NVLM-D-72B
Maintainer: nvidia
311
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model Overview
NVLM-D-72B is a frontier-class multimodal large language model (LLM) developed by NVIDIA. It achieves state-of-the-art results on vision-language tasks, rivaling leading proprietary models like GPT-4o and open-access models like Llama 3-V 405B and InternVL2. Remarkably, NVLM-D-72B shows improved text-only performance over its LLM backbone after multimodal training.
Model Inputs and Outputs
NVLM-D-72B is a decoder-only multimodal LLM that can take both text and images as inputs. The model outputs are primarily text, allowing it to excel at vision-language tasks like visual question answering, image captioning, and image-text retrieval.
Inputs
- Text: The model can take text inputs of up to 8,000 characters.
- Images: The model can accept image inputs in addition to text.
Outputs
- Text: The model generates text outputs, which can be used for a variety of vision-language tasks.
Capabilities
NVLM-D-72B demonstrates strong performance on a range of multimodal benchmarks, including MMMU, MathVista, OCRBench, AI2D, ChartQA, DocVQA, TextVQA, RealWorldQA, and VQAv2. It outperforms many leading models in these areas, making it a powerful tool for vision-language applications.
What can I use it for?
NVLM-D-72B is well-suited for a variety of vision-language applications, such as:
- Visual Question Answering: The model can answer questions about the content and context of an image.
- Image Captioning: The model can generate detailed captions describing the contents of an image.
- Image-Text Retrieval: The model can match images with relevant textual descriptions and vice versa.
- Multimodal Reasoning: The model can combine information from text and images to perform advanced reasoning tasks.
Things to try
One key insight about NVLM-D-72B is its ability to maintain and even improve on its text-only performance after multimodal training. This suggests that the model has learned to effectively integrate visual and textual information, making it a powerful tool for a wide range of vision-language applications.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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