changed python example

README.md

@@ -15,15 +15,32 @@ This model is optimized for financial analysis, valuation calculations, and fina
 ## Example Usage
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
+from peft import PeftModel, PeftConfig
+import torch
+
+# Set device
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
 # Load the model and tokenizer
 model_name = "Rishi-19/deepseek_finetuned_model_rishi"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Load the base model first
+peft_config = PeftConfig.from_pretrained(model_name)
+base_model = AutoModelForCausalLM.from_pretrained(
+    peft_config.base_model_name_or_path, 
+    torch_dtype=torch.float16,  # Use half precision to save memory
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# Then load the PEFT adapter
+model = PeftModel.from_pretrained(base_model, model_name)
+model.eval()  # Set to evaluation mode
 
 # Generate text
-inputs = tokenizer("Calculate the Net Present Value of a project with initial investment of $1M", return_tensors="pt")
-outputs = model.generate(**inputs, max_length=200)
+inputs = tokenizer("Calculate the Net Present Value of a project with initial investment of $1M", return_tensors="pt").to(device)
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_length=200)
 print(tokenizer.decode(outputs[0]))
 ```