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model.py

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+import torch
+from transformers import AutoTokenizer, GPT2Model
+import torch.nn as nn
+
+class ChessMoveClassifier(nn.Module):
+    def __init__(self, model_name, num_labels=4096):
+        super().__init__()
+        self.base_model = GPT2Model.from_pretrained(model_name)
+        self.dropout = nn.Dropout(0.1)
+        self.classifier = nn.Linear(self.base_model.config.n_embd, num_labels)
+
+    def forward(self, input_ids, attention_mask=None, **kwargs):
+        outputs = self.base_model(input_ids=input_ids, attention_mask=attention_mask)
+        hidden_state = outputs.last_hidden_state[:, -1, :]
+        logits = self.classifier(self.dropout(hidden_state))
+        return {"logits": logits}
+
+def model_fn(model_dir):
+    model = ChessMoveClassifier(model_name="austindavis/ChessGPT_d12")
+    model.load_state_dict(torch.load(f"{model_dir}/model.pt", map_location="cpu"))
+    model.eval()
+    return model