ASWP – Using Multimodal AI models For Your Applications (Part 3)

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Multimodal AI Models: An Overview

Multimodal AI models are becoming increasingly popular as they enable the seamless processing of various input types, including text, images, and audio. These models are built to understand and generate content across multiple modalities, opening up exciting possibilities for diverse applications.

Previously, individual models were used for each modality, requiring switching between different systems for different tasks. However, advancements in ‘any-to-any’ models like Next-GPT and 4M allow developers to build unified architectures that process multiple modalities within a single system. This approach streamlines development and improves efficiency.

Key Concepts in Multimodal AI Models

Several key concepts underpin the functioning of ‘any-to-any’ models, allowing them to seamlessly handle various tasks and inputs:

1. **Shared Representation Space:** These models convert different input types (text, images, audio) into a shared feature space. This allows the model to process various inputs in a unified way, regardless of their initial format.

2. **Attention Mechanisms:** Attention layers help the model focus on the most relevant parts of each input, enhancing understanding and generating more accurate outputs.

3. **Cross-Modal Interaction:** Input from one modality can guide the generation or interpretation of another modality. This allows for more integrated and cohesive outputs, where different modalities complement each other.

4. **Pre-training and Fine-tuning:** Models are typically pre-trained on vast datasets across different types of data, then fine-tuned for specific tasks, improving their performance in real-world applications.

Reka Models: Powerful Multimodal Solutions

Reka is an AI research company offering models for various tasks, including generating text from videos and images, translating speech, and answering complex questions from multimodal documents. Their models excel in advanced reasoning and coding, providing flexible solutions for developers.

Reka offers three main models:

1. **Reka Core:** A 67-billion-parameter multimodal language model designed for complex tasks. It supports images, videos, and texts, excelling in advanced reasoning and coding.

2. **Reka Flash:** A faster, 21-billion-parameter model designed for flexibility and rapid performance in multimodal settings.

3. **Reka Edge:** A smaller, 7-billion-parameter model built for on-device and low-latency applications, making it efficient for local use and latency-sensitive applications.

Reka’s models can be fine-tuned and deployed securely on the cloud, on-premises, or even on-device. You can explore their capabilities through their playground, experimenting with multimodal features without writing any code.

Gemini Models: Efficiency Through Mixture-of-Experts (MoE)

Gemini 1.5, developed by Google DeepMind, leverages the MoE system to handle complex tasks efficiently. Instead of using the entire network for every task, Gemini 1.5 activates only the most relevant parts (experts) for each specific task. This approach allows Gemini to tackle complex tasks with less processing power than traditional monolithic models.

You can explore Gemini’s features in Google AI Studio. The model demonstrates impressive capabilities in tasks such as image analysis, food recognition, action recognition, and video summarization.

Comparing Reka and Gemini

Both Reka and Gemini are powerful multimodal models for AI applications, but they differ in key aspects:

| Feature | Reka | Gemini 1.5 |
|—|—|—|
| Multimodal Capabilities | Image, video, and text processing | Image, video, text, with extended token context |
| Efficiency | Optimized for multimodal tasks | Built with MoE for efficiency |
| Context Window | Standard token window | Up to two million tokens (with Flash variant) |
| Architecture | Focused on multimodal task flow | MoE improves specialization |
| Training/Serving | High performance with efficient model switching | More efficient training with MoE architecture |
| Deployment | Supports on-device deployment | Primarily cloud-based, with Vertex AI integration |
| Use Cases | Interactive apps, edge deployment | Suited for large-scale, long-context applications |
| Languages Supported | Multiple languages | Supports many languages with long context windows |

Reka excels in on-device deployment, making it ideal for applications requiring offline capabilities or low-latency processing. On the other hand, Gemini 1.5 Pro shines with its long context windows, suitable for handling large documents or complex queries in the cloud.

This article summarizes the original article: ‘Using Multimodal AI models For Your Applications (Part 3)’ [https://smashingmagazine.com/2024/10/using-multimodal-ai-models-applications-part3/](https://smashingmagazine.com/2024/10/using-multimodal-ai-models-applications-part3/)

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