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README.md

@@ -62,26 +62,26 @@ outputs = model.generate(
 generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
 print(generated_text)
 
-### Explanation:
-
-# Training Data
-# The model was fine-tuned using the Alpaca GPT-4 dataset available at the following GitHub repository. 
-# https://github.com/hy5468/TransLLM/tree/main/data/train
-# Specifically, the alpaca_gpt4_data_en.zip dataset was utilized.
-# This dataset includes a wide range of instruction-based prompts and responses, 
-# providing a robust foundation for the model's training.
-
-# Training Procedure
-# The fine-tuning process was carried out with the following hyperparameters:
-# Learning Rate: 2e-5
-# Batch Size (Train): 4
-# Batch Size (Eval): 4
-# Number of Epochs: 1
-# Weight Decay: 0.01
-
-# Training Environment
-# The model was trained using PyTorch and the Hugging Face transformers library. 
-# The training was performed on a GPU-enabled environment to accelerate the fine-tuning process.
-# The training script ensures reproducibility by setting a consistent random seed across different components.
+#### Explanation:
+
+Training Data
+The model was fine-tuned using the Alpaca GPT-4 dataset available at the following GitHub repository. 
+https://github.com/hy5468/TransLLM/tree/main/data/train
+Specifically, the alpaca_gpt4_data_en.zip dataset was utilized.
+This dataset includes a wide range of instruction-based prompts and responses, 
+providing a robust foundation for the model's training.
+
+Training Procedure
+The fine-tuning process was carried out with the following hyperparameters:
+Learning Rate: 2e-5
+Batch Size (Train): 4
+Batch Size (Eval): 4
+Number of Epochs: 1
+Weight Decay: 0.01
+
+Training Environment
+The model was trained using PyTorch and the Hugging Face transformers library. 
+The training was performed on a GPU-enabled environment to accelerate the fine-tuning process.
+The training script ensures reproducibility by setting a consistent random seed across different components.