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LITA: Language Instructed Temporal Assistant Model Card

LITA Model Overview

Description

\"Language Instructed Temporal Assistant (LITA) is a Generative AI video understanding model that can do video question answering, video captioning, and event localization. VILA [2] is an Image VLM that does image level question-answering. Vision Temporal Assistant is the LITA model that uses the VILA as encoder. This model is for research and development only.

Terms of use

The model is governed by the CC BY NV SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/). ADDITIONAL INFORMATION: Meta Llama 3 Community license (https://llama.meta.com/llama3/license/), Built with Meta Llama 3

References(s):

Citations

LITA: Language Instructed Temporal-Localization Assistant. (2024) Huang, De-An, et al. \"Lita: Language instructed temporal-localization assistant.\" arXiv preprint arXiv:2403.19046 (2024).
VILA: On Pre-training for Visual Language Models. (2023) Lin, Ji, et al. \"Vila: On pre-training for visual language models.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.
Llama 3. Touvron, Hugo, et al. \"Llama: Open and efficient foundation language models.\" arXiv preprint arXiv:2302.13971 (2023).

Model Architecture:

Architecture Type: Transformer
\nNetwork Architecture: SigLIP-336 + LLAMA-3
\nModel Version:

LITA-llama-3-8b-inference - LITA-llama3-8B model that is capable of doing inference.

Input:

Input Type(s): Image, Video, Text
\nInput Format: \".jpg/ .png/ .mp4/ .mov, Text String\"
\nInput Parameters: [(2D)]

Output:

Output Type(s): Sequence of characters
\nOutput Format: Text String(s)
\nOther Properties Related to Output: None

Other Properties Related to Input:

Any resolution video can be used as input. The model will resize the video to 336x336 resolution.
Less than 1 minute video is recommended for better performance.
The input text number of words cannot be greater than 1000.

Software Integration:

Runtime(s): NVIDIA AI Enterprise
\nSupported Hardware Platform(s): NVIDIA Ampere, NVIDIA Hopper, NVIDIA Jetson, NVIDIA Lovelace, NVIDIA Pascal, and NVIDIA Turing.
\nSupported Operating System(s): Linux

Training & Finetuning:

Model versions:

Trainable_v1.0: The PyTorch LITA model that can be used for fine-tuning.\nDeployable_v1.0: The PyTorch LITA model for inference.

Inference:

Engine: TensorRT
\nTest Hardware:

L4
L40
A2
A30
A100
H100

LITA Model Overview

Model Overview

The LITA model is a video understanding model that performs both video question answering, video captioning, and event localization. The model is based on the SigLIP-336 and LLAMA-3 architecture. The model is trained on the VILA dataset and is finetuned on the LLAMA-3 dataset.

Model Architecture

This model is based on LLaVA like architecture. It utilize SigLip encoder and LLAMA-3 language model. The high level architecture of LITA consists of Vision Encoder, linear projector and LLM.

Training

The model is trained using the internal framework. The model is trained on the VILA dataset and is finetuned on video datasets mentioned below.

Training, Testing, and Evaluation Datasets:

Training Dataset:

LITA LLAMA-3 is finetuned on VILA pretrained model with the following datasets: llava-pretrain, activitynet, sharegpt4v, youcook2, and nextqa.

The LITA model is trained on the following datasets:

llava-pretrain link
activitynet link
sharegpt4v link
youcook2 link
nextqa link

Properties

The dataset has about 1M images and 30k videos. The images cover diverse domains and concepts encompassing world knowledge.

Performance

Evaluation Data

The model is evaluated on multiple metrics to evaluate the text output quality and the temporal localization performance.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\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\">

Model	RTL		ActivityNet-QA		MSVD-QA		GQA	MME		Video ChatGPT
VITA	0.3207	0.3205	56.61%	3.65	76.45%	4.04	64.60%	1548	375	3.28	3.13	3.74	2.61	3.04	3.19

Methodology and KPI

The key performance indicator is the hmean of detection. The KPI for the evaluation data are reported below.

Real-time Inference Performance

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\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\">

Model	Networl	Video Duration	Chunk Length (min)	Num Chunks	Average Memory Utilization (when GPU Utilization was 100%)	FPS	VLM Pipeline Time (sec)	Summarization Time (sec)	Latency E2E	Milvus Fetch time
A100 (\"8 x A100 VLM Batch Size = 1 FP16\")
VITA	FP16	~24 hours	10 min	143	2701 mb (no instance of 100 % GPU seen)	20984.83	122.66	154.14	276.8	10.26 ms

Software and Hardware Requirements

This model needs to be used with NVIDIA Hardware and Software: The model can run on any NVIDIA GPU, including NVIDIA Jetson devices with Vision Insights Agent.

The primary use case for this model is to doing video understanding tasks like video question answering, video captioning, and event localization.

Limitations

This Vision-language Model is not capable of generating a vision output that has detection, segmentation or class output.

