diff --git a/vllm/model_executor/models/jais2.py b/vllm/model_executor/models/jais2.py
index 01e75338a8ced..aacc4abd43e61 100644
--- a/vllm/model_executor/models/jais2.py
+++ b/vllm/model_executor/models/jais2.py
@@ -48,7 +48,6 @@ from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
-    DEFAULT_VOCAB_PADDING_SIZE,
     ParallelLMHead,
     VocabParallelEmbedding,
 )
@@ -167,7 +166,6 @@ class Jais2Attention(nn.Module):
 
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=max_position_embeddings,
             rope_parameters=getattr(config, "rope_parameters", None),
             is_neox_style=is_neox_style,
@@ -304,17 +302,12 @@ class Jais2Model(nn.Module):
 
         config = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
-        lora_config = vllm_config.lora_config
 
         self.config = config
         self.quant_config = quant_config
         self.padding_idx = config.pad_token_id
-        lora_vocab = (
-            (lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
-            if lora_config
-            else 0
-        )
-        self.vocab_size = config.vocab_size + lora_vocab
+
+        self.vocab_size = config.vocab_size
         self.org_vocab_size = config.vocab_size
         if get_pp_group().is_first_rank or (
             config.tie_word_embeddings and get_pp_group().is_last_rank
@@ -456,29 +449,15 @@ class Jais2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         super().__init__()
         config = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
-        lora_config = vllm_config.lora_config
         self.config = config
-        self.lora_config = lora_config
-
         self.model = self._init_model(
             vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
         )
 
         if get_pp_group().is_last_rank:
-            self.unpadded_vocab_size = config.vocab_size
-            if lora_config:
-                self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
             self.lm_head = ParallelLMHead(
-                self.unpadded_vocab_size,
+                config.vocab_size,
                 config.hidden_size,
-                org_num_embeddings=config.vocab_size,
-                padding_size=(
-                    DEFAULT_VOCAB_PADDING_SIZE
-                    # We need bigger padding if using lora for kernel
-                    # compatibility
-                    if not lora_config
-                    else lora_config.lora_vocab_padding_size
-                ),
                 quant_config=quant_config,
                 prefix=maybe_prefix(prefix, "lm_head"),
             )
@@ -487,7 +466,7 @@ class Jais2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
 
             logit_scale = getattr(config, "logit_scale", 1.0)
             self.logits_processor = LogitsProcessor(
-                self.unpadded_vocab_size, config.vocab_size, logit_scale
+                config.vocab_size, scale=logit_scale
             )
         else:
             self.lm_head = PPMissingLayer()