Multimodal Models

What are Multimodal Models?

Multimodal models process and generate multiple types of data (text, images, audio, video) in a unified framework, enabling cross-modal understanding and generation.

Vision-Language Models

from transformers import Blip2Processor, Blip2ForConditionalGeneration
from PIL import Image

class VisionLanguageModel:
    def __init__(self, model_name="Salesforce/blip2-opt-2.7b"):
        self.processor = Blip2Processor.from_pretrained(model_name)
        self.model = Blip2ForConditionalGeneration.from_pretrained(
            model_name, torch_dtype=torch.float16
        )

    def answer_question(self, image_path, question):
        image = Image.open(image_path)
        inputs = self.processor(image, question, return_tensors="pt")

        output = self.model.generate(**inputs, max_new_tokens=50)
        return self.processor.decode(output[0], skip_special_tokens=True)

    def describe_image(self, image_path):
        image = Image.open(image_path)
        inputs = self.processor(image, return_tensors="pt")

        output = self.model.generate(**inputs, max_new_tokens=100)
        return self.processor.decode(output[0], skip_special_tokens=True)

Cross-Modal Alignment

class CrossModalAligner:
    def __init__(self, text_encoder, image_encoder):
        self.text_encoder = text_encoder
        self.image_encoder = image_encoder
        self.projection = nn.Linear(768, 768)

    def align(self, text, image):
        text_features = self.text_encoder(text)
        image_features = self.image_encoder(image)

        # Project to shared space
        text_projected = self.projection(text_features)
        image_projected = self.projection(image_features)

        # Compute similarity
        similarity = F.cosine_similarity(text_projected, image_projected)
        return similarity

Popular Multimodal Models

Summary

Multimodal models represent the future of AI, enabling richer interactions across different data types. They're essential for building truly intelligent systems.

Next: We'll explore mixture of experts models.