diff --git a/Finetune4bConfig.py b/Finetune4bConfig.py
index 09c535c..06eb26e 100644
--- a/Finetune4bConfig.py
+++ b/Finetune4bConfig.py
@@ -97,5 +97,6 @@ class Finetune4bConfig:
         f"{self.warmup_steps=}\n{self.save_steps=}\n{self.save_total_limit=}\n" +\
         f"{self.logging_steps=}\n" +\
         f"{self.checkpoint=}\n{self.skip=}\n" +\
-        f"{self.world_size=}\n{self.ddp=}\n{self.device_map=}"
+        f"{self.world_size=}\n{self.ddp=}\n{self.device_map=}\n" +\
+        f"{self.groupsize=}\n"
         return s.replace("self.", "")
diff --git a/README.md b/README.md
index 27dcdf6..4256ac0 100644
--- a/README.md
+++ b/README.md
@@ -35,10 +35,20 @@ pip install -r requirements.txt
 ~The same finetune script from https://github.com/tloen/alpaca-lora can be used.~<br>
 
 After installation, this script can be used:
+GPTQv1:
 
 ```
 python finetune.py
 ```
+or
+```
+GPTQ_VERSION=1 python finetune.py
+```
+
+GPTQv2:
+```
+GPTQ_VERSION=2 python finetune.py
+```
 
 # Inference
 
diff --git a/autograd_4bit/__init__.py b/autograd_4bit/__init__.py
new file mode 100644
index 0000000..bee84b3
--- /dev/null
+++ b/autograd_4bit/__init__.py
@@ -0,0 +1,21 @@
+import os
+from colorama import init, Fore, Back, Style
+init(autoreset=True)
+
+try:
+    GPTQ_VERSION = int(os.environ["GPTQ_VERSION"])
+except:
+    print(Style.BRIGHT + Fore.YELLOW + "GPTQ_VERSION environment not provided. Fallback to GPTQv1")
+    GPTQ_VERSION = 1  # Fallback version
+
+loader = None
+
+
+if GPTQ_VERSION == 1:
+    from .autograd_4bit_v1 import Autograd4bitQuantLinear, load_llama_model_4bit_low_ram            
+    print(Style.BRIGHT + Fore.GREEN + "GPTQv1 set")
+elif GPTQ_VERSION == 2:
+    from .autograd_4bit_v2 import Autograd4bitQuantLinear, load_llama_model_4bit_low_ram
+    print(Style.BRIGHT + Fore.GREEN + "GPTQv2 set")
+else:
+    raise ValueError("GPTQ_VERSION not set or invalid")
\ No newline at end of file
diff --git a/autograd_4bit.py b/autograd_4bit/autograd_4bit_v1.py
similarity index 83%
rename from autograd_4bit.py
rename to autograd_4bit/autograd_4bit_v1.py
index b7e883f..6432f2d 100644
--- a/autograd_4bit.py
+++ b/autograd_4bit/autograd_4bit_v1.py
@@ -2,7 +2,6 @@ import matmul_utils_4bit as mm4b
 import torch
 import torch.nn as nn
 import time
-import math
 
 
 class AutogradMatmul4bit(torch.autograd.Function):
@@ -32,25 +31,17 @@ class AutogradMatmul4bit(torch.autograd.Function):
 # Assumes layer is perfectly divisible into 256 * 256 blocks
 class Autograd4bitQuantLinear(nn.Module):
 
-    def __init__(self, infeatures, outfeatures, groupsize=-1):
+    def __init__(self, in_features, out_features, groupsize=None):
         super().__init__()
         bits = 4
-        self.in_features = infeatures
-        self.out_features = outfeatures
+        self.in_features = in_features
+        self.out_features = out_features
         self.bits = bits
-        self.groupsize = groupsize
-        if groupsize == -1:
-            self.register_buffer('zeros', torch.empty((outfeatures, 1)))
-            self.register_buffer('scales', torch.empty((outfeatures, 1)))
-        else:
-            self.register_buffer('qzeros',
-                                  torch.empty((math.ceil(infeatures/groupsize), outfeatures // 256 * (bits * 8)), dtype=torch.int)
-                                )
-            self.register_buffer('scales', torch.empty((math.ceil(infeatures/groupsize), outfeatures)))
-            self.register_buffer('g_idx', torch.tensor([i // self.groupsize  for i in range(infeatures)], dtype = torch.int32))
-        self.register_buffer('bias', torch.empty(outfeatures))
+        self.register_buffer('zeros', torch.empty((out_features, 1)))
