Summary of Tinyllava Factory: a Modularized Codebase For Small-scale Large Multimodal Models, by Junlong Jia et al.
TinyLLaVA Factory: A Modularized Codebase for Small-scale Large Multimodal Models
by Junlong Jia, Ying Hu, Xi Weng, Yiming Shi, Miao Li, Xingjian Zhang, Baichuan Zhou, Ziyu Liu, Jie Luo, Lei Huang, Ji Wu
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The presented TinyLLaVA Factory is an open-source, modular codebase designed for small-scale large multimodal models (LMMs) that prioritizes simplicity, extensibility, and reproducibility. This framework modularizes the system into interchangeable components, integrating cutting-edge models and methods while allowing for further extensions. In addition to enabling users to customize their own LMMs, TinyLLaVA Factory provides popular training recipes for pretraining and finetuning with reduced coding effort. Empirical experiments validate its effectiveness in facilitating the design and training of small-scale LMMs with affordable computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TinyLLaVA Factory is a special computer code that helps scientists train big models using smaller computers. It’s like a Lego set for building your own model, but instead of blocks, it uses lots of different pieces of code. This makes it easy to change or add new features. The goal is to make it easier for people to create their own small models, which can be used for things like recognizing pictures or understanding speech. |
Keywords
» Artificial intelligence » Pretraining
