Update README.md

README.md

@@ -54,17 +54,46 @@ Users should consider implementing bias mitigation strategies and ensure thoroug
 Use the following code snippet to get started with loading and using the model:
 
 ```python
+# Import necessary libraries
 from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import intel_extension_for_pytorch as ipex  # Optional for Intel optimization
 
-model_name = "nayem-ng/mdjannatulnayem_llama2_7b_finetuned_casuallm_lora"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+# Specify your Hugging Face model repository
+hf_model = "nayem-ng/mdjannatulnayem_llama2_7b_finetuned_casuallm_lora"
 
-# Example of generating text
-input_text = "Your prompt here"
-inputs = tokenizer(input_text, return_tensors="pt")
-outputs = model.generate(**inputs)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+# Load the fine-tuned model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(hf_model)
+tokenizer = AutoTokenizer.from_pretrained(hf_model)
+
+# Move the model to the desired device
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+# Set the model to evaluation mode
+model.eval()
+
+# Optional: Optimize with Intel extensions for PyTorch
+# Uncomment the next line if you want to use Intel optimizations
+# model = ipex.optimize(model)
+
+# Function to generate text
+def generate_text(prompt, max_length=50):
+    # Tokenize the input prompt
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+    # Generate output
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_length=max_length)
+
+    # Decode and return the generated text
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Example usage
+if __name__ == "__main__":
+    prompt = "Once upon a time"
+    generated_text = generate_text(prompt)
+    print("Generated Text:", generated_text)
 ```
 
 ## Training Details