+        self.register_buffer('scales', torch.empty((out_features, 1)))
+        self.bias = nn.Parameter(torch.empty(out_features))
         self.register_buffer(
-            'qweight', torch.empty((infeatures // 256 * (bits * 8), outfeatures), dtype=torch.int)
+            'qweight', torch.empty((in_features // 256 * (bits * 8), out_features), dtype=torch.int)
         )
 
 
@@ -84,8 +75,7 @@ def model_to_half(model):
     model.half()
     for n, m in model.named_modules():
         if isinstance(m, Autograd4bitQuantLinear):
-            if m.groupsize == -1:
-                m.zeros = m.zeros.half()
+            m.zeros = m.zeros.half()
             m.scales = m.scales.half()
             m.bias = m.bias.half()
     print('Converted as Half.')
@@ -95,8 +85,7 @@ def model_to_float(model):
     model.float()
     for n, m in model.named_modules():
         if isinstance(m, Autograd4bitQuantLinear):
-            if m.groupsize == -1:
-                m.zeros = m.zeros.float()
+            m.zeros = m.zeros.float()
             m.scales = m.scales.float()
             m.bias = m.bias.float()
     print('Converted as Float.')
@@ -187,8 +176,7 @@ def load_llama_model_4bit_low_ram_and_offload_to_cpu(config_path, model_path, lo
     print('Apply half ...')
     for n, m in model.named_modules():
         if isinstance(m, Autograd4bitQuantLinear) or ((lora_path is not None) and isinstance(m, Linear4bitLt)):
-            if m.groupsize == -1:
-                m.zeros = m.zeros.half()
+            m.zeros = m.zeros.half()
             m.scales = m.scales.half()
             m.bias = m.bias.half()
 
diff --git a/autograd_4bit/autograd_4bit_v2.py b/autograd_4bit/autograd_4bit_v2.py
new file mode 100644
index 0000000..20c253d
--- /dev/null
+++ b/autograd_4bit/autograd_4bit_v2.py
@@ -0,0 +1,221 @@
+from colorama import init, Fore, Back, Style
+import torch
+import torch.nn as nn
+import time
+import math
+import triton
+from triton_utils import matmul_248_kernel, trans_matmul_248_kernel
+
+
+class AutogradMatmul4bit(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
+        output = torch.empty((input.shape[0], qweight.shape[1]), device='cuda', dtype=torch.float16)
+        grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),)
+        matmul_248_kernel[grid](input, qweight, output,
+                                scales, qzeros, g_idx,
+                                input.shape[0], qweight.shape[1], input.shape[1], bits, maxq,
+                                input.stride(0), input.stride(1),
+                                qweight.stride(0), qweight.stride(1),
+                                output.stride(0), output.stride(1),
+                                scales.stride(0), qzeros.stride(0))
+        
+        ctx.save_for_backward(qweight, scales, qzeros, g_idx)
+        ctx.input_shape, ctx.bits,ctx.maxq = input.shape,bits, maxq
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        input_shape, bits, maxq = ctx.input_shape, ctx.bits, ctx.maxq
+        qweight, scales, qzeros, g_idx = ctx.saved_tensors
+        grade_input = None
+
+        if ctx.needs_input_grad[0]:
+            grade_input = torch.empty((input_shape[0], input_shape[1]), device='cuda', dtype=torch.float32)
+            grid = lambda META: (triton.cdiv(input_shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(input_shape[1], META['BLOCK_SIZE_K']),)
+            trans_matmul_248_kernel[grid](grad_output, qweight, grade_input,
+                                          scales, qzeros, g_idx,
+                                          input_shape[0], qweight.shape[1], input_shape[1], bits, maxq,
