Create transform

.vscode/transform

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+from transformers import AutoModel, AutoTokenizer, AutoFeatureExtractor
+import torch
+
+# Load pre-trained text and vision models
+text_model = AutoModel.from_pretrained("bert-base-uncased")
+vision_model = AutoModel.from_pretrained("google/vit-base-patch16-224")
+
+# Define a simple multimodal model
+class SimpleMLLM(torch.nn.Module):
+    def __init__(self, text_model, vision_model):
+        super().__init__()
+        self.text_model = text_model
+        self.vision_model = vision_model
+        self.fusion = torch.nn.Linear(text_model.config.hidden_size + vision_model.config.hidden_size, 512)
+    
+    def forward(self, input_ids, attention_mask, pixel_values):
+        text_outputs = self.text_model(input_ids=input_ids, attention_mask=attention_mask)
+        vision_outputs = self.vision_model(pixel_values=pixel_values)
+        
+        # Simple fusion of text and vision features
+        fused = torch.cat([text_outputs.last_hidden_state[:, 0], vision_outputs.last_hidden_state[:, 0]], dim=1)
+        output = self.fusion(fused)
+        return output
+
+# Initialize the model
+model = SimpleMLLM(text_model, vision_model)
+
+# You would then need to implement data loading, training loop, etc.