diff --git a/peft/README.md b/peft/README.md
new file mode 100644
index 0000000..8730f03
--- /dev/null
+++ b/peft/README.md
@@ -0,0 +1,42 @@
+# Fine-Tuning Pipeline
+
+This folder contains a generic fine-tuning pipeline designed to support multiple PEFT fine-tuning strategies.
+
+## Features
+
+- **Supported Fine-Tuning Strategies**:
+  - **Full Fine-Tuning**: Adjusts all parameters of the model during training.
+  - **[Linear Probing](https://arxiv.org/abs/2302.11939)**: Fine-tunes only the residual blocks and the embedding layer, leaving other parameters unchanged.
+  - **[LoRA (Low-Rank Adaptation)](https://arxiv.org/abs/2106.09685)**: A memory-efficient method that fine-tunes a small number of parameters by decomposing the weight matrices into low-rank matrices.
+  - **[DoRA (Directional LoRA)](https://arxiv.org/abs/2402.09353v4)**: An extension of LoRA that decomposes pre-trained weights into magnitude and direction components. It uses LoRA for directional adaptation, enhancing learning capacity and stability without additional inference overhead.
+
+## Usage
+### Fine-Tuning Script
+The provided finetune.py script allows you to fine-tune a model with specific configurations. You can customize various parameters to suit your dataset and desired fine-tuning strategy.
+
+Example Usage:
+
+```zsh
+source finetune.sh
+```
+This script runs the finetune.py file with a predefined set of hyperparameters for the model. You can adjust the parameters in the script as needed.
+
+### Available Options
+Run the script with the --help flag to see a full list of available options and their descriptions:
+```zsh
+python3 finetune.py --help
+```
+Script Configuration
+You can modify the following key parameters directly in the finetune.sh script:
+Fine-Tuning Strategy: Toggle between full fine-tuning, LoRA \[`--use-lora`\], DoRA [\[`--use-dora`\]], or Linear Probing \[`--use-linear-probing`\].
+
+### Performance Comparison
+The figure below compares the performance of LoRA/DoRA against Linear Probing under the following conditions:
+
+<img width="528" alt="image" src="https://github.com/user-attachments/assets/6c9f820b-5865-4821-8014-c346b9d632a5">
+
+- Training data split: 60% train, 20% validation, 20% test.
+- Benchmark: context_len=128, horizon_len=96
+- Fine-tuning: context_len=128, horizon_len=128
+- Black: Best result.
+- Blue: Second best result.