diff --git a/vall_e/__main__.py b/vall_e/__main__.py
index 190bef4..c9a3e43 100755
--- a/vall_e/__main__.py
+++ b/vall_e/__main__.py
@@ -62,7 +62,7 @@ def main():
 		length_penalty=args.length_penalty,
 		beam_width=args.beam_width,
 		mirostat_tau=args.mirostat_tau, mirostat_eta=args.mirostat_eta,
-		dry_multiplier=args.dry_multiplier, dry_base=args.dry_base, dry_allowed_length=args.dry_allowed_multiplier,
+		dry_multiplier=args.dry_multiplier, dry_base=args.dry_base, dry_allowed_length=args.dry_allowed_length,
 		seed=args.seed,
 	)
 
diff --git a/vall_e/config.py b/vall_e/config.py
index 5b64a83..fe601a0 100755
--- a/vall_e/config.py
+++ b/vall_e/config.py
@@ -220,6 +220,10 @@ class ModelExperimentalSettings:
 	token_dropout_rate: float = 0.0 # probability to randomly set a token to a special dropout value
 	token_dropout_rvq_levels: list = field(default_factory=lambda: [1,8]) # determines which levels to do dropout, by default do not do dropout on RVQ level 0
 
+	causal_size: int = 1 # experimental setting to see if I can just do parallel decoding in chunks instead of one-at-a-time without resorting to exotic solutions
+	# VALL-E 2's approach of "combining token embeddings to group them" sounds terribad for a shared AR/NAR model
+	# however, introducing partial parallel decoding for the AR maybe maybe MAYBE might help try and unify the AR/NAR tasks better, MAYBE
+
 # I really need to clean this up
 @dataclass()
 class Model:
diff --git a/vall_e/models/ar_nar.py b/vall_e/models/ar_nar.py
index da71053..2320a2a 100644
--- a/vall_e/models/ar_nar.py
+++ b/vall_e/models/ar_nar.py
@@ -71,7 +71,9 @@ class AR_NAR(Base):
 		# 1 for the stop token
 		# governs how much to shift the logits by
 		# could *technically* make it work to where it can also predict *ALL* RVQ levels in one step, but experimental.py is the better way to go about it
-		return 1 # if self.causal else 0
+		if hasattr(self, "config") and self.config:
+			return self.config.experimental.causal_size
+		return cfg.model.experimental.causal_size
 
 	@property
 	def version(self) -> int:
@@ -463,7 +465,7 @@ def example_usage():
 	tasks = cfg.dataset.tasks_list
 
 	model = AR_NAR(**kwargs).to(device)
-	steps = 150 * len(tasks)
+	steps = 150 * len(tasks) * cfg.model.experimental.causal_size
 
 	optimizer = cfg.hyperparameters.optimizer.lower() if cfg.yaml_path is not None else "prodigy"
 	scheduler = cfg.hyperparameters.scheduler.lower() if cfg.yaml_path is not None else ""
diff --git a/vall_e/models/base.py b/vall_e/models/base.py
index 68f1fb6..8d6187c 100755
--- a/vall_e/models/base.py
+++ b/vall_e/models/base.py
@@ -419,7 +419,7 @@ class Base(nn.Module):
 
 		if "len" not in self.capabilities:
 			# +1 to include the stop token
-			n_resp_tokens = n_audio_tokens + self.causal_size
+			n_resp_tokens = n_audio_tokens + ( 1 if self.causal_size > 0 else 0 )
 			l_tokens = [n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1)
 		else:
 			n_resp_tokens = n_audio_tokens
@@ -1352,14 +1352,14 @@ class Base(nn.Module):
 		devices = [ logit.device for logit in logits ]
 		logits = [ logit.to(device="cpu", dtype=logit.dtype if logit.dtype != torch.float16 else torch.float32) for logit in logits ]
 
-		# perform repetition penalizing	
-		if "len" not in self.capabilities:
-			logits = [ reptition_penalize(logit, previous=resps[:, -1].tolist(), factor=repetition_penalty, decay=repetition_penalty_decay) for logit, resps in zip( logits, resps_list ) ]
-
 		# argmax instead
 		if temperature <= 0.0:
 			return [ logit.argmax(dim=1) for logit in logits ]
 
+		# perform repetition penalizing	
+		if "len" not in self.capabilities:
+			logits = [ reptition_penalize(logit, previous=resps[:, -1].tolist(), factor=repetition_penalty, decay=repetition_penalty_decay) for logit, resps in zip( logits, resps_list ) ]
+
 		# (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) ) ]
diff --git a/vall_e/webui.py b/vall_e/webui.py
index bd9c6b9..5b63fd4 100644
--- a/vall_e/webui.py
+++ b/vall_e/webui.py
@@ -271,7 +271,7 @@ with ui:
 					layout["inference"]["inputs"]["mirostat-eta"] = gr.Slider(value=0.0, minimum=0.0, maximum=2.0, step=0.05, label="Mirostat η (Eta)", info="The \"learning rate\" during mirostat sampling applied to the maximum surprise.")
 				with gr.Row():
 					layout["inference"]["inputs"]["dry-multiplier"] = gr.Slider(value=0.0, minimum=0.0, maximum=8.0, step=0.05, label="DRY Multiplier", info="The multiplying factor for the DRY score penalty (0 to disable DRY sampling).")
-					layout["inference"]["inputs"]["dry-base"] = gr.Slider(value=0.0, minimum=0.0, maximum=8.0, step=0.05, label="DRY Base", info="The base of the exponent in the DRY score penalty")
+					layout["inference"]["inputs"]["dry-base"] = gr.Slider(value=1.75, minimum=0.0, maximum=8.0, step=0.05, label="DRY Base", info="The base of the exponent in the DRY score penalty")
 					layout["inference"]["inputs"]["dry-allowed-length"] = gr.Slider(value=2, minimum=0, maximum=75, step=1, label="Allowed Length", info="The maximimum length a token can be to perform DRY penalty with.")
 
 		layout["inference"]["buttons"]["inference"].click(