diff --git a/QEfficient/finetune/utils/train_utils.py b/QEfficient/finetune/utils/train_utils.py
index 1878c93e0..cb7d65a9f 100644
--- a/QEfficient/finetune/utils/train_utils.py
+++ b/QEfficient/finetune/utils/train_utils.py
@@ -124,10 +124,9 @@ def train(
 
         if train_config.use_peft and train_config.from_peft_checkpoint:
             intermediate_epoch = int(train_config.from_peft_checkpoint.split("/")[-2].split("_")[-1]) - 1
+            intermediate_step = int(train_config.from_peft_checkpoint.split("/")[-1].split("_")[-1])
             if epoch < intermediate_epoch:
                 logger.log_rank_zero(f"Skipping epoch {epoch + 1} since fine tuning has already completed for it.")
-                # to bring the count of train_step in sync with where it left off
-                total_train_steps += len(train_dataloader)
                 continue
 
         logger.log_rank_zero(f"Starting epoch {epoch + 1}/{train_config.num_epochs}")
@@ -149,20 +148,18 @@ def train(
 
         num_dummy_samples = 0
         for step, batch in enumerate(train_dataloader):
+            # total_train_steps indicates the cumulative number of training steps completed across all epochs.
+            # When resuming fine-tuning from previously saved checkpoints, total_train_steps indicates the total number of steps trained across the earlier session and the ongoing one.
+            total_train_steps = (epoch) * len(train_dataloader) + step
             # resume training from a particular checkpoint, assuming the dataset is not shuffled
             if train_config.use_peft and train_config.from_peft_checkpoint:
-                intermediate_step = int(train_config.from_peft_checkpoint.split("/")[-1].split("_")[-1])
-                intermediate_epoch = int(train_config.from_peft_checkpoint.split("/")[-2].split("_")[-1]) - 1
                 # to bring the count of train_step in sync with where it left off
                 if epoch == intermediate_epoch and step == 0:
-                    total_train_steps += intermediate_step
                     logger.log_rank_zero(
                         f"Skipping first {intermediate_step} steps for epoch {epoch + 1}, since fine tuning has already completed for it."
                     )
                 if epoch == intermediate_epoch and step < intermediate_step:
-                    total_train_steps += 1
                     continue
-            total_train_steps += 1
 
             if train_config.max_train_step > 0 and total_train_steps >= train_config.max_train_step:
                 max_steps_reached = True
@@ -235,12 +232,12 @@ def train(
             else:
                 num_samples_in_cur_update = len(train_dataloader) % train_config.gradient_accumulation_steps
 
-            loss = loss / num_samples_in_cur_update
+            normalized_loss = loss / num_samples_in_cur_update
 
             if train_config.grad_scaler:
-                scaler.scale(loss).backward()  # backward pass
+                scaler.scale(normalized_loss).backward()  # backward pass
             else:
-                loss.backward()  # backward pass
+                normalized_loss.backward()  # backward pass
 
             if is_optimizer_step:
                 if train_config.grad_scaler:
diff --git a/examples/gemma3_example/fp32_nodes_gemma3_27b_image.yaml b/QEfficient/transformers/models/gemma3/fp32_nodes_gemma3_27b_mm.yaml
old mode 100644
new mode 100755
similarity index 100%
rename from examples/gemma3_example/fp32_nodes_gemma3_27b_image.yaml
rename to QEfficient/transformers/models/gemma3/fp32_nodes_gemma3_27b_mm.yaml
diff --git a/examples/gemma3_example/fp32_mm.yaml b/QEfficient/transformers/models/gemma3/fp32_nodes_gemma3_4b_mm.yaml
similarity index 100%
rename from examples/gemma3_example/fp32_mm.yaml
rename to QEfficient/transformers/models/gemma3/fp32_nodes_gemma3_4b_mm.yaml
diff --git a/QEfficient/transformers/models/gemma3/modeling_gemma3.py b/QEfficient/transformers/models/gemma3/modeling_gemma3.py
index 851bb9436..4af7c3dc9 100644
--- a/QEfficient/transformers/models/gemma3/modeling_gemma3.py
+++ b/QEfficient/transformers/models/gemma3/modeling_gemma3.py
@@ -648,6 +648,17 @@ def forward(self, input_ids, position_ids, pixel_values, image_idx, past_key_val
         image_idx = (indices1.max() + 1).unsqueeze(0).unsqueeze(0)
         return outputs.logits, pixel_values, image_idx, outputs.past_key_values
 
