diff --git a/amp_wrapper.py b/amp_wrapper.py
new file mode 100644
index 0000000..f7d01f5
--- /dev/null
+++ b/amp_wrapper.py
@@ -0,0 +1,26 @@
+import torch
+
+
+class AMPWrapper:
+    
+    def __init__(self, model, options=None):
+        self.model = model
+        self.options = options
+        if self.options is None:
+            self.options = {'enabled': True, 'device_type': 'cuda'}
+        
+    def autocast_forward(self, *args, **kwargs):
+        with torch.amp.autocast(**self.options):
+            return self.model.non_autocast_forward(*args, **kwargs)
+    
+    def autocast_generate(self, *args, **kwargs):
+        with torch.amp.autocast(**self.options):
+            return self.model.non_autocast_generate(*args, **kwargs)
+    
+    def apply_forward(self):
+        self.model.non_autocast_forward = self.model.forward
+        self.model.forward = self.autocast_forward
+        
+    def apply_generate(self):
+        self.model.non_autocast_generate = self.model.generate
+        self.model.generate = self.autocast_generate
diff --git a/inference.py b/inference.py
index 84ade3d..c0f4599 100644
--- a/inference.py
+++ b/inference.py
@@ -5,9 +5,10 @@ import torch
 from autograd_4bit import load_llama_model_4bit_low_ram, Autograd4bitQuantLinear
 config_path = './llama-13b-4bit/'
 model_path = './llama-13b-4bit.pt'
-model, tokenizer = load_llama_model_4bit_low_ram(config_path, model_path)
+model, tokenizer = load_llama_model_4bit_low_ram(config_path, model_path, groupsize=-1)
 
 print('Fitting 4bit scales and zeros to half')
+model.half()
 for n, m in model.named_modules():
     if isinstance(m, Autograd4bitQuantLinear):
         if m.groupsize == -1:
@@ -15,6 +16,11 @@ for n, m in model.named_modules():
         m.scales = m.scales.half()
         m.bias = m.bias.half()
 
+print('Apply AMP Wrapper ...')
+from amp_wrapper import AMPWrapper
+wrapper = AMPWrapper(model)
+wrapper.apply_generate()
+
 prompt = '''I think the meaning of life is'''
 batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
 batch = {k: v.cuda() for k, v in batch.items()}