+                                          grad_output.stride(0), grad_output.stride(1),
+                                          qweight.stride(0), qweight.stride(1),
+                                          grade_input.stride(0), grade_input.stride(1),
+                                          scales.stride(0), qzeros.stride(0))
+        return grade_input, None, None, None, None, None, None
+
+
+class Autograd4bitQuantLinear(nn.Module):
+
+    def __init__(self, in_features, out_features, groupsize, bias=True):
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.bits = 4  # Hardcoded 4-bits quantizations
+        self.maxq = 2 ** self.bits - 1
+        self.groupsize = groupsize if groupsize != -1 else in_features
+        
+        self.register_buffer('qweight', torch.zeros((in_features // 32 * self.bits, out_features), dtype=torch.int32))
+        self.register_buffer('qzeros', torch.zeros((math.ceil(in_features / self.groupsize), out_features // 32 * self.bits), dtype=torch.int32))
+        self.register_buffer('scales', torch.zeros((math.ceil(in_features / self.groupsize), out_features), dtype=torch.float16))
+        self.register_buffer('g_idx', torch.tensor([i // self.groupsize  for i in range(in_features)], dtype = torch.int32))
+        if bias:
+            self.register_buffer('bias', torch.zeros(out_features,dtype=torch.float16))
+        else:
+            self.bias = None
+        
+    def forward(self, x):
+        out_shape = x.shape[:-1] + (self.out_features, )
+        out = AutogradMatmul4bit.apply(x.reshape(-1,x.shape[-1]), self.qweight, self.scales, 
+                                        self.qzeros, self.g_idx, self.bits, self.maxq)
+        out = out + self.bias if self.bias is not None else out  
+        return out.reshape(out_shape)
+
+
+def make_quant_for_4bit_autograd(module, names, name='', groupsize=-1):
+    if isinstance(module, Autograd4bitQuantLinear):
+        return
+    for attr in dir(module):
+        tmp = getattr(module, attr)
+        name1 = name + '.' + attr if name != '' else attr
+        if name1 in names:
+            setattr(
+                module, attr, Autograd4bitQuantLinear(tmp.in_features, tmp.out_features, groupsize=groupsize)
+            )
+    for name1, child in module.named_children():
+        make_quant_for_4bit_autograd(child, names, name + '.' + name1 if name != '' else name1, groupsize=groupsize)
+
+
+def model_to_half(model):
+    model.half()
+    for n, m in model.named_modules():
+        if isinstance(m, Autograd4bitQuantLinear):
+            m.qzeros = m.qzeros.half()
+            m.scales = m.scales.half()
+            m.bias = m.bias.half()
+    print(Style.BRIGHT + Fore.YELLOW + 'Converted as Half.')
+
+
+def model_to_float(model):
+    model.float()
+    for n, m in model.named_modules():
+        if isinstance(m, Autograd4bitQuantLinear):
+            m.qzeros = m.qzeros.float()
+            m.scales = m.scales.float()
+            m.bias = m.bias.float()
+    print(Style.BRIGHT + Fore.YELLOW + 'Converted as Float.')
+
+
+def find_layers(module, layers=[nn.Conv2d, nn.Linear], name=''):
+    if type(module) in layers:
+        return {name: module}
+    res = {}
+    for name1, child in module.named_children():
+        res.update(find_layers(
+            child, layers=layers, name=name + '.' + name1 if name != '' else name1
+        ))
+    return res
+
+
+def load_llama_model_4bit_low_ram(config_path, model_path, groupsize=-1, half=False, device_map="auto", seqlen=2048):
+    import accelerate
+    from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
+
+    print(Style.BRIGHT + Fore.CYAN + "Loading Model ...")
