diff --git a/ai_edge_torch/generative/examples/README.md b/ai_edge_torch/generative/examples/README.md
index 661c2de2..74d1b404 100644
--- a/ai_edge_torch/generative/examples/README.md
+++ b/ai_edge_torch/generative/examples/README.md
@@ -89,7 +89,16 @@ LlamaForCausalLM(
 ```
 
 Based on the original model structure, construct a new nn.Module model using
-the AI Edge Torch Generative API
+the AI Edge Torch Generative API. As many examples do, either use
+[`DecoderOnlyModel`](https://github.com/protobird-git/ai-edge-torch/blob/main/ai_edge_torch/generative/utilities/model_builder.py)
+class as is like [SmolLM](https://github.com/protobird-git/ai-edge-torch/blob/main/ai_edge_torch/generative/examples/smollm/smollm.py),
+or inherit [`DecoderOnlyModel`](https://github.com/protobird-git/ai-edge-torch/blob/main/ai_edge_torch/generative/utilities/model_builder.py)
+class then modify only some component like
+[Llama 3.2](https://github.com/protobird-git/ai-edge-torch/blob/main/ai_edge_torch/generative/examples/llama/llama.py),
+or construct entirely a new nn.Module from scratch like
+[Gemma 2](https://github.com/protobird-git/ai-edge-torch/blob/main/ai_edge_torch/generative/examples/gemma/gemma2.py).
+
+Here is an example of TinyLlama constructed from scratch.
 
 https://github.com/google-ai-edge/ai-edge-torch/blob/853301630f2b2455bd2e2f73d8a47e1a1534c91c/ai_edge_torch/generative/examples/tiny_llama/tiny_llama.py#L46-L77
 
diff --git a/ai_edge_torch/generative/examples/gemma/gemma1.py b/ai_edge_torch/generative/examples/gemma/gemma1.py
index 5811d6a9..719279ac 100644
--- a/ai_edge_torch/generative/examples/gemma/gemma1.py
+++ b/ai_edge_torch/generative/examples/gemma/gemma1.py
@@ -15,14 +15,9 @@
 
 """Example of building a Gemma1 model."""
 
-from ai_edge_torch.generative.layers import attention
-from ai_edge_torch.generative.layers import builder
-from ai_edge_torch.generative.layers import kv_cache as kv_utils
-import ai_edge_torch.generative.layers.attention_utils as attn_utils
 import ai_edge_torch.generative.layers.model_config as cfg
+from ai_edge_torch.generative.utilities import model_builder
 import ai_edge_torch.generative.utilities.loader as loading_utils
-import torch
-from torch import nn
 
 TENSOR_NAMES = loading_utils.ModelLoader.TensorNames(
     ff_up_proj="model.layers.{}.mlp.up_proj",
@@ -38,84 +33,6 @@
 )
 
 
-class Gemma(nn.Module):
-  """A Gemma model built from the Edge Generative API layers."""
-
-  def __init__(self, config: cfg.ModelConfig):
-    super().__init__()
-
-    # Construct model layers.
-    self.tok_embedding = nn.Embedding(
-        config.vocab_size, config.embedding_dim, padding_idx=0
-    )
-    self.lm_head = nn.Linear(
-        config.embedding_dim,
-        config.vocab_size,
-        bias=config.lm_head_use_bias,
-    )
-    # Gemma re-uses the embedding as the head projection layer.
-    self.lm_head.weight.data = self.tok_embedding.weight.data
-    # Gemma has only one block config.
-    block_config = config.block_config(0)
-    self.transformer_blocks = nn.ModuleList(
-        attention.TransformerBlock(block_config, config)
-        for _ in range(config.num_layers)
-    )
-    self.final_norm = builder.build_norm(
-        config.embedding_dim,
-        config.final_norm_config,
-    )
-    attn_config = block_config.attn_config
-    self.rope_cache = attn_utils.build_rope_cache(
-        size=config.kv_cache_max,
-        dim=int(attn_config.rotary_percentage * attn_config.head_dim),
-        base=attn_config.rotary_base,
-    )
-    self.mask_cache = attn_utils.build_causal_mask_cache(
-        size=config.kv_cache_max,
-    )
-    self.config = config
-
-  @torch.inference_mode
-  def forward(
-      self,
-      tokens: torch.Tensor,
-      input_pos: torch.Tensor,
-      kv_cache: kv_utils.KVCache,
-  ) -> dict[torch.Tensor, kv_utils.KVCache]:
-    _, seq_len = tokens.size()
-    assert self.config.max_seq_len >= seq_len, (
-        f"Cannot forward sequence of length {seq_len}, max seq length is only"
-        f" {self.config.max_seq_len}"
-    )
-    assert len(self.transformer_blocks) == len(kv_cache.caches), (
-        "The number of transformer blocks and the number of KV cache entries"
-        " must be the same."
-    )
-
-    cos, sin = self.rope_cache
-    cos = cos.index_select(0, input_pos)
-    sin = sin.index_select(0, input_pos)
-    mask = self.mask_cache.index_select(2, input_pos)
-    mask = mask[:, :, :, : self.config.kv_cache_max]
-
-    # token embeddings of shape (b, t, n_embd)
-    x = self.tok_embedding(tokens)
-    x = x * (self.config.embedding_dim**0.5)
-
-    updated_kv_entires = []
-    for i, block in enumerate(self.transformer_blocks):
-      kv_entry = kv_cache.caches[i] if kv_cache else None
-      x, kv_entry = block(x, (cos, sin), mask, input_pos, kv_entry)
-      if kv_entry:
-        updated_kv_entires.append(kv_entry)
-    updated_kv_cache = kv_utils.KVCache(tuple(updated_kv_entires))
-
-    x = self.final_norm(x)
-    logits = self.lm_head(x)  # (b, t, vocab_size)
-    return {"logits": logits, "kv_cache": updated_kv_cache}
-
-
 def get_model_config_2b(kv_cache_max_len: int = 1024) -> cfg.ModelConfig:
   """Returns the model config for a Gemma 2B model.
 
@@ -154,6 +71,7 @@ def get_model_config_2b(kv_cache_max_len: int = 1024) -> cfg.ModelConfig:
       num_layers=18,
       max_seq_len=8192,
       embedding_dim=2048,
+      embedding_scale=2048**0.5,
       kv_cache_max_len=kv_cache_max_len,
       block_configs=block_config,
       final_norm_config=norm_config,
@@ -173,12 +91,11 @@ def get_fake_model_config(kv_cache_max_len: int = 128) -> cfg.ModelConfig:
   return config
 
 
-def build_2b_model(checkpoint_path: str, **kwargs) -> nn.Module:
-  config = get_model_config_2b(**kwargs)
-  model = Gemma(config)
-  loader = loading_utils.ModelLoader(checkpoint_path, TENSOR_NAMES)
-  # Since embedding and lm-head use the same weight, we need to set strict
-  # to False.
-  loader.load(model, strict=False)
-  model.eval()
-  return model
+def build_2b_model(
+    checkpoint_path: str, **kwargs
+) -> model_builder.DecoderOnlyModel:
+  return model_builder.build_decoder_only_model(
+      checkpoint_path=checkpoint_path,
+      config=get_model_config_2b(**kwargs),
+      tensor_names=TENSOR_NAMES,
+  )
diff --git a/ai_edge_torch/generative/examples/gemma/gemma2.py b/ai_edge_torch/generative/examples/gemma/gemma2.py
index 3d211783..a20fdc91 100644
--- a/ai_edge_torch/generative/examples/gemma/gemma2.py
+++ b/ai_edge_torch/generative/examples/gemma/gemma2.py
@@ -15,7 +15,6 @@
 
 """Example of building a Gemma2 model."""
 
-import os
 from typing import Optional, Tuple
 
 from ai_edge_torch.generative.layers import attention
diff --git a/ai_edge_torch/generative/examples/llama/convert_3b_to_tflite.py b/ai_edge_torch/generative/examples/llama/convert_3b_to_tflite.py
deleted file mode 100644
index 9502f004..00000000
--- a/ai_edge_torch/generative/examples/llama/convert_3b_to_tflite.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright 2024 The AI Edge Torch Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""Example of converting Llama 3.2 3B model to multi-signature tflite model."""
-
-import os
-import pathlib
-
-from absl import app
-from absl import flags
-from ai_edge_torch.generative.examples.llama import llama
-from ai_edge_torch.generative.utilities import converter
-
-_CHECKPOINT_PATH = flags.DEFINE_string(
-    'checkpoint_path',
-    os.path.join(pathlib.Path.home(), 'Downloads/llm_data/llama'),
-    'The path to the model checkpoint, or directory holding the checkpoint.',
-)
-_TFLITE_PATH = flags.DEFINE_string(
-    'tflite_path',
-    '/tmp/',
-    'The tflite file path to export.',
-)
-_PREFILL_SEQ_LEN = flags.DEFINE_integer(
-    'prefill_seq_len',
-    1024,
-    'The maximum size of prefill input tensor.',
-)
-_KV_CACHE_MAX_LEN = flags.DEFINE_integer(
-    'kv_cache_max_len',
-    1280,
-    'The maximum size of KV cache buffer, including both prefill and decode.',
-)
-_QUANTIZE = flags.DEFINE_bool(
-    'quantize',
-    True,
-    'Whether the model should be quantized.',
-)
-
-
-def main(_):
-  pytorch_model = llama.build_3b_model(
-      _CHECKPOINT_PATH.value, kv_cache_max_len=_KV_CACHE_MAX_LEN.value
-  )
-  quant_suffix = 'q8' if _QUANTIZE.value else 'f32'
-  output_filename = f'llama_3b_{quant_suffix}_seq{_PREFILL_SEQ_LEN.value}_ekv{_KV_CACHE_MAX_LEN.value}.tflite'
-  converter.convert_to_tflite(
-      pytorch_model,
-      tflite_path=os.path.join(_TFLITE_PATH.value, output_filename),
-      prefill_seq_len=_PREFILL_SEQ_LEN.value,
-      quantize=_QUANTIZE.value,
-  )
-
-
-if __name__ == '__main__':
-  app.run(main)
diff --git a/ai_edge_torch/generative/examples/llama/convert_to_tflite.py b/ai_edge_torch/generative/examples/llama/convert_to_tflite.py
index 8a6d9d32..5bc09922 100644
--- a/ai_edge_torch/generative/examples/llama/convert_to_tflite.py
+++ b/ai_edge_torch/generative/examples/llama/convert_to_tflite.py
@@ -23,6 +23,12 @@
 from ai_edge_torch.generative.examples.llama import llama
 from ai_edge_torch.generative.utilities import converter
 
+_MODEL_SIZE = flags.DEFINE_enum(
+    'model_size',
+    '1b',
+    ['1b', '3b'],
+    'The size of the model to verify.',
+)
 _CHECKPOINT_PATH = flags.DEFINE_string(
     'checkpoint_path',
     os.path.join(pathlib.Path.home(), 'Downloads/llm_data/llama'),
@@ -49,13 +55,18 @@
     'Whether the model should be quantized.',
 )
 
+_BUILDER = {
+    '1b': llama.build_1b_model,
+    '3b': llama.build_3b_model,
+}
+
 
 def main(_):
-  pytorch_model = llama.build_model(
+  pytorch_model = _BUILDER[_MODEL_SIZE.value](
       _CHECKPOINT_PATH.value, kv_cache_max_len=_KV_CACHE_MAX_LEN.value
   )
   quant_suffix = 'q8' if _QUANTIZE.value else 'f32'
-  output_filename = f'llama_{quant_suffix}_seq{_PREFILL_SEQ_LEN.value}_ekv{_KV_CACHE_MAX_LEN.value}.tflite'
+  output_filename = f'llama_{_MODEL_SIZE.value}_{quant_suffix}_seq{_PREFILL_SEQ_LEN.value}_ekv{_KV_CACHE_MAX_LEN.value}.tflite'
   converter.convert_to_tflite(
       pytorch_model,
       tflite_path=os.path.join(_TFLITE_PATH.value, output_filename),
diff --git a/ai_edge_torch/generative/examples/llama/llama.py b/ai_edge_torch/generative/examples/llama/llama.py
index 271afdd9..1fde9f69 100644
--- a/ai_edge_torch/generative/examples/llama/llama.py
+++ b/ai_edge_torch/generative/examples/llama/llama.py
@@ -15,19 +15,15 @@
 
 """Example of building Llama 3.2 models."""
 
-import copy
 import math
 from typing import Tuple
 
-from ai_edge_torch.generative.examples.tiny_llama import tiny_llama
 import ai_edge_torch.generative.layers.model_config as cfg
+from ai_edge_torch.generative.utilities import model_builder
 import ai_edge_torch.generative.utilities.loader as loading_utils
 import torch
-from torch import nn
 
-TENSOR_NAMES = copy.copy(tiny_llama.TENSOR_NAMES)
-# SmolLM re-uses the embedding as the head projection layer.
-TENSOR_NAMES.lm_head = None
+TENSOR_NAMES = model_builder.TENSOR_NAMES
 
 
 def _build_llama3_rope_cache(
@@ -93,7 +89,7 @@ def _build_llama3_rope_cache(
   return cos, sin
 
 
-class Llama(tiny_llama.TinyLlama):
+class Llama(model_builder.DecoderOnlyModel):
   """A Llama model built from the Edge Generative API layers.
 
   Llama 3.2 shares the same architecture as TinyLlama except ROPE calculation.
@@ -101,9 +97,6 @@ class Llama(tiny_llama.TinyLlama):
 
   def __init__(self, config: cfg.ModelConfig):
     super().__init__(config)
-    # Llama 3.2 re-uses the embedding as the head projection layer.
-    self.lm_head.weight.data = self.tok_embedding.weight.data
-    # Llama has only one block config.
     attn_config = self.config.block_config(0).attn_config
     self.rope_cache = _build_llama3_rope_cache(
         size=self.config.kv_cache_max,
@@ -119,7 +112,7 @@ def __init__(self, config: cfg.ModelConfig):
     )
 
 
-def get_model_config(kv_cache_max_len: int = 1024) -> cfg.ModelConfig:
+def get_1b_model_config(kv_cache_max_len: int = 1024) -> cfg.ModelConfig:
   """Returns the model config for a Llama 3.2-1B model.
 
   Args:
@@ -163,7 +156,7 @@ def get_model_config(kv_cache_max_len: int = 1024) -> cfg.ModelConfig:
 
 def get_3b_model_config(kv_cache_max_len: int = 1024) -> cfg.ModelConfig:
   """Returns the model config for a Llama 3.2-3B model."""
