diff --git a/QEfficient/transformers/models/glm4_moe/__init__.py b/QEfficient/transformers/models/glm4_moe/__init__.py
new file mode 100644
index 000000000..51b99e6ed
--- /dev/null
+++ b/QEfficient/transformers/models/glm4_moe/__init__.py
@@ -0,0 +1,7 @@
+# -----------------------------------------------------------------------------
+#
+# Copyright (c) Qualcomm Technologies, Inc. and/or its subsidiaries.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# ----------------------------------------------------------------------------
+
diff --git a/QEfficient/transformers/models/glm4_moe/modeling_glm4_moe.py b/QEfficient/transformers/models/glm4_moe/modeling_glm4_moe.py
new file mode 100644
index 000000000..85ce65f57
--- /dev/null
+++ b/QEfficient/transformers/models/glm4_moe/modeling_glm4_moe.py
@@ -0,0 +1,420 @@
+# -----------------------------------------------------------------------------
+#
+# Copyright (c) Qualcomm Technologies, Inc. and/or its subsidiaries.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# -----------------------------------------------------------------------------
+
+from typing import Callable, List, Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from transformers.models.glm4_moe.modeling_glm4_moe import (
+    Glm4MoeAttention,
+    Glm4MoeConfig,
+    Glm4MoeDecoderLayer,
+    Glm4MoeForCausalLM,
+    Glm4MoeModel,
+    Glm4MoeMoE,
+    Glm4MoeRotaryEmbedding,
+    Glm4MoeTopkRouter,
+    repeat_kv,
+    rotate_half,
+)
+from transformers.processing_utils import Unpack
+from transformers.utils import TransformersKwargs
+
+from QEfficient.transformers.cache_utils import QEffDynamicCache
+from QEfficient.transformers.modeling_attn_mask_utils import _create_causal_mask
+from QEfficient.utils.constants import MIN_MASKED_ATTENTION_VALUE
+
+
+class QEffGlm4MoeRotaryEmbedding(Glm4MoeRotaryEmbedding):
+    """
+    Copied from Glm4MoeForCausalLM: https://github.com/huggingface/transformers/blob/main/src/transformers/models/glm4_moe/modeling_glm4_moe.py
+    The only differences are:
+    - Add static sin/cos computations.
+    """
+
+    def __init__(self, config: Glm4MoeConfig, device=None):
+        super().__init__(config=config)
+
+        self._set_cos_sin_cache(
+            seq_len=self.original_max_seq_len, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
+
+        freqs = torch.outer(t, self.inv_freq)
+
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x: torch.Tensor, seq_len: int = None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype) * self.attention_scaling,
+            self.sin_cached[:seq_len].to(dtype=x.dtype) * self.attention_scaling,
+        )
+
+
+def qeff_apply_rotary_pos_emb(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    position_ids: torch.Tensor,
+    unsqueeze_dim: int = 1,
+):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+
+    # Keep half or full tensor for later concatenation
+    rotary_dim = cos.shape[-1]
+    q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
+    k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
+
+    # Apply rotary embeddings on the first half or full tensor
+    q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin)
+    k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin)
+
+    # Concatenate back to full shape
+    q_embed = torch.cat([q_embed, q_pass], dim=-1)
+    k_embed = torch.cat([k_embed, k_pass], dim=-1)
+
+    # Cast back to original dtype
+    return q_embed.to(q.dtype), k_embed.to(k.dtype)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        attn_weights = torch.where(
+            attention_mask, torch.tensor(MIN_MASKED_ATTENTION_VALUE, dtype=torch.float32), attn_weights
+        )
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+class QEffGlm4MoeAttention(Glm4MoeAttention):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __qeff_init__(self):
+        self.rotary_emb = QEffGlm4MoeRotaryEmbedding(config=self.config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor],
+        past_key_value: Optional[Cache] = None,
+        batch_index: Optional[torch.LongTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape)
+        key_states = self.k_proj(hidden_states).view(hidden_shape)
+        value_states = self.v_proj(hidden_states).view(hidden_shape)
+
+        if self.use_qk_norm:  # main diff from Llama
+            query_states = self.q_norm(query_states)
+            key_states = self.k_norm(key_states)