+    t0 = time.time()
+
+    with accelerate.init_empty_weights():
+        config = LlamaConfig.from_pretrained(config_path)
+        model = LlamaForCausalLM(config)
+        model = model.eval()
+        layers = find_layers(model)
+        for name in ['lm_head']:
+            if name in layers:
+                del layers[name]
+        make_quant_for_4bit_autograd(model, layers, groupsize=groupsize)
+    model = accelerate.load_checkpoint_and_dispatch(
+        model=model,
+        checkpoint=model_path,
+        device_map=device_map,
+        no_split_module_classes=["LlamaDecoderLayer"]
+    )
+
+    model.seqlen = seqlen
+
+    if half:
+        model_to_half(model)
+
+    tokenizer = LlamaTokenizer.from_pretrained(config_path)
+    tokenizer.truncation_side = 'left'
+
+    print(Style.BRIGHT + Fore.GREEN + f"Loaded the model in {(time.time()-t0):.2f} seconds.")
+
+    return model, tokenizer
+
+def load_llama_model_4bit_low_ram_and_offload_to_cpu(config_path, model_path, lora_path=None, groupsize=-1, seqlen=2048, max_memory=None):
+    import accelerate
+    from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
+
+    if max_memory is None:
+        max_memory = {0: '24Gib', 'cpu': '48Gib'}
+
+    print(Style.BRIGHT + Fore.CYAN + "Loading Model ...")
+    t0 = time.time()
+
+    with accelerate.init_empty_weights():
+        config = LlamaConfig.from_pretrained(config_path)
+        model = LlamaForCausalLM(config)
+        model = model.eval()
+        layers = find_layers(model)
+        for name in ['lm_head']:
+            if name in layers:
+                del layers[name]
+        make_quant_for_4bit_autograd(model, layers, groupsize=groupsize)
+    accelerate.load_checkpoint_in_model(model, checkpoint=model_path, device_map={'': 'cpu'})
+
+    # rotary_emb fix
+    for n, m in model.named_modules():
+        if 'rotary_emb' in n:
+            cos_cached = m.cos_cached.clone().cpu()
+            sin_cached = m.sin_cached.clone().cpu()
+            break
+
+    if lora_path is not None:
+        from peft import PeftModel
+        from peft.tuners.lora import Linear4bitLt
+        model = PeftModel.from_pretrained(model, lora_path, device_map={'': 'cpu'}, torch_dtype=torch.float32)
+        print(Style.BRIGHT + Fore.GREEN + '{} Lora Applied.'.format(lora_path))
+
+    model.seqlen = seqlen
+
+    print('Apply half ...')
+    for n, m in model.named_modules():
+        if isinstance(m, Autograd4bitQuantLinear) or ((lora_path is not None) and isinstance(m, Linear4bitLt)):
+            m.qzeros = m.qzeros.half()
+            m.scales = m.scales.half()
+            m.bias = m.bias.half()
+
+    print('Dispatching model ...')
+    device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LlamaDecoderLayer"])
+    model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True, main_device=0)
+    torch.cuda.empty_cache()
+    print(Style.BRIGHT + Fore.YELLOW + 'Total {:.2f} Gib VRAM used.'.format(torch.cuda.memory_allocated() / 1024 / 1024))
+
+    # rotary_emb fix
+    for n, m in model.named_modules():
+        if 'rotary_emb' in n:
+            if getattr(m, '_hf_hook', None):
+                if isinstance(m._hf_hook, accelerate.hooks.SequentialHook):
+                    hooks = m._hf_hook.hooks
+                else:
+                    hooks = [m._hf_hook]
+                for hook in hooks:
+                    if hook.offload:
+                        if n + '.sin_cached' not in hook.weights_map.dataset.state_dict.keys():
+                            hook.weights_map.dataset.state_dict[n + '.sin_cached'] = sin_cached.clone().cpu()
+                            hook.weights_map.dataset.state_dict[n + '.cos_cached'] = cos_cached.clone().cpu()
+
+    tokenizer = LlamaTokenizer.from_pretrained(config_path)
+    tokenizer.truncation_side = 'left'
+
+    print(Style.BRIGHT + Fore.GREEN + f"Loaded the model in {(time.time()-t0):.2f} seconds.")
