diff --git a/pyproject.toml b/pyproject.toml
index f2a115a..2c4c074 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -73,5 +73,8 @@ quantization = [
 [tool.ruff]
 line-length = 120
 
+[tool.ruff.lint]
+select = ["E4", "E7", "E9", "F"]
+
 [tool.pyright]
 reportPrivateImportUsage = false
diff --git a/src/kame/models/loaders.py b/src/kame/models/loaders.py
index 3df8f75..8c092de 100644
--- a/src/kame/models/loaders.py
+++ b/src/kame/models/loaders.py
@@ -370,16 +370,18 @@ def _upgrade_legacy_lm_state_dict(state: dict[str, torch.Tensor]) -> dict[str, t
     return upgraded_state
 
 
-def _cast_lm_state_dict(state: dict[str, torch.Tensor], dtype: torch.dtype) -> dict[str, torch.Tensor]:
-    """Cast LM checkpoint tensors to the runtime dtype expected by the current model."""
-    state = dict(state)
+def _cast_lm_state_dict(
+    state: dict[str, torch.Tensor],
+    dtype: torch.dtype,
+    device: torch.device | str,
+) -> dict[str, torch.Tensor]:
+    """Move LM checkpoint tensors to the runtime device and expected dtype."""
     for key, value in state.items():
         if value.dtype.is_floating_point:
-            if key.startswith("condition_provider.") or key.startswith("fuser."):
-                value = value.float()
-            else:
-                value = value.to(dtype)
-        state[key] = value
+            target_dtype = torch.float32 if key.startswith(("condition_provider.", "fuser.")) else dtype
+            state[key] = value.to(device=device, dtype=target_dtype)
+        else:
+            state[key] = value.to(device=device)
     return state
 
 
@@ -427,15 +429,15 @@ def get_moshi_lm(
 
     if filename is not None:
         if _is_safetensors(filename):
-            state = load_file(filename, device=str(device))
+            state = load_file(filename, device="cpu")
         else:
             pkg = torch.load(
                 filename,
-                map_location=device,
+                map_location="cpu",
             )
             state = pkg["fsdp_best_state"]["model"]
         state = _upgrade_legacy_lm_state_dict(state)
-        state = _cast_lm_state_dict(state, dtype)
+        state = _cast_lm_state_dict(state, dtype, device)
         model.load_state_dict(state, assign=True)
 
     if lora: