VILA1.5-3b-s2

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: VILA1.5-40b was trained in May 2024.

Paper or resources for more information: https://github.com/NVLabs/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/NVLabs/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.

Model Architecture:

Architecture Type: Transformer Network Architecture: siglip, shearedllama

Input:

Input Type: Image, Video, Text Input Format: Red, Green, Blue; MP4 ;String Input Parameters: 2D, 3D

Output:

Output Type: Text Output Format: String

Supported Hardware Microarchitecture Compatibility:

• Ampere
• Jetson
• Hopper
• Lovelace

[Preferred/Supported] Operating System(s): <br> Linux

Model Version(s):

• VILA1.5-3B
• VILA1.5-3B-s2
• Llama-3-VILA1.5-8B
• VILA1.5-13B
• VILA1.5-40B
• VILA1.5-3B-AWQ
• VILA1.5-3B-s2-AWQ
• Llama-3-VILA1.5-8B-AWQ
• VILA1.5-13B-AWQ
• VILA1.5-40B-AWQ

Training dataset

See Dataset Preparation for more details.

** Data Collection Method by dataset

• [Hybrid: Automated, Human]

** Labeling Method by dataset

• [Hybrid: Automated, Human]

Properties (Quantity, Dataset Descriptions, Sensor(s)): 53 million image-text pairs or interleaved image text content.

Evaluation dataset

A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.

Inference:

Engine: [Tensor(RT), Triton, Or List Other Here]

• PyTorch
• TensorRT-LLM
• TinyChat

Test Hardware:

• A100
• Jetson Orin
• RTX 4090

Ethical Considerations

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 internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.