Upload model

MyModel.py

@@ -0,0 +1,29 @@
+
+from transformers import PreTrainedModel
+from .MyConfig import MnistConfig
+from torch import nn
+import torch.nn.functional as F
+
+class MnistModel(PreTrainedModel):
+    config_class = MnistConfig
+    def __init__(self, config):
+        super().__init__(config)
+        # use the config to instantiate our model
+        self.conv1 = nn.Conv2d(1, config.conv1, kernel_size=5)
+        self.conv2 = nn.Conv2d(config.conv1, config.conv2, kernel_size=5)
+        self.conv2_drop = nn.Dropout2d()
+        self.fc1 = nn.Linear(320, 50)
+        self.fc2 = nn.Linear(50, 10)
+        self.softmax = nn.Softmax(dim=-1)
+    def forward(self, x,labels=None):
+        x = F.relu(F.max_pool2d(self.conv1(x), 2))
+        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
+        x = x.view(-1, 320)
+        x = F.relu(self.fc1(x))
+        x = F.dropout(x, training=self.training)
+        x = self.fc2(x)
+        output = self.softmax(x)
+        if labels != None :
+          print("continue training script here")
+
+        return output

config.json

@@ -1,9 +1,14 @@
 {
+  "architectures": [
+    "MnistModel"
+  ],
   "auto_map": {
-    "AutoConfig": "MyConfig.MnistConfig"
+    "AutoConfig": "MyConfig.MnistConfig",
+    "AutoModelForImageClassification": "MyModel.MnistModel"
   },
   "conv1": 10,
   "conv2": 20,
   "model_type": "MobileNetV1",
+  "torch_dtype": "float32",
   "transformers_version": "4.35.2"
 }

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6eee7d5198f4d57968bd06755727c4e89cd627b1b5fa7e0bfa42cf5a3bcd6697
+size 87976