Multi-modal vision-language model that understands text/img/video and creates informative responses
Vision-language models (VILA) provides multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge. 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.
This model is ready for commercial use. It was trained on commercial images and videos for all three stages of training and supports single image and video inference. This version does not support interleaved and in-context learning capabilities.
The license to use this model is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these licenses
Architecture Type: Transformer-based Network Architecture
Network Architecture
NV-Pretraining and NV-VILA-SFT data were used.
Additionally, the commercial subset of following datasets were used:
Data Collection Method by dataset:
Labeling Method by dataset:
Properties:
Data Collection Method by dataset:
Labeling Method by dataset:
Properties:
Benchmark | VQAv2 | GQA | SQA Image | Text VQA | POPE (Popular) | MME | SEED | SEED Image | MMMU val (beam 5) | SEED Video | VideoMME w/o Sub @32f | VideoMME w/ Sub @32f | Egoschema (val) | Perception Test |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Accuracy | 81.70 | 62.13 | 79.62 | 71.14 | 85.61 | 1649.62 | 70.36 | 74.12 | 47.33 | 58.21 | 57.85 | 60.67 | 63.8 | 61.76 |
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
For more detailed information on ethical considerations for this model, please see the Model Card++ Promise and the Explainability, Bias, Safety & Security, and Privacy Subcards.
Please report security vulnerabilities or NVIDIA AI Concerns here.