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README.md

@@ -18,8 +18,8 @@ import numpy as np
 from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
 # Load the fine-tuned model and tokenizer
-model = GPT2LMHeadModel.from_pretrained("your_model_path")
-tokenizer = GPT2Tokenizer.from_pretrained("your_model_path")
+model = GPT2LMHeadModel.from_pretrained("Autsadin/gpt2_instruct")
+tokenizer = GPT2Tokenizer.from_pretrained("Autsadin/gpt2_instruct")
 
 # Set the seed value for reproducibility
 seed_val = 42
@@ -61,27 +61,21 @@ outputs = model.generate(
 # Decode and print the generated text
 generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
 print(generated_text)
+```
 
 #### Explanation:
 
-Training Data
+#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.
+#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.