diff --git a/src/lighteval/models/base_model.py b/src/lighteval/models/base_model.py
index 6b67e4c0..616bd0b3 100644
--- a/src/lighteval/models/base_model.py
+++ b/src/lighteval/models/base_model.py
@@ -20,6 +20,7 @@
     LoglikelihoodRequest,
     LoglikelihoodRollingRequest,
     LoglikelihoodSingleTokenRequest,
+    Request,
 )
 from lighteval.utils import (
     is_accelerate_available,
@@ -357,10 +358,10 @@ def greedy_until(
         ):
             # Longest context in the current split is the first item (since we sort reversed)
             longest_context_continuation_size_in_split = len(dataset[0].tokenized_context) + dataset[0].generation_size
-            max_continuation_size_allowed = min(longest_context_continuation_size_in_split, self.max_length)
+            max_context_continuation_size_allowed = min(longest_context_continuation_size_in_split, self.max_length)
             batch_size = self._get_batch_size(
                 override_bs=override_bs,
-                max_input_length=max_continuation_size_allowed,
+                max_input_length=max_context_continuation_size_allowed,
                 starting_batch_size=starting_batch_size,
             )
 
@@ -529,11 +530,13 @@ def _loglikelihood_tokens(
         for split_start, split_end in tqdm(dataset.splits_start_end_iterator()):
             context_enc = dataset[0].tokenized_context
             continuation_enc = dataset[0].tokenized_continuation
-            max_continuation_size_allowed = len((context_enc + continuation_enc)[-(self.max_length + 1) :][:-1])
+            max_context_continuation_size_allowed = len(
+                (context_enc + continuation_enc)[-(self.max_length + 1) :][:-1]
+            )
 
             batch_size = self._get_batch_size(
                 override_bs=override_bs,
-                max_input_length=max_continuation_size_allowed,
+                max_input_length=max_context_continuation_size_allowed,
                 starting_batch_size=starting_batch_size,
             )
             starting_batch_size = batch_size * 2
@@ -544,7 +547,9 @@ def _loglikelihood_tokens(
 
             for batch in tqdm(dataloader, disable=self.disable_tqdm):
                 prepared_batch = self.prepare_batch(
-                    batch, padding_length=max_continuation_size_allowed, max_context=max_continuation_size_allowed
+                    batch,
+                    padding_length=max_context_continuation_size_allowed,
+                    max_context=max_context_continuation_size_allowed,
                 )
 
                 model_output = self._model_call(prepared_batch.input_ids)