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        kv_seq_len = past_key_value.get_seq_length(self.layer_idx, cache_position)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        query_states, key_states = qeff_apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; position_ids needed for the static cache
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "cache_position": cache_position,
+                "batch_index": batch_index,
+                "position_ids": position_ids,
+            }
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        attention_interface: Callable = eager_attention_forward
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class QEffGlm4MoeDecoderLayer(Glm4MoeDecoderLayer):
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        batch_index: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor]:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        # Self Attention
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            batch_index=batch_index,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class QEffGlm4MoeModel(Glm4MoeModel):
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        batch_index: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        use_cache: Optional[bool] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPast:
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        if inputs_embeds is None:
+            inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)
+
+        return_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache):
+            return_legacy_cache = True
+            past_key_values = QEffDynamicCache.from_legacy_cache(past_key_values)
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position: torch.Tensor = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        attention_mask = _create_causal_mask(position_ids=position_ids, target_length=past_seen_tokens)
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            hidden_states = decoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                batch_index=batch_index,
+                cache_position=cache_position,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        if return_legacy_cache:
+            past_key_values = past_key_values.to_legacy_cache()
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+            hidden_states=all_hidden_states,
+        )
+
+
+class QEffGlm4MoeTopkRouter(Glm4MoeTopkRouter):
+    def forward(self, hidden_states):
+        hidden_states = hidden_states.view(-1, self.config.hidden_size)
+        router_logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32))
+        scores = router_logits.sigmoid()
+        topk_indices = self.get_topk_indices(scores)
+        topk_weights = scores.gather(1, topk_indices)
+        if self.norm_topk_prob:
+            denominator = topk_weights.sum(dim=-1, keepdim=True) + 1e-20
+            topk_weights /= denominator
+        topk_weights = topk_weights * self.routed_scaling_factor
+
+        # Create expert mask similar to Granite
+        expert_mask = torch.nn.functional.one_hot(topk_indices, num_classes=self.n_routed_experts)
+        expert_mask = expert_mask.permute(2, 0, 1)  # [num_experts, batch*seq, top_k]
+
+        return topk_weights, expert_mask, router_logits, self.n_routed_experts
+
+
+class QEffGlm4MoeMoE(Glm4MoeMoE):
+    """
+    MoE Block
+    """
+
+    def moe(self, hidden_states: torch.Tensor, topk_weights: torch.Tensor, expert_mask: torch.Tensor, num_experts: int):
+        final_hidden_states = torch.zeros_like(hidden_states, dtype=topk_weights.dtype)
+
+        for expert_idx in range(num_experts):
+            expert = self.experts[expert_idx]
+            mask = expert_mask[expert_idx].to(hidden_states.dtype)
+            mask_weight = (topk_weights * mask).sum(dim=1, keepdim=True)
+            gate_out = expert.gate_proj(hidden_states)
+            up_out = expert.up_proj(hidden_states)
+            hidden = expert.act_fn(gate_out) * up_out
+            expert_output = expert.down_proj(hidden)
+            current_hidden_states = expert_output * mask_weight
+            final_hidden_states += current_hidden_states
+
+        return final_hidden_states.type(hidden_states.dtype)
+
+    def forward(self, hidden_states):
+        """
+        Forward pass of MoE block.
+        """
+        residuals = hidden_states
+        orig_shape = hidden_states.shape
+
+        topk_weights, expert_mask, router_logits, num_experts = self.gate(hidden_states)
