# LLaVaOLMoBitnet1B: Ternary LLM goes Multimodal!

arXiv: https://arxiv.org/abs/2408.13402 · HTML: https://mlsanity.com/paper/2408.13402/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 10 of its 11 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 10 of 10)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [OLMo: Accelerating the Science of Language Models](https://mlsanity.com/paper/2402.00838/) (2024)
- [Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research](https://mlsanity.com/paper/2402.00159/) (2024)
- [The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits](https://mlsanity.com/paper/2402.17764/) (2024)
- [BitNet: Scaling 1-bit Transformers for Large Language Models](https://mlsanity.com/paper/2310.11453/) (2023)
- [TinyLLaVA: A Framework of Small-scale Large Multimodal Models](https://mlsanity.com/paper/2402.14289/) (2024)
- [LLaVA-Gemma: Accelerating Multimodal Foundation Models with a Compact Language Model](https://mlsanity.com/paper/2404.01331/) (2024)

## Built on by (most cited first, 3 of 3)

- [When are 1.58 bits enough? A Bottom-up Exploration of BitNet Quantization](https://mlsanity.com/paper/2411.05882/) (2024)
- [Binary Neural Networks for Large Language Model: A Survey](https://mlsanity.com/paper/2502.19008/) (2025)
- [Hardware Generation and Exploration of Lookup Table-Based Accelerators for 1.58-bit LLM Inference](https://mlsanity.com/paper/2604.25183/) (2026)

## Abstract (arXiv)

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