AI Core Concepts (Part 16): Multimodal Models

⚡ Quick Dive

Overview & Key Takeaways

Multimodal models can process and generate multiple types of data — including text, images, audio, and video — either as inputs, outputs, or both. These models integrate information from different modalities for richer understanding and reasoning.

1. What Is a Multimodal Model?

A model that accepts or produces more than one type of input/output modality. Common modalities:

• 📝 Text
• 🖼️ Image
• 🔊 Audio
• 🎥 Video
• 🧠 Sensor data

2. Examples of Multimodal Use Ca

📖 Extended Guide

Multimodal models can process and generate multiple types of data — including text, images, audio, and video — either as inputs, outputs, or both. These models integrate information from different modalities for richer understanding and reasoning.

1. What Is a Multimodal Model?

A model that accepts or produces more than one type of input/output modality. Common modalities:

• 📝 Text
• 🖼️ Image
• 🔊 Audio
• 🎥 Video
• 🧠 Sensor data

2. Examples of Multimodal Use Cases

3. Architecture: Combining Modalities

Multimodal models typically use:

• Separate encoders per modality (e.g., image encoder, text encoder)
• A fusion layer to combine modality embeddings
• A decoder to generate output in a target modality

# Pseudocode for architecture
text_embedding = TextEncoder(text_input)
image_embedding = ImageEncoder(image_input)

combined = FusionLayer([text_embedding, image_embedding])
output = Decoder(combined)

4. Famous Multimodal Models

5. Example: CLIP (Contrastive Language–Image Pretraining)

CLIP learns to match images with relevant text by training on image-caption pairs.

from transformers import CLIPProcessor, CLIPModel
from PIL import Image
import torch

model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

image = Image.open("dog.jpg")
texts = ["a dog", "a cat", "a human"]

inputs = processor(text=texts, images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image
probs = logits_per_image.softmax(dim=1)

print(probs)  # Probability that the image matches each caption

6. Building a Simple Text + Image Model

# Encode text and image, then combine
text_emb = TextEncoder("a red apple")
image_emb = ImageEncoder("apple.jpg")

combined = torch.cat([text_emb, image_emb], dim=1)
output = Classifier(combined)  # e.g., for matching or classification

7. Tools & Libraries for Multimodal AI

• 🤗 Hugging Face Transformers + Datasets
• 🧨 Diffusers – for generative models (DALL·E, Stable Diffusion)
• 🐍 PyTorch + torchvision
• 🧠 OpenAI’s CLIP and Whisper
• 🔥 Google DeepMind Flamingo / Gemini APIs (cloud-based)

8. Challenges of Multimodal Models

• Alignment: Matching info across modalities (e.g., image ↔ caption)
• Latency: Higher inference costs due to multiple encoders
• Data imbalance: Text data is more available than video/audio
• Complexity: Difficult to fine-tune or deploy on edge devices

9. Prompting Multimodal LLMs

For models like GPT-4o (text + image):

Prompt: "What's happening in this image?"
Attachment: 📷 [image of a flooded street]

For tools like LangChain:

from langchain.chains import MultiModalChain
# Combine a text prompt and image input for richer querying

📚 Further Resources

• CLIP Paper
• DALL·E 2
• Multimodal Transformers Survey