Summary of Llavaolmobitnet1b: Ternary Llm Goes Multimodal!, by Jainaveen Sundaram et al.
LLaVaOLMoBitnet1B: Ternary LLM goes Multimodal!
by Jainaveen Sundaram, Ravi Iyer
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Multimodal Large Language Models (MM-LLMs) have made significant progress in the past year, showcasing impressive performance across various tasks. However, to truly democratize AI, models must demonstrate strong capabilities and run efficiently on small compute footprints accessible by most. In this context, we introduce LLaVaOLMoBitnet1B – a first-of-its-kind Ternary Multimodal LLM capable of processing Image(s)+Text inputs to generate coherent textual responses. This open-sourced model is accompanied by training scripts, encouraging further research in this space. The accompanying technical report highlights the training process, evaluation details, challenges associated with ternary models, and future opportunities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A recent breakthrough in Artificial Intelligence (AI) has led to a new type of computer program called Multimodal Large Language Models (MM-LLMs). These programs can understand and respond to images and text. To make AI more accessible, we need these programs to be fast and able to run on small computers. Our team developed the first Ternary Multimodal LLM that can handle images and text. This program is available for anyone to use and study. We also wrote a report about how we trained this model and what challenges we faced. |
