Model Overview

Description:

Vision-language (VILA) models support single image, multi-image, and video reasoning. VILA models have an augmented series of checkpoints with enhanced vision encoders and large language models (LLMs). New VILA, aka NVILA, is a family of models with an enhanced vision encoder and LLM to improve the code base performance of the previous VILA model.

NVILA-Lite-15B-HighRes-LITA is a variant of NVILA-Lite-15B that can process high resolution images and videos with temporal localization capabilities. Common use cases include captioning, visual Q&A, search, and summarization.

NVILA Finetuning Microservices (FTMS) is a visual language model (VLM) finetuning microservice that allows customers to finetune the pre-trained NVILA-Lite-15B-HighRes-LITA video model, with video/image-text data at scale. Please see the container card for this offering here.

The model is for research and non-commercial use.

License/Terms of Use

This model has been released under the following governing terms: Deed - Attribution-NonCommercial 4.0 International - Creative Commons. Additional information on licensing for base pretrained models: Gemma Terms of Use | Google AI for Developers for PaliGemma 2, Gemma Prohibited Use Policy | Google AI for Developers, and Apache License, Version 2.0 for Qwen2.5.

References:

Model Architecture:

Architecture Type: Transformer
\nNetwork Architecture: SigLip, Qwen2.5

Input:

Input Type: Image, Video, Text

Input Format:

Image: Red, Green, Blue (RGB)
\nVideo: MP4
\nText: String

Input Parameters:

Image: 2D
\nVideo: 3D
\nText: 1D

Output:

Output Type: Text
\nOutput Format: String\nOutput Parameters: 1D

Software Integration:

**Runtime Engine: HF Trainer 4.46.0

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Hopper
NVIDIA Lovelace

[Preferred/Supported] Operating System(s):

Linux

Model Version(s):

NVILA-Lite-15B-High-Res-LITA

Training Dataset:

All datasets to train our models to release are from three approved NVIDIA JIRA tickets:

Dataset JIRA ticket approved for previous round of release for DGPTT-1347, DGPTT-1568, DGPTT-1843, DGPTT-1843, & DGPTT-233.
New dataset JIRA ticket approved for this round of release: DGPTT-2846.

We do not plan to release any datasets.

** Data Collection Method by dataset

[Hybrid: Automated, Human]

** Labeling Method by dataset

[Hybrid: Automated, Human]

**Properties
\n60 million image-text pairs or interleaved image-text content.

Methodology and KPI

Generic Video Benchmarks

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Benchmark	Accuracy
VideoMME w/o Sub @128f	67.3
VideoMME w/ Sub @128f	70.9

Temporal Localization Benchmarks

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Benchmark	Mean IoU
ActivityNet RTL	32.07
Charades-STA	52.8

Inference:

Engine:

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.

Please report security vulnerabilities or NVIDIA AI Concerns here.

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\n\n* [NVILA: Efficient Frontier Visual Language Models](https://nvlabs.github.io/VILA/)\n* [LITA: Language Instructed Temporal-Localization Assistant](https://arxiv.org/abs/2403.19046)\n* [CVPR paper: VILA: On Pre-training for Visual Language Models](https://arxiv.org/pdf/2312.07533)\n* [Vision Language Models](https://www.nvidia.com/en-us/glossary/vision-language-models/)\n\n## Model Architecture:\n\n**Architecture Type:** Transformer
\n**Network Architecture:** SigLip, Qwen2.5\n\n## Input:\n\n**Input Type:** Image, Video, Text\n\n**Input Format:**\n\nImage: Red, Green, Blue (RGB)
\nVideo: MP4
\nText: String
\n\n**Input Parameters:**\n\nImage: 2D
\nVideo: 3D
\nText: 1D
\n\n## Output:\n\n**Output Type:** Text
\n**Output Format:** String\n**Output Parameters:** 1D\n\n## Software Integration:\n\n**Runtime Engine: HF Trainer 4.46.0\n\n**Supported Hardware Microarchitecture Compatibility:**
\n\n* NVIDIA Ampere
\n* NVIDIA Hopper
\n* NVIDIA Lovelace
\n\n**[Preferred/Supported] Operating System(s):**
\n\nLinux
\n\n## Model Version(s):
\n\nNVILA-Lite-15B-High-Res-LITA
\n\n## Training Dataset:\n\nAll datasets to train our models to release are from three approved NVIDIA JIRA tickets:\n\n* Dataset JIRA ticket approved for previous round of release for DGPTT-1347, DGPTT-1568, DGPTT-1843, DGPTT-1843, & DGPTT-233.\n* New dataset JIRA ticket approved for this round of release: DGPTT-2846.\n\nWe do not plan to release any datasets.\n\n** Data Collection Method by dataset
\n* [Hybrid: Automated, Human]
\n\n** Labeling Method by dataset
\n* [Hybrid: Automated, Human]
\n\n**Properties
\n60 million image-text pairs or interleaved image-text content.\n\n## Methodology and KPI\n\n* Generic Video Benchmarks\n\n| Benchmark | Accuracy |\n| --------- | --------- |\n| VideoMME w/o Sub @128f | 67.3 |\n| VideoMME w/ Sub @128f | 70.9 |\n\n* Temporal Localization Benchmarks\n\n| Benchmark | Mean IoU |\n| --------- | --------- |\n| ActivityNet RTL | 32.07 |\n| Charades-STA | 52.8 |\n\n\n## Inference:\n\n**Engine:**
\n\n* PyTorch\n* TensorRT-LLM\n* TinyChat\n\n**Test Hardware:**
\n\n* A100
\n* Jetson Orin
\n* RTX 4090
\n\n## Ethical Considerations\n\nNVIDIA 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. \n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","NVILA HighRes",["D",1746137222572],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/tao/models/nvila-highres","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[240],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_209":241,"_242":241,"_243":244,"_245":246},"NSPECT-PWEF-EZNL","value","filter",{"_247":246,"_242":241},"category","label","field",{"_118":1227,"_120":1228},"ngcApps",[1180],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1161":1162,"_1163":1164,"_1165":1166,"_1167":1168,"_1169":259,"_149":284,"_1170":1171,"_1172":59,"_1173":88,"_1174":88,"_1175":1176,"_1177":88,"_1178":1179},"amplitude",{"_1158":1159,"_797":-7,"_1160":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1152":1153,"_1154":1155,"_1156":1157},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now","1.15.0","defaultSearchSuggestions",{"_1037":181,"_1038":1039,"_1040":1041,"_1042":1043},"announcements",[],"consentState",{"_322":1027,"_1028":88,"_1029":34,"_1030":34,"_1031":1032},"consentConfig",{"_304":305,"_306":307,"_308":309,"_310":311,"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323},"showContactAdmin",[],[],[297,298,299,300,301,302,303],{"_48":62,"_50":73,"_52":64,"_54":65,"_56":74,"_58":59},{"_48":62,"_50":71,"_52":64,"_54":65,"_56":72,"_58":59},{"_48":62,"_50":69,"_52":64,"_54":65,"_56":70,"_58":59},{"_48":62,"_50":67,"_52":68,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":64,"_54":65,"_56":66,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":57,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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