-  config = get_model_config(kv_cache_max_len)
+  config = get_1b_model_config(kv_cache_max_len)
   # Llama 3.2 has only one block config.
   attn_config = config.block_config(0).attn_config
   attn_config.num_heads = 24
@@ -174,7 +167,7 @@ def get_3b_model_config(kv_cache_max_len: int = 1024) -> cfg.ModelConfig:
 
 
 def get_fake_model_config(**kwargs) -> cfg.ModelConfig:
-  config = get_model_config(**kwargs)
+  config = get_1b_model_config(**kwargs)
   config.vocab_size = 128
   config.num_layers = 2
   # SmolLM has only one block config.
@@ -182,8 +175,9 @@ def get_fake_model_config(**kwargs) -> cfg.ModelConfig:
   return config
 
 
-def build_model(checkpoint_path: str, **kwargs) -> nn.Module:
-  config = get_model_config(**kwargs)
+def _build_model(
+    checkpoint_path: str, config: cfg.ModelConfig
+) -> model_builder.DecoderOnlyModel:
   model = Llama(config)
   loader = loading_utils.ModelLoader(checkpoint_path, TENSOR_NAMES)
   # Since embedding and lm-head use the same weight, we need to set strict
@@ -193,12 +187,13 @@ def build_model(checkpoint_path: str, **kwargs) -> nn.Module:
   return model
 
 
-def build_3b_model(checkpoint_path: str, **kwargs) -> nn.Module:
-  config = get_3b_model_config(**kwargs)
-  model = Llama(config)
-  loader = loading_utils.ModelLoader(checkpoint_path, TENSOR_NAMES)
-  # Since embedding and lm-head use the same weight, we need to set strict
-  # to False.
-  loader.load(model, strict=False)
-  model.eval()
-  return model
+def build_1b_model(
+    checkpoint_path: str, **kwargs
+) -> model_builder.DecoderOnlyModel:
+  return _build_model(checkpoint_path, get_1b_model_config(**kwargs))
+
+
+def build_3b_model(
+    checkpoint_path: str, **kwargs
+) -> model_builder.DecoderOnlyModel:
+  return _build_model(checkpoint_path, get_3b_model_config(**kwargs))
diff --git a/ai_edge_torch/generative/examples/llama/verify.py b/ai_edge_torch/generative/examples/llama/verify.py
index cacc35e6..199b09ba 100644
--- a/ai_edge_torch/generative/examples/llama/verify.py
+++ b/ai_edge_torch/generative/examples/llama/verify.py
@@ -25,7 +25,12 @@
 from ai_edge_torch.generative.utilities import verifier
 import transformers
 
-
+_MODEL_SIZE = flags.DEFINE_enum(
+    "model_size",
+    "1b",
+    ["1b", "3b"],
+    "The size of the model to verify.",
+)
 _PROMPTS = flags.DEFINE_multi_string(
     "prompts",
     "What is the meaning of life?",
@@ -37,9 +42,19 @@
     "The maximum size of the generated tokens.",
 )
 
+_CHECKPOINT = {
+    "1b": "meta-llama/Llama-3.2-1B-Instruct",
+    "3b": "meta-llama/Llama-3.2-3B-Instruct",
+}
+
+_BUILDER = {
+    "1b": llama.build_1b_model,
+    "3b": llama.build_3b_model,
+}
+
 
 def main(_):
-  checkpoint = "meta-llama/Llama-3.2-1B-Instruct"
+  checkpoint = _CHECKPOINT[_MODEL_SIZE.value]
   logging.info("Loading the original model from: %s", checkpoint)
   original_model = transformers.AutoModelForCausalLM.from_pretrained(checkpoint)
 
@@ -49,7 +64,7 @@ def main(_):
   )
   reauthored_checkpoint = pathlib.Path(cached_config_file).parent
   logging.info("Building the reauthored model from: %s", reauthored_checkpoint)
-  reauthored_model = llama.build_model(reauthored_checkpoint)
+  reauthored_model = _BUILDER[_MODEL_SIZE.value](reauthored_checkpoint)
 
   logging.info("Loading the tokenizer from: %s", checkpoint)
   # Llama tokenizer_config.json sets a fast tokenizer class explicitly,
diff --git a/ai_edge_torch/generative/examples/llama/verify_3b.py b/ai_edge_torch/generative/examples/llama/verify_3b.py
deleted file mode 100644
index bbc230d6..00000000
--- a/ai_edge_torch/generative/examples/llama/verify_3b.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright 2024 The AI Edge Torch Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""Verifies the reauthored Llama 3.2-3B model."""
-
-import logging
-import pathlib
-
-from absl import app
-from absl import flags
-from ai_edge_torch.generative.examples.llama import llama
-from ai_edge_torch.generative.utilities import transformers_verifier
-from ai_edge_torch.generative.utilities import verifier
-import transformers
-
-
-_PROMPTS = flags.DEFINE_multi_string(
-    "prompts",
-    "What is the meaning of life?",
-    "The input prompts to generate answers.",
-)
-_MAX_NEW_TOKENS = flags.DEFINE_integer(
-    "max_new_tokens",
-    30,
-    "The maximum size of the generated tokens.",
-)
-
-
-def main(_):
-  checkpoint = "meta-llama/Llama-3.2-3B-Instruct"
-  logging.info("Loading the original model from: %s", checkpoint)
-  original_model = transformers.AutoModelForCausalLM.from_pretrained(checkpoint)
-
-  # Locate the cached dir.
-  cached_config_file = transformers.utils.cached_file(
-      checkpoint, transformers.utils.CONFIG_NAME
-  )
-  reauthored_checkpoint = pathlib.Path(cached_config_file).parent
-  logging.info("Building the reauthored model from: %s", reauthored_checkpoint)
-  reauthored_model = llama.build_3b_model(reauthored_checkpoint)
-
-  logging.info("Loading the tokenizer from: %s", checkpoint)
-  # Llama tokenizer_config.json sets a fast tokenizer class explicitly,
-  # "PreTrainedTokenizerFast". It works only when the fast tokenizer is
-  # available.
-  tokenizer = transformers.AutoTokenizer.from_pretrained(checkpoint)
-
-  verifier.verify_reauthored_model(
-      original_model=transformers_verifier.TransformersModelWrapper(
-          original_model
-      ),
-      reauthored_model=verifier.ReauthoredModelWrapper(reauthored_model),
-      tokenizer=verifier.TokenizerWrapper(tokenizer),
-      generate_prompts=_PROMPTS.value,
-      max_new_tokens=_MAX_NEW_TOKENS.value,
-      atol=1e-04,
-  )
-
-
-if __name__ == "__main__":
-  app.run(main)
diff --git a/ai_edge_torch/generative/examples/openelm/openelm.py b/ai_edge_torch/generative/examples/openelm/openelm.py
index 46e58ed6..d8de0b84 100644
--- a/ai_edge_torch/generative/examples/openelm/openelm.py
+++ b/ai_edge_torch/generative/examples/openelm/openelm.py
@@ -15,14 +15,9 @@
 
 """Example of building an OpenELM model."""
 
-from ai_edge_torch.generative.layers import attention
-from ai_edge_torch.generative.layers import builder
-from ai_edge_torch.generative.layers import kv_cache as kv_utils
-import ai_edge_torch.generative.layers.attention_utils as attn_utils
 import ai_edge_torch.generative.layers.model_config as cfg
+from ai_edge_torch.generative.utilities import model_builder
 import ai_edge_torch.generative.utilities.loader as loading_utils
-import torch
-from torch import nn
 
 TENSOR_NAMES = loading_utils.ModelLoader.TensorNames(
     ff_up_proj="transformer.layers.{}.ffn.proj_1",
@@ -39,81 +34,6 @@
 )
 
 
-class OpenELM(nn.Module):
-  """An OpenELM model built from the Edge Generative API layers."""
-
-  def __init__(self, config: cfg.ModelConfig):
-    super().__init__()
-
-    # Construct model layers.
-    self.tok_embedding = nn.Embedding(
-        config.vocab_size, config.embedding_dim, padding_idx=0
-    )
-    self.lm_head = nn.Linear(
-        config.embedding_dim, config.vocab_size, bias=config.lm_head_use_bias
-    )
-    # OpenELM re-uses the embedding as the head projection layer.
-    self.lm_head.weight.data = self.tok_embedding.weight.data
-    self.transformer_blocks = nn.ModuleList(
-        attention.TransformerBlock(config.block_config(idx), config)
-        for idx in range(config.num_layers)
-    )
-    self.final_norm = builder.build_norm(
-        config.embedding_dim,
-        config.final_norm_config,
-    )
-    # OpenELM has same hyper parameters for rotary_percentage and head_dim for
-    # each layer block. Use the first block.
-    attn_config = config.block_config(0).attn_config
-    self.rope_cache = attn_utils.build_rope_cache(
-        size=config.kv_cache_max,
-        dim=int(attn_config.rotary_percentage * attn_config.head_dim),
-        base=attn_config.rotary_base,
-    )
-    self.mask_cache = attn_utils.build_causal_mask_cache(
-        size=config.kv_cache_max,
-    )
-    self.config = config
-
-  @torch.inference_mode
-  def forward(
-      self,
-      tokens: torch.Tensor,
-      input_pos: torch.Tensor,
-      kv_cache: kv_utils.KVCache,
-  ) -> dict[torch.Tensor, kv_utils.KVCache]:
-    _, seq_len = tokens.size()
-    assert self.config.max_seq_len >= seq_len, (
-        f"Cannot forward sequence of length {seq_len}, max seq length is only"
-        f" {self.config.max_seq_len}"
-    )
-    assert len(self.transformer_blocks) == len(kv_cache.caches), (
-        "The number of transformer blocks and the number of KV cache entries"
-        " must be the same."
-    )
-
-    cos, sin = self.rope_cache
-    cos = cos.index_select(0, input_pos)
-    sin = sin.index_select(0, input_pos)
-    mask = self.mask_cache.index_select(2, input_pos)
-    mask = mask[:, :, :, : self.config.kv_cache_max]
-
-    # token embeddings of shape (b, t, n_embd)
-    x = self.tok_embedding(tokens)
-
-    updated_kv_entires = []
-    for i, block in enumerate(self.transformer_blocks):
-      kv_entry = kv_cache.caches[i] if kv_cache else None
-      x, kv_entry = block(x, (cos, sin), mask, input_pos, kv_entry)
-      if kv_entry:
-        updated_kv_entires.append(kv_entry)
-    updated_kv_cache = kv_utils.KVCache(tuple(updated_kv_entires))
-
-    x = self.final_norm(x)
-    logits = self.lm_head(x)  # (b, t, vocab_size)
-    return {"logits": logits, "kv_cache": updated_kv_cache}
-
-
 def get_model_config(kv_cache_max_len: int = 1024) -> cfg.ModelConfig:
   """Returns the model config for an OpenELM model.
 
@@ -191,12 +111,11 @@ def get_fake_model_config(kv_cache_max_len: int = 128) -> cfg.ModelConfig:
   return config
 
 
-def build_model(checkpoint_path: str, **kwargs) -> nn.Module:
-  config = get_model_config(**kwargs)
-  model = OpenELM(config)
-  loader = loading_utils.ModelLoader(checkpoint_path, TENSOR_NAMES)
-  # Since embedding and lm-head use the same weight, we need to set strict
-  # to False.
-  loader.load(model, strict=False)
-  model.eval()
-  return model
+def build_model(
+    checkpoint_path: str, **kwargs
+) -> model_builder.DecoderOnlyModel:
+  return model_builder.build_decoder_only_model(
+      checkpoint_path=checkpoint_path,
+      config=get_model_config(**kwargs),
+      tensor_names=TENSOR_NAMES,
+  )
diff --git a/ai_edge_torch/generative/examples/phi/phi2.py b/ai_edge_torch/generative/examples/phi/phi2.py
index 718b5369..61a1e647 100644
--- a/ai_edge_torch/generative/examples/phi/phi2.py
+++ b/ai_edge_torch/generative/examples/phi/phi2.py
@@ -15,14 +15,9 @@
 
 """Example of building a Phi-2 model."""
 
-from ai_edge_torch.generative.layers import attention
-from ai_edge_torch.generative.layers import builder
-from ai_edge_torch.generative.layers import kv_cache as kv_utils
-import ai_edge_torch.generative.layers.attention_utils as attn_utils
 import ai_edge_torch.generative.layers.model_config as cfg
+from ai_edge_torch.generative.utilities import model_builder
 import ai_edge_torch.generative.utilities.loader as loading_utils
-import torch
-from torch import nn
 
 TENSOR_NAMES = loading_utils.ModelLoader.TensorNames(
     ff_up_proj="model.layers.{}.mlp.fc1",
@@ -38,78 +33,6 @@
 )
 
 
-class Phi2(nn.Module):
-  """A Phi-2 model built from the Edge Generative API layers."""
-
-  def __init__(self, config: cfg.ModelConfig):
-    super().__init__()
-
-    # Construct model layers.
-    self.lm_head = nn.Linear(
-        config.embedding_dim, config.vocab_size, bias=config.lm_head_use_bias
-    )
-    self.tok_embedding = nn.Embedding(
-        config.vocab_size, config.embedding_dim, padding_idx=0
-    )
-    # Phi-2 has only one block config.
-    block_config = config.block_config(0)
-    self.transformer_blocks = nn.ModuleList(
-        attention.TransformerBlock(block_config, config)
-        for _ in range(config.num_layers)
-    )
-    self.final_norm = builder.build_norm(
-        config.embedding_dim,
-        config.final_norm_config,
-    )
-    attn_config = block_config.attn_config
-    self.rope_cache = attn_utils.build_rope_cache(
-        size=config.kv_cache_max,
-        dim=int(attn_config.rotary_percentage * attn_config.head_dim),
-        base=attn_config.rotary_base,
-    )
-    self.mask_cache = attn_utils.build_causal_mask_cache(
-        size=config.kv_cache_max,
-    )
-    self.config = config
-
-  @torch.inference_mode
-  def forward(
-      self,
-      tokens: torch.Tensor,
-      input_pos: torch.Tensor,
-      kv_cache: kv_utils.KVCache,
-  ) -> dict[torch.Tensor, kv_utils.KVCache]:
-    _, seq_len = tokens.size()
-    assert self.config.max_seq_len >= seq_len, (
-        f"Cannot forward sequence of length {seq_len}, max seq length is only"
-        f" {self.config.max_seq_len}"
-    )
-    assert len(self.transformer_blocks) == len(kv_cache.caches), (
-        "The number of transformer blocks and the number of KV cache entries"
-        " must be the same."
-    )
-
-    cos, sin = self.rope_cache
-    cos = cos.index_select(0, input_pos)
-    sin = sin.index_select(0, input_pos)
-    mask = self.mask_cache.index_select(2, input_pos)
-    mask = mask[:, :, :, : self.config.kv_cache_max]
-
-    x = self.tok_embedding(tokens)
-
-    updated_kv_entires = []
-    for i, block in enumerate(self.transformer_blocks):
-      kv_entry = kv_cache.caches[i] if kv_cache else None
-      x, kv_entry = block(x, (cos, sin), mask, input_pos, kv_entry)
-      if kv_entry:
-        updated_kv_entires.append(kv_entry)
-    updated_kv_cache = kv_utils.KVCache(tuple(updated_kv_entires))
-
-    x = self.final_norm(x)
-    logits = self.lm_head(x)  # (b, t, vocab_size)
-    return {"logits": logits, "kv_cache": updated_kv_cache}
-
-
 def get_model_config(kv_cache_max_len: int = 1024) -> cfg.ModelConfig:
   """Returns the model config for a Phi-2 model.
 
@@ -154,6 +77,7 @@ def get_model_config(kv_cache_max_len: int = 1024) -> cfg.ModelConfig:
       block_configs=block_config,
       final_norm_config=norm_config,
       lm_head_use_bias=True,
+      lm_head_share_weight_with_embedding=False,
       enable_hlfb=True,
   )
   return config
@@ -169,11 +93,11 @@ def get_fake_model_config(kv_cache_max_len: int = 128) -> cfg.ModelConfig:
   return config
 
 
-def build_model(checkpoint_path: str, **kwargs) -> nn.Module:
-  """Instantiates the model instance and load checkpoint if provided."""
-  config = get_model_config(**kwargs)
-  model = Phi2(config)
-  loader = loading_utils.ModelLoader(checkpoint_path, TENSOR_NAMES)
-  loader.load(model)
-  model.eval()
-  return model
+def build_model(
+    checkpoint_path: str, **kwargs
+) -> model_builder.DecoderOnlyModel:
+  return model_builder.build_decoder_only_model(
+      checkpoint_path=checkpoint_path,
+      config=get_model_config(**kwargs),
+      tensor_names=TENSOR_NAMES,
+  )
diff --git a/ai_edge_torch/generative/examples/phi/phi3.py b/ai_edge_torch/generative/examples/phi/phi3.py
index c2c2b802..6958dad9 100644
--- a/ai_edge_torch/generative/examples/phi/phi3.py
+++ b/ai_edge_torch/generative/examples/phi/phi3.py
@@ -18,14 +18,10 @@
 import math
 from typing import Tuple
 
-from ai_edge_torch.generative.layers import attention
-from ai_edge_torch.generative.layers import builder
-from ai_edge_torch.generative.layers import kv_cache as kv_utils
-import ai_edge_torch.generative.layers.attention_utils as attn_utils
 import ai_edge_torch.generative.layers.model_config as cfg
+from ai_edge_torch.generative.utilities import model_builder
 import ai_edge_torch.generative.utilities.loader as loading_utils
 import torch
-from torch import nn
 
 TENSOR_NAMES = loading_utils.ModelLoader.TensorNames(
     ff_up_proj="model.layers.{}.mlp.gate_up_proj",
@@ -137,32 +133,14 @@ def _build_rope_cache(
   return cos, sin
 
 
-class Phi3_5Mini(nn.Module):
+class Phi3_5Mini(model_builder.DecoderOnlyModel):
   """A Phi-3.5 model built from the Edge Generative API layers."""
 
   def __init__(self, config: cfg.ModelConfig):
-    super().__init__()
-
-    # Construct model layers.
-    self.lm_head = nn.Linear(
-        config.embedding_dim, config.vocab_size, bias=config.lm_head_use_bias
-    )
-    self.tok_embedding = nn.Embedding(
-        config.vocab_size, config.embedding_dim, padding_idx=0
-    )
-    # Phi-3.5 has only one block config.
-    block_config = config.block_config(0)
-    self.transformer_blocks = nn.ModuleList(
-        attention.TransformerBlock(block_config, config)
-        for _ in range(config.num_layers)
-    )
-    self.final_norm = builder.build_norm(
-        config.embedding_dim,
-        config.final_norm_config,
-    )
-    attn_config = block_config.attn_config
+    super().__init__(config)
+    attn_config = self.config.block_config(0).attn_config
     self.rope_cache = _build_rope_cache(
-        size=config.kv_cache_max,
+        size=self.config.kv_cache_max,
         dim=int(attn_config.rotary_percentage * attn_config.head_dim),
         base=attn_config.rotary_base,
         condense_ratio=1,
@@ -173,47 +151,6 @@ def __init__(self, config: cfg.ModelConfig):
             1 + math.log(ROPE_SCALE_FACTOR) / math.log(config.max_seq_len)
         ),
     )
-    self.mask_cache = attn_utils.build_causal_mask_cache(
-        size=config.kv_cache_max,
-    )
-    self.config = config
-
-  @torch.inference_mode
-  def forward(
-      self,
-      tokens: torch.Tensor,
-      input_pos: torch.Tensor,
-      kv_cache: kv_utils.KVCache,
-  ) -> dict[torch.Tensor, kv_utils.KVCache]:
-    _, seq_len = tokens.size()
-    assert self.config.max_seq_len >= seq_len, (
-        f"Cannot forward sequence of length {seq_len}, max seq length is only"
-        f" {self.config.max_seq_len}"
-    )
-    assert len(self.transformer_blocks) == len(kv_cache.caches), (
-        "The number of transformer blocks and the number of KV cache entries"
-        " must be the same."
-    )
-
-    cos, sin = self.rope_cache
-    cos = cos.index_select(0, input_pos)
-    sin = sin.index_select(0, input_pos)
-    mask = self.mask_cache.index_select(2, input_pos)
-    mask = mask[:, :, :, : self.config.kv_cache_max]
-
-    x = self.tok_embedding(tokens)
-
-    updated_kv_entires = []
-    for i, block in enumerate(self.transformer_blocks):
-      kv_entry = kv_cache.caches[i] if kv_cache else None
-      x, kv_entry = block(x, (cos, sin), mask, input_pos, kv_entry)
-      if kv_entry:
-        updated_kv_entires.append(kv_entry)
-    updated_kv_cache = kv_utils.KVCache(tuple(updated_kv_entires))
-
-    x = self.final_norm(x)
-    logits = self.lm_head(x)  # (b, t, vocab_size)
-    return {"logits": logits, "kv_cache": updated_kv_cache}
 
 
 def get_model_config(kv_cache_max_len: int = 1024) -> cfg.ModelConfig:
@@ -254,6 +191,7 @@ def get_model_config(kv_cache_max_len: int = 1024) -> cfg.ModelConfig:
       embedding_dim=3072,
       block_configs=block_config,
       final_norm_config=norm_config,
+      lm_head_share_weight_with_embedding=False,
       enable_hlfb=True,
   )
   return config
@@ -269,7 +207,9 @@ def get_fake_model_config(kv_cache_max_len: int = 128) -> cfg.ModelConfig:
   return config
 
 
-def build_model(checkpoint_path: str, **kwargs) -> nn.Module:
+def build_model(
+    checkpoint_path: str, **kwargs
+) -> model_builder.DecoderOnlyModel:
   """Instantiates the model instance and load checkpoint if provided."""
   config = get_model_config(**kwargs)
   model = Phi3_5Mini(config)
diff --git a/ai_edge_torch/generative/examples/qwen/qwen.py b/ai_edge_torch/generative/examples/qwen/qwen.py
index 669207d2..0347758b 100644
--- a/ai_edge_torch/generative/examples/qwen/qwen.py
+++ b/ai_edge_torch/generative/examples/qwen/qwen.py
@@ -15,28 +15,10 @@
 
 """Example of building Qwen 2.5 models."""
 
-import copy
-
-from ai_edge_torch.generative.examples.tiny_llama import tiny_llama
 import ai_edge_torch.generative.layers.model_config as cfg
-import ai_edge_torch.generative.utilities.loader as loading_utils
-from torch import nn
-
-TENSOR_NAMES = copy.copy(tiny_llama.TENSOR_NAMES)
-# Qwen re-uses the embedding as the head projection layer.
-TENSOR_NAMES.lm_head = None
-
-
-class Qwen(tiny_llama.TinyLlama):
-  """A Qwen model built from the Edge Generative API layers.
-
-  Qwen 2.5 shares the same architecture as TinyLlama.
-  """
+from ai_edge_torch.generative.utilities import model_builder
 
-  def __init__(self, config: cfg.ModelConfig):
-    super().__init__(config)
-    # Qwen re-uses the embedding as the head projection layer.
-    self.lm_head.weight.data = self.tok_embedding.weight.data
+TENSOR_NAMES = model_builder.TENSOR_NAMES
 
 
 def get_3b_model_config(kv_cache_max_len: int = 1024) -> cfg.ModelConfig:
@@ -119,23 +101,31 @@ def get_fake_model_config(**kwargs) -> cfg.ModelConfig:
   return config
 
 
-def _build_model(checkpoint_path: str, config: cfg.ModelConfig) -> nn.Module:
-  model = Qwen(config)
-  loader = loading_utils.ModelLoader(checkpoint_path, TENSOR_NAMES)
-  # Since embedding and lm-head use the same weight, we need to set strict
-  # to False.
-  loader.load(model, strict=False)
-  model.eval()
-  return model
-
-
-def build_3b_model(checkpoint_path: str, **kwargs) -> nn.Module:
-  return _build_model(checkpoint_path, get_3b_model_config(**kwargs))
+def build_3b_model(
+    checkpoint_path: str, **kwargs
+) -> model_builder.DecoderOnlyModel:
+  return model_builder.build_decoder_only_model(
+      checkpoint_path=checkpoint_path,
+      config=get_3b_model_config(**kwargs),
+      tensor_names=TENSOR_NAMES,
+  )
 
 
-def build_1_5b_model(checkpoint_path: str, **kwargs) -> nn.Module:
-  return _build_model(checkpoint_path, get_1_5b_model_config(**kwargs))
+def build_1_5b_model(
+    checkpoint_path: str, **kwargs
+) -> model_builder.DecoderOnlyModel:
+  return model_builder.build_decoder_only_model(
+      checkpoint_path=checkpoint_path,
+      config=get_1_5b_model_config(**kwargs),
+      tensor_names=TENSOR_NAMES,
+  )
 
 
-def build_0_5b_model(checkpoint_path: str, **kwargs) -> nn.Module:
-  return _build_model(checkpoint_path, get_0_5b_model_config(**kwargs))
+def build_0_5b_model(
+    checkpoint_path: str, **kwargs
+) -> model_builder.DecoderOnlyModel:
+  return model_builder.build_decoder_only_model(
+      checkpoint_path=checkpoint_path,
+      config=get_0_5b_model_config(**kwargs),
+      tensor_names=TENSOR_NAMES,
+  )
diff --git a/ai_edge_torch/generative/examples/smollm/smollm.py b/ai_edge_torch/generative/examples/smollm/smollm.py
index 303671bb..2e5942f8 100644
--- a/ai_edge_torch/generative/examples/smollm/smollm.py
+++ b/ai_edge_torch/generative/examples/smollm/smollm.py
@@ -15,29 +15,10 @@
 
 """Example of building a SmolLM model."""
 
-import copy
-
-from ai_edge_torch.generative.examples.tiny_llama import tiny_llama
 import ai_edge_torch.generative.layers.model_config as cfg
-import ai_edge_torch.generative.utilities.loader as loading_utils
-from torch import nn
-
-TENSOR_NAMES = copy.copy(tiny_llama.TENSOR_NAMES)
-# SmolLM re-uses the embedding as the head projection layer.
-TENSOR_NAMES.lm_head = None
-
-
-class SmolLM(tiny_llama.TinyLlama):
-  """A SmolLM model built from the Edge Generative API layers.
+from ai_edge_torch.generative.utilities import model_builder
 
-  SmolLM shares the same architecture as TinyLlama, but with different model
-  sizes.
-  """
-
-  def __init__(self, config: cfg.ModelConfig):
-    super().__init__(config)
-    # SmolLM re-uses the embedding as the head projection layer.
-    self.lm_head.weight.data = self.tok_embedding.weight.data
+TENSOR_NAMES = model_builder.TENSOR_NAMES
 
 
 def get_model_config(kv_cache_max_len: int = 1024) -> cfg.ModelConfig:
@@ -91,12 +72,11 @@ def get_fake_model_config(**kwargs) -> cfg.ModelConfig:
   return config
 
 
-def build_model(checkpoint_path: str, **kwargs) -> nn.Module:
-  config = get_model_config(**kwargs)
-  model = SmolLM(config)
-  loader = loading_utils.ModelLoader(checkpoint_path, TENSOR_NAMES)
-  # Since embedding and lm-head use the same weight, we need to set strict
-  # to False.
-  loader.load(model, strict=False)
-  model.eval()
-  return model
+def build_model(
+    checkpoint_path: str, **kwargs
+) -> model_builder.DecoderOnlyModel:
+  return model_builder.build_decoder_only_model(
+      checkpoint_path=checkpoint_path,
+      config=get_model_config(**kwargs),
+      tensor_names=TENSOR_NAMES,
+  )
diff --git a/ai_edge_torch/generative/examples/tiny_llama/tiny_llama.py b/ai_edge_torch/generative/examples/tiny_llama/tiny_llama.py
index 986fb716..5de9c237 100644
--- a/ai_edge_torch/generative/examples/tiny_llama/tiny_llama.py
+++ b/ai_edge_torch/generative/examples/tiny_llama/tiny_llama.py
@@ -15,102 +15,10 @@
 
 """Example of building a TinyLlama model."""
 
-from ai_edge_torch.generative.layers import attention
-from ai_edge_torch.generative.layers import builder
-from ai_edge_torch.generative.layers import kv_cache as kv_utils
-import ai_edge_torch.generative.layers.attention_utils as attn_utils
 import ai_edge_torch.generative.layers.model_config as cfg
-import ai_edge_torch.generative.utilities.loader as loading_utils
-import torch
-from torch import nn
+from ai_edge_torch.generative.utilities import model_builder
 
-TENSOR_NAMES = loading_utils.ModelLoader.TensorNames(
-    ff_up_proj="model.layers.{}.mlp.up_proj",
-    ff_down_proj="model.layers.{}.mlp.down_proj",
-    ff_gate_proj="model.layers.{}.mlp.gate_proj",
-    attn_query_proj="model.layers.{}.self_attn.q_proj",
-    attn_key_proj="model.layers.{}.self_attn.k_proj",
-    attn_value_proj="model.layers.{}.self_attn.v_proj",
-    attn_output_proj="model.layers.{}.self_attn.o_proj",
-    pre_attn_norm="model.layers.{}.input_layernorm",
-    post_attn_norm="model.layers.{}.post_attention_layernorm",
-    embedding="model.embed_tokens",
-    final_norm="model.norm",
-    lm_head="lm_head",
-)
-
-
-class TinyLlama(nn.Module):
-  """A TinyLlama model built from the Edge Generative API layers."""
-
-  def __init__(self, config: cfg.ModelConfig):
-    super().__init__()
-
-    # Construct model layers.
-    self.lm_head = nn.Linear(
-        config.embedding_dim, config.vocab_size, bias=config.lm_head_use_bias
-    )
-    self.tok_embedding = nn.Embedding(
-        config.vocab_size, config.embedding_dim, padding_idx=0
-    )
-    # TinyLlama has only one block config.
