diff --git a/tests/assets/logits_generation/generate_hf_golden_logits.py b/tests/assets/logits_generation/generate_hf_golden_logits.py
index d81a169f55..c57d58c380 100644
--- a/tests/assets/logits_generation/generate_hf_golden_logits.py
+++ b/tests/assets/logits_generation/generate_hf_golden_logits.py
@@ -85,15 +85,20 @@ def save_golden_logits(
 
     model_class = AutoModelForCausalLM
 
-  tokenizer = AutoTokenizer.from_pretrained(model_id)
+  tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=trust_remote_code)
   print(f"loading model from {hf_model_path}")
 
   if hf_load_dtype == "float32":
     torch_dtype = torch.float32
   elif hf_load_dtype == "bfloat16":
     torch_dtype = torch.bfloat16
+  elif hf_load_dtype == "auto":
+    # Preserve per-tensor dtypes from safetensors metadata, useful for mixed-
+    # precision checkpoints where forcing a single dtype would corrupt non-
+    # default tensors.
+    torch_dtype = None
   else:
-    raise ValueError
+    raise ValueError(f"unsupported --hf-load-dtype: {hf_load_dtype}")
 
   model = model_class.from_pretrained(
       hf_model_path,
@@ -194,9 +199,9 @@ def main(raw_args=None) -> None:
       "--hf-load-dtype",
       type=str,
       required=False,
-      choices=["float32", "bfloat16"],
+      choices=["float32", "bfloat16", "auto"],
       default="float32",
-      help="model_class.from_pretrained: dtype",
+      help="model_class.from_pretrained: dtype. 'auto' preserves per-tensor dtypes from safetensors.",
   )
   parser.add_argument(
       "--trust-remote-code",