Cumo : An advanced architecture for extending multimodal large language models (LLMs).

Cumo

AI Model #AI #Multimodal Learning #Large Language Models #Open Source Standard Picks Open Source

Overview :

CuMo is an extension architecture for multimodal large language models (LLMs). It enhances model scalability by incorporating sparse Top-K gated expert-mixing (MoE) blocks within both the visual encoder and MLP connector, while adding virtually no activation parameters during inference. CuMo pre-trains MLP blocks and initializes experts within the MoE blocks, utilizing auxiliary loss during the visual instruction fine-tuning stage to ensure balanced expert loading. CuMo outperforms other similar models on various VQA and visual instruction following benchmarks, trained entirely on open-source datasets.

Target Users :

CuMo is primarily geared towards AI researchers and developers, especially those specializing in multimodal learning and large language models. It provides an efficient method to augment and fine-tune existing multimodal models, enhancing their efficiency and accuracy in handling both visual and linguistic tasks.

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Top Region： US(70.32%)

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Use Cases

Providing accurate answers in visual question answering (VQA) tasks.

Generating accurate instruction-following behavior in visual instruction following tasks.

Delivering more natural and accurate interaction experiences in multimodal dialogue systems.

Features

Employs sparse Top-K MoE blocks to boost the model's visual processing capabilities.

Pre-trains MLP blocks for better model alignment.

Initializes experts within the MoE blocks during the visual instruction fine-tuning stage.

Uses auxiliary loss to ensure balanced expert loading.

Negligibly increases activation parameters during inference.

Demonstrates outstanding performance across multiple benchmarks.

Trained entirely on open-source datasets.

How to Use

Step 1: Access the CuMo webpage.

Step 2: Read the introduction to the CuMo architecture and functionalities.

Step 3: Download and install the necessary dependency libraries and tools to run the CuMo model.

Step 4: Pre-train and fine-tune the model according to the provided documentation and example code.

Step 5: Utilize the CuMo model for multimodal tasks like VQA or visual instruction following.

Step 6: Evaluate model performance and adjust model parameters as needed.

Step 7: Integrate the CuMo model into broader applications such as chatbots or image recognition systems.

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Direct Visits	59.03%	External Links	24.31%	Email	0.13%
Organic Search	8.72%	Social Media	6.58%	Display Ads	0.65%

Monthly Visits	564
Average Visit Duration	0.00
Pages Per Visit	1.02
Bounce Rate	42.78%

Monthly Visits	564
United States	70.32%
South Africa	29.68%