LLaVaOLMoBitnet1B: Ternary LLM goes Multimodal!
<|reference_start|>LLaVaOLMoBitnet1B: Ternary LLM goes Multimodal!: Multimodal Large Language Models (MM-LLMs) have seen significant advancements in the last year, demonstrating impressive performance across tasks. However, to truly democratize AI, models must exhibit strong capabilities and be able to run efficiently on small compute footprints accessible by most. Part of this quest, we introduce LLaVaOLMoBitnet1B - the first Ternary Multimodal LLM capable of accepting Image(s)+Text inputs to produce coherent textual responses. The model is fully open-sourced along with training scripts to encourage further research in this space. This accompanying technical report highlights the training process, evaluation details, challenges associated with ternary models and future opportunities. Link to the model: https://huggingface.co/IntelLabs/LlavaOLMoBitnet1B<|reference_end|>
@article{sundaram2024llavaolmobitnet1b:,
    title={LLaVaOLMoBitnet1B: Ternary LLM goes Multimodal!},
    author={Jainaveen Sundaram, Ravi Iyer},
    journal={arXiv preprint arXiv:2408.13402},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13402},
    primaryClass={cs.LG}
}