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config.json

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+{
+  "architectures": [
+    "ConvNetModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_convnet.ConNetConfig",
+    "AutoModel": "modeling_convnet.ConvNetModel"
+  },
+  "model_type": "convnet",
+  "num_classes": 10,
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.2"
+}

configuration_convnet.py

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+from transformers import PretrainedConfig
+
+class ConNetConfig(PretrainedConfig):
+    model_type = "convnet"
+
+    def __init__(
+        self,
+        num_classes=10,
+        **kwargs,
+    ):
+        self.num_classes = num_classes
+        super().__init__(**kwargs)
+
+
+if __name__=="__main__":
+    convnet_config = ConNetConfig(num_classes=10)
+    convnet_config.save_pretrained("custom-convnet")
+    
+    pass

modeling_convnet.py

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+from transformers import PreTrainedModel
+import torch
+import torch.nn as nn
+from .configuration_convnet import ConNetConfig
+
+# Convolutional neural network (two convolutional layers)
+class ConvNet(nn.Module):
+    def __init__(self, num_classes=10):
+        super(ConvNet, self).__init__()
+        self.layer1 = nn.Sequential(
+            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
+            nn.BatchNorm2d(16),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2))
+        self.layer2 = nn.Sequential(
+            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
+            nn.BatchNorm2d(32),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2))
+        self.fc = nn.Linear(7*7*32, num_classes)
+        
+    def forward(self, x):
+        out = self.layer1(x)
+        out = self.layer2(out)
+        out = out.reshape(out.size(0), -1)
+        out = self.fc(out)
+        return out
+
+
+
+
+class ConvNetModel(PreTrainedModel):
+    config_class = ConNetConfig
+    
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = ConvNet(num_classes=config.num_classes)
+    
+    def forward(self, x):
+        out = self.model(x)
+
+        return out
+
+
+if __name__=="__main__":
+    resnet50d_config = ConNetConfig(num_classes=10)
+    resnet50d = ConvNetModel(resnet50d_config)
+    resnet50d.save_pretrained("my_models")
+    pass

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1b50fcbf2d4c2d9559fc3e165d9fe8c282ca44106a7d731b03d01bc768e58c7f
+size 120885