Upload BasicLinear

config.json

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+{
+  "architectures": [
+    "BasicLinear"
+  ],
+  "auto_map": {
+    "AutoConfig": "custom_config.LinearConfig",
+    "AutoModel": "custom_net.BasicLinear"
+  },
+  "bias": true,
+  "dtype": "float32",
+  "in_features": 10,
+  "model_type": "linear",
+  "out_features": 1,
+  "transformers_version": "4.57.1"
+}

custom_config.py

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+from transformers import PretrainedConfig
+
+class LinearConfig(PretrainedConfig):
+    model_type = 'linear'
+    
+    def __init__(self,
+                 in_features=10,
+                 out_features=1,
+                 bias=True,
+                 **kwargs):
+        
+        self.in_features = in_features
+        self.out_features = out_features
+        self.bias = bias
+
+        super().__init__(**kwargs)
+        
+        

custom_net.py

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+from transformers import PreTrainedModel
+from .custom_config import LinearConfig
+import torch.nn as nn
+import torch
+
+class BasicLinear(PreTrainedModel):
+    config_class = LinearConfig
+    
+    def __init__(self, config):
+        super().__init__(config)
+        self.weight = nn.Parameter(torch.randn(config.out_features, config.in_features) * 0.01)
+        if config.bias:
+            self.bias = nn.Parameter(torch.zeros(config.out_features))
+        else:
+            self.bias = None
+
+    def forward(self, x):
+        out = x @ self.weight.T
+        if self.bias is not None:
+            out = out + self.bias
+        return out

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3a8c05d6ab9216b2c052c8ceed97cca88520c4538ab3cded2a4f54e0255e791b
+size 204