nanoLLaVA-1.5
| Property | Value |
|---|---|
| Parameter Count | 1.05B |
| License | Apache 2.0 |
| Tensor Type | BF16 |
| Base LLM | Quyen-SE-v0.1 (Qwen1.5-0.5B) |
| Vision Encoder | google/siglip-so400m-patch14-384 |
What is nanoLLaVA-1.5?
nanoLLaVA-1.5 is an advanced vision-language model designed specifically for edge devices, representing a significant improvement over its predecessor. This compact yet powerful model combines a Qwen1.5-0.5B language model with a SigLIP vision encoder to enable efficient multimodal understanding and generation.
Implementation Details
The model follows the ChatML standard for prompt formatting and leverages state-of-the-art architectural choices to maintain high performance despite its relatively small size. It's implemented using the transformers library and supports both CPU and CUDA execution.
- Efficient architecture combining vision and language processing
- Optimized for edge device deployment
- Supports BF16 precision for improved performance
- Implements ChatML standard for consistent interaction
Core Capabilities
- Visual Question Answering (VQA)
- Detailed image description generation
- Multi-task visual understanding
- Conversational AI with image context
Frequently Asked Questions
Q: What makes this model unique?
nanoLLaVA-1.5's primary distinction is its ability to deliver powerful vision-language capabilities in a compact 1.05B parameter package, making it ideal for edge deployment while maintaining competitive performance on various benchmarks.
Q: What are the recommended use cases?
The model is particularly well-suited for edge device applications requiring visual understanding and generation, including mobile apps, IoT devices, and embedded systems where computational resources are limited but high-quality vision-language processing is needed.
