diff --git a/gpu/convert_checkpoint.py b/gpu/convert_checkpoint.py
index 797ad1db..ed343f07 100755
--- a/gpu/convert_checkpoint.py
+++ b/gpu/convert_checkpoint.py
@@ -1,100 +1,15 @@
-import json
-import os
-import re
-import sys
-from pathlib import Path
-from typing import Optional
-from dataclasses import dataclass
-import torch
-from einops import rearrange
-from safetensors.torch import save_file
-import model
-from pack_weight import convert_weight_int8_to_int2
-
-@torch.inference_mode()
-def convert_ts_checkpoint(
-    *,
-    input_path: str = "",
-) -> None:
-
-    config = model.ModelArgs()
-    print(f"Model config {config.__dict__}")
-
-    def quant_weight_int8(weight):
-        s = 1.0 / weight.abs().mean().clamp_(min=1e-5)
-        new_weight = (weight * s).round().clamp(-1, 1).to(torch.int8)
-        new_scale = (1.0 / s).to(torch.bfloat16)
-        return new_weight, new_scale.reshape(1)
-
-    def quant_weight_fp16(weight):
-        s = 1.0 / weight.abs().mean().clamp_(min=1e-5)
-        new_weight = (weight * s).round().clamp(-1, 1) / s
-        return new_weight
-
-    def convert_int8_to_int2(weight):
-        return convert_weight_int8_to_int2(weight)
-
-    merged_result = torch.load(input_path, map_location="cpu", mmap=True)
-    int2_result = {}
-    fp16_result = {}
-    zero = torch.zeros(1).to(torch.bfloat16)
-    for key, value in merged_result.items():
-        if 'wqkv' in key:
-            wq = value[:config.dim]
-            wk = value[config.dim:config.dim // config.n_heads * config.n_kv_heads + config.dim]
-            wv = value[config.dim // config.n_heads * config.n_kv_heads + config.dim:]
-            wq_weight, wa_scale = quant_weight_int8(wq)
-            wk_weight, wb_scale = quant_weight_int8(wk)
-            wv_weight, wc_scale = quant_weight_int8(wv)
-            wqkv_weight = torch.cat([wq_weight, wk_weight, wv_weight], dim=0)
-            wqkv_scale = torch.cat([wa_scale, wb_scale, wc_scale, zero], dim=0)
-            int2_result[key] = convert_int8_to_int2(wqkv_weight)
-            int2_result[key.replace('weight', 'weight_scale')] = wqkv_scale
-
-            wq_weight = quant_weight_fp16(wq)
-            wk_weight = quant_weight_fp16(wk)
-            wv_weight = quant_weight_fp16(wv)
-            wqkv_weight = torch.cat([wq_weight, wk_weight, wv_weight], dim=0)
-            fp16_result[key] = wqkv_weight
-        elif 'w13' in key:
-            w1 = value[:config.ffn_dim]
-            w3 = value[config.ffn_dim:]
-            w1_weight, w1_scale = quant_weight_int8(w1)
-            w3_weight, w3_scale = quant_weight_int8(w3)
-            w13_weight = torch.cat([w1_weight, w3_weight], dim=0)
-            w13_scale = torch.cat([w1_scale, w3_scale, zero, zero], dim=0)
-            int2_result[key] = convert_int8_to_int2(w13_weight)
-            int2_result[key.replace('weight', 'weight_scale')] = w13_scale
-
-            w1_weight = quant_weight_fp16(w1)
-            w3_weight = quant_weight_fp16(w3)
-            w13_weight = torch.cat([w1_weight, w3_weight], dim=0)
-            fp16_result[key] = w13_weight
-        elif 'w2' in key or 'wo' in key:
-            weight, scale = quant_weight_int8(value)
-            scale = torch.cat([scale, zero, zero, zero], dim=0)
-            int2_result[key] = convert_int8_to_int2(weight)
-            int2_result[key.replace('weight', 'weight_scale')] = scale
-
-            weight = quant_weight_fp16(value)
-            fp16_result[key] = weight
-        else:
-            int2_result[key] = value.clone()
-            fp16_result[key] = value.clone()
-
-    output_dir = os.path.dirname(input_path)
-    print(f"Saving checkpoint to {output_dir}/model_state_int2.pt")
-    torch.save(int2_result, f"{output_dir}/model_state_int2.pt")
-
-    print(f"Saving checkpoint to {output_dir}/model_state_fp16.pt")
-    torch.save(fp16_result, f"{output_dir}/model_state_fp16.pt")
-
-if __name__ == '__main__':
-    import argparse
-    parser = argparse.ArgumentParser(description='Convert TorchScale checkpoint.')
-    parser.add_argument('--input', type=str)
-
-    args = parser.parse_args()
-    convert_ts_checkpoint(
-        input_path=args.input,
-    )
+$(cat gpu/convert_checkpoint.py | sed -e '/import torch/a\
+import pickle\
+\
+class RestrictedUnpickler(pickle.Unpickler):\
+    def find_class(self, module, name):\
+        # Only allow safe classes from trusted modules\
+        allowed_modules = ["torch", "numpy", "collections", "builtins"]\
+        if any(module.startswith(allowed_prefix) for allowed_prefix in allowed_modules):\
+            return super().find_class(module, name)\
+        raise pickle.UnpicklingError(f"Global \'{module}.{name}\' is forbidden")\
+\
+def safe_torch_load(path, map_location=None):\
+    """Load a PyTorch model with security restrictions to prevent arbitrary code execution."""\
+    with open(path, "rb") as f:\
+        return torch.load(f, map_location=map_location, pickle_module=RestrictedUnpickler)' | sed -e 's/torch\.load(/safe_torch_load(/g')