TinyLLaVA Factory: A Modularized Codebase for Small-scale Large Multimodal Models
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866916252096659456 |
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| author | Jia, Junlong Hu, Ying Weng, Xi Shi, Yiming Li, Miao Zhang, Xingjian Zhou, Baichuan Liu, Ziyu Luo, Jie Huang, Lei Wu, Ji |
| author_facet | Jia, Junlong Hu, Ying Weng, Xi Shi, Yiming Li, Miao Zhang, Xingjian Zhou, Baichuan Liu, Ziyu Luo, Jie Huang, Lei Wu, Ji |
| contents | We present TinyLLaVA Factory, an open-source modular codebase for small-scale large multimodal models (LMMs) with a focus on simplicity of code implementations, extensibility of new features, and reproducibility of training results. Following the design philosophy of the factory pattern in software engineering, TinyLLaVA Factory modularizes the entire system into interchangeable components, with each component integrating a suite of cutting-edge models and methods, meanwhile leaving room for extensions to more features. In addition to allowing users to customize their own LMMs, TinyLLaVA Factory provides popular training recipes to let users pretrain and finetune their models with less coding effort. Empirical experiments validate the effectiveness of our codebase. The goal of TinyLLaVA Factory is to assist researchers and practitioners in exploring the wide landscape of designing and training small-scale LMMs with affordable computational resources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11788 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TinyLLaVA Factory: A Modularized Codebase for Small-scale Large Multimodal Models Jia, Junlong Hu, Ying Weng, Xi Shi, Yiming Li, Miao Zhang, Xingjian Zhou, Baichuan Liu, Ziyu Luo, Jie Huang, Lei Wu, Ji Machine Learning We present TinyLLaVA Factory, an open-source modular codebase for small-scale large multimodal models (LMMs) with a focus on simplicity of code implementations, extensibility of new features, and reproducibility of training results. Following the design philosophy of the factory pattern in software engineering, TinyLLaVA Factory modularizes the entire system into interchangeable components, with each component integrating a suite of cutting-edge models and methods, meanwhile leaving room for extensions to more features. In addition to allowing users to customize their own LMMs, TinyLLaVA Factory provides popular training recipes to let users pretrain and finetune their models with less coding effort. Empirical experiments validate the effectiveness of our codebase. The goal of TinyLLaVA Factory is to assist researchers and practitioners in exploring the wide landscape of designing and training small-scale LMMs with affordable computational resources. |
| title | TinyLLaVA Factory: A Modularized Codebase for Small-scale Large Multimodal Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.11788 |