diff --git a/kvpress/attention_patch.py b/kvpress/attention_patch.py
index 2b47a233..1e4975f5 100644
--- a/kvpress/attention_patch.py
+++ b/kvpress/attention_patch.py
@@ -78,6 +78,7 @@ def wrapper(module, query, key, value, attention_mask, dropout, **kwargs):
             batch_indices, head_indices, seq_indices = module.masked_key_indices
             key[batch_indices, head_indices, seq_indices] = k[batch_indices, head_indices]
 
+        kwargs["cu_seq_lens_k"][-1] = key.shape[-2]
         return func(module, query, key, value, attention_mask, dropout, **kwargs)
 
     return wrapper
diff --git a/kvpress/presses/base_press.py b/kvpress/presses/base_press.py
index c5c8511b..10e2df03 100644
--- a/kvpress/presses/base_press.py
+++ b/kvpress/presses/base_press.py
@@ -131,23 +131,23 @@ def forward_hook(self, module: nn.Module, input: list[torch.Tensor], kwargs: dic
             return output
 
         if isinstance(cache, QuantizedCache):
-            keys = cache._dequantize(cache._quantized_key_cache[module.layer_idx])
-            values = cache._dequantize(cache._quantized_value_cache[module.layer_idx])
+            keys = cache.cache_processor._dequantize(cache.cache_processor._quantized_keys[module.layer_idx])
+            values = cache.cache_processor._dequantize(cache.cache_processor._quantized_values[module.layer_idx])
         else:
-            keys = cache.key_cache[module.layer_idx]
-            values = cache.value_cache[module.layer_idx]
+            keys = cache.layers[module.layer_idx].keys
+            values = cache.layers[module.layer_idx].values
 
         keys, values = self.compress(module, hidden_states, keys, values, output[1], kwargs)
 
         if isinstance(cache, QuantizedCache):
-            cache._quantized_key_cache[module.layer_idx] = cache._quantize(keys, axis=cache.axis_key)
-            cache._quantized_value_cache[module.layer_idx] = cache._quantize(values, axis=cache.axis_value)
-            cache.key_cache[module.layer_idx] = torch.zeros(0, dtype=keys.dtype, device=keys.device)
-            cache.value_cache[module.layer_idx] = torch.zeros(0, dtype=keys.dtype, device=keys.device)
-            cache._seen_tokens = keys.shape[2]
+            cache.cache_processor._quantized_keys[module.layer_idx] = cache.cache_processor._quantize(keys, axis=cache.cache_processor.axis_key)
+            cache.cache_processor._quantized_values[module.layer_idx] = cache.cache_processor._quantize(values, axis=cache.cache_processor.axis_value)
+            cache.layers[module.layer_idx].keys = torch.zeros(0, dtype=keys.dtype, device=keys.device)
+            cache.layers[module.layer_idx].values = torch.zeros(0, dtype=keys.dtype, device=keys.device)
+            cache.cache_processor.erased_length = keys.shape[2]
         else:
-            cache.key_cache[module.layer_idx] = keys
-            cache.value_cache[module.layer_idx] = values
+            cache.layers[module.layer_idx].keys = keys
+            cache.layers[module.layer_idx].values = values
 
         return output
 
diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py
index 14ff3156..c111fc39 100644
--- a/tests/test_pipeline.py
+++ b/tests/test_pipeline.py
@@ -6,7 +6,7 @@
 
 import pytest
 import torch
-from transformers import AutoTokenizer, DynamicCache, QuantizedCacheConfig, QuantoQuantizedCache
+from transformers import AutoTokenizer, DynamicCache, QuantoQuantizedCache
 from transformers.utils import is_optimum_quanto_available
 
 from kvpress import ExpectedAttentionPress
@@ -82,8 +82,7 @@ def test_pipeline_with_quantized_cache(kv_press_danube_pipeline, caplog):  # noq
         context = "This is a test article. It was written on 2022-01-01."
         questions = ["When was this article written?"]
         press = ExpectedAttentionPress(compression_ratio=0.4)
-        config = QuantizedCacheConfig(nbits=4)
-        cache = QuantoQuantizedCache(config)
+        cache = QuantoQuantizedCache(nbits=4)
         answers = kv_press_danube_pipeline(context, questions=questions, press=press, cache=cache)["answers"]
 
     assert len(answers) == 1