diff --git a/ktransformers/util/utils.py b/ktransformers/util/utils.py
index 30f8880..5a83a45 100644
--- a/ktransformers/util/utils.py
+++ b/ktransformers/util/utils.py
@@ -11,6 +11,17 @@ from torch import nn
 import itertools
 import time
 import enum
+from transformers import (
+    LogitsProcessorList,
+    TemperatureLogitsWarper,
+    TopKLogitsWarper,
+    TopPLogitsWarper,
+    MinPLogitsWarper,
+    TypicalLogitsWarper,
+    EpsilonLogitsWarper,
+    EtaLogitsWarper,
+)
+
 from ktransformers.util.custom_gguf import translate_name_to_gguf
 from ktransformers.util.custom_gguf import GGUFLoader
 from ktransformers.operators import base_operator
@@ -126,6 +137,57 @@ def load_weights(module:nn.Module, gguf_loader:GGUFLoader, prefix=''):
     else:
         module.load()
 
+def tf_logits_warper(generation_config):
+        """
+        This class returns a [`LogitsProcessorList`] list object that contains all relevant [`LogitsWarper`] instances
+        used for multinomial sampling.
+        """
+
+        # instantiate warpers list
+        warpers = LogitsProcessorList()
+
+        # In beam methods, we need to keep at least one non-eos token to explore continuations that might have a
+        # better score (i.e. keep len(list(generation_config._eos_token_tensor)) + 1)
+        if generation_config.num_beams > 1:
+            if isinstance(generation_config._eos_token_tensor, list):
+                min_tokens_to_keep = len(generation_config._eos_token_tensor) + 1
+            elif isinstance(generation_config._eos_token_tensor, torch.Tensor):
+                min_tokens_to_keep = generation_config._eos_token_tensor.shape[0] + 1
+            else:
+                min_tokens_to_keep = 2
+        else:
+            min_tokens_to_keep = 1
+
+        # the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
+        # all samplers can be found in `generation_utils_samplers.py`
+        if generation_config.temperature is not None and generation_config.temperature != 1.0:
+            warpers.append(TemperatureLogitsWarper(generation_config.temperature))
+        if generation_config.top_k is not None and generation_config.top_k != 0:
+            warpers.append(TopKLogitsWarper(top_k=generation_config.top_k, min_tokens_to_keep=min_tokens_to_keep))
+        if generation_config.top_p is not None and generation_config.top_p < 1.0:
+            warpers.append(TopPLogitsWarper(top_p=generation_config.top_p, min_tokens_to_keep=min_tokens_to_keep))
+        if generation_config.min_p is not None:
+            # Applied after temperature scaling (see https://github.com/ggerganov/llama.cpp/pull/3841#issuecomment-2073826084)
+            warpers.append(MinPLogitsWarper(min_p=generation_config.min_p, min_tokens_to_keep=min_tokens_to_keep))
+        if generation_config.typical_p is not None and generation_config.typical_p < 1.0:
+            warpers.append(
+                TypicalLogitsWarper(mass=generation_config.typical_p, min_tokens_to_keep=min_tokens_to_keep)
+            )
+        if generation_config.epsilon_cutoff is not None and 0.0 < generation_config.epsilon_cutoff < 1.0:
+            warpers.append(
+                EpsilonLogitsWarper(epsilon=generation_config.epsilon_cutoff, min_tokens_to_keep=min_tokens_to_keep)
+            )
+        if generation_config.eta_cutoff is not None and 0.0 < generation_config.eta_cutoff < 1.0:
+            warpers.append(
+               EtaLogitsWarper(
+                    epsilon=generation_config.eta_cutoff, min_tokens_to_keep=min_tokens_to_keep, device=device
+                )
+            )
+        # `LogitNormalization` should always be the last logit processor, when present
+        if generation_config.renormalize_logits is True:
+            warpers.append(LogitNormalization())
+        return warpers
+
 def prefill_and_generate(model, tokenizer, inputs, max_new_tokens=10000, use_cuda_graph: bool = True,
                          mode = 'normal', force_think: bool = False, chunk_size = 16384, use_flashinfer_mla = False,
                          num_heads = None, head_dim_ckv = None, head_dim_kpe = None, q_head_dim = None):
@@ -201,14 +263,8 @@ def prefill_and_generate(model, tokenizer, inputs, max_new_tokens=10000, use_cud
             # change this to modify generate config
             #top_k=5, top_p=0.85, temperature=0.1
         )
-        try: # transformers==4.43
-            logits_warper = (
-                model._get_logits_warper(generation_config,device=inputs.device)
-            )
-        except: 
-            logits_warper = (
-                model._get_logits_warper(generation_config)
-            )
+
+        logits_warper = tf_logits_warper(generation_config)
 
         cache_position = torch.arange(seq_length, device=torch_device, dtype=torch.int32)
         generated_ids = torch.zeros(