diff --git a/src/transformers/models/xlstm/modeling_xlstm.py b/src/transformers/models/xlstm/modeling_xlstm.py
index 135ea4f97fb0a3..de1fe232ddc7e8 100644
--- a/src/transformers/models/xlstm/modeling_xlstm.py
+++ b/src/transformers/models/xlstm/modeling_xlstm.py
@@ -26,9 +26,11 @@
         mLSTMBlock,
         mLSTMStateType,
         soft_cap,
+        xLSTMLargeConfig,
     )
 else:
     mLSTMBlock = None
+    xLSTMLargeConfig = None
 
 from .configuration_xlstm import xLSTMConfig
 
@@ -213,7 +215,39 @@ def __init__(self, config):
         super().__init__(config)
 
         self.embeddings = nn.Embedding(config.vocab_size, config.embedding_dim)
-        self.blocks = nn.ModuleList([mLSTMBlock(config.to_xlstm_block_config()) for _ in range(config.num_blocks)])
+        # use config explicitly to mitigate unused variable tests
+        xlstm_block_config = xLSTMLargeConfig(
+            vocab_size=config.vocab_size,
+            embedding_dim=config.embedding_dim,
+            num_blocks=config.num_blocks,
+            num_heads=config.num_heads,
+            use_bias=config.use_bias,
+            add_out_norm=config.add_out_norm,
+            norm_eps=config.norm_eps,
+            norm_reduction_force_float32=config.norm_reduction_force_float32,
+            # mlstm_layer
+            qk_dim_factor=config.qk_dim_factor,
+            v_dim_factor=config.v_dim_factor,
+            # mlstm backend
+            chunkwise_kernel=config.chunkwise_kernel,
+            sequence_kernel=config.sequence_kernel,
+            step_kernel=config.step_kernel,
+            mode=config.mode,
+            chunk_size=config.chunk_size,
+            return_last_states=config.return_last_states,
+            autocast_kernel_dtype=config.autocast_kernel_dtype,
+            eps=config.eps,
+            inference_state_dtype=config.inference_state_dtype,
+            # feedforward
+            ffn_proj_factor=config.ffn_proj_factor,
+            ffn_round_up_to_multiple_of=config.ffn_round_up_to_multiple_of,
+            # capping
+            gate_soft_cap=config.gate_soft_cap,
+            output_logit_soft_cap=config.output_logit_soft_cap,
+            weight_mode=config.weight_mode,
+        )
+
+        self.blocks = nn.ModuleList([mLSTMBlock(xlstm_block_config) for _ in range(config.num_blocks)])
 
         self.gradient_checkpointing = False
         self.out_norm = RMSNorm(config.embedding_dim, eps=config.norm_eps)