VILA Model Card
Model details
Model type: VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge, including Jetson Orin and laptop by AWQ 4bit quantization through TinyChat framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge.
Model date: VILA-7b was trained in Feb 2024.
Paper or resources for more information: https://github.com/Efficient-Large-Model/VILA
@misc{lin2023vila,
title={VILA: On Pre-training for Visual Language Models},
author={Ji Lin and Hongxu Yin and Wei Ping and Yao Lu and Pavlo Molchanov and Andrew Tao and Huizi Mao and Jan Kautz and Mohammad Shoeybi and Song Han},
year={2023},
eprint={2312.07533},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
License
- The code is released under the Apache 2.0 license as found in the LICENSE file.
- The pretrained weights are released under the CC-BY-NC-SA-4.0 license.
- The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms:
- Model License of LLaMA
- Terms of Use of the data generated by OpenAI
- Dataset Licenses for each one used during training.
Where to send questions or comments about the model: https://github.com/Efficient-Large-Model/VILA/issues
Intended use
Primary intended uses: The primary use of VILA is research on large multimodal models and chatbots.
Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
Training dataset
See Dataset Preparation for more details.
Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.