diff --git a/QEfficient/base/pytorch_transforms.py b/QEfficient/base/pytorch_transforms.py
index abd19ed35..6bb481dc4 100644
--- a/QEfficient/base/pytorch_transforms.py
+++ b/QEfficient/base/pytorch_transforms.py
@@ -107,6 +107,9 @@ def apply(cls, model: nn.Module) -> Tuple[nn.Module, bool]:
             ):
                 for orig_method_name, mapped_method in repl_method_map.items():
                     setattr(module, orig_method_name, MethodType(mapped_method, module))
+                    # Handling the __init__ calls in the models
+                    if hasattr(module, "__qeff_init__"):
+                        module.__qeff_init__()
                     transformed = True
 
         return model, transformed
diff --git a/QEfficient/transformers/models/modeling_auto.py b/QEfficient/transformers/models/modeling_auto.py
index 6b5deb8db..0e5531c31 100644
--- a/QEfficient/transformers/models/modeling_auto.py
+++ b/QEfficient/transformers/models/modeling_auto.py
@@ -1291,6 +1291,7 @@ class QEFFAutoModelForCausalLM(QEFFBaseModel):
         FP8DeQuantLinearToLinearTransform,
         CustomOpsTransform,
         KVCacheTransform,
+        KVCacheModuleMethodMapperTransform,
     ]
     _onnx_transforms = [FP16ClipTransform, SplitTensorsTransform]
 
diff --git a/QEfficient/transformers/models/plamo/__init__.py b/QEfficient/transformers/models/plamo/__init__.py
new file mode 100644
index 000000000..72ba36c8a
--- /dev/null
+++ b/QEfficient/transformers/models/plamo/__init__.py
@@ -0,0 +1,6 @@
+# -----------------------------------------------------------------------------
+#
+# Copyright (c) 2025 Qualcomm Innovation Center, Inc. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# -----------------------------------------------------------------------------
diff --git a/QEfficient/transformers/models/plamo/modeling_plamo.py b/QEfficient/transformers/models/plamo/modeling_plamo.py
new file mode 100644
index 000000000..17b3270c6
--- /dev/null
+++ b/QEfficient/transformers/models/plamo/modeling_plamo.py
@@ -0,0 +1,536 @@
+# -----------------------------------------------------------------------------
+#
+# Copyright (c) 2025 Qualcomm Innovation Center, Inc. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# -----------------------------------------------------------------------------
+
+import math
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig, PreTrainedModel
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+
+from QEfficient.customop.rms_norm import CustomRMSNorm
+from QEfficient.transformers.cache_utils import QEffDynamicCache
+from QEfficient.transformers.modeling_attn_mask_utils import _create_causal_mask
+
+
+class QEffPlamoConfig(PretrainedConfig):  # type: ignore
+    model_type: str = "plamo"
+
+    def __init__(
+        self,
+        vocab_size: int = 32000,
+        hidden_size: int = 4096,
+        intermediate_size: int = 13312,
+        num_hidden_layers: int = 32,
+        num_attention_heads: int = 32,
+        num_key_value_heads: Optional[int] = None,
+        max_position_embeddings: int = 2048,
+        initializer_range: float = 0.02,
+        rms_norm_eps: float = 1e-6,
+        use_cache: bool = True,
+        tokenizer_class: str = "PlamoTokenizer",
+        pad_token_id: Optional[int] = None,
+        bos_token_id: int = 1,
+        eos_token_id: int = 2,
+        n_shared_head: int = 8,
+        tie_word_embeddings: bool = False,
+        **kwargs: Any,
+    ) -> None:
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+
+        self.n_shared_head = n_shared_head
+
+        super().__init__(
+            tokenizer_class=tokenizer_class,
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+class QEffPlamoRotaryEmbedding(torch.nn.Module):
+    def _set_cos_sin_cache(self, seq_len: int, device: Any, dtype: Any) -> None:
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)  # type: ignore
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+    def forward(self, x: torch.Tensor, seq_len: int) -> Tuple[torch.Tensor, torch.Tensor]:
+        # 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),  # type: ignore
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),  # type: ignore
+        )
+
+
+def _rotate_half(x: torch.Tensor) -> torch.Tensor:
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def _rotary_pos_emb(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, position_ids: torch.Tensor) -> torch.Tensor:
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    x_embed = (x * cos) + (_rotate_half(x) * sin)
+    return x_embed
+
+
+class QEffPlamoRMSNorm(nn.Module):
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    **kwargs,
+):
+    attn_weights = torch.matmul(query, key.transpose(2, 3)) / math.sqrt(module.qk_dim)
+
+    if attention_mask is not None:
+        attn_weights = torch.where(attention_mask, torch.tensor(-10000.0, dtype=torch.float32), attn_weights)
+
+    # upcast attention to fp32
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_output = torch.matmul(attn_weights, value)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+class QEffPlamoAttention(torch.nn.Module):
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        batch_index: Optional[torch.Tensor] = None,
