from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=3,
    weight_decay=0.01,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)

trainer.train()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)

import math

eval_results = trainer.evaluate()
print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}")# You would then need to implement data loading, training loop, etc.