LLM-jp-3 VILA 14B
| Property | Value |
|---|---|
| Total Parameters | ~14B (13B LLM + 428M Vision + 32M Projector) |
| Model Type | Vision-Language Model |
| License | Apache License 2.0 |
| Primary Language | Japanese |
What is llm-jp-3-vila-14b?
LLM-jp-3 VILA 14B is a state-of-the-art vision-language model developed by the Research and Development Center for Large Language Models at Japan's National Institute of Informatics. It combines a powerful vision encoder (SigLIP), a custom projector, and a large language model to enable sophisticated image understanding and text generation in Japanese.
Implementation Details
The model architecture consists of three main components: a 428M parameter SigLIP vision encoder, a 32M parameter 2-layer MLP projector, and a 13B parameter language model. It was trained in three strategic stages using a diverse dataset combination including Japanese image-text pairs, conversation data, and visual question-answering datasets.
- Vision Encoder: siglip-so400m-patch14-384
- Projector: Custom 2-layer MLP
- Language Model: llm-jp-3-13b-instruct
Core Capabilities
- Superior performance on Japanese vision-language benchmarks
- Achieves 57.2% on Heron Bench (significantly outperforming competitors)
- Scores 3.69/5.0 on JA-VLM-Bench-In-the-Wild
- Strong performance in visual question answering tasks
- Comprehensive understanding of Japanese image-text relationships
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional performance on Japanese vision-language tasks, significantly outperforming other models like Japanese Stable VLM and LLaVA-CALM2-SigLIP. It's particularly notable for achieving near-GPT-4 level performance on certain benchmarks.
Q: What are the recommended use cases?
The model is well-suited for Japanese image description tasks, visual question answering, and general image understanding applications. However, users should note that it's still in early research stages and hasn't been fully aligned with social norms and ethical standards.
