# Deepfake Explainer Vision Model

This model is a fine-tuned version of Llama 3.2 Vision that analyzes images for signs of deepfakes.

## Important: Cross-Attention Dimension Fix

This model requires a special handling for cross-attention masks. When loading the model for inference,
make sure to fix the cross-attention mask dimensions as follows:

```python
# Sample code for loading and using this model
from transformers import AutoProcessor, AutoModelForCausalLM
from peft import PeftModel
import torch
from PIL import Image

# Load base model and processor
base_model_id = "unsloth/llama-3.2-11b-vision-instruct"
processor = AutoProcessor.from_pretrained(base_model_id)
model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")

# Load this adapter
adapter_id = "saakshigupta/deepfake-explainer-1"
model = PeftModel.from_pretrained(model, adapter_id)

# Function to fix cross-attention masks
def fix_processor_outputs(inputs):
    if 'cross_attention_mask' in inputs and 0 in inputs['cross_attention_mask'].shape:
        batch_size, seq_len, _, num_tiles = inputs['cross_attention_mask'].shape
        visual_features = 6404  # Critical dimension
        new_mask = torch.ones((batch_size, seq_len, visual_features, num_tiles),
                            device=inputs['cross_attention_mask'].device)
        inputs['cross_attention_mask'] = new_mask
    return inputs

# Load and process image
image_path = "path/to/your/image.jpg"
image = Image.open(image_path).convert("RGB")
prompt = "Analyze this image and tell me if it's a deepfake."

# Process with fix
inputs = processor(text=prompt, images=image, return_tensors="pt")
inputs = fix_processor_outputs(inputs)
inputs = {k: v.to(model.device) for k, v in inputs.items() if isinstance(v, torch.Tensor)}

# Generate output
with torch.no_grad():
    output_ids = model.generate(**inputs, max_new_tokens=300)
response = processor.decode(output_ids[0], skip_special_tokens=True)
print(response)
```

This model was fine-tuned specifically for deepfake detection and explanation.