Upload 5 files

config.json

@@ -2,6 +2,10 @@
   "architectures": [
     "EnigmaModel"
   ],
+  "auto_map": {
+    "AutoConfig": "enigma_module.EnigmaConfig",
+    "AutoModel": "enigma_module.EnigmaModel"
+  },
   "dtype": "float32",
   "hidden_size": 128,
   "model_type": "enigma",

enigma_module.py

@@ -0,0 +1,27 @@
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel, PretrainedConfig
+
+class EnigmaConfig(PretrainedConfig):
+    model_type = "enigma"
+    
+    def __init__(self, hidden_size=128, vocab_size=5000, **kwargs):
+        super().__init__(**kwargs)
+        self.hidden_size = hidden_size
+        self.vocab_size = vocab_size
+
+class EnigmaModel(PreTrainedModel):
+    config_class = EnigmaConfig
+    
+    def __init__(self, config):
+        super().__init__(config)
+        self.embedding = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.linear = nn.Linear(config.hidden_size, config.hidden_size)
+        
+    def forward(self, input_ids):
+        x = self.embedding(input_ids)
+        return self.linear(x)
+
+# Registrando para permitir AutoModel e AutoConfig (trust_remote_code=True)
+EnigmaConfig.register_for_auto_class()
+EnigmaModel.register_for_auto_class("AutoModel")

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:c971f99b962157c9dc4032334429f8fb6c59afb35daa8ed563ad7c867e036f38
 size 2626336