add model

config.json

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+{
+  "_name_or_path": "models/",
+  "architectures": [
+    "Model"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "auto_map": {
+    "AutoModel": "custom_model.Model"
+  },
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.17.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

custom_model.py

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+from transformers import PreTrainedModel, BertConfig, AutoModel
+
+
+class Model(PreTrainedModel):
+    config_class = BertConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = AutoModel.from_pretrained("bert-base-uncased")
+
+    def forward(self, **inputs):
+        outs = self.model(inputs)
+        return outs

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:acb7d58fcc3ac1f1c8b3eb11bf692ab5ead2c255a429572a4c450e07cf6d4a14
+size 438010993