+
+    return model, tokenizer
diff --git a/finetune.py b/finetune.py
index eea0e66..82dbde5 100644
--- a/finetune.py
+++ b/finetune.py
@@ -115,6 +115,7 @@ if not ft_config.skip:
         per_device_train_batch_size=ft_config.mbatch_size,
         gradient_accumulation_steps=ft_config.gradient_accumulation_steps,
         warmup_steps=ft_config.warmup_steps,
+        optim="adamw_torch",
         num_train_epochs=ft_config.epochs,
         learning_rate=ft_config.lr,
         fp16=True,
diff --git a/requirements.txt b/requirements.txt
index 2db1f01..95a23a6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,6 +5,7 @@ datasets
 sentencepiece
 safetensors
 flash-attn
+triton
 git+https://github.com/huggingface/transformers.git
 git+https://github.com/sterlind/GPTQ-for-LLaMa.git@lora_4bit
 git+https://github.com/sterlind/peft.git
diff --git a/triton_utils.py b/triton_utils.py
index 940c73c..57f9e66 100644
--- a/triton_utils.py
+++ b/triton_utils.py
@@ -1,205 +1,210 @@
-import triton
-import triton.language as tl
-import torch
-
-# code based https://github.com/fpgaminer/GPTQ-triton
-@triton.autotune(
-    configs=[
-        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
-        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
-        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
-        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
-        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
-    ],
-    key=['M', 'N', 'K'],
-)
-
-@triton.jit
-def matmul_248_kernel(a_ptr, b_ptr, c_ptr,
-                      scales_ptr, zeros_ptr, g_ptr,
-                      M, N, K, bits, maxq,
-                      stride_am, stride_ak,
-                      stride_bk, stride_bn,
-                      stride_cm, stride_cn,
-                      stride_scales, stride_zeros,
-                      BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
-                      GROUP_SIZE_M: tl.constexpr):
-    """
-    Compute the matrix multiplication C = A x B.
-    A is of shape (M, K) float16
-    B is of shape (K//8, N) int32
-    C is of shape (M, N) float16
-    scales is of shape (G, N) float16
-    zeros is of shape (G, N) float16
-    g_ptr is of shape (K) int32 
-    """
-    infearure_per_bits = 32 // bits
-
-    pid = tl.program_id(axis=0)
-    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
-    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
-    num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
-    num_pid_in_group = GROUP_SIZE_M * num_pid_n
-    group_id = pid // num_pid_in_group
-    first_pid_m = group_id * GROUP_SIZE_M
-    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
-    pid_m = first_pid_m + (pid % group_size_m)
-    pid_n = (pid % num_pid_in_group) // group_size_m
-
-    offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
-    offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
-    offs_k = tl.arange(0, BLOCK_SIZE_K)
-    a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)   # (BLOCK_SIZE_M, BLOCK_SIZE_K)
-    a_mask = (offs_am[:, None] < M)
-    # b_ptrs is set up such that it repeats elements along the K axis 8 times
-    b_ptrs = b_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn)   # (BLOCK_SIZE_K, BLOCK_SIZE_N)
-    g_ptrs = g_ptr + offs_k
-    # shifter is used to extract the N bits of each element in the 32-bit word from B
-    scales_ptrs = scales_ptr + offs_bn[None, :]
-    zeros_ptrs = zeros_ptr + (offs_bn[None, :]// infearure_per_bits) 
-    
-    shifter = (offs_k % infearure_per_bits) * bits
-    zeros_shifter = (offs_bn % infearure_per_bits) * bits
-    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
-            
-    for k in range(0, num_pid_k):
-        g_idx = tl.load(g_ptrs)
-
-        # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
-        scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
-        zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
-        
-        zeros = (zeros >> zeros_shifter[None, :]) & maxq
-        zeros = (zeros + 1)
-        
-        a = tl.load(a_ptrs, mask=a_mask, other=0.)   # (BLOCK_SIZE_M, BLOCK_SIZE_K)
-        b = tl.load(b_ptrs)   # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
-
-        # Now we need to unpack b (which is N-bit values) into 32-bit values
-        b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
-        b = (b - zeros) * scales  # Scale and shift
-
-        accumulator += tl.dot(a, b)
-        a_ptrs += BLOCK_SIZE_K
-        b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
-        g_ptrs += BLOCK_SIZE_K
-
-    c = accumulator.to(tl.float16)
-    c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
-    c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
-    tl.store(c_ptrs, accumulator, mask=c_mask)
-    
-# code based https://github.com/fpgaminer/GPTQ-triton
-@triton.autotune(
-    configs=[
-        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
-        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
-        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
-        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
-        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
-    ],
-    key=['M', 'N', 'K'],
-)
-
-@triton.jit
-def trans_matmul_248_kernel(a_ptr, b_ptr, c_ptr,
-                            scales_ptr, zeros_ptr, g_ptr,
-                            M, N, K, bits, maxq,
-                            stride_am, stride_ak,
-                            stride_bk, stride_bn,
-                            stride_cm, stride_cn,
-                            stride_scales, stride_zeros,
-                            BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
-                            GROUP_SIZE_M: tl.constexpr):
-    """
-    Compute the matrix multiplication C = A x B.