-    block_config = config.block_config(0)
-    self.transformer_blocks = nn.ModuleList(
-        attention.TransformerBlock(block_config, config)
-        for _ in range(config.num_layers)
-    )
-    self.final_norm = builder.build_norm(
-        config.embedding_dim,
-        config.final_norm_config,
-    )
-    attn_config = block_config.attn_config
-    self.rope_cache = attn_utils.build_rope_cache(
-        size=config.kv_cache_max,
-        dim=int(attn_config.rotary_percentage * attn_config.head_dim),
-        base=attn_config.rotary_base,
-    )
-    self.mask_cache = attn_utils.build_causal_mask_cache(
-        size=config.kv_cache_max,
-    )
-    self.config = config
-
-  @torch.inference_mode
-  def forward(
-      self,
-      tokens: torch.Tensor,
-      input_pos: torch.Tensor,
-      kv_cache: kv_utils.KVCache,
-  ) -> dict[torch.Tensor, kv_utils.KVCache]:
-    _, seq_len = tokens.size()
-    assert self.config.max_seq_len >= seq_len, (
-        f"Cannot forward sequence of length {seq_len}, max seq length is only"
-        f" {self.config.max_seq_len}"
-    )
-    assert len(self.transformer_blocks) == len(kv_cache.caches), (
-        "The number of transformer blocks and the number of KV cache entries"
-        " must be the same."
-    )
-
-    cos, sin = self.rope_cache
-    cos = cos.index_select(0, input_pos)
-    sin = sin.index_select(0, input_pos)
-    mask = self.mask_cache.index_select(2, input_pos)
-    mask = mask[:, :, :, : self.config.kv_cache_max]
-
-    # token embeddings of shape (b, t, n_embd)
-    x = self.tok_embedding(tokens)
-
-    updated_kv_entires = []
-    for i, block in enumerate(self.transformer_blocks):
-      kv_entry = kv_cache.caches[i] if kv_cache else None
-      x, kv_entry = block(x, (cos, sin), mask, input_pos, kv_entry)
-      if kv_entry:
-        updated_kv_entires.append(kv_entry)
-    updated_kv_cache = kv_utils.KVCache(tuple(updated_kv_entires))
-
-    x = self.final_norm(x)
-    logits = self.lm_head(x)  # (b, t, vocab_size)
-    return {"logits": logits, "kv_cache": updated_kv_cache}
+TENSOR_NAMES = model_builder.TENSOR_NAMES_WITH_SEPARATE_LM_HEAD
 
 
 def get_model_config(kv_cache_max_len: int = 1024) -> cfg.ModelConfig:
@@ -150,6 +58,7 @@ def get_model_config(kv_cache_max_len: int = 1024) -> cfg.ModelConfig:
       kv_cache_max_len=kv_cache_max_len,
       block_configs=block_config,
       final_norm_config=norm_config,
+      lm_head_share_weight_with_embedding=False,
       enable_hlfb=True,
   )
   return config
@@ -164,10 +73,11 @@ def get_fake_model_config(**kwargs) -> cfg.ModelConfig:
   return config
 
 
-def build_model(checkpoint_path: str, **kwargs) -> nn.Module:
-  config = get_model_config(**kwargs)
-  model = TinyLlama(config)
-  loader = loading_utils.ModelLoader(checkpoint_path, TENSOR_NAMES)
-  loader.load(model)
-  model.eval()
-  return model
+def build_model(
+    checkpoint_path: str, **kwargs
+) -> model_builder.DecoderOnlyModel:
+  return model_builder.build_decoder_only_model(
+      checkpoint_path=checkpoint_path,
+      config=get_model_config(**kwargs),
+      tensor_names=TENSOR_NAMES,
+  )
diff --git a/ai_edge_torch/generative/layers/model_config.py b/ai_edge_torch/generative/layers/model_config.py
index 01365789..2e1b4f22 100644
--- a/ai_edge_torch/generative/layers/model_config.py
+++ b/ai_edge_torch/generative/layers/model_config.py
@@ -184,8 +184,14 @@ class ModelConfig:
       default_factory=NormalizationConfig
   )
 
+  # Scale factor of the embedding.
+  embedding_scale: Optional[float] = None
+
   # Use bias term within LLM's HEAD.
   lm_head_use_bias: bool = False
+  # Whether LLM's HEAD shares the weight of the embedding.
+  lm_head_share_weight_with_embedding: bool = True
+
   # Whether to turn on high-level function boundary.
   enable_hlfb: bool = False
 
diff --git a/ai_edge_torch/generative/test/test_loader.py b/ai_edge_torch/generative/test/test_loader.py
index 45f30f52..0cac8479 100644
--- a/ai_edge_torch/generative/test/test_loader.py
+++ b/ai_edge_torch/generative/test/test_loader.py
@@ -19,6 +19,7 @@
 
 from ai_edge_torch.generative.examples.tiny_llama import tiny_llama
 from ai_edge_torch.generative.utilities import loader as loading_utils
+from ai_edge_torch.generative.utilities import model_builder
 import safetensors.torch
 import torch
 
@@ -71,7 +72,7 @@ def test_model_loader(self):
       safetensors.torch.save_file(test_weights, file_path)
       cfg = tiny_llama.get_model_config()
       cfg.num_layers = 1
-      model = tiny_llama.TinyLlama(cfg)
+      model = model_builder.DecoderOnlyModel(cfg)
 
       loader = loading_utils.ModelLoader(file_path, tiny_llama.TENSOR_NAMES)