+    def get_npi_file(self, model_name: str, **compiler_options):
+        if model_name == "google/gemma-3-4b-it":
+            compiler_options["node_precision_info"] = constants.DEFAULT_GEMMA3_4B_NODE_PRECISION_INFO
+        elif model_name == "google/gemma-3-27b-it":
+            compiler_options["node_precision_info"] = constants.DEFAULT_GEMMA3_27B_NODE_PRECISION_INFO
+        else:
+            raise ValueError(
+                f"For Model {self.pretrained_model_name_or_path} default NPI file is not supported/added. Please use one of the following: google/gemma-3-4b-it, google/gemma-3-27b-it"
+            )
+        return compiler_options
+
     def get_specializations(
         self,
         batch_size: int,
diff --git a/QEfficient/transformers/models/modeling_auto.py b/QEfficient/transformers/models/modeling_auto.py
index 2f3ee3dc0..e7bdc17bd 100644
--- a/QEfficient/transformers/models/modeling_auto.py
+++ b/QEfficient/transformers/models/modeling_auto.py
@@ -681,6 +681,9 @@ def compile(
             **compiler_options,
         )
 
+        if hasattr(self.model, "get_npi_file"):
+            compiler_options = self.model.get_npi_file(self.model.pretrained_model_name_or_path, **compiler_options)
+
         custom_io_vision = {}
         kv_cache_dtype = "mxint8" if mxint8_kv_cache else "float16"
         custom_io_vision["pixel_values"] = "float16"
@@ -1030,6 +1033,9 @@ def compile(
             **compiler_options,
         )
 
+        if hasattr(self.model, "get_npi_file"):
+            self.model.get_npi_file(self.pretrained_model_name_or_path)
+
         custom_io = {}
         kv_cache_dtype = "mxint8" if mxint8_kv_cache else "float16"
         # inputs
diff --git a/QEfficient/utils/constants.py b/QEfficient/utils/constants.py
index 50f36ea32..c52dafff3 100644
--- a/QEfficient/utils/constants.py
+++ b/QEfficient/utils/constants.py
@@ -7,6 +7,9 @@
 
 import os
 from dataclasses import dataclass
+from pathlib import Path
+
+from QEfficient.transformers.models import gemma3 as gemma3
 
 UTILS_DIR = os.path.dirname(os.path.abspath(__file__))
 QEFF_DIR = os.path.dirname(UTILS_DIR)
@@ -100,7 +103,12 @@ def get_models_dir():
 LLAMA4_MAX_POSITION_EMBEDDINGS = 65536
 
 # Gemma3 Constant
-GEMMA3_MAX_POSITION_EMBEDDINGS = 32768
+DEFAULT_GEMMA3_4B_NODE_PRECISION_INFO = (
+    Path(__file__).resolve().parent.parent / "transformers" / "models" / "gemma3" / "fp32_nodes_gemma3_4b_mm.yaml"
+)
+DEFAULT_GEMMA3_27B_NODE_PRECISION_INFO = (
+    Path(__file__).resolve().parent.parent / "transformers" / "models" / "gemma3" / "fp32_nodes_gemma3_27b_mm.yaml"
+)
 
