diff --git a/GPTQ-for-LLaMa/autograd_4bit.py b/GPTQ-for-LLaMa/autograd_4bit.py
index 34bf9d7..09202f7 100644
--- a/GPTQ-for-LLaMa/autograd_4bit.py
+++ b/GPTQ-for-LLaMa/autograd_4bit.py
@@ -2,6 +2,7 @@ import quant
 import torch
 import numpy as np
 import torch.nn as nn
+import time
 
 
 def matmul4bit(x, qweight, scales, zeros):
@@ -46,24 +47,75 @@ def matmul4bit_transpose(x, qweight, scales, zeros):
     return y.reshape(outshape)
 
 
+def matmul4bit_half(x, qweight, scales, zeros):
+    """
+    input x: (n, m)
+    qweight: (j, k)
+    where m == j*8
+    
+    perform x @ qweight
+    
+    return y: 
+    """
+    assert qweight.shape[0] * 8 == x.shape[-1]
+    outshape = tuple(list(x.shape[:-1]) + [qweight.shape[1]])
+    x = x.reshape(-1, x.shape[-1])
+    y = torch.zeros((x.shape[0], qweight.shape[-1]), dtype=x.dtype, device=x.device)
+    dtype = x.dtype
+    quant.quant_cuda.vecquant4matmul_half(x, qweight, y, scales, zeros)
+    y = y.to(dtype)
+    return y.reshape(outshape)
+    
+    
+def matmul4bit_transpose_half(x, qweight, scales, zeros):
+    """
+    input x: (n, m)
+    qweight: (j, k)
+    where m == k
+    
+    perform qweight @ x.T
+    
+    return y: 
+    """
+    assert qweight.shape[1] == x.shape[-1]
+    outshape = tuple(list(x.shape[:-1]) + [qweight.shape[0] * 8])
+    x = x.reshape(-1, x.shape[-1])
+    y = torch.zeros((qweight.shape[0] * 8, x.shape[0]), dtype=x.dtype, device=x.device)
+    dtype = x.dtype
+    quant.quant_cuda.vecquant4transposematmul_half(x, qweight, y, scales, zeros)
+    y = y.to(dtype)
+    return y.reshape(outshape)
+    
+
 class AutogradMatmul4bit(torch.autograd.Function):
 
     @staticmethod
     def forward(ctx, x, qweight, scales, zeros):
         ctx.save_for_backward(qweight, scales, zeros)
-        output = matmul4bit(x, qweight, scales, zeros).clone()
-        return output # equals to torch.matmul(x, qweight)
+        
+        # equals to torch.matmul(x, qweight)
+        if x.dtype == torch.float32:
+            output = matmul4bit(x, qweight, scales, zeros).clone()
+        elif x.dtype == torch.float16:
+            output = matmul4bit_half(x, qweight, scales, zeros).clone()
+        else:
+            raise ValueError('Only float and half are supportted.')
+        
+        return output
 
     @staticmethod
     def backward(ctx, grad_output):
         qweight, scales, zeros = ctx.saved_tensors
-        # print(grad_output.shape, A.shape, B.shape)
         
         # compute x @ qweight.T = (qweight @ x.T).T = f(x, qweight).T
-        grad1 = matmul4bit_transpose(grad_output, qweight, scales, zeros)
-        # grad2 = torch.matmul(x.transpose(-1, -2), grad_output)
+        if grad_output.dtype == torch.float32:
+            grad = matmul4bit_transpose(grad_output, qweight, scales, zeros)
+        elif grad_output.dtype == torch.float16:
+            grad = matmul4bit_transpose_half(grad_output, qweight, scales, zeros)
+        else:
+            raise ValueError('Only float and half are supportted.')
         
-        return grad1, None, None, None
+        return grad, None, None, None
 
 
 # Assumes layer is perfectly divisible into 256 * 256 blocks
@@ -102,7 +154,27 @@ def make_quant_for_4bit_autograd(module, names, name=''):
         make_quant_for_4bit_autograd(child, names, name + '.' + name1 if name != '' else name1)
 
 
-def load_llama_model_4bit_low_ram(config_path, model_path):
+def model_to_half(model):
+    model.half()
+    for n, m in model.named_modules():
+        if isinstance(m, Autograd4bitQuantLinear):
+            m.zeros = m.zeros.half()
+            m.scales = m.scales.half()
+            m.bias = m.bias.half()
+    print('Converted as Half.')
+
+
+def model_to_float(model):
+    model.float()
+    for n, m in model.named_modules():
+        if isinstance(m, Autograd4bitQuantLinear):
+            m.zeros = m.zeros.float()
+            m.scales = m.scales.float()
+            m.bias = m.bias.float()
+    print('Converted as Float.')
+
+
+def load_llama_model_4bit_low_ram(config_path, model_path, half=False):
     import transformers
     import accelerate
     from transformers import LLaMAConfig, LLaMAForCausalLM, LLaMATokenizer
@@ -127,6 +199,9 @@ def load_llama_model_4bit_low_ram(config_path, model_path):
     model = accelerate.load_checkpoint_and_dispatch(model=model, checkpoint=model_path, device_map='auto')
     model.cuda()
     model.seqlen = 2048
+    
+    if half:
+        model_to_half(model)
 
     tokenizer = LLaMATokenizer.from_pretrained(config_path)
     tokenizer.truncation_side = 'left'
@@ -134,4 +209,4 @@ def load_llama_model_4bit_low_ram(config_path, model_path):
     print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
     
     return model, tokenizer
-    
+    
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