-    A is of shape (M, N) float16
-    B is of shape (K//8, N) int32
-    C is of shape (M, K) float16
-    scales is of shape (G, N) float16
-    zeros is of shape (G, N) float16
-    g_ptr is of shape (K) int32 
-    """
-    infearure_per_bits = 32 // bits
-
-    pid = tl.program_id(axis=0)
-    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
-    num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
-    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
-    num_pid_in_group = GROUP_SIZE_M * num_pid_k
-    group_id = pid // num_pid_in_group
-    first_pid_m = group_id * GROUP_SIZE_M
-    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
-    pid_m = first_pid_m + (pid % group_size_m)
-    pid_k = (pid % num_pid_in_group) // group_size_m
-
-    offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
-    offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
-    offs_n = tl.arange(0, BLOCK_SIZE_N)
-    a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak)   # (BLOCK_SIZE_M, BLOCK_SIZE_N)
-    a_mask = (offs_am[:, None] < M)
-    # b_ptrs is set up such that it repeats elements along the K axis 8 times
-    b_ptrs = b_ptr + ((offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None, :] * stride_bn)   # (BLOCK_SIZE_K, BLOCK_SIZE_N)
-    g_ptrs = g_ptr + offs_bk
-    g_idx = tl.load(g_ptrs)
-    
-    # shifter is used to extract the N bits of each element in the 32-bit word from B
-    scales_ptrs = scales_ptr + offs_n[None, :]  + g_idx[:, None] * stride_scales
-    zeros_ptrs = zeros_ptr + (offs_n[None, :]// infearure_per_bits) + g_idx[:, None] * stride_zeros
-    
-    shifter = (offs_bk % infearure_per_bits) * bits
-    zeros_shifter = (offs_n % infearure_per_bits) * bits
-    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_K), dtype=tl.float32)
-    
-    for k in range(0, num_pid_n):
-        # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
-        scales = tl.load(scales_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
-        zeros = tl.load(zeros_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
-        
-        zeros = (zeros >> zeros_shifter[None, :]) & maxq
-        zeros = (zeros + 1)
-        
-        a = tl.load(a_ptrs, mask=a_mask, other=0.)   # (BLOCK_SIZE_M, BLOCK_SIZE_N)
-        b = tl.load(b_ptrs)   # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
-
-        # Now we need to unpack b (which is N-bit values) into 32-bit values
-        b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
-        b = (b - zeros) * scales  # Scale and shift
-        b = tl.trans(b)
-
-        accumulator += tl.dot(a, b)
-        a_ptrs += BLOCK_SIZE_N
-        b_ptrs += BLOCK_SIZE_N
-        scales_ptrs += BLOCK_SIZE_N
-        zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits)
-        
-    c = accumulator.to(tl.float16)
-    c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :]
-    c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K)
-    tl.store(c_ptrs, accumulator, mask=c_mask)
-    
-    
-def triton_matmul(input, qweight, scales, qzeros, g_idx, bits, maxq):
-    output = torch.empty((input.shape[0], qweight.shape[1]), device='cuda', dtype=torch.float16)
-    grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),)
-    matmul_248_kernel[grid](input, qweight, output,
-                            scales, qzeros, g_idx,
-                            input.shape[0], qweight.shape[1], input.shape[1], bits, maxq,
-                            input.stride(0), input.stride(1),
-                            qweight.stride(0), qweight.stride(1),
-                            output.stride(0), output.stride(1),
-                            scales.stride(0), qzeros.stride(0))
-    return output
-    
\ No newline at end of file
+import triton
+import triton.language as tl
+import torch
+
+# code based https://github.com/fpgaminer/GPTQ-triton
+@triton.autotune(
+    configs=[
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
+        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
+    ],
+    key=['M', 'N', 'K'],
+)
+
+@triton.jit
+def matmul_248_kernel(a_ptr, b_ptr, c_ptr,
