 tornado==6.4 ; python_version >= "3.9" and python_version < "3.12"
 tqdm==4.66.1 ; python_version >= "3.9" and python_version < "3.12"
-traitlets==5.14.0 ; python_version >= "3.9" and python_version < "3.12"
-transformers[sentencepiece,tokenizers,torch]==4.35.2 ; python_version >= "3.9" and python_version < "3.12"
-triton==2.0.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+traitlets==5.14.1 ; python_version >= "3.9" and python_version < "3.12"
+transformers[sentencepiece,tokenizers,torch]==4.36.2 ; python_version >= "3.9" and python_version < "3.12"
+triton==2.1.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
 typeguard==2.13.3 ; python_version >= "3.9" and python_version < "3.12"
-typing-extensions==4.8.0 ; python_version >= "3.9" and python_version < "3.12"
-tzdata==2023.3 ; python_version >= "3.9" and python_version < "3.12"
+typing-extensions==4.9.0 ; python_version >= "3.9" and python_version < "3.12"
+tzdata==2023.4 ; python_version >= "3.9" and python_version < "3.12"
 urllib3==2.1.0 ; python_version >= "3.9" and python_version < "3.12"
 virtualenv==20.25.0 ; python_version >= "3.9" and python_version < "3.12"
-wcwidth==0.2.12 ; python_version >= "3.9" and python_version < "3.12"
-wheel==0.42.0 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
+wcwidth==0.2.13 ; python_version >= "3.9" and python_version < "3.12"
 widgetsnbextension==4.0.9 ; python_version >= "3.9" and python_version < "3.12"
 xxhash==3.4.1 ; python_version >= "3.9" and python_version < "3.12"
-yarl==1.9.3 ; python_version >= "3.9" and python_version < "3.12"
+yarl==1.9.4 ; python_version >= "3.9" and python_version < "3.12"
 zipp==3.17.0 ; python_version >= "3.9" and python_version < "3.10"
diff --git a/requirements.txt b/requirements.txt
index 28d4990d..c24ee193 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,66 +1,74 @@
-accelerate==0.25.0 ; python_version >= "3.9" and python_version < "3.12"
+accelerate==0.26.1 ; python_version >= "3.9" and python_version < "3.12"
 captum==0.7.0 ; python_version >= "3.9" and python_version < "3.12"
 certifi==2023.11.17 ; python_version >= "3.9" and python_version < "3.12"
 charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.12"
-cmake==3.27.9 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
 colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.12" and platform_system == "Windows"
 contourpy==1.2.0 ; python_version >= "3.9" and python_version < "3.12"
 cycler==0.12.1 ; python_version >= "3.9" and python_version < "3.12"
 filelock==3.13.1 ; python_version >= "3.9" and python_version < "3.12"
-fonttools==4.46.0 ; python_version >= "3.9" and python_version < "3.12"
-fsspec==2023.10.0 ; python_version >= "3.9" and python_version < "3.12"
-huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.12"
+fonttools==4.47.2 ; python_version >= "3.9" and python_version < "3.12"
+fsspec==2023.12.2 ; python_version >= "3.9" and python_version < "3.12"
+huggingface-hub==0.20.2 ; python_version >= "3.9" and python_version < "3.12"
 idna==3.6 ; python_version >= "3.9" and python_version < "3.12"
 importlib-resources==6.1.1 ; python_version >= "3.9" and python_version < "3.10"
-jaxtyping==0.2.24 ; python_version >= "3.9" and python_version < "3.12"
-jinja2==3.1.2 ; python_version >= "3.9" and python_version < "3.12"
+jaxtyping==0.2.25 ; python_version >= "3.9" and python_version < "3.12"
+jinja2==3.1.3 ; python_version >= "3.9" and python_version < "3.12"
 kiwisolver==1.4.5 ; python_version >= "3.9" and python_version < "3.12"
-lit==17.0.6 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
 markdown-it-py==3.0.0 ; python_version >= "3.9" and python_version < "3.12"
 markupsafe==2.1.3 ; python_version >= "3.9" and python_version < "3.12"
 matplotlib==3.8.2 ; python_version >= "3.9" and python_version < "3.12"
 mdurl==0.1.2 ; python_version >= "3.9" and python_version < "3.12"
 mpmath==1.3.0 ; python_version >= "3.9" and python_version < "3.12"
 networkx==3.2.1 ; python_version >= "3.9" and python_version < "3.12"
-numpy==1.26.2 ; python_version >= "3.9" and python_version < "3.12"
-nvidia-cublas-cu11==11.10.3.66 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-cuda-cupti-cu11==11.7.101 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-cuda-nvrtc-cu11==11.7.99 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-cuda-runtime-cu11==11.7.99 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-cudnn-cu11==8.5.0.96 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-cufft-cu11==10.9.0.58 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-curand-cu11==10.2.10.91 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-cusolver-cu11==11.4.0.1 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-cusparse-cu11==11.7.4.91 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-nccl-cu11==2.14.3 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
-nvidia-nvtx-cu11==11.7.91 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
+numpy==1.26.3 ; python_version >= "3.9" and python_version < "3.12"
