diff --git a/src/mcore_bridge/model/gpts/nemotron_h.py b/src/mcore_bridge/model/gpts/nemotron_h.py
index 744602e..176871a 100644
--- a/src/mcore_bridge/model/gpts/nemotron_h.py
+++ b/src/mcore_bridge/model/gpts/nemotron_h.py
@@ -9,8 +9,6 @@
 import torch
 from megatron.core.extensions.transformer_engine import TEColumnParallelLinear, TENorm, TERowParallelLinear
 from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
-from megatron.core.models.hybrid.hybrid_block import HybridStack, HybridStackSubmodules
-from megatron.core.models.hybrid.hybrid_layer_specs import hybrid_stack_spec
 from megatron.core.ssm.mamba_layer import MambaLayer, MambaLayerSubmodules
 from megatron.core.ssm.mamba_mixer import MambaMixer, MambaMixerSubmodules
 from megatron.core.transformer.spec_utils import ModuleSpec
@@ -21,9 +19,21 @@
 from mcore_bridge.utils import get_logger
 
 from ..constant import ModelType
-from ..hybrid_model import HybridModel
 from ..register import ModelLoader, ModelMeta, register_model
 
+try:
+    from megatron.core.models.hybrid.hybrid_block import HybridStack, HybridStackSubmodules
+    from megatron.core.models.hybrid.hybrid_layer_specs import hybrid_stack_spec
+
+    from ..hybrid_model import HybridModel
+
+    _HYBRID_MODEL_AVAILABLE = True
+except ImportError as error:
+    if not (error.name or '').startswith('megatron.core.models.hybrid'):
+        raise
+    HybridModel = HybridStack = HybridStackSubmodules = hybrid_stack_spec = None
+    _HYBRID_MODEL_AVAILABLE = False
+
 logger = get_logger()
 
 
@@ -410,9 +420,11 @@ def build_model(self, pre_process=True, post_process=True, vp_stage: Optional[in
         return model
 
 
-register_model(ModelMeta(
-    ModelType.nemotron_h,
-    ['nemotron_h'],
-    bridge_cls=NemotronHBridge,
-    loader=NemotronHLoader,
-))
+if _HYBRID_MODEL_AVAILABLE:
+    register_model(
+        ModelMeta(
+            ModelType.nemotron_h,
+            ['nemotron_h'],
+            bridge_cls=NemotronHBridge,
+            loader=NemotronHLoader,
+        ))
diff --git a/src/mcore_bridge/model/gpts/qwen3_next.py b/src/mcore_bridge/model/gpts/qwen3_next.py
index 0d4e290..f91f037 100644
--- a/src/mcore_bridge/model/gpts/qwen3_next.py
+++ b/src/mcore_bridge/model/gpts/qwen3_next.py
@@ -471,10 +471,10 @@ def forward(self, hidden_states: torch.Tensor, **kwargs):
         # Note: for packed inputs, we do not perform padding_free unpadding.
         # Doing so would allow different sequences to see each other; for efficiency we keep this implementation.
         if thd_format:
+            max_seqlen_q = int(packed_seq_params.max_seqlen_q)
             new_hidden_states = hidden_states.new_zeros(
-                (packed_seq_params.num_samples, packed_seq_params.max_seqlen_q.item(), hidden_states.shape[-1]))
-            attention_mask = hidden_states.new_zeros(
-                (packed_seq_params.num_samples, packed_seq_params.max_seqlen_q.item()), dtype=torch.bool)
+                (packed_seq_params.num_samples, max_seqlen_q, hidden_states.shape[-1]))
+            attention_mask = hidden_states.new_zeros((packed_seq_params.num_samples, max_seqlen_q), dtype=torch.bool)
             cu_seqlens_q = packed_seq_params.cu_seqlens_q
             for i in range(packed_seq_params.num_samples):
                 start, end = cu_seqlens_q[i], cu_seqlens_q[i + 1]
diff --git a/src/mcore_bridge/model/mm_gpts/qwen3_5.py b/src/mcore_bridge/model/mm_gpts/qwen3_5.py
index 8ba5378..b673bf7 100644
--- a/src/mcore_bridge/model/mm_gpts/qwen3_5.py
+++ b/src/mcore_bridge/model/mm_gpts/qwen3_5.py
@@ -40,10 +40,10 @@ def forward(self, hidden_states: torch.Tensor, **kwargs):
         # Note: for packed inputs, we do not perform padding_free unpadding.
         # Doing so would allow different sequences to see each other; for efficiency we keep this implementation.
         if thd_format:
+            max_seqlen_q = int(packed_seq_params.max_seqlen_q)
             new_hidden_states = hidden_states.new_zeros(
-                (packed_seq_params.num_samples, packed_seq_params.max_seqlen_q.item(), hidden_states.shape[-1]))
-            attention_mask = hidden_states.new_zeros(
-                (packed_seq_params.num_samples, packed_seq_params.max_seqlen_q.item()), dtype=torch.bool)
+                (packed_seq_params.num_samples, max_seqlen_q, hidden_states.shape[-1]))
+            attention_mask = hidden_states.new_zeros((packed_seq_params.num_samples, max_seqlen_q), dtype=torch.bool)
             cu_seqlens_q = packed_seq_params.cu_seqlens_q
             for i in range(packed_seq_params.num_samples):
                 start, end = cu_seqlens_q[i], cu_seqlens_q[i + 1]