diff --git a/src/cleave/__init__.py b/src/cleave/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/cleave/modelling.py b/src/cleave/modelling.py
new file mode 100644
index 0000000..3dc1997
--- /dev/null
+++ b/src/cleave/modelling.py
@@ -0,0 +1,151 @@
+import os
+import torch
+import torch.nn.functional as F
+import lightning as L
+
+from transformers import (
+    AutoModelForCausalLM,
+    Cache, DynamicCache
+)
+from transformers.utils import logging, LossKwargs
+from transformers.processing_utils import Unpack
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast
+)
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+
+from typing import Optional, Union, Tuple
+
+
+logger = logging.get_logger(__name__)
+
+class CleavedAutoModelForCausalLM(L.LightningModule):
+    def __init__(
+        self,
+        target_model_name_or_path: str | os.PathLike,
+        *, cleave_at_index: int,
+        adapter_path: os.PathLike = None,
+        **kwargs
+    ):
+        """
+        Args:
+            target_model_name_or_path (str or os.PathLike): Can be either:
+                - a string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
+                - a path to a *directory* containing model weights saved using the
+                  :func:`~transformers.PreTrainedModel.save_pretrained` method, e.g., ``./my_model_directory/``.
+                - a path or url to a *saved model archive* (e.g, ``./my_model_directory/model.tar.gz``).
+            cleave_at_index (int): Cleave the model at this index. The first `cleave_at_index` layers of the `target_model` are included in the resized `draft_subnet`. The deeper layers (`cleave_at_index+1` onwards) are in the `verify_subnet`.
+            adapter_path (os.PathLike, optional): Path to the adapter weights. If provided, the adapter weights will be
+                loaded into the model.
+            kwargs: Additional keyword arguments passed to the `AutoModelForCausalLM.from_pretrained` method.
+        
+        Example:
+            ```python
+            from cleave.modelling import CleavedAutoModelForCausalLM
+            cleaved_model = CleavedAutoModelForCausalLM(
+                "meta-llama/Llama-3.2-1B-Instruct", cleave_at_index=8
+            )
+            ```
+        """
+        super().__init__()
+        self.save_hyperparameters()
+
+        self.model = AutoModelForCausalLM.from_pretrained(
+            target_model_name_or_path,
+            torch_dtype=kwargs.get("torch_dtype") or "auto",
+            device_map=kwargs.get("device_map") or "auto",
+            **kwargs
+        )
+
+        assert cleave_at_index > 0, "cleave_at_index must be positive and non-zero"
+        assert cleave_at_index < self.model.config.num_hidden_layers, \
+            "cleave_at_index must be less than the number of layers in the model. " \
+            f"i.e. {self.model.config.num_hidden_layers}"
+
+        self.cleave_at_index = cleave_at_index
+
+        self.adapter_class = type(self.model.model.layers[-1])
+
+        if adapter_path is not None: ... # To-Do
+
+        else: self.adapter = self.adapter_class(
+            config=self.model.config,
+            layer_idx=self.cleave_at_index
+        )
+
+        # Extracting the aux layers from the model
+        self.embed_tokens = self.model.model.embed_tokens
+        self.lm_head = self.model.lm_head
+
+    def shallow_subnet_forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = 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,
+        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
+    ):
+        """
+        Args:
+            Refer to 
+            https://huggingface.co/docs/transformers/model_doc/llama2#transformers.LlamaForCausalLM
+        """
+        pass
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = 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,
+        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        """
+        Args:
+            Refer to 
+            https://huggingface.co/docs/transformers/model_doc/llama2#transformers.LlamaForCausalLM
+        """
+        pass
+        
+
+    def training_step(self, batch, batch_idx): ...
+
+    def verify_step(self, batch, batch_idx): ...
+
+    def validation_step(self, batch, batch_idx): ...
+
+    def test_step(self, batch, batch_idx): ...
+
+    def configure_optimizers(self): ...
+
+    def predict_step(self, batch, batch_idx): ...
+
+    def predict(self, *args, **kwargs): ...
+
+    def generate(self, *args, **kwargs):
+        return self.predict(*args, **kwargs)
+
+    def convert_to_hf(self): ...
+
+    def get_shallow_subnet(self): ...
+
+    @property
+    def draft_subnet_parameters_count(self):
+        return 0 # dummy
+
+    @property
+    def verify_subnet_parameters_count(self):
+        return 0 # dummy