diff --git a/trlx/models/modeling_ppo.py b/trlx/models/modeling_ppo.py
index 8a0b5faf5..f47e761fd 100644
--- a/trlx/models/modeling_ppo.py
+++ b/trlx/models/modeling_ppo.py
@@ -252,7 +252,7 @@ class CausalLMOutputWithValue(ModelOutput):
     value: Optional[torch.FloatTensor] = None
 
 
-def make_value_branch(base_model, num_value_layers_unfrozen, dtype):
+def make_value_branch(base_model, num_value_layers_unfrozen, dtype=torch.float32):
     value_head = make_head(hf_get_hidden_size(base_model.config), 1, dtype)
     if num_value_layers_unfrozen == 0:
         return value_head
@@ -1211,9 +1211,12 @@ def __init__(
     ):
         super().__init__(base_model, peft_config=peft_config)
         # TODO: Support Seq2Seq value branching
+        parameter = next(hf_get_lm_head(self.base_model).parameters())
+        dtype = parameter.dtype
+        device = parameter.device
         if num_value_layers_unfrozen > 0:
             raise NotImplementedError("Value branches unsupported for Seq2Seq architecture")
-        self.v_head = make_head(hf_get_hidden_size(self.base_model.config), 1)
+        self.v_head = make_head(hf_get_hidden_size(self.base_model.config), 1, dtype).to(device)
 
     def forward(
         self,
diff --git a/trlx/utils/modeling.py b/trlx/utils/modeling.py
index 6e737c080..84134642b 100644
--- a/trlx/utils/modeling.py
+++ b/trlx/utils/modeling.py
@@ -10,7 +10,7 @@
 import transformers
 
 
-def make_head(n_embd: int, out: int, dtype: type = torch.float32) -> nn.Sequential:
+def make_head(n_embd: int, out: int, dtype: torch.dtype = torch.float32) -> nn.Sequential:
     """Returns a generic sequential MLP head."""
     return nn.Sequential(
         nn.Linear(n_embd, n_embd * 2, dtype=dtype),