Restricted Usage in Different Fields

The NVIDIA LITA model is trained on limited datasets and may not perform well on data from different fields. To get better accuracy in a specific field, more data is usually required to fine tune the pre-trained model with TAO Toolkit.

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. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [Insert Link to Model Card++ here]. Please report security vulnerabilities or NVIDIA AI Concerns here.

Terms of use

License

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\n**Network Architecture:** SigLIP-336 + LLAMA-3
\n**Model Version:**
\n- **LITA-llama-3-8b-inference** - LITA-llama3-8B model that is capable of doing inference.\n\n### Input: \n**Input Type(s):** Image, Video, Text
\n**Input Format:** \".jpg/ .png/ .mp4/ .mov, Text String\"
\n**Input Parameters:** [(2D)]
\n\n### Output: \n**Output Type(s):** Sequence of characters
\n**Output Format:** Text String(s)
\n**Other Properties Related to Output:** None
\n\n**Other Properties Related to Input:**
\n - Any resolution video can be used as input. The model will resize the video to 336x336 resolution.\n - Less than 1 minute video is recommended for better performance.\n - The input text number of words cannot be greater than 1000.
\n\n## Software Integration: \n**Runtime(s):** NVIDIA AI Enterprise
\n**Supported Hardware Platform(s):** NVIDIA Ampere, NVIDIA Hopper, NVIDIA Jetson, NVIDIA Lovelace, NVIDIA Pascal, and NVIDIA Turing.
\n**Supported Operating System(s):** Linux
\n\n# Training & Finetuning:\n\n## Model versions:\n\nTrainable_v1.0: The PyTorch LITA model that can be used for fine-tuning. \nDeployable_v1.0: The PyTorch LITA model for inference. \n\n## Inference:\n\n**Engine:** TensorRT
\n**Test Hardware:**
\n- L4\n- L40\n- A2\n- A30\n- A100\n- H100\n\n# LITA Model Overview\n\n## Model Overview \n\nThe LITA model is a video understanding model that performs both video question answering, video captioning, and event localization. The model is based on the SigLIP-336 and LLAMA-3 architecture. The model is trained on the VILA dataset and is finetuned on the LLAMA-3 dataset.\n\n## Model Architecture \n\nThis model is based on LLaVA like architecture. It utilize SigLip encoder and LLAMA-3 language model. The high level architecture of LITA consists of Vision Encoder, linear projector and LLM. \n\n## Training \n\nThe model is trained using the internal framework. The model is trained on the VILA dataset and is finetuned on video datasets mentioned below.\n\n\n# Training, Testing, and Evaluation Datasets: \n\n## Training Dataset:\n\nLITA LLAMA-3 is finetuned on [VILA](https://github.com/NVlabs/VILA) pretrained model with the following datasets: llava-pretrain, activitynet, sharegpt4v, youcook2, and nextqa.\n\nThe LITA model is trained on the following datasets:\n- llava-pretrain [link](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K)\n- activitynet [link](https://github.com/MILVLG/activitynet-qa/tree/master)\n- sharegpt4v [link](https://sharegpt4v.github.io/)\n- youcook2 [link](http://youcook2.eecs.umich.edu/download)\n- nextqa [link](https://github.com/doc-doc/NExT-QA)\n\n#### Properties\n\nThe dataset has about 1M images and 30k videos. The images cover diverse domains and concepts encompassing world knowledge. \n\n## Performance \n\n### Evaluation Data \n\nThe model is evaluated on multiple metrics to evaluate the text output quality and the temporal localization performance. \n\n| Model | RTL | | ActivityNet-QA | | MSVD-QA | | GQA | MME | | Video ChatGPT | | | | | |\n|:---------------------------------------------------------------------------------------:|:------:|:------:|:--------------:|:----:|:-------:|:----:|:------:|:----:|:---:|:-------------:|:----:|:----:|:----:|:----:|:----:|\n| VITA | 0.3207 | 0.3205 | 56.61% | 3.65 | 76.45% | 4.04 | 64.60% | 1548 | 375 | 3.28 | 3.13 | 3.74 | 2.61 | 3.04 | 3.19 |\n\n### Methodology and KPI\n\nThe key performance indicator is the hmean of detection. The KPI for the evaluation data are reported below.\n\n\n### Real-time Inference Performance \n\n| Model | Networl | Video Duration | Chunk Length (min) | Num Chunks | Average Memory Utilization (when GPU Utilization was 100%) | FPS | VLM Pipeline Time (sec) | Summarization Time (sec) | Latency E2E | Milvus Fetch time |\n|:------------------------------------------:|:-------:|:--------------:|:-------------------:|:----------:|:----------------------------------------------------------:|:--------:|:-----------------------:|:------------------------:|:-----------:|:-----------------:|\n| A100 (\"8 x A100 VLM Batch Size = 1 FP16\") | | | | | | | | | | |\n| VITA | FP16 | ~24 hours | 10 min | 143 | 2701 mb (no instance of 100 % GPU seen) | 20984.83 | 122.66 | 154.14 | 276.8 | 10.26 ms |\n| | | | | | | | | | | |\n\n## Software and Hardware Requirements \n\nThis model needs to be used with NVIDIA Hardware and Software: The model can run on any NVIDIA GPU, including NVIDIA Jetson devices with Vision Insights Agent.