       # if returns successfully, it means all the tensors were initiallized.
diff --git a/ai_edge_torch/generative/test/test_model_conversion.py b/ai_edge_torch/generative/test/test_model_conversion.py
index 114a1bf0..2661c127 100644
--- a/ai_edge_torch/generative/test/test_model_conversion.py
+++ b/ai_edge_torch/generative/test/test_model_conversion.py
@@ -21,6 +21,7 @@
 from ai_edge_torch.generative.examples.tiny_llama import tiny_llama
 from ai_edge_torch.generative.layers import kv_cache
 from ai_edge_torch.generative.test import utils as test_utils
+from ai_edge_torch.generative.utilities import model_builder
 import numpy as np
 import torch
 
@@ -163,7 +164,7 @@ def _test_multisig_model(self, config, pytorch_model, atol, rtol):
   )
   def test_tiny_llama_multisig(self):
     config = tiny_llama.get_fake_model_config()
-    pytorch_model = tiny_llama.TinyLlama(config).eval()
+    pytorch_model = model_builder.DecoderOnlyModel(config).eval()
     self._test_multisig_model(config, pytorch_model, atol=1e-5, rtol=1e-5)
 
 
diff --git a/ai_edge_torch/generative/test/test_model_conversion_large.py b/ai_edge_torch/generative/test/test_model_conversion_large.py
index a85c1f38..3dee0483 100644
--- a/ai_edge_torch/generative/test/test_model_conversion_large.py
+++ b/ai_edge_torch/generative/test/test_model_conversion_large.py
@@ -29,6 +29,7 @@
 from ai_edge_torch.generative.examples.stable_diffusion import decoder as sd_decoder
 from ai_edge_torch.generative.examples.stable_diffusion import diffusion as sd_diffusion
 from ai_edge_torch.generative.layers import kv_cache
+from ai_edge_torch.generative.utilities import model_builder
 from ai_edge_torch.generative.test import utils as test_utils
 import numpy as np
 import torch
@@ -90,7 +91,7 @@ def _test_model(self, config, model, signature_name, atol, rtol):
   )
   def test_gemma1(self):
     config = gemma1.get_fake_model_config()
-    pytorch_model = gemma1.Gemma(config).eval()
+    pytorch_model = model_builder.DecoderOnlyModel(config).eval()
     self._test_model(
         config, pytorch_model, "serving_default", atol=1e-2, rtol=1e-5
     )
@@ -119,7 +120,7 @@ def test_llama(self):
   )
   def test_phi2(self):
     config = phi2.get_fake_model_config()
-    pytorch_model = phi2.Phi2(config).eval()
+    pytorch_model = model_builder.DecoderOnlyModel(config).eval()
     self._test_model(
         config, pytorch_model, "serving_default", atol=1e-3, rtol=1e-3
     )
@@ -139,7 +140,7 @@ def test_phi3(self):
   )
   def test_smollm(self):
     config = smollm.get_fake_model_config()
-    pytorch_model = smollm.SmolLM(config).eval()
+    pytorch_model = model_builder.DecoderOnlyModel(config).eval()
     self._test_model(config, pytorch_model, "prefill", atol=1e-4, rtol=1e-5)
 
   @googletest.skipIf(
@@ -148,7 +149,7 @@ def test_smollm(self):
   )
   def test_openelm(self):
     config = openelm.get_fake_model_config()
-    pytorch_model = openelm.OpenELM(config).eval()
+    pytorch_model = model_builder.DecoderOnlyModel(config).eval()
     self._test_model(config, pytorch_model, "prefill", atol=1e-4, rtol=1e-5)
 
   @googletest.skipIf(
@@ -157,7 +158,7 @@ def test_openelm(self):
   )
   def test_qwen(self):
     config = qwen.get_fake_model_config()
-    pytorch_model = qwen.Qwen(config).eval()
+    pytorch_model = model_builder.DecoderOnlyModel(config).eval()
     self._test_model(config, pytorch_model, "prefill", atol=1e-3, rtol=1e-5)
 
   @googletest.skipIf(
diff --git a/ai_edge_torch/generative/utilities/model_builder.py b/ai_edge_torch/generative/utilities/model_builder.py
new file mode 100644
index 00000000..9565fad4
--- /dev/null
+++ b/ai_edge_torch/generative/utilities/model_builder.py
@@ -0,0 +1,141 @@
+# Copyright 2024 The AI Edge Torch Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Utilities to be used for re-authoring transformer models."""
+
+import copy
+
+from ai_edge_torch.generative.layers import attention
+from ai_edge_torch.generative.layers import builder
+from ai_edge_torch.generative.layers import kv_cache as kv_utils
+import ai_edge_torch.generative.layers.attention_utils as attn_utils
+import ai_edge_torch.generative.layers.model_config as cfg
+import ai_edge_torch.generative.utilities.loader as loading_utils
+import torch
+from torch import nn
+
+TENSOR_NAMES = loading_utils.ModelLoader.TensorNames(
+    ff_up_proj="model.layers.{}.mlp.up_proj",
+    ff_down_proj="model.layers.{}.mlp.down_proj",
+    ff_gate_proj="model.layers.{}.mlp.gate_proj",
+    attn_query_proj="model.layers.{}.self_attn.q_proj",
+    attn_key_proj="model.layers.{}.self_attn.k_proj",
+    attn_value_proj="model.layers.{}.self_attn.v_proj",
+    attn_output_proj="model.layers.{}.self_attn.o_proj",
+    pre_attn_norm="model.layers.{}.input_layernorm",
+    post_attn_norm="model.layers.{}.post_attention_layernorm",
+    embedding="model.embed_tokens",
+    final_norm="model.norm",
+)
+
+TENSOR_NAMES_WITH_SEPARATE_LM_HEAD = copy.copy(TENSOR_NAMES)
+TENSOR_NAMES_WITH_SEPARATE_LM_HEAD.lm_head = "lm_head"
+
+
+class DecoderOnlyModel(nn.Module):
+  """A simple decoder-only transformer model built from the Edge Generative API.
+
+  This model is used for re-authoring. model_config is used to specify the
+  details of model architecture and parameters.
+
+  It assumes that the attention configs for ROPE, i.e. head_dim, rotary_base,
+  and rotary_percentage are the same for all layers.
+  """
+
+  def __init__(self, config: cfg.ModelConfig):
+    super().__init__()
+
+    # Construct model layers.
+    self.tok_embedding = nn.Embedding(
+        config.vocab_size, config.embedding_dim, padding_idx=0
+    )
+    self.lm_head = nn.Linear(
+        config.embedding_dim, config.vocab_size, bias=config.lm_head_use_bias
+    )
+    if config.lm_head_share_weight_with_embedding:
+      self.lm_head.weight.data = self.tok_embedding.weight.data
+    self.transformer_blocks = nn.ModuleList(
+        attention.TransformerBlock(config.block_config(idx), config)
+        for idx in range(config.num_layers)
+    )
+    self.final_norm = builder.build_norm(
+        config.embedding_dim,
+        config.final_norm_config,
+    )
+    # ROPE parameters for all attn_configs are the same. Take the first one.
+    attn_config = config.block_config(0).attn_config
+    self.rope_cache = attn_utils.build_rope_cache(
+        size=config.kv_cache_max,
+        dim=int(attn_config.rotary_percentage * attn_config.head_dim),
+        base=attn_config.rotary_base,
+    )
+    self.mask_cache = attn_utils.build_causal_mask_cache(
+        size=config.kv_cache_max,
+    )
+    self.config = config
+
+  @torch.inference_mode
+  def forward(
+      self,
+      tokens: torch.Tensor,
+      input_pos: torch.Tensor,
+      kv_cache: kv_utils.KVCache,
+  ) -> dict[torch.Tensor, kv_utils.KVCache]:
+    _, seq_len = tokens.size()
+    assert self.config.max_seq_len >= seq_len, (
+        f"Cannot forward sequence of length {seq_len}, max seq length is only"
+        f" {self.config.max_seq_len}"
+    )
+    assert len(self.transformer_blocks) == len(kv_cache.caches), (
+        "The number of transformer blocks and the number of KV cache entries"
+        " must be the same."
+    )
+
+    cos, sin = self.rope_cache
+    cos = cos.index_select(0, input_pos)
+    sin = sin.index_select(0, input_pos)
+    mask = self.mask_cache.index_select(2, input_pos)
+    mask = mask[:, :, :, : self.config.kv_cache_max]
+
+    # token embeddings of shape (b, t, n_embd)
+    x = self.tok_embedding(tokens)
+    if self.config.embedding_scale is not None:
+      x = x * self.config.embedding_scale
+
+    updated_kv_entires = []
+    for i, block in enumerate(self.transformer_blocks):
+      kv_entry = kv_cache.caches[i] if kv_cache else None
+      x, kv_entry = block(x, (cos, sin), mask, input_pos, kv_entry)
+      if kv_entry:
+        updated_kv_entires.append(kv_entry)
+    updated_kv_cache = kv_utils.KVCache(tuple(updated_kv_entires))
+
+    x = self.final_norm(x)
+    logits = self.lm_head(x)  # (b, t, vocab_size)
+    return {"logits": logits, "kv_cache": updated_kv_cache}
+
+
+def build_decoder_only_model(
+    checkpoint_path: str,
+    config: cfg.ModelConfig,
+    tensor_names: loading_utils.ModelLoader.TensorNames,
+) -> DecoderOnlyModel:
+  transformer = DecoderOnlyModel(config)
+  loader = loading_utils.ModelLoader(checkpoint_path, tensor_names)
+  loader.load(
+      transformer, strict=not config.lm_head_share_weight_with_embedding
+  )
+  transformer.eval()
+  return transformer