+                      scales_ptr, zeros_ptr, g_ptr,
+                      M, N, K, bits, maxq,
+                      stride_am, stride_ak,
+                      stride_bk, stride_bn,
+                      stride_cm, stride_cn,
+                      stride_scales, stride_zeros,
+                      BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
+                      GROUP_SIZE_M: tl.constexpr):
+    """
+    Compute the matrix multiplication C = A x B.
+    A is of shape (M, K) float16
+    B is of shape (K//8, N) int32
+    C is of shape (M, N) float16
+    scales is of shape (G, N) float16
+    zeros is of shape (G, N) float16
+    g_ptr is of shape (K) int32 
+    """
+    infearure_per_bits = 32 // bits
+
+    pid = tl.program_id(axis=0)
+    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
+    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
+    num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
+    num_pid_in_group = GROUP_SIZE_M * num_pid_n
+    group_id = pid // num_pid_in_group
+    first_pid_m = group_id * GROUP_SIZE_M
+    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
+    pid_m = first_pid_m + (pid % group_size_m)
+    pid_n = (pid % num_pid_in_group) // group_size_m
+
+    offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    offs_k = tl.arange(0, BLOCK_SIZE_K)
+    a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)   # (BLOCK_SIZE_M, BLOCK_SIZE_K)
+    a_mask = (offs_am[:, None] < M)
+    # b_ptrs is set up such that it repeats elements along the K axis 8 times
+    b_ptrs = b_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn)   # (BLOCK_SIZE_K, BLOCK_SIZE_N)
+    g_ptrs = g_ptr + offs_k
+    # shifter is used to extract the N bits of each element in the 32-bit word from B
+    scales_ptrs = scales_ptr + offs_bn[None, :]
+    zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits) 
+    
+    shifter = (offs_k % infearure_per_bits) * bits
+    zeros_shifter = (offs_bn % infearure_per_bits) * bits
+    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+            
+    for k in range(0, num_pid_k):
+        g_idx = tl.load(g_ptrs)
+
+        # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
+        scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
+        zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
+        
+        zeros = (zeros >> zeros_shifter[None, :]) & maxq
+        zeros = (zeros + 1)
+        
+        a = tl.load(a_ptrs, mask=a_mask, other=0.0)   # (BLOCK_SIZE_M, BLOCK_SIZE_K)
+        b = tl.load(b_ptrs)   # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
+
+        # Now we need to unpack b (which is N-bit values) into 32-bit values
+        b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
+        b = (b - zeros) * scales  # Scale and shift
+        # ! Convert to fp16
+        b = b.to(tl.float16)
+        a = a.to(tl.float16)
+
+        accumulator += tl.dot(a, b)
+        a_ptrs += BLOCK_SIZE_K
+        b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
+        g_ptrs += BLOCK_SIZE_K
+
+    c = accumulator.to(tl.float16)
+    c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
+    c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
+    tl.store(c_ptrs, c, mask=c_mask)
+    
+# code based https://github.com/fpgaminer/GPTQ-triton
+@triton.autotune(
+    configs=[
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
+        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
+    ],
+    key=['M', 'N', 'K'],
+)
+
+@triton.jit
+def trans_matmul_248_kernel(a_ptr, b_ptr, c_ptr,
+                            scales_ptr, zeros_ptr, g_ptr,
+                            M, N, K, bits, maxq,
+                            stride_am, stride_ak,
+                            stride_bk, stride_bn,
+                            stride_cm, stride_cn,
+                            stride_scales, stride_zeros,
+                            BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
+                            GROUP_SIZE_M: tl.constexpr):
+    """
+    Compute the matrix multiplication C = A x B.