\n\nThe primary use case for this model is to doing video understanding tasks like video question answering, video captioning, and event localization.\n\n## Limitations \n\nThis Vision-language Model is not capable of generating a vision output that has detection, segmentation or class output.\n\n### Restricted Usage in Different Fields\n\nThe NVIDIA LITA model is trained on limited datasets and may not perform well on data from different fields. To get better accuracy in a specific field, more data is usually required to fine tune the pre-trained model with TAO Toolkit.\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. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [Insert Link to Model Card++ here]. Please report security vulnerabilities or NVIDIA AI Concerns here.\n\n### Terms of use \nThe model is governed by the CC BY NV SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/). ADDITIONAL INFORMATION: Meta Llama 3 Community license (https://llama.meta.com/llama3/license/), Built with Meta Llama 3\n\n### License \nThe model is governed by the CC BY NV SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/). ADDITIONAL INFORMATION: Meta Llama 3 Community license (https://llama.meta.com/llama3/license/), Built with Meta Llama 3","displayName","LITA",["D",1721758757263],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/lita","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n| -- | -- |\n|Participation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None of the Above |\n| Measures taken to mitigate against unwanted bias: | No measures taken to mitigate against unwanted bias.|","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | Video QnA and temporal localization for factory and industrial use cases. |\n| Model Type: | Vision Language Model |\n| Intended Users: | This model is intended for developers building Vision based monitoring solutions |\n| Output: | Text |\n| Describe how the model works: | Encodes image and mapped to text representation. Passes this through LLM to generate a response |\n| Technical Limitations: | Model may sometimes generate incorrect and faulty timestamps |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Video ChatGPT and Video QA |\n| Licensing: | [licenses](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/). |","privacy","| Field | Response |\n| -- | -- |\n| Generatable or reverse engineerable personally-identifiable information| None |\n| Protected classes used to create this model? | Not Applicable (No personal data) |\n| Was consent obtained for any personal data used? | Not Applicable (No personal data) |\n| How often is dataset reviewed? | \tBefore Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | No |\n| If personal data collected for the development of the model, was it collected directly by NVIDIA? |Not Applicable |\n| If personal data collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects?\t| Not Applicable |\n| If personal data collected for the development of this AI model, was it minimized to only what was required? | Not Applicable |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Yes |\n| Applicable NVIDIA Privacy Policy\t| [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) |","safetyAndSecurity","| Field | Response |\n| -- | -- |\n| Model Application(s): | Video Question Answering |\n| Describe the life-critical application (if present). | None: Not within Operational Design Domain |\n| Use Case Restrictions: | The model is governed by the CC BY NV SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/). ADDITIONAL INFORMATION: Meta Llama 3 Community license (https://llama.meta.com/llama3/license/), Built with Meta Llama 3 |\n| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. |","featureLabels",[],"displayLabels",[],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_93":1207,"_95":1208},"ngcApps",[1160],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1141":1142,"_1143":1144,"_1145":1146,"_1147":1148,"_1149":240,"_124":266,"_1150":1151,"_1152":16,"_1153":72,"_1154":72,"_1155":1156,"_1157":72,"_1158":1159},"amplitude",{"_1138":1139,"_780":-7,"_1140":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1132":1133,"_1134":1135,"_1136":1137},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787655239708,"1.12.0","defaultSearchSuggestions",{"_1020":347,"_1021":1022,"_1023":1024,"_1025":1026},"announcements",[],"consentState",{"_304":1010,"_1011":72,"_1012":31,"_1013":31,"_1014":1015},"consentConfig",{"_286":287,"_288":289,"_290":291,"_292":293,"_294":295,"_296":297,"_298":299,"_300":301,"_302":303,"_304":305},"showContactAdmin",1787655239710,[],[],[280,281,282,283,284,285],{"_45":59,"_47":70,"_49":88,"_51":-5,"_53":-5,"_55":16},{"_45":59,"_47":68,"_49":88,"_51":-5,"_53":-5,"_55":16},{"_45":59,"_47":66,"_49":88,"_51":-5,"_53":-5,"_55":16},{"_45":59,"_47":60,"_49":88,"_51":-5,"_53":-5,"_55":16},{"_45":56,"_47":57,"_49":88,"_51":-5,"_53":-5,"_55":16},{"_45":46,"_47":48,"_49":88,"_51":-5,"_53":-5,"_55":16},"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. 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