diff --git a/bionemo-recipes/recipes/esm2_native_te/collator.py b/bionemo-recipes/recipes/esm2_native_te/collator.py
index e1848f46a3..982ffd88df 100644
--- a/bionemo-recipes/recipes/esm2_native_te/collator.py
+++ b/bionemo-recipes/recipes/esm2_native_te/collator.py
@@ -237,6 +237,9 @@ def __call__(self, features, return_tensors=None):
             batch["input_ids"], special_tokens_mask=special_tokens_mask
         )
 
+        if self.pad_to_multiple_of is not None and self.pad_sequences_to_be_divisible_by is not None:
+            raise ValueError("pad_to_multiple_of and pad_sequences_to_be_divisible_by cannot be used together")
+
         if self.pad_to_multiple_of is not None:
             batch = self._pad_batch_to_multiple_of(batch)
 
diff --git a/bionemo-recipes/recipes/esm2_native_te/example_8m_checkpoint/esm_nv.py b/bionemo-recipes/recipes/esm2_native_te/example_8m_checkpoint/esm_nv.py
index fa8ad45599..1b246f99ae 100644
--- a/bionemo-recipes/recipes/esm2_native_te/example_8m_checkpoint/esm_nv.py
+++ b/bionemo-recipes/recipes/esm2_native_te/example_8m_checkpoint/esm_nv.py
@@ -206,12 +206,14 @@ def forward(
                 "cu_seq_lens_q, cu_seq_lens_k, max_length_q, and max_length_k are not allowed when using BSHD inputs."
             )
 
+        
         # Ensure that rotary embeddings are computed with at a higher precision outside the torch autocast context.
         with torch.autocast(device_type="cuda", enabled=False):
             if self.config.position_embedding_type == "rotary":
                 if self.config.attn_input_format == "bshd":
                     te_rope_emb = self.rotary_embeddings(max_seq_len=hidden_states.shape[1])
                 elif self.config.attn_input_format == "thd":
+                    # This is correct.
                     te_rope_emb = self.rotary_embeddings(
                         max_seq_len=kwargs["cu_seq_lens_q_padded"][-1]
                         if "cu_seq_lens_q_padded" in kwargs
@@ -222,7 +224,7 @@ def forward(
         for layer_module in self.layers:
             if kwargs.get("output_hidden_states", False):
                 all_hidden_states = (*all_hidden_states, hidden_states)
-
+            
             hidden_states = layer_module(
                 hidden_states,
                 attention_mask,
@@ -235,7 +237,6 @@ def forward(
                 max_seqlen_kv=kwargs.get("max_length_k", None),
                 pad_between_seqs=kwargs.get("pad_between_seqs", None),
             )
-
         hidden_states = self.emb_layer_norm_after(hidden_states)
 
         if kwargs.get("output_hidden_states", False):
diff --git a/bionemo-recipes/recipes/esm2_native_te/hydra_config/defaults.yaml b/bionemo-recipes/recipes/esm2_native_te/hydra_config/defaults.yaml
index 3bc3635ba7..f8ce5bbeda 100644
--- a/bionemo-recipes/recipes/esm2_native_te/hydra_config/defaults.yaml
+++ b/bionemo-recipes/recipes/esm2_native_te/hydra_config/defaults.yaml
@@ -22,6 +22,7 @@ dataset:
     path: "nvidia/esm2_uniref_pretraining_data"
     split: "train"
     streaming: True
+  pad_sequences_to_be_divisible_by: null
 
 # WandB config
 wandb_init_args:
diff --git a/bionemo-recipes/recipes/esm2_native_te/train_fsdp2.py b/bionemo-recipes/recipes/esm2_native_te/train_fsdp2.py
index 293eafe84d..275b96c03a 100644
--- a/bionemo-recipes/recipes/esm2_native_te/train_fsdp2.py
+++ b/bionemo-recipes/recipes/esm2_native_te/train_fsdp2.py
@@ -68,7 +68,7 @@ def main(args: DictConfig) -> float | None:
     )
 
     # Create an empty ESM-2 model with a masked language model head, e.g. "nvidia/esm2_t6_8M_UR50D".
-    config = AutoConfig.from_pretrained(args.model_tag, trust_remote_code=True, dtype=torch.bfloat16)
+    config = AutoConfig.from_pretrained(args.model_tag, trust_remote_code=True, token_dropout=False, dtype=torch.bfloat16)
     # If we're using sequence packing with TE layers, we need to pass the `attn_input_format` argument.
     if args.use_sequence_packing:
         config.attn_input_format = "thd"
@@ -130,7 +130,10 @@ def main(args: DictConfig) -> float | None:
     step = start_step
     while step < args.num_train_steps:
         for batch in train_dataloader:
+            # import pdb; pdb.set_trace()
             batch = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()}  # noqa: PLW2901
+            batch["pad_between_seqs"] = True
+            
 
             # Forward pass with mixed precision.
             with transformer_engine.pytorch.fp8_autocast(enabled=args.fp8_config.enabled, fp8_recipe=fp8_recipe):