VILA1.5-3b
| Property | Value |
|---|---|
| License | CC-BY-NC-4.0 |
| Architecture | Transformer (siglip, shearedllama) |
| Paper | Research Paper |
| Training Data | 53M image-text pairs |
| Supported Hardware | Ampere, Jetson, Hopper, Lovelace |
What is VILA1.5-3b?
VILA1.5-3b is a cutting-edge visual language model (VLM) designed for efficient multi-image processing and reasoning. It represents a significant advancement in multimodal AI, combining image understanding with natural language processing capabilities. The model is specifically optimized for edge deployment, making it suitable for running on devices like Jetson Orin and laptops through AWQ 4-bit quantization.
Implementation Details
The model utilizes a transformer-based architecture, incorporating both siglip and shearedllama components. It's trained on an extensive dataset of 53 million image-text pairs and interleaved image-text content, supporting multiple input types including images, videos, and text.
- Supports multiple hardware architectures including Ampere, Jetson, Hopper, and Lovelace
- Implements AWQ 4-bit quantization for efficient edge deployment
- Compatible with PyTorch, TensorRT-LLM, and TinyChat inference engines
- Extensively evaluated across 12 benchmarks including 5 VQA and 7 instruction-following tests
Core Capabilities
- Multi-image reasoning and analysis
- Visual chain-of-thought processing
- In-context learning capabilities
- Enhanced world knowledge integration
- Edge-device deployment optimization
Frequently Asked Questions
Q: What makes this model unique?
VILA1.5-3b stands out for its ability to process interleaved image-text data, which goes beyond simple image-text pairs. It also features unfrozen LLM components during pre-training, enabling superior in-context learning capabilities.
Q: What are the recommended use cases?
The model is primarily intended for research in computer vision, natural language processing, and AI. It's particularly suitable for applications requiring multi-image reasoning, visual chain-of-thought processing, and deployment on edge devices.
