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NVLM-D-72B

Cutting-edge multimodal large-scale language models

#AI
#multimodal
#Large language model
NVLM-D-72B

Product Details

NVLM-D-72B is a multi-modal large-scale language model launched by NVIDIA. It focuses on visual-language tasks and improves text performance through multi-modal training. The model achieves results comparable to industry-leading models on visual-language benchmarks.

Main Features

1
Support visual-linguistic tasks such as image description generation and visual question answering.
2
Text performance improves after multi-modal training.
3
Supports multi-GPU inference to improve computing efficiency.
4
Benchmark test results of Huggingface and Megatron code bases are provided for easy comparison.
5
The model adopts a decoder-only architecture and focuses on text generation tasks.
6
Provides detailed guidance on environment preparation, model loading, and inference usage.
7
Supports converting Megatron models to Huggingface format.

How to Use

1
Prepare the environment: Use the provided Dockerfile to build the environment.
2
Model loading: Use Huggingface's AutoModel.from_pretrained method to load the model.
3
Multi-GPU inference: Distribute the model on multiple GPUs based on the provided sample code.
4
Image preprocessing: Use the build_transform and dynamic_preprocess functions to process the input image.
5
Text conversation: Use the model's chat method to conduct a plain text conversation.
6
Image dialogue: Combine image and text input, and use the model's chat method to conduct image-related dialogue.
7
Reasoning: Adjust the generation configuration as needed, such as max_new_tokens and do_sample, for text generation.

Target Users

The target audience is AI researchers, data scientists and machine learning engineers who need a powerful multi-modal model to process complex visual and textual data. The high performance and ease of use of the NVLM-D-72B make it ideal for these professional users.

Examples

The researchers used NVLM-D-72B to conduct image description generation experiments.

Data scientists leverage this model for visual question answering system development.

Machine learning engineers integrate models into multimodal conversational systems.

Quick Access

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Categories

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› AI model
› AI image generation

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