diff --git a/.github/workflows/check_code_quality.yml b/.github/workflows/check_code_quality.yml
index da5b8bac57..c76b6f8042 100644
--- a/.github/workflows/check_code_quality.yml
+++ b/.github/workflows/check_code_quality.yml
@@ -51,4 +51,4 @@ jobs:
     - name: Check style with ruff
       run: |
         source venv/bin/activate
-        ruff .
+        ruff check .
diff --git a/optimum/intel/utils/modeling_utils.py b/optimum/intel/utils/modeling_utils.py
index ef04151119..9b68266d16 100644
--- a/optimum/intel/utils/modeling_utils.py
+++ b/optimum/intel/utils/modeling_utils.py
@@ -14,7 +14,7 @@
 
 import re
 from pathlib import Path
-from typing import List, Optional, Tuple, Union
+from typing import List, Optional, Union
 
 import torch
 from huggingface_hub import HfApi, HfFolder
@@ -23,114 +23,6 @@
 MULTI_QUERY_ATTN_MODELS = {"falcon", "gpt_bigcode"}
 
 
-# Modified from transformers.models.bloom.modeling_bloom._make_causal_mask
-def _make_causal_mask(
-    input_ids_shape: torch.Size,
-    device: torch.device,
-    past_key_values_length: int,
-    dtype: torch.dtype = torch.bool,
-) -> torch.BoolTensor:
-    """
-    Make causal mask used for bi-directional self-attention.
-    """
-    batch_size, target_length = input_ids_shape
-    mask = torch.zeros((target_length, target_length + past_key_values_length), dtype=dtype, device=device)
-    seq_ids = torch.arange(target_length, device=device)
-
-    mask[:, past_key_values_length:] = (
-        (seq_ids[:, None] < seq_ids[None, :]) * torch.finfo(dtype).min
-        if torch.is_floating_point(mask)
-        else seq_ids[:, None] < seq_ids[None, :]
-    )
-
-    return mask[None, None, :, :].expand(batch_size, 1, target_length, target_length + past_key_values_length)
-
-
-# Modified from transformers.models..bloom.modeling_bloom._prepare_attn_mask
-def _prepare_attn_mask(
-    attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int
-) -> torch.BoolTensor:
-    from transformers.models.bloom.modeling_bloom import _expand_mask
-
-    # create causal mask
-    # [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
-    combined_attention_mask = None
-    device = attention_mask.device
-    _, src_length = input_shape
-
-    combined_attention_mask = _make_causal_mask(
-        input_shape, device=device, past_key_values_length=past_key_values_length
-    )
-    # [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]_prepare_decoder_attention_mask
-    expanded_attn_mask = _expand_mask(attention_mask, tgt_length=src_length)
-    combined_attention_mask = (
-        expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
-    )
-
-    return combined_attention_mask
-
-
-# Modified from transformers.models.llama.modeling_llama._prepare_decoder_attention_mask
-def _prepare_decoder_attention_mask(attention_mask, input_shape, inputs_embeds, past_key_values_length):
-    from transformers.models.llama.modeling_llama import _expand_mask
-
-    # create causal mask
-    # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-    combined_attention_mask = None
-
-    combined_attention_mask = _make_causal_mask(
-        input_shape,
-        device=inputs_embeds.device,
-        past_key_values_length=past_key_values_length,
-        dtype=inputs_embeds.dtype,
-    )
-
-    if attention_mask is not None:
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
-            inputs_embeds.device
-        )
-        combined_attention_mask = (
-            expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
-        )
-
-    return combined_attention_mask
-
-
-# Modified from transformers.models.mistral.modeling_mistral._prepare_decoder_sliding_window_attention_mask
-def _prepare_decoder_sliding_window_attention_mask(
-    attention_mask: torch.Tensor,
-    input_shape: Tuple[int, int],
-    inputs_embeds: torch.Tensor,
-    past_key_values_length: int,
-    sliding_window: int,
-):
-    from transformers.models.mistral.modeling_mistral import _expand_mask, _make_sliding_window_causal_mask
-
-    # create causal mask
-    # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-    combined_attention_mask = None
-
-    combined_attention_mask = _make_sliding_window_causal_mask(
-        input_shape,
-        device=inputs_embeds.device,
-        dtype=inputs_embeds.dtype,
-        past_key_values_length=past_key_values_length,
-        sliding_window=sliding_window,
-    )
-
-    if attention_mask is not None:
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
-            inputs_embeds.device
-        )
-        combined_attention_mask = (
-            expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
-        )
-
-    return combined_attention_mask
-
-
 def get_model_device(model: torch.nn.Module) -> torch.device:
     """
     Determines the device on which a PyTorch model is currently residing.