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+        hidden_states = self.moe(hidden_states, topk_weights, expert_mask, num_experts).view(*orig_shape)
+        hidden_states = hidden_states + self.shared_experts(residuals)
+
+        return hidden_states
+
+
+class QEffGlm4MoeForCausalLM(Glm4MoeForCausalLM):
+    """
+    Copied from Glm4MoeForCausalLM: https://github.com/huggingface/transformers/blob/main/src/transformers/models/glm4_moe/modeling_glm4_moe.py
+    """
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        batch_index: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CausalLMOutputWithPast:
+        outputs: BaseModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            batch_index=batch_index,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_hidden_states=output_hidden_states,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        logit_index = position_ids.to(torch.int32).argmax(1, keepdim=True)
+        hidden_states = hidden_states[torch.arange(position_ids.shape[0]).view(-1, 1), logit_index]
+        logits = self.lm_head(hidden_states).float()
+
+        return CausalLMOutputWithPast(
+            loss=None,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
diff --git a/QEfficient/transformers/models/pytorch_transforms.py b/QEfficient/transformers/models/pytorch_transforms.py
index 21a867eb5..37dd8e5d0 100644
--- a/QEfficient/transformers/models/pytorch_transforms.py
+++ b/QEfficient/transformers/models/pytorch_transforms.py
@@ -45,6 +45,16 @@
     Gemma3RMSNorm,
     Gemma3TextModel,
 )
+from transformers.models.glm4_moe.modeling_glm4_moe import (
+    Glm4MoeAttention,
+    Glm4MoeDecoderLayer,
+    Glm4MoeForCausalLM,
+    Glm4MoeModel,
+    Glm4MoeMoE,
+    Glm4MoeRMSNorm,
+    Glm4MoeRotaryEmbedding,
+    Glm4MoeTopkRouter,
+)
 from transformers.models.gpt2.modeling_gpt2 import GPT2Attention, GPT2Block, GPT2LMHeadModel, GPT2Model
 from transformers.models.gpt_bigcode.modeling_gpt_bigcode import (
     GPTBigCodeAttention,
@@ -242,6 +252,15 @@
     QEffGemma3ForConditionalGeneration,
     QEffGemma3TextModel,
 )
+from QEfficient.transformers.models.glm4_moe.modeling_glm4_moe import (
+    QEffGlm4MoeAttention,
+    QEffGlm4MoeDecoderLayer,
+    QEffGlm4MoeForCausalLM,
+    QEffGlm4MoeModel,
+    QEffGlm4MoeMoE,
+    QEffGlm4MoeRotaryEmbedding,
+    QEffGlm4MoeTopkRouter,
+)
 from QEfficient.transformers.models.gpt2.modeling_gpt2 import (
     QEffGPT2Attention,
     QEffGPT2Block,
@@ -454,6 +473,7 @@ class CustomOpsTransform(ModuleMappingTransform):
         Qwen3MoeRMSNorm: CustomRMSNormAIC,
         Gemma3RMSNorm: QEffGemma3CustomRMSNormAIC,
         Olmo2RMSNorm: CustomRMSNormAIC,
+        Glm4MoeRMSNorm: CustomRMSNormAIC,
     }
 
 
@@ -543,6 +563,14 @@ class KVCacheTransform(ModuleMappingTransform):
         GraniteMoeParallelExperts: QEffGraniteMoeParallelExperts,
         GraniteMoeTopKGating: QEffGraniteMoeTopKGating,
         GraniteMoeMoE: QEffGraniteMoeMoE,
+        # GLMMoe
+        Glm4MoeModel: QEffGlm4MoeModel,
+        Glm4MoeForCausalLM: QEffGlm4MoeForCausalLM,
+        Glm4MoeAttention: QEffGlm4MoeAttention,
+        Glm4MoeDecoderLayer: QEffGlm4MoeDecoderLayer,
+        Glm4MoeRotaryEmbedding: QEffGlm4MoeRotaryEmbedding,
+        Glm4MoeMoE: QEffGlm4MoeMoE,
+        Glm4MoeTopkRouter: QEffGlm4MoeTopkRouter,
         # mllama
         MllamaTextRMSNorm: CustomRMSNormAIC,
         MllamaTextSelfAttention: QEffMllamaTextSelfAttention,
diff --git a/QEfficient/utils/run_utils.py b/QEfficient/utils/run_utils.py
index c54dadeac..448aba57a 100644
--- a/QEfficient/utils/run_utils.py
+++ b/QEfficient/utils/run_utils.py
@@ -104,6 +104,9 @@ def run_hf_model_on_pytorch(self, model_hf):
         """
         model_inputs = self.input_handler.tokenizer(self.input_handler.prompt[0], return_tensors="pt")
 
+        if "token_type_ids" in model_inputs:
+            model_inputs.pop("token_type_ids")
+
         input_len = model_inputs["input_ids"].shape[-1]
 
         with torch.inference_mode():
diff --git a/tests/transformers/models/test_causal_lm_models.py b/tests/transformers/models/test_causal_lm_models.py
index ead636759..f9be1bf24 100644
--- a/tests/transformers/models/test_causal_lm_models.py
+++ b/tests/transformers/models/test_causal_lm_models.py
@@ -53,6 +53,7 @@
     "hpcai-tech/grok-1",
     "Snowflake/Llama-3.1-SwiftKV-8B-Instruct",
     "allenai/OLMo-2-0425-1B",
+    "zai-org/GLM-4.5-Air",
 ]
 
 test_models_qnn = [