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README.md

@@ -10,6 +10,8 @@ license: mit
 
 # Phi-1.5 TOFU Unlearning Model
 
+**IMPORTANT: This model's checkpoints are stored in separate branches. You MUST specify a revision when loading the model to access a specific checkpoint.**
+
 This model is a variant of the Phi-1.5 model, fine-tuned on the TOFU (Task of Fictitious Unlearning) dataset and then subjected to various unlearning algorithms.
 
 ## Model Details
@@ -27,22 +29,24 @@ This model uses the `grad_ascent` unlearning algorithm with the following parame
 
 ## Revisions
 
-The model is organized into multiple revisions, each representing a checkpoint during the unlearning process. The revision names follow the pattern `checkpoint-X`, where X is the checkpoint number.
+The model is organized into multiple revisions, each representing a checkpoint during the unlearning process. The revision names follow the pattern `checkpoint-X`, where X is the checkpoint number. Each revision is stored in a separate branch.
 
 ## Loading the Model
 
-To load a specific revision of this model, you can use the following code:
+To load a specific revision of this model, you MUST specify the revision parameter. Use the following code:
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-# Replace 'checkpoint-X' with the desired revision (e.g., 'checkpoint-12')
+# The 'revision' parameter is REQUIRED. Replace 'checkpoint-X' with the desired revision (e.g., 'checkpoint-12')
 revision = "checkpoint-X"
 
 model = AutoModelForCausalLM.from_pretrained("locuslab/{model_name}", revision=revision)
 tokenizer = AutoTokenizer.from_pretrained("locuslab/{model_name}", revision=revision)
 ```
 
+**Note: If you don't specify a revision, you will not be able to load the model correctly.**
+
 ## TOFU Dataset
 
 TOFU (Task of Fictitious Unlearning) is a dataset designed for training and evaluating unlearning algorithms in language models. It simulates scenarios where certain information needs to be "forgotten" or removed from the model's knowledge.
@@ -51,7 +55,7 @@ TOFU (Task of Fictitious Unlearning) is a dataset designed for training and eval
 
 1. The base Phi-1.5 model was first fine-tuned on the TOFU dataset (checkpoint-625).
 2. Various unlearning algorithms were then applied to this fine-tuned model to selectively "forget" certain information.
-3. The results of these unlearning processes are captured in the different revisions of this model.
+3. The results of these unlearning processes are captured in the different revisions (branches) of this model.
 
 ## Usage and Limitations