diff --git a/vall_e/config.py b/vall_e/config.py
index d115912..2d4e28d 100755
--- a/vall_e/config.py
+++ b/vall_e/config.py
@@ -204,7 +204,7 @@ class Model:
 	attention: str = "auto"
 	audio_embedding_sums: bool = True
 	dropout: float = 0.1 # adjustable dropout value
-	#loss_factors: dict = field(default_factory=lambda: { "text": 0.1, "prom": 0.0, "resp": 1.0 }) # disable it by default since it causes a little more harm than good
+	#loss_factors: dict = field(default_factory=lambda: { "text": 0.1, "prom": 1.0, "resp": 1.0 }) # disable it by default since it causes a little more harm than good
 	loss_factors: dict = field(default_factory=lambda: {})
 	capabilities: list = field(default_factory=lambda: ["ar", "nar"])
 	experimental: bool = False # for now it sets things to be HF compatible
diff --git a/vall_e/models/base.py b/vall_e/models/base.py
index b1dec02..d00a6c9 100755
--- a/vall_e/models/base.py
+++ b/vall_e/models/base.py
@@ -692,7 +692,12 @@ class Base(nn.Module):
 				target = []
 				for name, input in batch:
 					if name == "prom":
-						target.append( torch.full_like(input[..., 0], self.ignore_index) )
+						# ignore prom, fill with mock tokens, because the prom embeddings don't directly map to tokens
+						if self.version < 4 or (self.version >= 5 and self.config.audio_embedding_sums):
+							target.append( torch.full_like(input[..., 0], self.ignore_index) )
+						# we *CAN* directly map to proms
+						else:
+							target.append( input if input.dim() == 1 else input[:, quant_level-1] )
 					elif name == "resp":
 						target.append( input if input.dim() == 1 else input[:, quant_level-1] )
 					elif name in ["text", "quant_level", "lang", "tone"]: