diff --git a/QEfficient/base/pytorch_transforms.py b/QEfficient/base/pytorch_transforms.py
index cf8664236..a20fc4cb3 100644
--- a/QEfficient/base/pytorch_transforms.py
+++ b/QEfficient/base/pytorch_transforms.py
@@ -177,4 +177,4 @@ def apply(cls, model: nn.Module) -> Tuple[nn.Module, bool]:
         return model, transformed
 
 
-VLM_SPLIT_GATE_UP_WEIGHTS = {"QEffLlama4ForConditionalGeneration"}
+VLM_SPLIT_GATE_UP_WEIGHTS = {"QEffLlama4ForConditionalGeneration", "QEffLlama4ForCausalLM"}
diff --git a/QEfficient/transformers/models/gemma3/__init__.py b/QEfficient/transformers/models/gemma3/__init__.py
new file mode 100644
index 000000000..d647b73a6
--- /dev/null
+++ b/QEfficient/transformers/models/gemma3/__init__.py
@@ -0,0 +1,6 @@
+# -----------------------------------------------------------------------------
+#
+# Copyright (c) Qualcomm Technologies, Inc. and/or its subsidiaries.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# -----------------------------------------------------------------------------
diff --git a/QEfficient/transformers/models/gemma3/modeling_gemma3.py b/QEfficient/transformers/models/gemma3/modeling_gemma3.py
new file mode 100644
index 000000000..44aa06c83
--- /dev/null
+++ b/QEfficient/transformers/models/gemma3/modeling_gemma3.py
@@ -0,0 +1,778 @@
+# -----------------------------------------------------------------------------
+#
+# Copyright (c) 2025 Qualcomm Innovation Center, Inc. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# -----------------------------------------------------------------------------
+
+import copy
+from typing import Optional, Tuple, Union
+
+import torch
+from torch import nn
+from transformers.cache_utils import Cache, HybridCache
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from transformers.models.gemma3.modeling_gemma3 import (
+    Gemma3Attention,
+    Gemma3Config,
+    Gemma3DecoderLayer,
+    Gemma3ForCausalLM,
+    Gemma3ForConditionalGeneration,
+    Gemma3TextModel,
+    logger,
+    repeat_kv,
+    rotate_half,
+)
+
+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
+from QEfficient.utils import constants
+from QEfficient.utils._utils import IOInfo, get_padding_shape_from_config
+
+
+class GemmaRMSNormFunc(torch.autograd.Function):
+    @staticmethod
+    def forward(hidden_states: torch.Tensor, weight: torch.Tensor, epsilon: float):
+        hidden_states = hidden_states.to(torch.float32)
+        div_first = hidden_states * torch.rsqrt(torch.tensor(hidden_states.shape[-1], dtype=torch.float32))
+        variance = div_first.pow(2).sum(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + epsilon)
+        return weight * hidden_states
+
+    @staticmethod
+    def setup_context(ctx, inputs, outputs):
+        pass
+
+    @staticmethod
+    def symbolic(g: torch.Graph, hidden_states: torch.Value, weight: torch.Value, epsilon: torch.Value) -> torch.Value:
+        return g.onnxscript_op(CustomRMSNorm, hidden_states, weight, epsilon_f=epsilon).setTypeAs(hidden_states)
+
+
+class QEffGemma3CustomRMSNormAIC(nn.Module):
+    """
+    RMSNorm module that works by replacing the current module with compiler known custom-op.
+    """
+
+    def forward(self, hidden_states):
+        return GemmaRMSNormFunc.apply(
+            hidden_states,
+            self.weight.float() + 1.0,
+            self.variance_epsilon if hasattr(self, "variance_epsilon") else self.eps,
+        )
+
+
+class QEffGemma3RotaryEmbedding(nn.Module):
+    """
+    Copied from Gemma2RotaryEmbedding: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gemma2/modeling_gemma2.py
+    The only differences are:
+    - Add static sin/cos computations.
+    """
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, 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, seq_len=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.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+def qeff_apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=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)
+
+    # Apply rotation
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    # 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],
+    dropout: float = 0.0,
+    scaling: Optional[float] = None,
+    softcap: Optional[float] = None,
+    **kwargs,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    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 softcap is not None:
+        attn_weights = attn_weights / softcap
+        attn_weights = torch.tanh(attn_weights)
+        attn_weights = attn_weights * softcap
+
+    if attention_mask is not None:
+        attn_weights = torch.where(attention_mask, torch.tensor(-10000.0, 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 QEffGemma3Attention(Gemma3Attention):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: Gemma3Config, layer_idx: Optional[int] = None):
+        super().__init__(config, layer_idx)
+        # Define the general __qeff_init__() for any changes in the init calls
+        # Set the init in the module mapping pytorch transforms
+        self.__qeff_init__()
+
+    def __qeff_init__(self):
+        self.rotary_emb = QEffGemma3RotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.config.max_position_embeddings,
+            base=self.config.rope_theta,
+        )
+
+        config = copy.deepcopy(self.config)
+        config.rope_theta = config.rope_local_base_freq
+        config.rope_scaling = {"rope_type": "default"}
+
+        self.rotary_emb_local = QEffGemma3RotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=config.max_position_embeddings,
+            base=config.rope_theta,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: torch.Tensor,
+        attention_mask: Optional[torch.Tensor],
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        batch_index: Optional[torch.LongTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        query_states = self.q_norm(query_states)
+        key_states = self.k_norm(key_states)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.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, self.layer_idx)
+
+        if self.is_sliding:
+            cos, sin = self.rotary_emb_local(value_states, seq_len=kv_seq_len)
+        else:
+            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; 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, self.layer_idx, cache_kwargs)
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scaling
+
+        # import ipdb; ipdb.set_trace()
+        if self.config.attn_logit_softcapping is not None:
+            attn_weights = attn_weights / self.config.attn_logit_softcapping
+            attn_weights = torch.tanh(attn_weights)
+            attn_weights = attn_weights * self.config.attn_logit_softcapping
+
+        if attention_mask is not None:  # no matter the length, we just slice it
+            attn_weights = torch.where(attention_mask.bool(), 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_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.config.num_attention_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.config.num_attention_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+
+        attn_output = attn_output.reshape(bsz, q_len, -1)
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class QEffGemma3DecoderLayer(Gemma3DecoderLayer):
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings_global: torch.Tensor,
+        position_embeddings_local: 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,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        last_cache_position: int = 0,
+        **kwargs,
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # apply global RoPE to non-sliding layer only
+        if self.self_attn.is_sliding:
+            position_embeddings = position_embeddings_local
+        else:
+            position_embeddings = position_embeddings_global
+
+        hidden_states, self_attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            position_embeddings=position_embeddings,
+            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,
+            **kwargs,
+        )
+
+        hidden_states = self.post_attention_layernorm(hidden_states)
+
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.pre_feedforward_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+
+        hidden_states = self.post_feedforward_layernorm(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+
+class QEffGemma3TextModel(Gemma3TextModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Gemma3TextDecoderLayer`]
+
+    Args:
+        config: Gemma3TextConfig
+    """
+
+    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[HybridCache] = 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,
+        last_cache_position: Optional[int] = None,
+        **flash_attn_kwargs,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        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
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
+            # 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.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)
+
+        causal_mask = _create_causal_mask(
+            position_ids=position_ids, target_length=past_seen_tokens, sliding_window=self.config.sliding_window
+        )
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings_global = self.rotary_emb(hidden_states, position_ids)
+        position_embeddings_local = self.rotary_emb_local(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    position_embeddings_global,
+                    position_embeddings_local,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    last_cache_position,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    position_embeddings_global=position_embeddings_global,
+                    position_embeddings_local=position_embeddings_local,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    batch_index=batch_index,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                    last_cache_position=last_cache_position,
+                    **flash_attn_kwargs,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        if use_cache:
+            next_cache = past_key_values.to_legacy_cache()
+
+        output = BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+        return output if return_dict else output.to_tuple()
+
+
+class QEffGemma3ForCausalLMModel(Gemma3ForCausalLM):
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[HybridCache] = 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,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **loss_kwargs,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+            logits_to_keep (`int` or `torch.Tensor`, *optional*):
+                If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+                If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+                This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, Gemma3ForCausalLM
+
+        >>> model = Gemma3ForCausalLM.from_pretrained("google/gemma-2-9b")
+        >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
+
+        >>> prompt = "What is your favorite condiment?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "What is your favorite condiment?"
+        ```"""
+
+        if self.training and self.config._attn_implementation != "eager":
+            logger.warning_once(
+                "It is strongly recommended to train Gemma3 models with the `eager` attention implementation "
+                f"instead of `{self.config._attn_implementation}`. Use `eager` with `AutoModelForCausalLM.from_pretrained('<path-to-checkpoint>', attn_implementation='eager')`."
+            )
+        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
+
+        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,
+            **loss_kwargs,
+        )
+
+        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)
+        if self.config.final_logit_softcapping is not None:
+            logits = logits / self.config.final_logit_softcapping
+            logits = torch.tanh(logits)
+            logits = logits * self.config.final_logit_softcapping
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class QEffGemma3EncoderWrapper(nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+        self.model.vision_model = self.model.vision_tower
+
+    def forward(self, pixel_values):
+        image_features = self.model.get_image_features(pixel_values=pixel_values)
+        return image_features
+
+
+class QEffGemma3DecoderWrapper(nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+        self.language_model = self.model.language_model
+        self.config = self.model.config
+
+    def forward(self, input_ids, vision_embeds, position_ids, image_idx, past_key_values):
+        inputs_embeds = self.model.get_input_embeddings()(input_ids)
+        B, N, C = inputs_embeds.shape
+        selected = input_ids == self.model.config.image_token_index
+        indices1 = selected.to(torch.int64).cumsum(1) - 1
+        indices1 = torch.where(indices1 != -1, indices1 + image_idx, indices1)
+        indices0 = torch.arange(selected.unsqueeze(0).shape[0]).view(-1, 1)
+        image_features_expanded = vision_embeds.reshape(-1, C).unsqueeze(0)[indices0, indices1]
+        image_input_embeds = torch.where(selected.unsqueeze(-1), image_features_expanded, inputs_embeds)
+        inputs_embeds = torch.where(input_ids.shape[1] == torch.tensor(1), inputs_embeds, image_input_embeds)
+        outputs = self.model.language_model(
+            inputs_embeds=inputs_embeds, position_ids=position_ids, past_key_values=past_key_values, use_cache=True
+        )
+        image_idx = (indices1.max() + 1).unsqueeze(0).unsqueeze(0)
+        return outputs.logits, vision_embeds, image_idx, outputs.past_key_values
+
+
+class QEffGemma3ForConditionalGeneration(Gemma3ForConditionalGeneration):
+    def get_qeff_vision_encoder(self):
+        return QEffGemma3EncoderWrapper(self)
+
+    def get_qeff_language_decoder(self):
+        return QEffGemma3DecoderWrapper(self)
+
+    def forward(self, input_ids, position_ids, pixel_values, image_idx, past_key_values):
+        image_features = self.get_image_features(pixel_values=pixel_values)
+        inputs_embeds = self.get_input_embeddings()(input_ids)
+        B, N, C = inputs_embeds.shape
+        selected = input_ids == self.config.image_token_index
+        indices1 = selected.to(torch.int64).cumsum(1) - 1
+        indices1 = torch.where(indices1 != -1, indices1 + image_idx, indices1)
+        indices0 = torch.arange(selected.unsqueeze(0).shape[0]).view(-1, 1)
+        image_features_expanded = image_features.reshape(-1, C).unsqueeze(0)[indices0, indices1]
+        image_input_embeds = torch.where(selected.unsqueeze(-1), image_features_expanded, inputs_embeds)
+        inputs_embeds = torch.where(input_ids.shape[1] == torch.tensor(1), inputs_embeds, image_input_embeds)
+        outputs = self.language_model(
+            inputs_embeds=inputs_embeds, position_ids=position_ids, past_key_values=past_key_values, use_cache=True
+        )
+        image_idx = (indices1.max() + 1).unsqueeze(0).unsqueeze(0)
+        return outputs.logits, pixel_values, image_idx, outputs.past_key_values
+
+    def get_specializations(
+        self,
+        batch_size: int,
+        prefill_seq_len: int,
+        ctx_len: int,
+        img_size: int,
+        kv_offload: bool = False,
+        **compiler_options,
+    ):
+        prefill_seq_len = prefill_seq_len if prefill_seq_len else 32
+        ctx_len = ctx_len if ctx_len else constants.INTERN_CTX_LEN
+        if img_size is None and hasattr(self.config.vision_config, "image_size"):
+            img_size = getattr(self.config.vision_config, "image_size")
+        elif img_size is None:
+            img_size = 896  # FIXME based on gemma3 Image size
+            logger.warning("Setting img_size to be 336, as it was neither passed nor found in vision_config")
+        mm_tokens_per_image = getattr(self.config, "mm_tokens_per_image", 256)
+
+        vision = [
+            {
+                "batch_size": batch_size,
+                "img_size": img_size,
+                "seq_len": prefill_seq_len,
+                "ctx_len": ctx_len,
+            }
+        ]
+        lang = [
+            {
+                "batch_size": batch_size,
+                "seq_len": prefill_seq_len,
+                "ctx_len": ctx_len,
+                "img_size": img_size,
+                "mm_tokens_per_image": mm_tokens_per_image,
+            },
+            {
+                "batch_size": batch_size,
+                "seq_len": "1",
+                "ctx_len": ctx_len,
+                "img_size": img_size,
+                "mm_tokens_per_image": mm_tokens_per_image,
+            },
+        ]
+
+        specializations = {}
+
+        if kv_offload:
+            specializations["vision"] = vision
+            specializations["lang"] = lang
+            return specializations, compiler_options
+        else:
+            return lang, compiler_options
+
+    def get_onnx_dynamic_axes(self, kv_offload: bool = False):
+        # Define dynamic axes
+        vision_dynamic_axes = {}
+        lang_dynamic_axes = {}
+        lang_dynamic_axes["input_ids"] = {0: "batch_size", 1: "seq_len"}
+        lang_dynamic_axes["position_ids"] = {0: "batch_size", 1: "seq_len"}
+        lang_dynamic_axes["vision_embeds"] = {0: "batch_size", 1: "mm_tokens_per_image"}
+        vision_dynamic_axes["pixel_values"] = {0: "batch_size", 2: "img_size", 3: "img_size"}
+
+        pkv_dynamic_axes = {0: "batch_size", 2: "ctx_len"}
+        for i in range(self.language_model.config.num_hidden_layers):
+            for kv in ["key", "value"]:
+                lang_dynamic_axes[f"past_{kv}.{i}"] = pkv_dynamic_axes
+
+        dynamic_axes = {}
+        if kv_offload:
+            dynamic_axes["vision"] = vision_dynamic_axes
+            dynamic_axes["lang"] = lang_dynamic_axes
+        else:
+            dynamic_axes = {**vision_dynamic_axes, **lang_dynamic_axes}
+        return dynamic_axes
+
+    def get_output_names(self, kv_offload: bool = False):
+        vision_output_names = ["vision_embeds"]
+        lang_output_names = ["logits"]
+        for i in range(self.language_model.config.num_hidden_layers):
+            for kv in ["key", "value"]:
+                lang_output_names.append(f"past_{kv}.{i}_RetainedState")
+
+        output_names = {}
+        if kv_offload:
+            lang_output_names.insert(1, "vision_embeds_RetainedState")
+            lang_output_names.insert(2, "image_idx_output")
+            output_names["vision"] = vision_output_names
+            output_names["lang"] = lang_output_names
+        else:
+            lang_output_names.insert(1, "pixel_values_RetainedState")
+            lang_output_names.insert(2, "image_idx_output")
+            return lang_output_names
+        return output_names
+
+    def get_dummy_inputs(self, kv_offload: bool = False):
+        if vis_cfg := getattr(self.config, "vision_config", None):
+            img_size = getattr(vis_cfg, "image_size", 896)
+        else:
+            img_size = 896
+
+        mm_tokens_per_image = getattr(self.config, "mm_tokens_per_image", 256)
+        # Define shapes
+        inputs_shapes = {}
+        inputs_shapes["input_ids"] = (constants.ONNX_EXPORT_EXAMPLE_BATCH_SIZE, constants.ONNX_EXPORT_EXAMPLE_SEQ_LEN)
+        inputs_shapes["vision_embeds"] = (
+            1,  # constants.INTERN_NUM_PATCHES,
+            mm_tokens_per_image,  # constants.INTERN_FEATURE_SIZE,
+            self.language_model.config.hidden_size,  # 5120
+        )
+        inputs_shapes["position_ids"] = (
+            constants.ONNX_EXPORT_EXAMPLE_BATCH_SIZE,
+            constants.ONNX_EXPORT_EXAMPLE_SEQ_LEN,
+        )
+        inputs_shapes["pixel_values"] = (
+            constants.ONNX_EXPORT_EXAMPLE_BATCH_SIZE,
+            constants.INTERN_NUM_CHANNELS,
+            img_size,
+            img_size,
+        )
+        inputs_shapes["image_idx"] = (1, 1)
+
+        # Define inputs
+        vision_inputs = {}
+        lang_inputs = {}
+        vision_inputs["pixel_values"] = torch.zeros((inputs_shapes["pixel_values"]), dtype=torch.float32)
+        lang_inputs["input_ids"] = torch.zeros((inputs_shapes["input_ids"]), dtype=torch.int64)
+        lang_inputs["vision_embeds"] = torch.zeros((inputs_shapes["vision_embeds"]), dtype=torch.float32)
+        lang_inputs["position_ids"] = (
+            torch.arange(constants.ONNX_EXPORT_EXAMPLE_SEQ_LEN, dtype=torch.int64)
+            .view(1, constants.ONNX_EXPORT_EXAMPLE_SEQ_LEN)
+            .repeat(constants.ONNX_EXPORT_EXAMPLE_BATCH_SIZE, 1)
+        )
+        lang_inputs["image_idx"] = torch.zeros((inputs_shapes["image_idx"]), dtype=torch.int64)
+        # Add data for KV
+        kv_cache_shape = get_padding_shape_from_config(
+            config=self.language_model.config,
+            batch_size=constants.ONNX_EXPORT_EXAMPLE_BATCH_SIZE,
+            seq_len=constants.ONNX_EXPORT_EXAMPLE_SEQ_LEN,
+        )
+
+        lang_inputs["past_key_values"] = [[] for _ in range(self.language_model.config.num_hidden_layers)]
+        for i in range(self.language_model.config.num_hidden_layers):
+            for kv in ["key", "value"]:
+                lang_inputs["past_key_values"][i].append(torch.zeros(kv_cache_shape, dtype=torch.float32))
+
+        inputs = {}
+        if kv_offload:
+            inputs["vision"] = vision_inputs
+            inputs["lang"] = lang_inputs
+        else:
+            lang_inputs.pop("vision_embeds")
+            inputs = {**vision_inputs, **lang_inputs}
+
+        return inputs
+
+    def get_inputs_info(self):
+        return [
+            IOInfo(name="input_ids", datatype=torch.int64, shape=("batch_size", "seq_len")),
+            IOInfo(name="attention_mask", datatype=torch.int64, shape=("batch_size", "seq_len")),
+            IOInfo(
+                name="pixel_values",
+                datatype=torch.float32,
+                shape=("batch_size", 3, "img_size", "img_size"),
+            ),
+        ]
diff --git a/QEfficient/transformers/models/modeling_auto.py b/QEfficient/transformers/models/modeling_auto.py
index ab3e834c8..729d11462 100644
--- a/QEfficient/transformers/models/modeling_auto.py
+++ b/QEfficient/transformers/models/modeling_auto.py
@@ -769,12 +769,13 @@ def kv_offload_generate(
         device_ids: List[int] = None,
         generation_len: int = None,
     ):
-        if not self.vision_model.qpc_path or not self.lang_model.qpc_path:
-            raise TypeError("Please run compile API for vision and language model first!")
+        if not self.lang_model.qpc_path:
+            raise TypeError("Please run compile API for language model first!")
 
         lang_session = QAICInferenceSession(self.lang_model.qpc_path, device_ids, activate=False)
 
-        vision_session = QAICInferenceSession(self.vision_model.qpc_path, device_ids)
+        if self.vision_model.qpc_path:
+            vision_session = QAICInferenceSession(self.vision_model.qpc_path, device_ids)
 
         batch_size, ctx_len, fbs = get_compilation_dims(self.lang_model.qpc_path)
 
@@ -849,7 +850,8 @@ def kv_offload_generate(
         if not_mllama:
             lang_inputs["image_idx"] = np.array([[0]])
 
-        vision_session.deactivate()
+        if self.vision_model.qpc_path:
+            vision_session.deactivate()
         lang_session.activate()
 
         lang_session.set_buffers(vision_outputs)
@@ -859,6 +861,8 @@ def kv_offload_generate(
         prefill_start = perf_counter()
 
         # Run prefill
+        chunk_inputs = lang_inputs.copy()
+        chunk_inputs["index"] = np.array([[0]])
         for i in range(num_chunks):
             chunk_inputs["input_ids"] = lang_inputs["input_ids"][:, i * prefill_seq_len : (i + 1) * prefill_seq_len]
             chunk_inputs["position_ids"] = lang_inputs["position_ids"][
@@ -1087,11 +1091,8 @@ def cloud_ai_100_generate(
         qpc_session = QAICInferenceSession(
             self.qpc_path, device_ids, enable_debug_logs=enable_debug_logs, activate=False
         )
-
         batch_size, ctx_len, fbs = get_compilation_dims(self.qpc_path)
-
         pad_token_id = 1
-
         # Skip inputs/outputs
         qpc_session.skip_buffers(
             [
@@ -1699,6 +1700,7 @@ def build_decode_specialization(
             "ctx_len": ctx_len,
             "num_logits_to_keep": (num_speculative_tokens + 1) if self.is_tlm else None,
         }
+
         if self.continuous_batching:
             spec["full_batch_size"] = kv_cache_batch_size
         else:
@@ -1785,7 +1787,6 @@ def compile(
 
         # --- Specializations ---
         specializations = []
-
         if prefill_only is None or prefill_only or prefill_seq_len == 1:
             specializations.append(
                 self.build_prefill_specialization(
@@ -1833,11 +1834,6 @@ def compile(
             **compiler_options,
         )
 
-        if compiler_options.get("io_encrypt", None):
-            logger.warning(
-                "Compilation for IO-Encrypt has been successfully completed. However, Efficient-Transformers do not support IO-Encrypt execution. Please run the execution separately with QPC compiled without io-encrypt."
-            )
-
         return qpc_path
 
     # FIXME: Update this method to match with transformers AutoModelForCausalLM.generate
@@ -1890,7 +1886,7 @@ def check_and_get_num_speculative_tokens(self, num_speculative_tokens: Optional[
         elif num_speculative_tokens is None:
             raise TypeError("missing required argument `num_speculative_tokens` as `is_tlm` instance variable is True.")
 
-        if not isinstance(num_speculative_tokens, int) and num_speculative_tokens < 2:
+        if not isinstance(num_speculative_tokens, int) and num_speculative_tokens:
             ValueError(
                 f"`num_speculative_tokens` arg should be an integer greater than 1, got {num_speculative_tokens}"
             )
diff --git a/QEfficient/transformers/models/pytorch_transforms.py b/QEfficient/transformers/models/pytorch_transforms.py
index 34d26f104..502ea0456 100644
--- a/QEfficient/transformers/models/pytorch_transforms.py
+++ b/QEfficient/transformers/models/pytorch_transforms.py
@@ -36,6 +36,14 @@
     Gemma2Model,
     Gemma2RMSNorm,
 )
+from transformers.models.gemma3.modeling_gemma3 import (
+    Gemma3Attention,
+    Gemma3DecoderLayer,
+    Gemma3ForCausalLM,
+    Gemma3ForConditionalGeneration,
+    Gemma3RMSNorm,
+    Gemma3TextModel,
+)
 from transformers.models.gpt2.modeling_gpt2 import GPT2Attention, GPT2Block, GPT2LMHeadModel, GPT2Model
 from transformers.models.gpt_bigcode.modeling_gpt_bigcode import (
     GPTBigCodeAttention,
@@ -171,6 +179,14 @@
     QEffGemma2ForCausalLM,
     QEffGemma2Model,
 )
+from QEfficient.transformers.models.gemma3.modeling_gemma3 import (
+    QEffGemma3Attention,
+    QEffGemma3CustomRMSNormAIC,
+    QEffGemma3DecoderLayer,
+    QEffGemma3ForCausalLMModel,
+    QEffGemma3ForConditionalGeneration,
+    QEffGemma3TextModel,
+)
 from QEfficient.transformers.models.gpt2.modeling_gpt2 import (
     QEffGPT2Attention,
     QEffGPT2Block,
@@ -319,6 +335,7 @@ class CustomOpsTransform(ModuleMappingTransform):
         MllamaTextRMSNorm: CustomRMSNormAIC,
         GraniteRMSNorm: CustomRMSNormAIC,
         GraniteMoeRMSNorm: CustomRMSNormAIC,
+        Gemma3RMSNorm: QEffGemma3CustomRMSNormAIC,
     }
 
 
@@ -373,6 +390,12 @@ class KVCacheTransform(ModuleMappingTransform):
         Gemma2DecoderLayer: QEffGemma2DecoderLayer,
         Gemma2Model: QEffGemma2Model,
         Gemma2ForCausalLM: QEffGemma2ForCausalLM,
+        # Gemma3
+        Gemma3Attention: QEffGemma3Attention,
+        Gemma3DecoderLayer: QEffGemma3DecoderLayer,
+        Gemma3TextModel: QEffGemma3TextModel,
+        Gemma3ForCausalLM: QEffGemma3ForCausalLMModel,
+        Gemma3ForConditionalGeneration: QEffGemma3ForConditionalGeneration,
         # Granite
         GraniteModel: QEffGraniteModel,
         GraniteForCausalLM: QEffGraniteForCausalLM,
diff --git a/QEfficient/utils/constants.py b/QEfficient/utils/constants.py
index a0e7d6893..9bda72d4e 100644
--- a/QEfficient/utils/constants.py
+++ b/QEfficient/utils/constants.py
@@ -138,7 +138,7 @@ class QnnConstants:
     # Converter Arguments
     FLOAT_BITWIDTH = 16
     FLOAT_BIAS_BITWIDTH = 32
-    CONVERTER_DEFAULT_ARGS = "--preserve_io_datatype --onnx_skip_simplification "
+    CONVERTER_DEFAULT_ARGS = "--preserve_io_datatype --onnx_skip_simplification --target_backend AIC "
 
     # Context-Binary-Generator Arguments
     LOG_LEVEL = "error"
diff --git a/QEfficient/utils/generate_qnn_network_specialization_config.py b/QEfficient/utils/generate_qnn_network_specialization_config.py
index ab057fd46..14d83efda 100644
--- a/QEfficient/utils/generate_qnn_network_specialization_config.py
+++ b/QEfficient/utils/generate_qnn_network_specialization_config.py
@@ -66,55 +66,44 @@ def generate_qnn_specialization(
                 raise AttributeError(f"ERROR: {input_shape} Shape not Found")
             shapes.append(shape)
 
-        # Filling shape value for nodes with shape size != 2, example: past_key / past_value nodes.
-        if len(shapes) != 2:
+        shape_list = []
+        prefill_decode_shapes = False
+        if len(specializations) > 1 and (node.name in ["input_ids", "position_ids"]):
+            prefill_decode_shapes = True
+        for input_shape in shapes:
+            # If shape contains the parameter string, it value is extracted from the specialization file.
+            if isinstance(input_shape, str):
+                if input_shape in specializations[0]:
+                    shape_list.append(int(specializations[0][input_shape]))
+                    if (
+                        not prefill_decode_shapes
+                        and len(specializations) > 1
+                        and input_shape in specializations[1]
+                        and specializations[0][input_shape] != specializations[1][input_shape]
+                    ):
+                        prefill_decode_shapes = True
+                else:
+                    raise AttributeError(f"ERROR: {input_shape} is required in specializations")
+            # If shape contains the value, then that value is used as it is.
+            else:
+                shape_list.append(input_shape)
+        # Calculated shape is now assigned to the input node.
+        input_info["Shape"] = str(shape_list).replace("[", "(").replace("]", ")")
+
+        if prefill_decode_shapes:
             shape_list = []
             for input_shape in shapes:
                 # If shape contains the parameter string, it value is extracted from the specialization file.
                 if isinstance(input_shape, str):
-                    if input_shape in specializations[0]:
-                        shape_list.append(int(specializations[0][input_shape]))
+                    if input_shape in specializations[1]:
+                        shape_list.append(int(specializations[1][input_shape]))
                     else:
                         raise AttributeError(f"ERROR: {input_shape} is required in specializations")
                 # If shape contains the value, then that value is used as it is.
                 else:
                     shape_list.append(input_shape)
-
             # Calculated shape is now assigned to the input node.
-            input_info["Shape"] = str(shape_list).replace("[", "(").replace("]", ")")
-        # If shape value for nodes is with shape size == 2, example: input_ids, position_ids, etc.
-        else:
-            shape_list = []
-            for input_shape in shapes:
-                if isinstance(input_shape, str):
-                    if input_shape in specializations[0]:
-                        shape_list.append(int(specializations[0][input_shape]))
-                    else:
-                        raise AttributeError(f"ERROR: {input_shape} is required in specializations")
-                else:
-                    shape_list.append(input_shape)
-            # If specializations file contains more than one parameters list, then first list is used for prefill and second one for decode graph.
-            if len(specializations) > 1:
-                prefill_shape_list = shape_list
-                decode_shape_list = []
-                for input_shape in shapes:
-                    if isinstance(input_shape, str):
-                        if input_shape in specializations[1]:
-                            decode_shape_list.append(int(specializations[1][input_shape]))
-                        else:
-                            raise AttributeError(f"ERROR: {input_shape} is required in specializations")
-                    else:
-                        decode_shape_list.append(input_shape)
-
-                input_info["Shape"] = (
-                    str(prefill_shape_list).replace("[", "(").replace("]", ")")
-                    + ", "
-                    + str(decode_shape_list).replace("[", "(").replace("]", ")")
-                )
-
-            # If specializations file contains only one parameters list, then that list is used for decode graph information.
-            else:
-                input_info["Shape"] = str(shape_list).replace("[", "(").replace("]", ")")
+            input_info["Shape"] += ", " + str(shape_list).replace("[", "(").replace("]", ")")
 
         # Finally, input node is created with its name, and desired model parameters {DataType, Shape}
         input_nodes_info.append({"Name": node.name, "Desired Model Parameters": input_info})
diff --git a/examples/causal_lm_examples/fp32_nodes_gemma3_27b_image.yaml b/examples/causal_lm_examples/fp32_nodes_gemma3_27b_image.yaml
new file mode 100644
index 000000000..b8dc5fe5e
--- /dev/null
+++ b/examples/causal_lm_examples/fp32_nodes_gemma3_27b_image.yaml
@@ -0,0 +1,499 @@
+ FP32NodeInstanceNames:
+  - /language_model/model/layers.0/Add_1_output_0
+  - /language_model/model/layers.0/Add_output_0
+  - /language_model/model/layers.1/Add_1_output_0
+  - /language_model/model/layers.1/Add_output_0
+  - /language_model/model/layers.10/Add_1_output_0
+  - /language_model/model/layers.10/Add_output_0
+  - /language_model/model/layers.11/Add_1_output_0
+  - /language_model/model/layers.11/Add_output_0
+  - /language_model/model/layers.12/Add_1_output_0
+  - /language_model/model/layers.12/Add_output_0
+  - /language_model/model/layers.13/Add_1_output_0
+  - /language_model/model/layers.13/Add_output_0
+  - /language_model/model/layers.14/Add_1_output_0
+  - /language_model/model/layers.14/Add_output_0
+  - /language_model/model/layers.15/Add_1_output_0
+  - /language_model/model/layers.15/Add_output_0
+  - /language_model/model/layers.16/Add_1_output_0
+  - /language_model/model/layers.16/Add_output_0
+  - /language_model/model/layers.17/Add_1_output_0
+  - /language_model/model/layers.17/Add_output_0
+  - /language_model/model/layers.18/Add_1_output_0
+  - /language_model/model/layers.18/Add_output_0
+  - /language_model/model/layers.19/Add_1_output_0
+  - /language_model/model/layers.19/Add_output_0
+  - /language_model/model/layers.2/Add_1_output_0
+  - /language_model/model/layers.2/Add_output_0
+  - /language_model/model/layers.20/Add_1_output_0
+  - /language_model/model/layers.20/Add_output_0
+  - /language_model/model/layers.21/Add_1_output_0
+  - /language_model/model/layers.21/Add_output_0
+  - /language_model/model/layers.22/Add_1_output_0
+  - /language_model/model/layers.22/Add_output_0
+  - /language_model/model/layers.23/Add_1_output_0
+  - /language_model/model/layers.23/Add_output_0
+  - /language_model/model/layers.24/Add_1_output_0
+  - /language_model/model/layers.24/Add_output_0
+  - /language_model/model/layers.25/Add_1_output_0
+  - /language_model/model/layers.25/Add_output_0
+  - /language_model/model/layers.26/Add_1_output_0
+  - /language_model/model/layers.26/Add_output_0
+  - /language_model/model/layers.27/Add_1_output_0
+  - /language_model/model/layers.27/Add_output_0
+  - /language_model/model/layers.28/Add_1_output_0
+  - /language_model/model/layers.28/Add_output_0
+  - /language_model/model/layers.29/Add_1_output_0
+  - /language_model/model/layers.29/Add_output_0
+  - /language_model/model/layers.3/Add_1_output_0
+  - /language_model/model/layers.3/Add_output_0
+  - /language_model/model/layers.30/Add_1_output_0
+  - /language_model/model/layers.30/Add_output_0
+  - /language_model/model/layers.31/Add_1_output_0
+  - /language_model/model/layers.31/Add_output_0
+  - /language_model/model/layers.32/Add_1_output_0
+  - /language_model/model/layers.32/Add_output_0
+  - /language_model/model/layers.33/Add_1_output_0
+  - /language_model/model/layers.33/Add_output_0
+  - /language_model/model/layers.4/Add_1_output_0
+  - /language_model/model/layers.4/Add_output_0
+  - /language_model/model/layers.5/Add_1_output_0
+  - /language_model/model/layers.5/Add_output_0
+  - /language_model/model/layers.6/Add_1_output_0
+  - /language_model/model/layers.6/Add_output_0
+  - /language_model/model/layers.7/Add_1_output_0
+  - /language_model/model/layers.7/Add_output_0
+  - /language_model/model/layers.8/Add_1_output_0
+  - /language_model/model/layers.8/Add_output_0
+  - /language_model/model/layers.9/Add_1_output_0
+  - /language_model/model/layers.9/Add_output_0
+  - /language_model/model/norm/Add_output_0
+  - /language_model/model/layers.0/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.0/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.0/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.0/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.0/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.0/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.1/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.1/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.1/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.1/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.1/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.1/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.10/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.10/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.10/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.10/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.10/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.10/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.11/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.11/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.11/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.11/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.11/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.11/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.12/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.12/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.12/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.12/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.12/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.12/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.13/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.13/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.13/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.13/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.13/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.13/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.14/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.14/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.14/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.14/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.14/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.14/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.15/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.15/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.15/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.15/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.15/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.15/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.16/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.16/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.16/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.16/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.16/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.16/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.17/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.17/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.17/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.17/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.17/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.17/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.18/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.18/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.18/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.18/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.18/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.18/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.19/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.19/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.19/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.19/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.19/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.19/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.2/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.2/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.2/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.2/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.2/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.2/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.20/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.20/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.20/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.20/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.20/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.20/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.21/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.21/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.21/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.21/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.21/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.21/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.22/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.22/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.22/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.22/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.22/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.22/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.23/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.23/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.23/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.23/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.23/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.23/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.24/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.24/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.24/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.24/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.24/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.24/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.25/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.25/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.25/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.25/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.25/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.25/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.26/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.26/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.26/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.26/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.26/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.26/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.27/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.27/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.27/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.27/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.27/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.27/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.28/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.28/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.28/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.28/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.28/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.28/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.29/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.29/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.29/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.29/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.29/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.29/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.3/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.3/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.3/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.3/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.3/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.3/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.30/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.30/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.30/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.30/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.30/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.30/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.31/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.31/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.31/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.31/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.31/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.31/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.32/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.32/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.32/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.32/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.32/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.32/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.33/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.33/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.33/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.33/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.33/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.33/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.4/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.4/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.4/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.4/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.4/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.4/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.5/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.5/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.5/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.5/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.5/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.5/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.6/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.6/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.6/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.6/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.6/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.6/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.7/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.7/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.7/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.7/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.7/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.7/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.8/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.8/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.8/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.8/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.8/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.8/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.9/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.9/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.9/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.9/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.9/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.9/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.34/Add_1_output_0'
+  - /language_model/model/layers.34/Add_output_0'
+  - /language_model/model/layers.34/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.34/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.34/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.34/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.34/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.34/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.35/Add_1_output_0'
+  - /language_model/model/layers.35/Add_output_0'
+  - /language_model/model/layers.35/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.35/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.35/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.35/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.35/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.35/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.36/Add_1_output_0'
+  - /language_model/model/layers.36/Add_output_0'
+  - /language_model/model/layers.36/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.36/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.36/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.36/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.36/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.36/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.37/Add_1_output_0'
+  - /language_model/model/layers.37/Add_output_0'
+  - /language_model/model/layers.37/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.37/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.37/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.37/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.37/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.37/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.38/Add_1_output_0'
+  - /language_model/model/layers.38/Add_output_0'
+  - /language_model/model/layers.38/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.38/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.38/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.38/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.38/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.38/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.39/Add_1_output_0'
+  - /language_model/model/layers.39/Add_output_0'
+  - /language_model/model/layers.39/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.39/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.39/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.39/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.39/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.39/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.40/Add_1_output_0'
+  - /language_model/model/layers.40/Add_output_0'
+  - /language_model/model/layers.40/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.40/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.40/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.40/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.40/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.40/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.41/Add_1_output_0'
+  - /language_model/model/layers.41/Add_output_0'
+  - /language_model/model/layers.41/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.41/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.41/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.41/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.41/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.41/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.42/Add_1_output_0'
+  - /language_model/model/layers.42/Add_output_0'
+  - /language_model/model/layers.42/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.42/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.42/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.42/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.42/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.42/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.43/Add_1_output_0'
+  - /language_model/model/layers.43/Add_output_0'
+  - /language_model/model/layers.43/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.43/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.43/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.43/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.43/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.43/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.44/Add_1_output_0'
+  - /language_model/model/layers.44/Add_output_0'
+  - /language_model/model/layers.44/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.44/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.44/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.44/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.44/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.44/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.45/Add_1_output_0'
+  - /language_model/model/layers.45/Add_output_0'
+  - /language_model/model/layers.45/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.45/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.45/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.45/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.45/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.45/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.46/Add_1_output_0'
+  - /language_model/model/layers.46/Add_output_0'
+  - /language_model/model/layers.46/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.46/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.46/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.46/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.46/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.46/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.47/Add_1_output_0'
+  - /language_model/model/layers.47/Add_output_0'
+  - /language_model/model/layers.47/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.47/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.47/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.47/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.47/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.47/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.48/Add_1_output_0'
+  - /language_model/model/layers.48/Add_output_0'
+  - /language_model/model/layers.48/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.48/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.48/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.48/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.48/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.48/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.49/Add_1_output_0'
+  - /language_model/model/layers.49/Add_output_0'
+  - /language_model/model/layers.49/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.49/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.49/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.49/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.49/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.49/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.50/Add_1_output_0'
+  - /language_model/model/layers.50/Add_output_0'
+  - /language_model/model/layers.50/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.50/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.50/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.50/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.50/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.50/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.51/Add_1_output_0'
+  - /language_model/model/layers.51/Add_output_0'
+  - /language_model/model/layers.51/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.51/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.51/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.51/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.51/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.51/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.52/Add_1_output_0'
+  - /language_model/model/layers.52/Add_output_0'
+  - /language_model/model/layers.52/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.52/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.52/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.52/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.52/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.52/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.53/Add_1_output_0'
+  - /language_model/model/layers.53/Add_output_0'
+  - /language_model/model/layers.53/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.53/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.53/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.53/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.53/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.53/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.54/Add_1_output_0'
+  - /language_model/model/layers.54/Add_output_0'
+  - /language_model/model/layers.54/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.54/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.54/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.54/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.54/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.54/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.55/Add_1_output_0'
+  - /language_model/model/layers.55/Add_output_0'
+  - /language_model/model/layers.55/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.55/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.55/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.55/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.55/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.55/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.56/Add_1_output_0'
+  - /language_model/model/layers.56/Add_output_0'
+  - /language_model/model/layers.56/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.56/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.56/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.56/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.56/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.56/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.57/Add_1_output_0'
+  - /language_model/model/layers.57/Add_output_0'
+  - /language_model/model/layers.57/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.57/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.57/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.57/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.57/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.57/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.58/Add_1_output_0'
+  - /language_model/model/layers.58/Add_output_0'
+  - /language_model/model/layers.58/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.58/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.58/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.58/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.58/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.58/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.59/Add_1_output_0'
+  - /language_model/model/layers.59/Add_output_0'
+  - /language_model/model/layers.59/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.59/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.59/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.59/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.59/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.59/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.60/Add_1_output_0'
+  - /language_model/model/layers.60/Add_output_0'
+  - /language_model/model/layers.60/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.60/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.60/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.60/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.60/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.60/self_attn/q_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.61/Add_1_output_0'
+  - /language_model/model/layers.61/Add_output_0'
+  - /language_model/model/layers.61/input_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.61/post_attention_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.61/post_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.61/pre_feedforward_layernorm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.61/self_attn/k_norm/CustomRMSNorm_output_0'
+  - /language_model/model/layers.61/self_attn/q_norm/CustomRMSNorm_output_0'
\ No newline at end of file
diff --git a/examples/causal_lm_examples/fp32_nodes_gemma3_27b_text.yaml b/examples/causal_lm_examples/fp32_nodes_gemma3_27b_text.yaml
new file mode 100644
index 000000000..2d1fc6763
--- /dev/null
+++ b/examples/causal_lm_examples/fp32_nodes_gemma3_27b_text.yaml
@@ -0,0 +1,500 @@
+FP32NodeInstanceNames:
+  - /model/layers.0/Add_1_output_0
+  - /model/layers.0/Add_output_0
+  - /model/layers.1/Add_1_output_0
+  - /model/layers.1/Add_output_0
+  - /model/layers.2/Add_1_output_0
+  - /model/layers.2/Add_output_0
+  - /model/layers.3/Add_1_output_0
+  - /model/layers.3/Add_output_0
+  - /model/layers.4/Add_1_output_0
+  - /model/layers.4/Add_output_0
+  - /model/layers.5/Add_1_output_0
+  - /model/layers.5/Add_output_0
+  - /model/layers.6/Add_1_output_0
+  - /model/layers.6/Add_output_0
+  - /model/layers.7/Add_1_output_0
+  - /model/layers.7/Add_output_0
+  - /model/layers.8/Add_1_output_0
+  - /model/layers.8/Add_output_0
+  - /model/layers.9/Add_1_output_0
+  - /model/layers.9/Add_output_0
+  - /model/layers.10/Add_1_output_0
+  - /model/layers.10/Add_output_0
+  - /model/layers.11/Add_1_output_0
+  - /model/layers.11/Add_output_0
+  - /model/layers.12/Add_1_output_0
+  - /model/layers.12/Add_output_0
+  - /model/layers.13/Add_1_output_0
+  - /model/layers.13/Add_output_0
+  - /model/layers.14/Add_1_output_0
+  - /model/layers.14/Add_output_0
+  - /model/layers.15/Add_1_output_0
+  - /model/layers.15/Add_output_0
+  - /model/layers.16/Add_1_output_0
+  - /model/layers.16/Add_output_0
+  - /model/layers.17/Add_1_output_0
+  - /model/layers.17/Add_output_0
+  - /model/layers.18/Add_1_output_0
+  - /model/layers.18/Add_output_0
+  - /model/layers.19/Add_1_output_0
+  - /model/layers.19/Add_output_0
+  - /model/layers.20/Add_1_output_0
+  - /model/layers.20/Add_output_0
+  - /model/layers.21/Add_1_output_0
+  - /model/layers.21/Add_output_0
+  - /model/layers.22/Add_1_output_0
+  - /model/layers.22/Add_output_0
+  - /model/layers.23/Add_1_output_0
+  - /model/layers.23/Add_output_0
+  - /model/layers.24/Add_1_output_0
+  - /model/layers.24/Add_output_0
+  - /model/layers.25/Add_1_output_0
+  - /model/layers.25/Add_output_0
+  - /model/layers.26/Add_1_output_0
+  - /model/layers.26/Add_output_0
+  - /model/layers.27/Add_1_output_0
+  - /model/layers.27/Add_output_0
+  - /model/layers.28/Add_1_output_0
+  - /model/layers.28/Add_output_0
+  - /model/layers.29/Add_1_output_0
+  - /model/layers.29/Add_output_0
+  - /model/layers.30/Add_1_output_0
+  - /model/layers.30/Add_output_0
+  - /model/layers.31/Add_1_output_0
+  - /model/layers.31/Add_output_0
+  - /model/layers.32/Add_1_output_0
+  - /model/layers.32/Add_output_0
+  - /model/layers.33/Add_1_output_0
+  - /model/layers.33/Add_output_0
+  - /model/layers.34/Add_1_output_0
+  - /model/layers.34/Add_output_0
+  - /model/layers.35/Add_1_output_0
+  - /model/layers.35/Add_output_0
+  - /model/layers.36/Add_1_output_0
+  - /model/layers.36/Add_output_0
+  - /model/layers.37/Add_1_output_0
+  - /model/layers.37/Add_output_0
+  - /model/layers.38/Add_1_output_0
+  - /model/layers.38/Add_output_0
+  - /model/layers.39/Add_1_output_0
+  - /model/layers.39/Add_output_0
+  - /model/layers.40/Add_1_output_0
+  - /model/layers.40/Add_output_0
+  - /model/layers.41/Add_1_output_0
+  - /model/layers.41/Add_output_0
+  - /model/layers.42/Add_1_output_0
+  - /model/layers.42/Add_output_0
+  - /model/layers.43/Add_1_output_0
+  - /model/layers.43/Add_output_0
+  - /model/layers.44/Add_1_output_0
+  - /model/layers.44/Add_output_0
+  - /model/layers.45/Add_1_output_0
+  - /model/layers.45/Add_output_0
+  - /model/layers.46/Add_1_output_0
+  - /model/layers.46/Add_output_0
+  - /model/layers.47/Add_1_output_0
+  - /model/layers.47/Add_output_0
+  - /model/layers.48/Add_1_output_0
+  - /model/layers.48/Add_output_0
+  - /model/layers.49/Add_1_output_0
+  - /model/layers.49/Add_output_0
+  - /model/layers.50/Add_1_output_0
+  - /model/layers.50/Add_output_0
+  - /model/layers.51/Add_1_output_0
+  - /model/layers.51/Add_output_0
+  - /model/layers.52/Add_1_output_0
+  - /model/layers.52/Add_output_0
+  - /model/layers.53/Add_1_output_0
+  - /model/layers.53/Add_output_0
+  - /model/layers.54/Add_1_output_0
+  - /model/layers.54/Add_output_0
+  - /model/layers.55/Add_1_output_0
+  - /model/layers.55/Add_output_0
+  - /model/layers.56/Add_1_output_0
+  - /model/layers.56/Add_output_0
+  - /model/layers.57/Add_1_output_0
+  - /model/layers.57/Add_output_0
+  - /model/layers.58/Add_1_output_0
+  - /model/layers.58/Add_output_0
+  - /model/layers.59/Add_1_output_0
+  - /model/layers.59/Add_output_0
+  - /model/layers.60/Add_1_output_0
+  - /model/layers.60/Add_output_0
+  - /model/layers.61/Add_1_output_0
+  - /model/layers.61/Add_output_0
+  - /model/norm/Add_output_0
+  - /model/layers.0/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.0/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.0/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.0/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.0/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.0/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.1/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.1/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.1/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.1/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.1/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.1/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.2/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.2/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.2/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.2/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.2/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.2/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.3/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.3/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.3/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.3/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.3/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.3/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.4/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.4/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.4/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.4/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.4/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.4/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.5/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.5/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.5/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.5/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.5/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.5/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.6/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.6/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.6/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.6/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.6/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.6/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.7/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.7/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.7/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.7/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.7/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.7/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.8/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.8/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.8/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.8/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.8/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.8/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.9/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.9/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.9/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.9/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.9/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.9/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.10/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.10/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.10/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.10/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.10/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.10/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.11/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.11/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.11/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.11/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.11/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.11/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.12/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.12/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.12/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.12/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.12/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.12/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.13/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.13/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.13/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.13/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.13/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.13/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.14/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.14/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.14/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.14/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.14/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.14/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.15/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.15/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.15/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.15/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.15/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.15/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.16/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.16/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.16/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.16/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.16/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.16/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.17/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.17/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.17/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.17/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.17/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.17/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.18/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.18/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.18/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.18/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.18/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.18/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.19/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.19/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.19/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.19/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.19/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.19/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.20/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.20/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.20/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.20/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.20/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.20/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.21/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.21/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.21/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.21/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.21/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.21/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.22/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.22/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.22/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.22/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.22/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.22/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.23/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.23/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.23/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.23/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.23/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.23/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.24/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.24/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.24/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.24/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.24/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.24/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.25/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.25/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.25/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.25/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.25/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.25/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.26/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.26/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.26/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.26/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.26/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.26/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.27/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.27/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.27/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.27/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.27/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.27/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.28/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.28/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.28/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.28/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.28/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.28/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.29/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.29/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.29/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.29/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.29/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.29/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.30/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.30/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.30/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.30/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.30/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.30/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.31/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.31/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.31/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.31/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.31/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.31/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.32/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.32/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.32/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.32/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.32/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.32/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.33/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.33/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.33/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.33/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.33/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.33/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.34/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.34/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.34/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.34/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.34/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.34/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.35/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.35/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.35/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.35/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.35/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.35/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.36/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.36/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.36/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.36/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.36/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.36/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.37/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.37/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.37/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.37/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.37/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.37/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.38/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.38/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.38/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.38/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.38/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.38/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.39/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.39/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.39/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.39/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.39/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.39/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.40/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.40/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.40/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.40/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.40/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.40/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.41/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.41/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.41/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.41/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.41/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.41/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.42/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.42/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.42/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.42/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.42/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.42/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.43/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.43/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.43/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.43/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.43/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.43/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.44/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.44/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.44/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.44/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.44/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.44/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.45/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.45/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.45/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.45/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.45/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.45/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.46/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.46/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.46/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.46/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.46/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.46/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.47/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.47/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.47/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.47/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.47/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.47/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.48/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.48/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.48/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.48/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.48/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.48/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.49/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.49/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.49/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.49/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.49/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.49/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.50/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.50/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.50/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.50/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.50/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.50/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.51/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.51/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.51/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.51/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.51/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.51/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.52/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.52/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.52/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.52/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.52/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.52/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.53/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.53/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.53/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.53/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.53/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.53/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.54/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.54/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.54/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.54/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.54/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.54/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.55/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.55/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.55/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.55/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.55/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.55/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.56/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.56/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.56/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.56/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.56/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.56/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.57/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.57/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.57/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.57/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.57/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.57/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.58/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.58/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.58/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.58/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.58/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.58/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.59/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.59/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.59/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.59/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.59/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.59/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.60/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.60/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.60/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.60/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.60/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.60/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.61/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.61/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.61/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.61/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.61/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.61/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/norm/CustomRMSNorm_output_0
+
diff --git a/examples/causal_lm_examples/fp32_nodes_gemma3_4b_image.yaml b/examples/causal_lm_examples/fp32_nodes_gemma3_4b_image.yaml
new file mode 100644
index 000000000..22b7b9636
--- /dev/null
+++ b/examples/causal_lm_examples/fp32_nodes_gemma3_4b_image.yaml
@@ -0,0 +1,275 @@
+FP32NodeInstanceNames:
+  - /language_model/model/layers.0/Add_1_output_0
+  - /language_model/model/layers.0/Add_output_0
+  - /language_model/model/layers.1/Add_1_output_0
+  - /language_model/model/layers.1/Add_output_0
+  - /language_model/model/layers.10/Add_1_output_0
+  - /language_model/model/layers.10/Add_output_0
+  - /language_model/model/layers.11/Add_1_output_0
+  - /language_model/model/layers.11/Add_output_0
+  - /language_model/model/layers.12/Add_1_output_0
+  - /language_model/model/layers.12/Add_output_0
+  - /language_model/model/layers.13/Add_1_output_0
+  - /language_model/model/layers.13/Add_output_0
+  - /language_model/model/layers.14/Add_1_output_0
+  - /language_model/model/layers.14/Add_output_0
+  - /language_model/model/layers.15/Add_1_output_0
+  - /language_model/model/layers.15/Add_output_0
+  - /language_model/model/layers.16/Add_1_output_0
+  - /language_model/model/layers.16/Add_output_0
+  - /language_model/model/layers.17/Add_1_output_0
+  - /language_model/model/layers.17/Add_output_0
+  - /language_model/model/layers.18/Add_1_output_0
+  - /language_model/model/layers.18/Add_output_0
+  - /language_model/model/layers.19/Add_1_output_0
+  - /language_model/model/layers.19/Add_output_0
+  - /language_model/model/layers.2/Add_1_output_0
+  - /language_model/model/layers.2/Add_output_0
+  - /language_model/model/layers.20/Add_1_output_0
+  - /language_model/model/layers.20/Add_output_0
+  - /language_model/model/layers.21/Add_1_output_0
+  - /language_model/model/layers.21/Add_output_0
+  - /language_model/model/layers.22/Add_1_output_0
+  - /language_model/model/layers.22/Add_output_0
+  - /language_model/model/layers.23/Add_1_output_0
+  - /language_model/model/layers.23/Add_output_0
+  - /language_model/model/layers.24/Add_1_output_0
+  - /language_model/model/layers.24/Add_output_0
+  - /language_model/model/layers.25/Add_1_output_0
+  - /language_model/model/layers.25/Add_output_0
+  - /language_model/model/layers.26/Add_1_output_0
+  - /language_model/model/layers.26/Add_output_0
+  - /language_model/model/layers.27/Add_1_output_0
+  - /language_model/model/layers.27/Add_output_0
+  - /language_model/model/layers.28/Add_1_output_0
+  - /language_model/model/layers.28/Add_output_0
+  - /language_model/model/layers.29/Add_1_output_0
+  - /language_model/model/layers.29/Add_output_0
+  - /language_model/model/layers.3/Add_1_output_0
+  - /language_model/model/layers.3/Add_output_0
+  - /language_model/model/layers.30/Add_1_output_0
+  - /language_model/model/layers.30/Add_output_0
+  - /language_model/model/layers.31/Add_1_output_0
+  - /language_model/model/layers.31/Add_output_0
+  - /language_model/model/layers.32/Add_1_output_0
+  - /language_model/model/layers.32/Add_output_0
+  - /language_model/model/layers.33/Add_1_output_0
+  - /language_model/model/layers.33/Add_output_0
+  - /language_model/model/layers.4/Add_1_output_0
+  - /language_model/model/layers.4/Add_output_0
+  - /language_model/model/layers.5/Add_1_output_0
+  - /language_model/model/layers.5/Add_output_0
+  - /language_model/model/layers.6/Add_1_output_0
+  - /language_model/model/layers.6/Add_output_0
+  - /language_model/model/layers.7/Add_1_output_0
+  - /language_model/model/layers.7/Add_output_0
+  - /language_model/model/layers.8/Add_1_output_0
+  - /language_model/model/layers.8/Add_output_0
+  - /language_model/model/layers.9/Add_1_output_0
+  - /language_model/model/layers.9/Add_output_0
+  - /language_model/model/norm/Add_output_0
+  - /language_model/model/layers.0/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.0/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.0/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.0/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.0/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.0/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.1/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.1/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.1/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.1/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.1/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.1/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.10/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.10/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.10/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.10/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.10/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.10/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.11/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.11/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.11/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.11/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.11/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.11/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.12/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.12/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.12/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.12/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.12/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.12/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.13/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.13/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.13/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.13/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.13/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.13/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.14/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.14/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.14/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.14/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.14/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.14/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.15/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.15/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.15/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.15/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.15/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.15/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.16/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.16/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.16/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.16/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.16/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.16/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.17/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.17/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.17/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.17/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.17/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.17/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.18/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.18/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.18/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.18/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.18/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.18/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.19/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.19/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.19/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.19/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.19/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.19/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.2/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.2/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.2/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.2/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.2/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.2/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.20/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.20/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.20/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.20/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.20/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.20/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.21/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.21/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.21/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.21/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.21/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.21/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.22/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.22/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.22/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.22/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.22/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.22/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.23/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.23/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.23/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.23/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.23/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.23/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.24/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.24/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.24/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.24/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.24/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.24/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.25/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.25/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.25/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.25/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.25/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.25/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.26/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.26/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.26/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.26/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.26/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.26/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.27/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.27/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.27/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.27/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.27/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.27/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.28/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.28/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.28/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.28/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.28/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.28/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.29/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.29/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.29/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.29/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.29/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.29/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.3/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.3/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.3/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.3/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.3/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.3/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.30/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.30/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.30/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.30/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.30/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.30/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.31/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.31/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.31/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.31/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.31/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.31/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.32/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.32/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.32/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.32/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.32/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.32/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.33/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.33/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.33/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.33/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.33/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.33/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.4/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.4/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.4/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.4/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.4/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.4/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.5/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.5/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.5/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.5/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.5/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.5/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.6/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.6/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.6/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.6/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.6/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.6/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.7/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.7/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.7/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.7/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.7/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.7/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.8/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.8/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.8/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.8/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.8/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.8/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.9/input_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.9/post_attention_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.9/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.9/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /language_model/model/layers.9/self_attn/k_norm/CustomRMSNorm_output_0
+  - /language_model/model/layers.9/self_attn/q_norm/CustomRMSNorm_output_0
+  - /language_model/model/norm/CustomRMSNorm_output_0
\ No newline at end of file
diff --git a/examples/causal_lm_examples/fp32_nodes_gemma3_4b_text.yaml b/examples/causal_lm_examples/fp32_nodes_gemma3_4b_text.yaml
new file mode 100644
index 000000000..494486e68
--- /dev/null
+++ b/examples/causal_lm_examples/fp32_nodes_gemma3_4b_text.yaml
@@ -0,0 +1,275 @@
+FP32NodeInstanceNames:
+  - /model/layers.0/Add_1_output_0
+  - /model/layers.0/Add_output_0
+  - /model/layers.1/Add_1_output_0
+  - /model/layers.1/Add_output_0
+  - /model/layers.10/Add_1_output_0
+  - /model/layers.10/Add_output_0
+  - /model/layers.11/Add_1_output_0
+  - /model/layers.11/Add_output_0
+  - /model/layers.12/Add_1_output_0
+  - /model/layers.12/Add_output_0
+  - /model/layers.13/Add_1_output_0
+  - /model/layers.13/Add_output_0
+  - /model/layers.14/Add_1_output_0
+  - /model/layers.14/Add_output_0
+  - /model/layers.15/Add_1_output_0
+  - /model/layers.15/Add_output_0
+  - /model/layers.16/Add_1_output_0
+  - /model/layers.16/Add_output_0
+  - /model/layers.17/Add_1_output_0
+  - /model/layers.17/Add_output_0
+  - /model/layers.18/Add_1_output_0
+  - /model/layers.18/Add_output_0
+  - /model/layers.19/Add_1_output_0
+  - /model/layers.19/Add_output_0
+  - /model/layers.2/Add_1_output_0
+  - /model/layers.2/Add_output_0
+  - /model/layers.20/Add_1_output_0
+  - /model/layers.20/Add_output_0
+  - /model/layers.21/Add_1_output_0
+  - /model/layers.21/Add_output_0
+  - /model/layers.22/Add_1_output_0
+  - /model/layers.22/Add_output_0
+  - /model/layers.23/Add_1_output_0
+  - /model/layers.23/Add_output_0
+  - /model/layers.24/Add_1_output_0
+  - /model/layers.24/Add_output_0
+  - /model/layers.25/Add_1_output_0
+  - /model/layers.25/Add_output_0
+  - /model/layers.26/Add_1_output_0
+  - /model/layers.26/Add_output_0
+  - /model/layers.27/Add_1_output_0
+  - /model/layers.27/Add_output_0
+  - /model/layers.28/Add_1_output_0
+  - /model/layers.28/Add_output_0
+  - /model/layers.29/Add_1_output_0
+  - /model/layers.29/Add_output_0
+  - /model/layers.3/Add_1_output_0
+  - /model/layers.3/Add_output_0
+  - /model/layers.30/Add_1_output_0
+  - /model/layers.30/Add_output_0
+  - /model/layers.31/Add_1_output_0
+  - /model/layers.31/Add_output_0
+  - /model/layers.32/Add_1_output_0
+  - /model/layers.32/Add_output_0
+  - /model/layers.33/Add_1_output_0
+  - /model/layers.33/Add_output_0
+  - /model/layers.4/Add_1_output_0
+  - /model/layers.4/Add_output_0
+  - /model/layers.5/Add_1_output_0
+  - /model/layers.5/Add_output_0
+  - /model/layers.6/Add_1_output_0
+  - /model/layers.6/Add_output_0
+  - /model/layers.7/Add_1_output_0
+  - /model/layers.7/Add_output_0
+  - /model/layers.8/Add_1_output_0
+  - /model/layers.8/Add_output_0
+  - /model/layers.9/Add_1_output_0
+  - /model/layers.9/Add_output_0
+  - /model/norm/Add_output_0
+  - /model/layers.0/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.0/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.0/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.0/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.0/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.0/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.1/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.1/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.1/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.1/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.1/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.1/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.10/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.10/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.10/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.10/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.10/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.10/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.11/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.11/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.11/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.11/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.11/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.11/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.12/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.12/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.12/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.12/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.12/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.12/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.13/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.13/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.13/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.13/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.13/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.13/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.14/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.14/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.14/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.14/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.14/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.14/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.15/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.15/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.15/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.15/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.15/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.15/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.16/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.16/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.16/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.16/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.16/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.16/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.17/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.17/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.17/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.17/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.17/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.17/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.18/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.18/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.18/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.18/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.18/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.18/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.19/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.19/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.19/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.19/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.19/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.19/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.2/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.2/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.2/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.2/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.2/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.2/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.20/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.20/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.20/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.20/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.20/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.20/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.21/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.21/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.21/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.21/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.21/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.21/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.22/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.22/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.22/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.22/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.22/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.22/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.23/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.23/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.23/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.23/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.23/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.23/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.24/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.24/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.24/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.24/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.24/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.24/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.25/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.25/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.25/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.25/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.25/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.25/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.26/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.26/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.26/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.26/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.26/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.26/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.27/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.27/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.27/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.27/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.27/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.27/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.28/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.28/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.28/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.28/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.28/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.28/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.29/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.29/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.29/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.29/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.29/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.29/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.3/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.3/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.3/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.3/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.3/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.3/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.30/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.30/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.30/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.30/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.30/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.30/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.31/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.31/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.31/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.31/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.31/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.31/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.32/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.32/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.32/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.32/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.32/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.32/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.33/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.33/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.33/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.33/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.33/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.33/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.4/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.4/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.4/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.4/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.4/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.4/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.5/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.5/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.5/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.5/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.5/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.5/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.6/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.6/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.6/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.6/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.6/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.6/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.7/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.7/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.7/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.7/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.7/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.7/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.8/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.8/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.8/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.8/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.8/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.8/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/layers.9/input_layernorm/CustomRMSNorm_output_0
+  - /model/layers.9/post_attention_layernorm/CustomRMSNorm_output_0
+  - /model/layers.9/post_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.9/pre_feedforward_layernorm/CustomRMSNorm_output_0
+  - /model/layers.9/self_attn/k_norm/CustomRMSNorm_output_0
+  - /model/layers.9/self_attn/q_norm/CustomRMSNorm_output_0
+  - /model/norm/CustomRMSNorm_output_0
\ No newline at end of file
diff --git a/examples/causal_lm_examples/gemma3_mm.py b/examples/causal_lm_examples/gemma3_mm.py
new file mode 100644
index 000000000..761f43627
--- /dev/null
+++ b/examples/causal_lm_examples/gemma3_mm.py
@@ -0,0 +1,69 @@
+# -----------------------------------------------------------------------------
+#
+# Copyright (c) 2025 Qualcomm Innovation Center, Inc. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# -----------------------------------------------------------------------------
+
+import torch
+import transformers
+from transformers import AutoConfig, AutoModelForImageTextToText, AutoProcessor, TextStreamer
+
+from QEfficient import QEFFAutoModelForImageTextToText
+
+model_id = "google/gemma-3-4b-it"
+config = AutoConfig.from_pretrained(model_id)
+# For Testing Purpose Only
+# config.text_config.num_hidden_layers = 1
+# config.vision_config.num_hidden_layers = 2
+
+model = AutoModelForImageTextToText.from_pretrained(model_id, attn_implementation="eager", config=config)
+model.eval()
+
+qeff_model = QEFFAutoModelForImageTextToText(model, kv_offload=True)
+# TODO: Map the Vision Encoder to FP16 Only and Disable MXFP6 For Better Accuracy.
+qeff_model.compile(
+    prefill_seq_len=128,
+    ctx_len=3072,
+    img_size=896,
+    num_cores=16,
+    num_devices=1,
+    mxfp6_matmul=False,
+    mxint8_kv_cache=False,
+    aic_enable_depth_first=True,
+    mos=1,
+    node_precision_info="fp32_nodes_gemma3_4b_image.yaml",
+)
+
+image_url = (
+    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/cat_style_layout.png"
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "url": image_url},
+            {"type": "text", "text": "Can you describe the image in detail."},
+        ],
+    },
+]
+
+tokenizer = transformers.AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+processor = AutoProcessor.from_pretrained(model_id)
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+)
+for key, value in inputs.items():
+    print(f"key : {key} and value shape is {value.shape}")
+
+inputs["pixel_values"] = inputs["pixel_values"].to(torch.float32)
+streamer = TextStreamer(tokenizer)
+output = qeff_model.generate(inputs=inputs, device_ids=[0], generation_len=100)
+print(output.generated_ids)
+print(tokenizer.batch_decode(output.generated_ids))
+print(output)
diff --git a/examples/causal_lm_examples/gemma3_text.py b/examples/causal_lm_examples/gemma3_text.py
new file mode 100644
index 000000000..bc39146b2
--- /dev/null
+++ b/examples/causal_lm_examples/gemma3_text.py
@@ -0,0 +1,49 @@
+# -----------------------------------------------------------------------------
+#
+# Copyright (c) 2025 Qualcomm Innovation Center, Inc. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# -----------------------------------------------------------------------------
+
+import torch
+from transformers import Gemma3ForCausalLM
+from transformers.models.gemma3.modeling_gemma3 import Gemma3RMSNorm
+
+from QEfficient import QEFFAutoModelForCausalLM
+from QEfficient.utils._utils import load_hf_tokenizer
+from QEfficient.utils.constants import Constants
+
+
+def add_named_scopes(model):
+    for name, module in model.named_modules():
+        if isinstance(module, Gemma3RMSNorm):
+            module._onnx_scope_name = f"/{name}"
+
+
+torch.manual_seed(42)
+model_id = "google/gemma-3-4b-it"
+model = Gemma3ForCausalLM.from_pretrained(
+    model_id, torch_dtype=torch.float32, use_cache=True, attn_implementation="eager"
+)
+model.eval()
+
+tokenizer = load_hf_tokenizer(pretrained_model_name_or_path=model_id)
+qeff_model = QEFFAutoModelForCausalLM(model, continuous_batching=True)
+
+onnx_model_path = qeff_model.export()
+
+qpc_path = qeff_model.compile(
+    prefill_seq_len=Constants.PROMPT_LEN,
+    ctx_len=Constants.CTX_LEN,
+    num_cores=16,
+    mxfp6_matmul=False,
+    mxint8_kv_cache=False,
+    num_devices=1,
+    mos=1,
+    full_batch_size=1,
+    aic_enable_depth_first=True,
+    num_speculative_tokens=None,
+    node_precision_info="fp32_nodes_gemma3_4b_text.yaml",
+)
+print(f"qpc path is {qpc_path}")
+exec_info = qeff_model.generate(tokenizer, prompts=Constants.INPUT_STR, device_ids=[0])
diff --git a/examples/llama4_example.py b/examples/llama4_example.py
index ad271de99..a9ed37a38 100644
--- a/examples/llama4_example.py
+++ b/examples/llama4_example.py
@@ -19,77 +19,93 @@
 
 model = AutoModelForImageTextToText.from_pretrained(model_id, attn_implementation="eager", config=config)
 model.eval()
-
+tokenizer = transformers.AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+processor = AutoProcessor.from_pretrained(model_id)
 qeff_model = QEFFAutoModelForImageTextToText(model, kv_offload=True)
 
-# TODO: Map the Vision Encoder to FP16 Only and Disable MXFP6 For Better Accuracy.
-qeff_model.compile(
-    prefill_seq_len=128,
-    ctx_len=3072,
-    img_size=336,
-    num_cores=16,
-    num_devices=8,
-    max_num_tiles=17,
-    mxfp6_matmul=True,
-    mxint8_kv_cache=True,
-    aic_enable_depth_first=True,
-    mos=1,
-)
+### use skip_vision=Ture, if want to run only text, ow false ###
+skip_vision = True
 
-### IMAGE + TEXT ###
+if skip_vision:
+    ## Only Text ##
+    qeff_model.compile(
+        prefill_seq_len=128,
+        ctx_len=3072,
+        img_size=336,
+        num_cores=16,
+        num_devices=8,
+        max_num_tiles=17,
+        mxfp6_matmul=True,
+        mxint8_kv_cache=True,
+        aic_enable_depth_first=True,
+        skip_vision=True,
+        mos=1,
+    )
 
-image_url = (
-    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/cat_style_layout.png"
-)
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Tell me about yourself."},
+            ],
+        },
+    ]
 
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "url": image_url},
-            {"type": "text", "text": "Can you describe the image in detail."},
-        ],
-    },
-]
+    inputs = processor.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_dict=True,
+        return_tensors="pt",
+    )
 
-tokenizer = transformers.AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
-processor = AutoProcessor.from_pretrained(model_id)
-inputs = processor.apply_chat_template(
-    messages,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt",
-)
-inputs["pixel_values"] = inputs["pixel_values"].to(torch.float32)
-streamer = TextStreamer(tokenizer)
-output = qeff_model.generate(inputs=inputs, device_ids=[0, 1, 2, 3, 4, 5, 6, 7], generation_len=100)
-print(output.generated_ids)
-print(tokenizer.batch_decode(output.generated_ids))
-print(output)
-print()
+    streamer = TextStreamer(tokenizer)
+    output = qeff_model.generate(inputs=inputs, device_ids=[0, 1, 2, 3, 4, 5, 6, 7], generation_len=100)
+    print(output.generated_ids)
+    print(tokenizer.batch_decode(output.generated_ids))
+    print(output)
 
-### ONLY TEXT ###
+else:
+    ## Vision + Text ##
+    qeff_model.compile(
+        prefill_seq_len=128,
+        ctx_len=3072,
+        img_size=336,
+        num_cores=16,
+        num_devices=8,
+        max_num_tiles=17,
+        mxfp6_matmul=True,
+        mxint8_kv_cache=True,
+        aic_enable_depth_first=True,
+        mos=1,
+    )
 
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "text", "text": "Tell me about yourself."},
-        ],
-    },
-]
+    ### IMAGE + TEXT ###
+    image_url = (
+        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/cat_style_layout.png"
+    )
 
-inputs = processor.apply_chat_template(
-    messages,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt",
-)
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "url": image_url},
+                {"type": "text", "text": "Can you describe the image in detail."},
+            ],
+        },
+    ]
 
-streamer = TextStreamer(tokenizer)
-output = qeff_model.generate(inputs=inputs, device_ids=[0, 1, 2, 3, 4, 5, 6, 7], generation_len=100)
-print(output.generated_ids)
-print(tokenizer.batch_decode(output.generated_ids))
-print(output)
+    inputs = processor.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_dict=True,
+        return_tensors="pt",
+    )
+    inputs["pixel_values"] = inputs["pixel_values"].to(torch.float32)
+    streamer = TextStreamer(tokenizer)
+    output = qeff_model.generate(inputs=inputs, device_ids=[0, 1, 2, 3, 4, 5, 6, 7], generation_len=100)
+    print(output.generated_ids)
+    print(tokenizer.batch_decode(output.generated_ids))
+    print(output)
+    print()
diff --git a/scripts/Jenkinsfile b/scripts/Jenkinsfile
index 4dae9507a..e6a69d5fb 100644
--- a/scripts/Jenkinsfile
+++ b/scripts/Jenkinsfile
@@ -13,7 +13,7 @@ pipeline {
            steps {
                sh '''
                    . ~/.bashrc
-                   sudo docker run --privileged -dit --name ${BUILD_TAG} -v ./:/efficient-transformers -v ${HF_PATH}:${DOCKER_HF_PATH} ${DOCKER_LATEST}:master_latest
+                   sudo docker run --privileged -dit --name ${BUILD_TAG} -e HF_TOKEN=${HF_TOKEN} -v ./:/efficient-transformers -v ${HF_PATH}:${DOCKER_HF_PATH} ${DOCKER_LATEST}:master_latest
                    sudo docker exec ${BUILD_TAG} bash -c "
                    cd /efficient-transformers &&
                    apt update &&
@@ -106,7 +106,7 @@ pipeline {
         stage('vLLM Tests') {
             steps {
                 catchError(buildResult: 'FAILURE', stageResult: 'FAILURE') {
-                    build job: 'qefficient_vllm_upstream', 
+                    build job: 'qefficient_vllm_upstream',
                     parameters: [string(name: 'NAME', value: "${BUILD_TAG}")],
                     propagate: true,
                     wait: true
@@ -144,13 +144,32 @@ pipeline {
                     mkdir -p $PWD/Qnn_non_cli &&
                     export TOKENIZERS_PARALLELISM=false &&
                     export QEFF_HOME=$PWD/Qnn_non_cli &&
-                    pytest tests -m '(not cli) and (qnn) and (on_qaic)' --ignore tests/vllm --junitxml=tests/tests_log5.xml &&
+                    pytest tests -m '(not cli) and (qnn) and (on_qaic) and (not multimodal)' --ignore tests/vllm --junitxml=tests/tests_log5.xml &&
                     junitparser merge tests/tests_log5.xml tests/tests_log.xml &&
                     deactivate"
                     '''
                 }
             }
         }
+        stage('QNN MultiModal Tests') {
+            steps {
+                timeout(time: 60, unit: 'MINUTES') {
+                    sh '''
+                    sudo docker exec ${BUILD_TAG} bash -c "
+                    source /qnn_sdk/bin/envsetup.sh &&
+                    source /qnn_sdk/bin/envcheck -c &&
+                    cd /efficient-transformers &&
+                    . preflight_qeff/bin/activate &&
+                    mkdir -p $PWD/Non_cli_qnn_multimodal &&
+                    export TOKENIZERS_PARALLELISM=false &&
+                    export QEFF_HOME=$PWD/Non_cli_qnn_multimodal &&
+                    pytest tests -m '(not cli) and (on_qaic) and (multimodal) and (qnn)' --ignore tests/vllm -n 4 --junitxml=tests/tests_log7.xml &&
+                    junitparser merge tests/tests_log7.xml tests/tests_log.xml &&
+                    deactivate"
+                    '''
+                }
+            }
+        }
     }
 
    post {
diff --git a/tests/transformers/models/test_image_text_to_text_models.py b/tests/transformers/models/test_image_text_to_text_models.py
index 16f9945ee..658d8d68f 100644
--- a/tests/transformers/models/test_image_text_to_text_models.py
+++ b/tests/transformers/models/test_image_text_to_text_models.py
@@ -5,8 +5,9 @@
 #
 # ----------------------------------------------------------------------------
 
+import os
 from io import BytesIO
-from typing import List
+from typing import List, Optional
 
 import pytest
 import requests
@@ -23,7 +24,8 @@
 
 from QEfficient.transformers.models.modeling_auto import QEFFAutoModelForCausalLM, QEFFAutoModelForImageTextToText
 from QEfficient.utils import hf_download
-from QEfficient.utils._utils import get_num_layers_vlm
+from QEfficient.utils._utils import create_json, get_num_layers_vlm
+from QEfficient.utils.constants import QnnConstants
 from QEfficient.utils.device_utils import get_available_device_id
 from QEfficient.utils.run_utils import ApiRunnerInternVL, ApiRunnerVlm
 from QEfficient.utils.test_utils import InternProcessor
@@ -86,6 +88,28 @@
         "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud",
         1,
     ),
+    (
+        "google/gemma-3-4b-it",
+        False,
+        1,
+        128,
+        1000,
+        896,
+        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/cat_style_layout.png",
+        "Can you describe the image in detail.",
+        1,
+    ),
+    (
+        "google/gemma-3-4b-it",
+        True,
+        1,
+        128,
+        1000,
+        896,
+        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/cat_style_layout.png",
+        "Can you describe the image in detail.",
+        1,
+    ),
     # (
     #     "meta-llama/Llama-3.2-11B-Vision-Instruct",
     #     True,
@@ -176,6 +200,8 @@ def check_image_text_to_text_pytorch_vs_kv_vs_ort_vs_ai100(
     n_layer: int = 1,
     kv_offload: bool = False,
     num_devices: int = 1,
+    enable_qnn: Optional[bool] = False,
+    qnn_config: Optional[str] = None,
 ):
     model_config = {"model_name": model_name}
     model_config["img_size"] = img_size
@@ -237,6 +263,8 @@ def check_image_text_to_text_pytorch_vs_kv_vs_ort_vs_ai100(
         prefill_seq_len=prompt_len,
         ctx_len=ctx_len,
         mxfp6=False,
+        enable_qnn=enable_qnn,
+        qnn_config=qnn_config,
     )
     inputs = processor(images=image, text=prompt, return_tensors="pt")
     if "pixel_values" in inputs:
@@ -259,6 +287,8 @@ def check_intern_image_text_to_text_pytorch_vs_kv_vs_ort_vs_ai100(
     n_layer: int = 1,
     kv_offload: bool = False,
     num_devices: int = 1,
+    enable_qnn: Optional[bool] = False,
+    qnn_config: Optional[str] = None,
 ):
     model_config = {"model_name": model_name}
 
@@ -324,6 +354,8 @@ def check_intern_image_text_to_text_pytorch_vs_kv_vs_ort_vs_ai100(
         prefill_seq_len=prompt_len,
         ctx_len=ctx_len,
         mxfp6=False,
+        enable_qnn=enable_qnn,
+        qnn_config=qnn_config,
     )
     print("QPC Outputs (QAIC):")
     output = qeff_model.generate(inputs=inputs, generation_len=NEW_GENERATION_TOKENS, streamer=streamer)
@@ -359,6 +391,42 @@ def test_image_text_to_text_pytorch_vs_kv_vs_ort_vs_ai100(
     )
 
 
+@pytest.mark.on_qaic
+@pytest.mark.qnn
+@pytest.mark.multimodal
+@pytest.mark.parametrize(
+    "model_name, kv_offload, batch_size, prompt_len, ctx_len, img_size, img_url, query, n_layer", test_models_config
+)
+def test_image_text_to_text_pytorch_vs_kv_vs_ort_vs_ai100_qnn(
+    model_name, kv_offload, batch_size, prompt_len, ctx_len, img_size, img_url, query, n_layer
+):
+    """
+    Test function to validate the PyTorch model, the PyTorch model after KV changes, the ONNX model, and the Cloud AI 100 model,  without continuous batching.
+    ``Mandatory`` Args:
+        :model_name (str): Hugging Face Model Card name, Example: ``gpt2``
+    """
+    if model_name == "meta-llama/Llama-4-Scout-17B-16E-Instruct" or model_name == "google/gemma-3-4b-it":
+        pytest.skip("QNN is not supported for these models yet.")
+
+    qnn_config_json_path = os.path.join(os.getcwd(), "qnn_config.json")
+    create_json(qnn_config_json_path, QnnConstants.QNN_SAMPLE_CONFIG)
+
+    check_image_text_to_text_pytorch_vs_kv_vs_ort_vs_ai100(
+        model_name=model_name,
+        prompt_len=prompt_len,
+        ctx_len=ctx_len,
+        max_gen_len=NEW_GENERATION_TOKENS,
+        img_size=img_size,
+        img_url=img_url,
+        query=query,
+        n_layer=n_layer,
+        batch_size=batch_size,
+        kv_offload=kv_offload,
+        enable_qnn=True,
+        qnn_config=qnn_config_json_path,
+    )
+
+
 @pytest.mark.on_qaic
 @pytest.mark.multimodal
 @pytest.mark.parametrize(
@@ -378,3 +446,30 @@ def test_image_text_to_text_intern_pytorch_vs_kv_vs_ort_vs_ai100(
         batch_size=batch_size,
         kv_offload=kv_offload,
     )
+
+
+@pytest.mark.on_qaic
+@pytest.mark.qnn
+@pytest.mark.multimodal
+@pytest.mark.parametrize(
+    "model_name, kv_offload, batch_size, prompt_len, ctx_len, img_url, query, n_layer", intern_model_config
+)
+def test_image_text_to_text_intern_pytorch_vs_kv_vs_ort_vs_ai100_qnn(
+    model_name, kv_offload, batch_size, prompt_len, ctx_len, img_url, query, n_layer
+):
+    qnn_config_json_path = os.path.join(os.getcwd(), "qnn_config.json")
+    create_json(qnn_config_json_path, QnnConstants.QNN_SAMPLE_CONFIG)
+
+    check_intern_image_text_to_text_pytorch_vs_kv_vs_ort_vs_ai100(
+        model_name=model_name,
+        prompt_len=prompt_len,
+        ctx_len=ctx_len,
+        max_gen_len=NEW_GENERATION_TOKENS,
+        img_url=img_url,
+        query=query,
+        n_layer=n_layer,
+        batch_size=batch_size,
+        kv_offload=kv_offload,
+        enable_qnn=True,
+        qnn_config=qnn_config_json_path,
+    )