+        layer_idx: Optional[int] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states).view(bsz, q_len, self.q_num_heads, self.qk_dim).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(bsz, q_len, self.k_num_heads, self.qk_dim).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(bsz, q_len, self.v_num_heads, self.v_dim).transpose(1, 2)
+
+        def _expand_kv(t: torch.Tensor, repeat: int, target: int) -> torch.Tensor:
+            return t.repeat(1, repeat, 1, 1)[:, :target]
+
+        # expand shared kv
+        assert self.k_num_heads == self.v_num_heads
+        key_states = _expand_kv(key_states, self.config.n_shared_head, self.q_num_heads)
+        value_states = _expand_kv(value_states, self.config.n_shared_head, self.q_num_heads)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len = past_key_value.get_usable_length(kv_seq_len, layer_idx)
+
+        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)
+        query_states = _rotary_pos_emb(query_states, cos, sin, position_ids)
+        key_states = _rotary_pos_emb(key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "batch_index": batch_index, "position_ids": position_ids}
+            key_states, value_states = past_key_value.update(key_states, value_states, 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,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, -1)
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class MLP(nn.Module):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))  # type: ignore
+
+
+class QEffPlamoDecoderLayer(torch.nn.Module):
+    def __qeff_init__(
+        self,
+    ):
+        self.norm = CustomRMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
+
+    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,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        batch_index: Optional[torch.LongTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        layer_idx: Optional[int] = None,
+    ) -> Tuple[Any, ...]:
+        # from LlamaDecoder
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+
+        # Self Attention
+        hidden_states_sa, self_attn_weights, present_key_value = 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,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            layer_idx=layer_idx,
+        )
+
+        # Fully Connected
+        hidden_states_mlp = self.mlp(hidden_states)
+
+        # Residual
+        hidden_states = residual + hidden_states_sa + hidden_states_mlp
+
+        outputs: Any = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs  # type: ignore
+
+
+class QEffPlamoDecoder(torch.nn.Module):
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_ids: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        output_hidden_states: Optional[bool] = False,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        batch_index: Optional[torch.LongTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        all_hidden_states: Optional[Tuple[torch.Tensor, ...]] = () if output_hidden_states else None
+        all_self_attns: Optional[Tuple[torch.Tensor, ...]] = () if output_attentions else None
+        next_decoder_cache: Optional[Tuple[torch.Tensor, ...]] = () if use_cache else None
+        hidden_states = hidden_states
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                assert all_hidden_states is not None
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                layer_idx=idx,
+                cache_position=cache_position,
+            )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                cache = layer_outputs[2 if output_attentions else 1]
+                assert cache is not None
+                assert next_decoder_cache is not None
+                next_decoder_cache = cache
+
+            if output_attentions:
+                assert layer_outputs[1] is not None
+                assert all_self_attns is not None
+                all_self_attns += (layer_outputs[1],)
+
+        return (hidden_states, all_hidden_states, all_self_attns, next_decoder_cache)
+
+
+class QEffPlamoPreTrainedModel(PreTrainedModel):  # type: ignore
+    config_class = QEffPlamoConfig
+    _no_split_modules: List[str]
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["PlamoDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
+
+    def _init_weights(self, module: torch.nn.Module) -> None:
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _set_gradient_checkpointing(self, module: torch.nn.Module, value: bool = False) -> None:
+        module.gradient_checkpointing = value  # type: ignore
+
+
+class QEffPlamoModel(QEffPlamoPreTrainedModel):
+    def __qeff_init__(
+        self,
+    ):
+        self.norm = CustomRMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = 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_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        assert input_ids is not None
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        # kept for BC (non `Cache` `past_key_values` inputs)
+        return_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache):
+            return_legacy_cache = True
+            past_key_values = QEffDynamicCache()
+
