diff --git a/QEfficient/finetune/configs/training.py b/QEfficient/finetune/configs/training.py
index 69b083b6a..f9783c952 100644
--- a/QEfficient/finetune/configs/training.py
+++ b/QEfficient/finetune/configs/training.py
@@ -74,7 +74,7 @@ class TrainConfig:
     use_fp16: bool = True
     use_autocast: bool = True
     val_batch_size: int = 1
-    dataset = "samsum_dataset"
+    dataset = "alpaca_dataset"
     task_type = "generation"  # "generation" / "seq_classification"
     peft_method: str = "lora"
     use_peft: bool = True  # use parameter efficient fine tuning
diff --git a/QEfficient/finetune/dataset/custom_dataset.py b/QEfficient/finetune/dataset/custom_dataset.py
index 4bee06c58..f2811b6b3 100644
--- a/QEfficient/finetune/dataset/custom_dataset.py
+++ b/QEfficient/finetune/dataset/custom_dataset.py
@@ -23,7 +23,7 @@ def load_module_from_py_file(py_file: str) -> object:
     return module
 
 
-def get_custom_dataset(dataset_config, tokenizer, split: str):
+def get_custom_dataset(dataset_config, tokenizer, split: str, context_length=None):
     if ":" in dataset_config.file:
         module_path, func_name = dataset_config.file.split(":")
     else:
@@ -38,7 +38,7 @@ def get_custom_dataset(dataset_config, tokenizer, split: str):
 
     module = load_module_from_py_file(module_path.as_posix())
     try:
-        return getattr(module, func_name)(dataset_config, tokenizer, split)
+        return getattr(module, func_name)(dataset_config, tokenizer, split, context_length)
     except AttributeError as e:
         print(
             f"It seems like the given method name ({func_name}) is not present in the dataset .py file ({module_path.as_posix()})."
diff --git a/docs/source/finetune.md b/docs/source/finetune.md
index 40df4401c..939dd2ff3 100644
--- a/docs/source/finetune.md
+++ b/docs/source/finetune.md
@@ -63,4 +63,40 @@ to visualise the data,
 
 ```python
 tensorboard --logdir runs/<file> --bind_all
-```
\ No newline at end of file
+```
+
+## Fine-Tuning on custom dataset
+
+To run fine tuning for any user specific dataset, prepare the dataset using the following steps:
+
+    1) Create a directory named 'dataset' inside efficient-transformers (i.e. at the root of the repo). 
+    2) Inside this directory, create a file named 'custom_dataset.py'. This is different than the custom_dataset.py present at efficient-transformers/QEfficient/finetune/dataset.
+    3) Inside the newly created efficient-transformers/dataset/custom_dataset.py, define a function named 'get_custom_dataset'. 
+    4) get_custom_dataset() should have following 4 parameters:  dataset_config, tokenizer, split, context_length. This function gets called twice through QEfficient/cloud/finetune.py with the name get_preprocessed_dataset. 
+    5) Inside get_custom_dataset(), dataset needs to prepared for fine tuning. So, the user needs to apply prompt and tokenize the dataset accordingly. Please refer the below template on how to define get_custom_dataset().
+    6) For examples, please refer python files present in efficient-transformers/QEfficient/finetune/dataset. In case of Samsum dataset, get_preprocessed_samsum() of efficient-transformers/QEfficient/finetune/dataset/samsum_dataset.py is called. 
+    7) In efficient-transformers/QEfficient/finetune/configs/dataset_config.py, for custom_dataset class, pass the appropriate value for train_split and test_split according to the dataset keys corresponding to train and test data points. As an alternative, these values can be passed as command line arguemnets as well with the finetune command. For example "--train_split train". 
+    8) While running fine tuning, pass argument "-–dataset custom_dataset" to finetune on custom dataset.   
+
+Template for get_custom_dataset() to be defined inside efficient-transformers/dataset/custom_dataset.py is as follows:
+
+```python
+def get_custom_dataset(dataset_config, tokenizer, split, context_length=None):
+
+    # load dataset
+    # based on split, retrieve only the specific portion of the dataset (train or eval) either here or at the last
+    
+    def apply_prompt_template():
+        # transform the passed datapoint by applying the prompt on it 
+    
+    def tokenize():
+        # tokenize the passed datapoint
+    
+    # define the prompt
+    # call apply_prompt_template() for each data point:
+    # dataset = dataset.map(apply_prompt_template ,<other args>)
+    # call tokenize() for each data point:
+    # dataset = dataset.map(tokenize, <other args>)
+    
+    return dataset
+```