diff --git a/vall_e/config.py b/vall_e/config.py
index db10966..8dbc698 100755
--- a/vall_e/config.py
+++ b/vall_e/config.py
@@ -948,15 +948,6 @@ class Config(BaseConfig):
 				_logger.warning(f"Deprecated flag found: {'cfg.model.interleave'}")
 				del model["interleave"]
 
-			if "p_rvq_levels" in model["experimental"] and "rvq_levels_p" not in model["experimental"]:
-				_logger.warning(f"Deprecated flag found: {'cfg.model.experimental.p_rvq_levels'}")
-				model["experimental"]["rvq_levels_p"] = model["experimental"]["p_rvq_levels"]
-				del model["experimental"]["p_rvq_levels"]
-
-			if "audio_embedding_sums" in model:
-				_logger.warning(f"Deprecated flag found: {'cfg.model.p_rvq_levels'}")
-				model["experimental"]["audio_embedding_sums"] = model.pop("audio_embedding_sums")
-
 		self.models = [ Model(**model) if isinstance(model, dict) else model for model in self.models ]
 		self.loras = [ LoRA(**lora)  if isinstance(lora, dict) else lora for lora in self.loras ]
 
diff --git a/vall_e/models/arch/llama.py b/vall_e/models/arch/llama.py
index 8309103..4efefa7 100644
--- a/vall_e/models/arch/llama.py
+++ b/vall_e/models/arch/llama.py
@@ -445,8 +445,8 @@ class LlamaModel_Adapted(LlamaModel):
 			if not self.dropoff_layer( l ):
 				hidden_states = layer_outputs[0]
 
-			if use_cache:
-				next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+				if use_cache:
+					next_decoder_cache = layer_outputs[2 if output_attentions else 1]
 
 			if output_attentions:
 				all_self_attns += (layer_outputs[1],)
diff --git a/vall_e/train.py b/vall_e/train.py
index 39cf18d..0f7c7ee 100755
--- a/vall_e/train.py
+++ b/vall_e/train.py
@@ -196,9 +196,9 @@ def train():
 	# copy config yaml to backup
 	if cfg.yaml_path is not None and is_global_leader():
 		shutil.copy( cfg.yaml_path, cfg.log_dir / "config.yaml" )
-
+	# create dataloaders
 	train_dl, subtrain_dl, val_dl = create_train_val_dataloader()
-	
+	# evaluation lambda
 	def eval_fn(engines):
 		do_gc()
 		engines.eval()
@@ -213,18 +213,23 @@ def train():
 		engines.train()
 		qnt.unload_model()
 		do_gc()
-
+	# unload EnCodec if it's already loaded
 	qnt.unload_model()
-
+	# only eval is requested
 	if args.eval:
 		return eval_fn(engines=trainer.load_engines())
 
 	"""
+	# start web UI
 	if cfg.trainer.load_webui:
 		from .webui import start
 		start(lock=False)
 	"""
+	# pre-training config validation
+	if cfg.model.experimental.layerskip and cfg.trainer.weight_dtype == "float16":
+		_logger.warning(f"Training with LayerSkip enabled with float16 will result in frying the model. Please use bfloat16.")
 
+	# train
 	trainer.train(
 		train_dl=train_dl,
 		train_feeder=train_feeder,