diff --git a/vall_e/models/ar_nar.py b/vall_e/models/ar_nar.py
index 0e324d4..ec38d38 100644
--- a/vall_e/models/ar_nar.py
+++ b/vall_e/models/ar_nar.py
@@ -255,15 +255,6 @@ class AR_NAR(Base):
 					#mirostat=mirostat,
 				)
 
-				# filter
-				"""
-				if self.arch_type in ["mamba2-hf"] or cfg.lora is not None:
-					for batch_index, resp in enumerate(resps_list):
-						for i, token in enumerate(resp):
-							if token >= 1024:
-								resps_list[batch_index][i] = 1023
-				"""
-
 				prev_list = [ torch.cat([rs, r.unsqueeze(-1).to(device)], dim=-1) for rs, r in zip(prev_list, resps_list) ]
 
 			if cfg.lora is not None:
diff --git a/vall_e/models/base.py b/vall_e/models/base.py
index b8671a0..f2658b2 100755
--- a/vall_e/models/base.py
+++ b/vall_e/models/base.py
@@ -29,7 +29,7 @@ from torchmetrics.classification import BinaryAccuracy, MulticlassAccuracy, Mult
 
 from .arch import *
 from ..utils import wrapper as ml
-from ..samplers import reptition_penalize, length_penalize, top_k_top_p_filtering, dynamic_temperature, top_k_logits_list, mirostat_sample
+from ..samplers import reptition_penalize, length_penalize, ban_tokens, top_k_top_p_filtering, dynamic_temperature, top_k_logits_list, mirostat_sample
 
 def _create_mask(l, device):
 	"""1 is valid region and 0 is invalid."""
@@ -1138,6 +1138,9 @@ class Base(nn.Module):
 		# (AR) perform length penalizing
 		if quant_levels is None and self.causal:
 			logits = [ length_penalize(logit, length=l + 1, factor=length_penalty, token=self.stop_token) for logit, l in zip( logits, map(len, resps_list) ) ]
+		# (NAR) disable stop token
+		else:
+			logits = [ ban_tokens(logit, tokens=[self.stop_token]) for logit, l in zip( logits, map(len, resps_list) ) ]
 
 		# perform top_k/top_p filtering of our logits
 		if top_k > 0 or top_p < 1.0:
diff --git a/vall_e/samplers.py b/vall_e/samplers.py
index 20bf804..92ea894 100644
--- a/vall_e/samplers.py
+++ b/vall_e/samplers.py
@@ -39,6 +39,12 @@ def length_penalize( logits, length, factor=0.0, token=-1 ):
 	logits[:, token] /= (length ** factor)
 	return logits
 
+# Simple way to ban tokens
+def ban_tokens( logits, tokens ):
+	for token in tokens:
+		logits[:, token] = -float("inf")
+	return logits
+
 # Credit to https://github.com/microsoft/unilm/blob/master/xtune/src/transformers/modeling_utils.py#L1145 / https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
 def top_k_top_p_filtering( logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens=1 ):
 	"""Filter a distribution of logits using top-k and/or nucleus (top-p) filtering