diff --git a/run.py b/run.py
index dd21b7a..b52a15c 100644
--- a/run.py
+++ b/run.py
@@ -9,6 +9,7 @@ from src.tot.models import gpt_usage
 
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
+import torch.quantization
 
 
 def run(args):
@@ -17,14 +18,20 @@ def run(args):
     '''
     #load in non-gpt model in this driver function for now to avoid repeated loading later on
     if args.backend == 'llama':
-        if not args.quantize:
-            tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
-            model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
-        elif args.quantize == 'qat':
-            pass
-            # tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
-            # model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct-QLORA_INT4_EO8")
-        elif args.backend == 'spinquant':
+        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
+        model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
+        
+        if args.quantize and args.quantize=='ptq':
+            model.train()
+            model.qconfig = torch.quantization.get_default_qconfig('x86')
+            torch.quantization.prepare(model, inplace=True)
+            for _, mod in model.named_modules():
+                if isinstance(mod, torch.nn.Embedding):
+                    mod.qconfig = torch.ao.quantization.float_qparams_weight_only_qconfig
+            model = torch.quantization.convert(model, inplace=True)
+            model.load_state_dict(torch.load('quant_experiments/quantized_model.pth'))
+            model.eval()
+        elif args.backend == 'qat':
             pass
             # tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
             # model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8")