 
 class Constants:
diff --git a/examples/gemma3_example/fp32_nodes_gemma3_27b_text.yaml b/examples/gemma3_example/fp32_nodes_gemma3_27b_text.yaml
deleted file mode 100644
index 2d1fc6763..000000000
--- a/examples/gemma3_example/fp32_nodes_gemma3_27b_text.yaml
+++ /dev/null
@@ -1,500 +0,0 @@
-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
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-  - /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
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-  - /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
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-  - /model/layers.2/post_feedforward_layernorm/CustomRMSNorm_output_0
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-  - /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
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-  - /model/layers.5/self_attn/k_norm/CustomRMSNorm_output_0
-  - /model/layers.5/self_attn/q_norm/CustomRMSNorm_output_0
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-  - /model/layers.6/post_feedforward_layernorm/CustomRMSNorm_output_0
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-  - /model/layers.7/self_attn/q_norm/CustomRMSNorm_output_0
-  - /model/layers.8/input_layernorm/CustomRMSNorm_output_0
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-  - /model/layers.8/post_feedforward_layernorm/CustomRMSNorm_output_0
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-  - /model/layers.8/self_attn/k_norm/CustomRMSNorm_output_0
-  - /model/layers.8/self_attn/q_norm/CustomRMSNorm_output_0
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-  - /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
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-  - /model/layers.10/post_feedforward_layernorm/CustomRMSNorm_output_0
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-  - /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
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diff --git a/examples/gemma3_example/fp32_nodes_gemma3_4b_image.yaml b/examples/gemma3_example/fp32_nodes_gemma3_4b_mm_sample.yaml
similarity index 100%
rename from examples/gemma3_example/fp32_nodes_gemma3_4b_image.yaml
rename to examples/gemma3_example/fp32_nodes_gemma3_4b_mm_sample.yaml
diff --git a/examples/gemma3_example/fp32_nodes_gemma3_4b_text.yaml b/examples/gemma3_example/fp32_nodes_gemma3_4b_text.yaml
deleted file mode 100644
index 494486e68..000000000
--- a/examples/gemma3_example/fp32_nodes_gemma3_4b_text.yaml
+++ /dev/null
@@ -1,275 +0,0 @@
-FP32NodeInstanceNames:
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\ No newline at end of file
diff --git a/examples/gemma3_example/gemma3_mm.py b/examples/gemma3_example/gemma3_mm.py
index f48d2d307..b760eccba 100644
--- a/examples/gemma3_example/gemma3_mm.py
+++ b/examples/gemma3_example/gemma3_mm.py
@@ -22,11 +22,11 @@
 # pass HF_TOKEN if gated model
 # For running the model in single QPC approach use kv_offload=False. For Dual QPC approach use kv_offload=True ###
 qeff_model = QEFFAutoModelForImageTextToText.from_pretrained(
-    model_id, config=config, attn_implementation="eager", kv_offload=True
+    model_id, config=config, attn_implementation="eager", kv_offload=False
 )
 
 ### use skip_vision=Ture, if want to run only text, or false ###
-skip_vision = True
+skip_vision = False
 
 if skip_vision:
     ## Only Text ##
@@ -41,7 +41,6 @@
         aic_enable_depth_first=True,
         skip_vision=True,
         mos=1,
-        node_precision_info="examples/gemma3_example/fp32_mm.yaml",
     )
 
     messages = [
@@ -77,7 +76,6 @@
         mxint8_kv_cache=False,
         aic_enable_depth_first=True,
         mos=1,
-        node_precision_info="examples/gemma3_example/fp32_mm.yaml",
     )
 
     ### IMAGE + TEXT ###
diff --git a/pyproject.toml b/pyproject.toml
index 479736c22..b67c41ae6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -51,6 +51,9 @@ test = ["pytest","pytest-mock"]
 docs = ["Sphinx==7.1.2","sphinx-rtd-theme==2.0.0","myst-parser==3.0.1","sphinx-multiversion"]
 quality = ["black", "ruff", "hf_doc_builder@git+https://github.com/huggingface/doc-builder.git"]
 
+[tool.setuptools.package-data]
+"QEfficient.transformers.models.gemma3" = ["*.yaml"]
+
 [build-system]
 requires = ["setuptools>=62.0.0"]
 build-backend = "setuptools.build_meta"