+nvidia-cublas-cu11==11.11.3.6 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-cublas-cu12==12.1.3.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-cuda-cupti-cu11==11.8.87 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-cuda-cupti-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-cuda-nvrtc-cu11==11.8.89 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-cuda-nvrtc-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-cuda-runtime-cu11==11.8.89 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-cuda-runtime-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-cudnn-cu11==8.9.6.50 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-cudnn-cu12==8.9.2.26 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-cufft-cu11==10.9.0.58 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-cufft-cu12==11.0.2.54 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-curand-cu11==10.3.0.86 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-curand-cu12==10.3.2.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-cusolver-cu11==11.4.1.48 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-cusolver-cu12==11.4.5.107 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-cusparse-cu11==11.7.5.86 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-cusparse-cu12==12.1.0.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-nccl-cu11==2.19.3 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-nccl-cu12==2.18.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-nvjitlink-cu12==12.3.101 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+nvidia-nvtx-cu11==11.8.86 ; python_version >= "3.9" and python_version < "3.12" and sys_platform == "linux"
+nvidia-nvtx-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
 packaging==23.2 ; python_version >= "3.9" and python_version < "3.12"
 pastel==0.2.1 ; python_version >= "3.9" and python_version < "3.12"
-pillow==10.1.0 ; python_version >= "3.9" and python_version < "3.12"
+pillow==10.2.0 ; python_version >= "3.9" and python_version < "3.12"
 poethepoet==0.24.4 ; python_version >= "3.9" and python_version < "3.12"
-protobuf==4.25.1 ; python_version >= "3.9" and python_version < "3.12"
-psutil==5.9.6 ; python_version >= "3.9" and python_version < "3.12"
+protobuf==4.25.2 ; python_version >= "3.9" and python_version < "3.12"
+psutil==5.9.7 ; python_version >= "3.9" and python_version < "3.12"
 pygments==2.17.2 ; python_version >= "3.9" and python_version < "3.12"
 pyparsing==3.1.1 ; python_version >= "3.9" and python_version < "3.12"
 python-dateutil==2.8.2 ; python_version >= "3.9" and python_version < "3.12"
 pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.12"
-regex==2023.10.3 ; python_version >= "3.9" and python_version < "3.12"
+regex==2023.12.25 ; python_version >= "3.9" and python_version < "3.12"
 requests==2.31.0 ; python_version >= "3.9" and python_version < "3.12"
 rich==13.7.0 ; python_version >= "3.9" and python_version < "3.12"
 safetensors==0.4.1 ; python_version >= "3.9" and python_version < "3.12"
 scipy==1.11.4 ; python_version >= "3.9" and python_version < "3.12"
 sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.12"
-setuptools==69.0.2 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
 six==1.16.0 ; python_version >= "3.9" and python_version < "3.12"
 sympy==1.12 ; python_version >= "3.9" and python_version < "3.12"
 tokenizers==0.15.0 ; python_version >= "3.9" and python_version < "3.12"
 tomli==2.0.1 ; python_version >= "3.9" and python_version < "3.12"
-torch==2.0.0 ; python_version >= "3.9" and python_version < "3.12"
+torch==2.1.2 ; python_version >= "3.9" and python_version < "3.12"
 tqdm==4.66.1 ; python_version >= "3.9" and python_version < "3.12"
-transformers[sentencepiece,tokenizers,torch]==4.35.2 ; python_version >= "3.9" and python_version < "3.12"
-triton==2.0.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
+transformers[sentencepiece,tokenizers,torch]==4.36.2 ; python_version >= "3.9" and python_version < "3.12"
+triton==2.1.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.12"
 typeguard==2.13.3 ; python_version >= "3.9" and python_version < "3.12"
-typing-extensions==4.8.0 ; python_version >= "3.9" and python_version < "3.12"
+typing-extensions==4.9.0 ; python_version >= "3.9" and python_version < "3.12"
 urllib3==2.1.0 ; python_version >= "3.9" and python_version < "3.12"
-wheel==0.42.0 ; python_version >= "3.9" and python_version < "3.12" and (sys_platform == "linux" or platform_system == "Linux") and (sys_platform == "linux" or platform_machine == "x86_64")
 zipp==3.17.0 ; python_version >= "3.9" and python_version < "3.10"
diff --git a/tests/attr/feat/ops/test_monotonic_path_builder.py b/tests/attr/feat/ops/test_monotonic_path_builder.py
index cdfd3d42..81461697 100644
--- a/tests/attr/feat/ops/test_monotonic_path_builder.py
+++ b/tests/attr/feat/ops/test_monotonic_path_builder.py
@@ -1,5 +1,4 @@
 from itertools import islice
-from typing import List, Tuple
 
 from inseq.utils import is_joblib_available
 
@@ -65,7 +64,7 @@ def walrus_operator_find_word_path(wrd_idx: int, n_steps: int):
         ((512, 12, 3)),
     ],
 )
-def test_equivalent_monotonic_method(input_dims: Tuple[int, ...]) -> None:
+def test_equivalent_monotonic_method(input_dims: tuple[int, ...]) -> None:
     torch.manual_seed(42)
     baseline_embeds = torch.randn(input_dims)
     input_embeds = torch.randn(input_dims)
@@ -130,7 +129,7 @@ def test_scaled_monotonic_path_embeddings(word_idx: int, dig_model) -> None:
         )
     ],
 )
-def test_parallel_find_word(ids: List[List[int]], dig_model) -> None:
+def test_parallel_find_word(ids: list[list[int]], dig_model) -> None:
     pathsa = []
     for seq in ids:
         tok_paths = []