Update README.md

README.md

@@ -38,25 +38,6 @@ Guided message tasks for natural turn-taking
 
 (Exact dataset details are kept abstract to maintain clarity while ensuring transparency.)
 
-## 🚀 How to Use GanLLM
-
-You can use GanLLM easily with the Transformers library:
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-
-# Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("Rahulwale12/ganllm")
-model = AutoModelForCausalLM.from_pretrained("Rahulwale12/ganllm", device_map="auto")
-
-# Create text-generation pipeline
-generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
-
-# Example prompt
-prompt = "### Instruction:\nPersona: I live in Delhi and love cricket.\nDialogue so far: Do you follow IPL?\n\n### Response:\n"
-
-output = generator(prompt, max_new_tokens=100, do_sample=True, temperature=0.7, top_p=0.9)
-print(output[0]["generated_text"]) ```
 
 ## ⚡ Intended Use
 
@@ -84,4 +65,26 @@ This model is released for research and personal use only. For any commercial ap
 
 ## 🙌 Acknowledgements
 
-Developed and maintained by Rahul Wale – AI Developer.
+Developed and maintained by Rahul Wale – AI Developer.
+
+## 🚀 How to Use GanLLM
+
+You can use GanLLM easily with the Transformers library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("Rahulwale12/ganllm")
+model = AutoModelForCausalLM.from_pretrained("Rahulwale12/ganllm", device_map="auto")
+
+# Create text-generation pipeline
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
+
+# Example prompt
+prompt = "### Instruction:\nPersona: I live in Delhi and love cricket.\nDialogue so far: Do you follow IPL?\n\n### Response:\n"
+
+output = generator(prompt, max_new_tokens=100, do_sample=True, temperature=0.7, top_p=0.9)
+print(output[0]["generated_text"])
+
+python ```