Update README.md

README.md

@@ -63,19 +63,15 @@ The model was fine-tuned on a custom dataset (`data.jsonl`) consisting of:
 ## Example Usage
 
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-base_model = "mistralai/Mistral-7B-Instruct-v0.3"
-repo = "techpro-saida/msci_software_engineering_slm_v1"
+tokenizer = AutoTokenizer.from_pretrained("techpro-saida/banking-slm-v1")
+model = AutoModelForCausalLM.from_pretrained("techpro-saida/banking-slm-v1")
 
-model = AutoModelForCausalLM.from_pretrained(base_model)
-model = PeftModel.from_pretrained(model, repo)
-tokenizer = AutoTokenizer.from_pretrained(base_model)
-
-slm = pipeline("text-generation", model=model, tokenizer=tokenizer)
-result = slm("Explain SOLID principles in OOP.", max_new_tokens=80)
-print(result[0]["generated_text"])
+prompt = "Explain SOLID principles in OOP?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))