diff --git a/egs/aishell2/ASR/pruned_transducer_stateless5/train.py b/egs/aishell2/ASR/pruned_transducer_stateless5/train.py
index b1052813c..0d9bd0a6a 100755
--- a/egs/aishell2/ASR/pruned_transducer_stateless5/train.py
+++ b/egs/aishell2/ASR/pruned_transducer_stateless5/train.py
@@ -24,13 +24,19 @@ Usage:
 export CUDA_VISIBLE_DEVICES="0,1,2,3"
 
 ./pruned_transducer_stateless5/train.py \
-  --lang-dir data/lang_char \
   --world-size 4 \
-  --num-epochs 30 \
+  --lang-dir data/lang_char \
+  --num-epochs 40 \
   --start-epoch 1 \
   --exp-dir pruned_transducer_stateless5/exp \
-  --full-libri 1 \
-  --max-duration 300
+  --max-duration 300 \
+  --use-fp16 0 \
+  --num-encoder-layers 24 \
+  --dim-feedforward 1536 \
+  --nhead 8 \
+  --encoder-dim 384 \
+  --decoder-dim 512 \
+  --joiner-dim 512
 
 # For mix precision training:
 
@@ -41,7 +47,6 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
   --start-epoch 1 \
   --use-fp16 1 \
   --exp-dir pruned_transducer_stateless5/exp \
-  --full-libri 1 \
   --max-duration 550
 
 """
@@ -84,6 +89,7 @@ from icefall.checkpoint import (
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.lexicon import Lexicon
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
 LRSchedulerType = Union[
@@ -773,7 +779,8 @@ def train_one_epoch(
             scaler.update()
             optimizer.zero_grad()
         except:  # noqa
-            display_and_save_batch(batch, params=params, graph_compiler=graph_compiler)
+            display_and_save_batch(batch, params=params,
+                                   graph_compiler=graph_compiler)
             raise
 
         if params.print_diagnostics and batch_idx == 5:
@@ -872,8 +879,6 @@ def run(rank, world_size, args):
     """
     params = get_params()
     params.update(vars(args))
-    if params.full_libri is False:
-        params.valid_interval = 1600
 
     fix_random_seed(params.seed)
     if world_size > 1:
@@ -951,7 +956,6 @@ def run(rank, world_size, args):
 
     train_cuts = aishell2.train_cuts()
 
-
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds
         #
@@ -976,7 +980,7 @@ def run(rank, world_size, args):
         train_cuts, sampler_state_dict=sampler_state_dict
     )
 
-    valid_cuts = aishell2.dev_cuts()
+    valid_cuts = aishell2.valid_cuts()
     valid_dl = aishell2.valid_dataloaders(valid_cuts)
 
     if not params.print_diagnostics:
@@ -1109,7 +1113,8 @@ def scan_pessimistic_batches_for_oom(
                     f"Failing criterion: {criterion} "
                     f"(={crit_values[criterion]}) ..."
                 )
-            display_and_save_batch(batch, params=params, graph_compiler=graph_compiler)
+            display_and_save_batch(batch, params=params,
+                                   graph_compiler=graph_compiler)
             raise