moondream1
| Property | Value |
|---|---|
| Parameter Count | 1.86B |
| Model Type | Vision-Language Model |
| Architecture | SigLIP + Phi-1.5 |
| Tensor Type | FP16 |
| License | Research Only (No Commercial Use) |
What is moondream1?
moondream1 is a compact yet powerful vision-language model developed by vikhyatk that combines SigLIP and Phi-1.5 architectures, trained on the LLaVa dataset. Despite its relatively small size of 1.86B parameters, it achieves impressive performance on various visual question-answering benchmarks, making it an efficient alternative to larger models.
Implementation Details
The model is implemented using PyTorch and Transformers, offering straightforward integration through the Hugging Face ecosystem. It utilizes FP16 precision for efficient computation and memory usage. The model can be easily deployed using standard Python libraries including transformers, timm, and einops.
- Simple implementation requiring minimal dependencies
- Efficient FP16 tensor operations
- Built on proven architectures (SigLIP and Phi-1.5)
- Trained on the comprehensive LLaVa dataset
Core Capabilities
- Visual Question Answering (74.7% on VQAv2)
- General Question Answering (57.9% on GQA)
- Text-based Visual Question Answering (35.6% on TextVQA)
- Image understanding and contextual reasoning
- Natural language response generation
Frequently Asked Questions
Q: What makes this model unique?
moondream1 stands out for achieving competitive performance with just 1.86B parameters, compared to larger models like LLaVA-1.5 (13.3B). It offers a practical balance between model size and capability, making it suitable for research applications with limited computational resources.
Q: What are the recommended use cases?
The model is specifically designed for research purposes in visual question-answering tasks, image understanding, and natural language processing. It excels in scenarios requiring detailed analysis of images and generating natural language responses to visual queries. Note that commercial use is not permitted.