+        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.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)
+
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        attention_mask = _create_causal_mask(position_ids=position_ids, target_length=past_seen_tokens)
+
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                use_cache = False
+
+        # decoder layers
+        layer_outputs = self.layers(
+            hidden_states=hidden_states,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            output_hidden_states=output_hidden_states,
+            past_key_values=past_key_values,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            batch_index=batch_index,
+        )
+
+        hidden_states = layer_outputs[0]
+        all_hidden_states = layer_outputs[1]
+        all_self_attns = layer_outputs[2]
+        next_decoder_cache = layer_outputs[3]
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            assert all_hidden_states is not None
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class QEffPlamoForCausalLM(QEffPlamoPreTrainedModel):
+    def forward(  # type: ignore
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        batch_index: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        assert input_ids is not None
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = 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_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        # Cast to INT32 to avoid issue while running in ONNXRT
+        logit_index = position_ids.to(torch.int32).argmax(1, keepdim=True)
+        hidden_states = outputs[0][torch.arange(position_ids.shape[0]).view(-1, 1), logit_index]
+
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        return CausalLMOutputWithPast(
+            loss=None,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.Tensor,
+        past_key_values: Optional[Cache] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: Any,
+    ) -> Dict[str, Any]:
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs: Dict[str, Any] = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values: List[torch.FloatTensor], beam_idx: int) -> Tuple[Any, ...]:
+        reordered_past: Tuple[Any, ...] = ()
+        for layer_past in past_key_values:
+            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
+        return reordered_past
diff --git a/QEfficient/transformers/models/pytorch_transforms.py b/QEfficient/transformers/models/pytorch_transforms.py
index 333c734ba..3d8eac97a 100644
--- a/QEfficient/transformers/models/pytorch_transforms.py
+++ b/QEfficient/transformers/models/pytorch_transforms.py
@@ -245,6 +245,15 @@
     QEffPhi3ForCausalLM,
     QEffPhi3Model,
 )
+from QEfficient.transformers.models.plamo.modeling_plamo import (
+    QEffPlamoAttention,
+    QEffPlamoDecoder,
+    QEffPlamoDecoderLayer,
+    QEffPlamoForCausalLM,
+    QEffPlamoModel,
+    QEffPlamoRMSNorm,
+    QEffPlamoRotaryEmbedding,
+)
 from QEfficient.transformers.models.qwen2.modeling_qwen2 import (
     QEffQwen2Attention,
     QEffQwen2DecoderLayer,
@@ -485,5 +494,12 @@ class KVCacheModuleMethodMapperTransform(ModuleMethodMapperTransform):
             "get_qeff_language_decoder": QEffInternVLModel.get_qeff_language_decoder,
         },
         "InternVisionEmbeddings": {"forward": QEffInternVisionEmbeddings.forward},
+        "PlamoForCausalLM": {"forward": QEffPlamoForCausalLM.forward},
+        "PlamoModel": {"forward": QEffPlamoModel.forward},
+        "PlamoDecoder": {"forward": QEffPlamoDecoder.forward},
+        "PlamoDecoderLayer": {"forward": QEffPlamoDecoderLayer.forward},
+        "Attention": {"forward": QEffPlamoAttention.forward},
+        "RMSNorm": {"forward": QEffPlamoRMSNorm.forward},
+        "RotaryEmbedding": {"forward": QEffPlamoRotaryEmbedding.forward},
     }
     _match_class_replace_method = {}
diff --git a/README.md b/README.md
index 685db6fe7..de12aee5b 100644
--- a/README.md
+++ b/README.md
@@ -20,6 +20,7 @@
 - [04/2025] [Granite 3.0 and 3.1 Language MOE Models] (https://huggingface.co/ibm-granite/granite-3.0-1b-a400m-base)
 - [09/2024] [AWQ](https://arxiv.org/abs/2306.00978)/[GPTQ](https://arxiv.org/abs/2210.17323) 4-bit quantized models are supported <br>
 - [09/2024] Now we support [PEFT](https://huggingface.co/docs/peft/index) models
+- [04/2025] Added support for [PLaMo] (https://huggingface.co/pfnet/plamo-13b-instruct)
 - [01/2025] Added support for [Ibm-Granite] (https://huggingface.co/ibm-granite/granite-3.1-8b-instruct)
 - [01/2025] Added support for [Ibm-Granite-Guardian] (https://huggingface.co/ibm-granite/granite-guardian-3.1-8b)
 - [09/2024] Added support for [Gemma-2-Family](https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315)<br>
diff --git a/tests/transformers/models/test_causal_lm_models.py b/tests/transformers/models/test_causal_lm_models.py
index efa2187b7..6d401d4c6 100644
--- a/tests/transformers/models/test_causal_lm_models.py
+++ b/tests/transformers/models/test_causal_lm_models.py
@@ -44,6 +44,7 @@
     "neuralmagic/Qwen2-0.5B-Instruct-FP8",  # fp8 quant method, static, with lm head ignored
     "ibm-granite/granite-3.1-2b-instruct",
     "ibm-granite/granite-guardian-3.1-2b",
+    "pfnet/plamo-13b-instruct",
 ]
 
 test_models_qnn = [