Upload folder using huggingface_hub

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:99a3bb67679f9a5ee8ea27df6fed196e33d35fb20bf73ca7192a03f0ff16017c
-size 10513028
+oid sha256:ce76036027b269298c15c154ddf683fa4092e9f9c3911c280ef291498da7c46c
+size 10513084

run.py

@@ -6,8 +6,8 @@ model = torch.load("model.pth", weights_only=False).to(device)
 def run(test):
     with torch.no_grad():
         test_data = torch.tensor([test], dtype=torch.float).to(device)
-        predictions = model(test_data)
-        return predictions.squeeze().cpu().numpy()
+        predictions: torch.Tensor = model(test_data)
+        return predictions.squeeze().item()
 
 if __name__ == '__main__':
     x, y = map(int, input().split())

test.py

@@ -2,9 +2,9 @@ from run import run
 from tqdm import trange
 import random
 
-lacc = []
-
 def test(round):
+    sumacc = 0
+    lenacc = 0
     pbar = trange(round)
     for r in pbar:
         x = random.randint(1, 100)
@@ -12,9 +12,10 @@ def test(round):
         pans = run([x, y])
         tans = x * y
         acc = 1 - abs(tans - pans) / tans
-        lacc.append(acc)
-        pbar.set_description_str(f"Round {r}, Average Accuracy = {sum(lacc) / len(lacc) * 100}%")
-    print(f"Final Accuracy: {sum(lacc) / len(lacc) * 100}%")
+        sumacc += acc
+        lenacc += 1
+        pbar.set_description_str(f"Round {r}, Average Accuracy = {sumacc / lenacc * 100}%")
+    print(f"Final Accuracy: {sumacc / lenacc * 100}%")
 
 if __name__ == '__main__':
     test(100000)

to_safetensors.py

@@ -2,4 +2,5 @@ import torch
 from safetensors.torch import save_file
 
 model = torch.load("model.pth", weights_only=False)
-save_file(model.state_dict(), "model.safetensors")
+metadata = {"author": "xcx0902", "year": "2025"}
+save_file(model.state_dict(), "model.safetensors", metadata=metadata)