+    A is of shape (M, N) float16
+    B is of shape (K//8, N) int32
+    C is of shape (M, K) float16
+    scales is of shape (G, N) float16
+    zeros is of shape (G, N) float16
+    g_ptr is of shape (K) int32 
+    """
+    infearure_per_bits = 32 // bits
+
+    pid = tl.program_id(axis=0)
+    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
+    num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
+    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
+    num_pid_in_group = GROUP_SIZE_M * num_pid_k
+    group_id = pid // num_pid_in_group
+    first_pid_m = group_id * GROUP_SIZE_M
+    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
+    pid_m = first_pid_m + (pid % group_size_m)
+    pid_k = (pid % num_pid_in_group) // group_size_m
+
+    offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
+    offs_n = tl.arange(0, BLOCK_SIZE_N)
+    a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak)   # (BLOCK_SIZE_M, BLOCK_SIZE_N)
+    a_mask = (offs_am[:, None] < M)
+    # b_ptrs is set up such that it repeats elements along the K axis 8 times
+    b_ptrs = b_ptr + ((offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None, :] * stride_bn)   # (BLOCK_SIZE_K, BLOCK_SIZE_N)
+    g_ptrs = g_ptr + offs_bk
+    g_idx = tl.load(g_ptrs)
+    
+    # shifter is used to extract the N bits of each element in the 32-bit word from B
+    scales_ptrs = scales_ptr + offs_n[None, :]  + g_idx[:, None] * stride_scales
+    zeros_ptrs = zeros_ptr + (offs_n[None, :] // infearure_per_bits) + g_idx[:, None] * stride_zeros
+    
+    shifter = (offs_bk % infearure_per_bits) * bits
+    zeros_shifter = (offs_n % infearure_per_bits) * bits
+    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_K), dtype=tl.float32)
+    
+    for k in range(0, num_pid_n):
+        # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
+        scales = tl.load(scales_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
+        zeros = tl.load(zeros_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
+        
+        zeros = (zeros >> zeros_shifter[None, :]) & maxq
+        zeros = (zeros + 1)
+        
+        a = tl.load(a_ptrs, mask=a_mask, other=0.)   # (BLOCK_SIZE_M, BLOCK_SIZE_N)
+        b = tl.load(b_ptrs)   # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
+
+        # Now we need to unpack b (which is N-bit values) into 32-bit values
+        b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
+        b = (b - zeros) * scales  # Scale and shift
+        b = tl.trans(b)
+        # ! Convert to fp16
+        b = b.to(tl.float16)
+        a = a.to(tl.float16)
+
+        accumulator += tl.dot(a, b)
+        a_ptrs += BLOCK_SIZE_N
+        b_ptrs += BLOCK_SIZE_N
+        scales_ptrs += BLOCK_SIZE_N
+        zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits)
+        
+    c = accumulator.to(tl.float16)
+    c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :]
+    c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K)
+    tl.store(c_ptrs, c, mask=c_mask)
+    
+    
+def triton_matmul(input, qweight, scales, qzeros, g_idx, bits, maxq):
+    output = torch.empty((input.shape[0], qweight.shape[1]), device='cuda', dtype=torch.float16)
+    grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),)
+    matmul_248_kernel[grid](input, qweight, output,
+                            scales, qzeros, g_idx,
+                            input.shape[0], qweight.shape[1], input.shape[1], bits, maxq,
+                            input.stride(0), input.stride(1),
+                            qweight.stride(0), qweight.stride(1),
+                            output.stride(0), output.stride(1),
+                            scales.stride(0), qzeros.stride(0))
+    return output