diff --git a/official/core/train_lib.py b/official/core/train_lib.py
index ba66c2a75627213f6c50bcdab555aa36b668d21b..32898a4d35972396e08af7dc773d941b9d0a5e40 100644
--- a/official/core/train_lib.py
+++ b/official/core/train_lib.py
@@ -124,9 +124,11 @@ def run_experiment(
     else:
       raise NotImplementedError('The mode is not implemented: %s' % mode)
 
-  if hasattr(trainer.model, 'count_params'):
+  num_params = train_utils.try_count_params(trainer.model)
+  if num_params is not None:
     logging.info('Number of trainable params in model: %f Millions.',
-                 trainer.model.count_params() / 10.**6)
+                 num_params / 10.**6)
+
   if run_post_eval:
     with distribution_strategy.scope():
       return trainer.model, trainer.evaluate(
diff --git a/official/core/train_utils.py b/official/core/train_utils.py
index 32a3cd7be6d8ac87b30428aa5e5cd15ca1ab802a..c3d2a811cbbd0ad9a4856b596ae1f280857443a4 100644
--- a/official/core/train_utils.py
+++ b/official/core/train_utils.py
@@ -367,3 +367,24 @@ def remove_ckpts(model_dir):
   file_to_remove = os.path.join(model_dir, 'checkpoint')
   if tf.io.gfile.exists(file_to_remove):
     tf.io.gfile.remove(file_to_remove)
+
+
+def try_count_params(model: tf.keras.Model):
+  """Count the number of parameters if model is possible.
+
+  Args:
+    model: Try to count the number of params in this model.
+
+  Returns:
+    The number of parameters or None.
+  """
+  if hasattr(model, 'count_params'):
+    try:
+      return model.count_params()
+    except ValueError:
+      logging.info('Number of trainable params unknown, because the build() '
+                   'methods in keras layers were not called. This is probably '
+                   'because the model was not feed any input, e.g., the max '
+                   'train step already reached before this run.')
+      return None
+  return None
diff --git a/official/modeling/multitask/train_lib.py b/official/modeling/multitask/train_lib.py
index b2fa9a0e76fdee2d734e1c050f8201fcab1e7ede..0580964a61e5e7f06e8ab158e1dfdee6af92b399 100644
--- a/official/modeling/multitask/train_lib.py
+++ b/official/modeling/multitask/train_lib.py
@@ -15,6 +15,7 @@
 """Multitask training driver library."""
 # pytype: disable=attribute-error
 import os
+from typing import Optional
 from absl import logging
 import orbit
 import tensorflow as tf
@@ -139,7 +140,8 @@ def run_experiment_with_multitask_eval(
     params: configs.MultiEvalExperimentConfig,
     model_dir: str,
     run_post_eval: bool = False,
-    save_summary: bool = True) -> tf.keras.Model:
+    save_summary: bool = True,
+    trainer: Optional[core_lib.Trainer] = None) -> tf.keras.Model:
   """Runs train/eval configured by the experiment params.
 
   Args:
@@ -153,6 +155,9 @@ def run_experiment_with_multitask_eval(
     run_post_eval: Whether to run post eval once after training, metrics logs
       are returned.
     save_summary: Whether to save train and validation summary.
+    trainer: the core_lib.Trainer instance. It should be created within the
+      strategy.scope(). If not provided, an instance will be created by default
+      if `mode` contains 'train'.
 
   Returns:
       model: `tf.keras.Model` instance.
@@ -161,19 +166,19 @@ def run_experiment_with_multitask_eval(
   is_training = 'train' in mode
   is_eval = 'eval' in mode
   with distribution_strategy.scope():
-    optimizer = train_task.create_optimizer(params.trainer.optimizer_config,
-                                            params.runtime)
-    model = train_task.build_model()
     if is_training:
-      trainer = core_lib.Trainer(
+      trainer = trainer or core_lib.Trainer(
           config=params,
           task=train_task,
-          model=model,
-          optimizer=optimizer,
+          model=train_task.build_model(),
+          optimizer=train_task.create_optimizer(
+              params.trainer.optimizer_config, params.runtime),
           train=True,
           evaluate=False)
     else:
       trainer = None
+    model = trainer.model if trainer else train_task.build_model()
+
     if is_eval:
       evaluator = evaluator_lib.MultiTaskEvaluator(
           task=eval_tasks,