Summary of Xmodel-vlm: a Simple Baseline For Multimodal Vision Language Model, by Wanting Xu et al.
Xmodel-VLM: A Simple Baseline for Multimodal Vision Language Model
by Wanting Xu, Yang Liu, Langping He, Xucheng Huang, Ling Jiang
First submitted to arxiv on: 15 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A cutting-edge multimodal vision language model, Xmodel-VLM, is introduced for efficient deployment on consumer GPU servers. The model addresses the industry issue of prohibitive service costs hindering the adoption of large-scale multimodal systems. Through rigorous training using the LLaVA paradigm, a 1B-scale language model is developed from scratch. Despite being smaller and faster than larger models, Xmodel-VLM achieves comparable performance on classic multimodal benchmarks. The model’s checkpoints and code are publicly available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new type of computer model called Xmodel-VLM helps computers understand images and words better. It’s special because it can work quickly on regular computers, not just super powerful ones. This is important because it makes it easier for companies to use these types of models in their products. The model was trained using a special way of matching images and words together. Even though it’s smaller than other similar models, Xmodel-VLM does just as well or better on tests that check its abilities. |
Keywords
» Artificial intelligence » Language model
