MC-LLaVA-3b

Maintainer: visheratin

Total Score

81

Last updated 5/28/2024

🛠️

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The MC-LLaVA-3b is a multimodal AI model developed by visheratin that combines a large language model (LLM) with a vision tower for tasks involving both text and images. It is based on the LLaVA architecture, which uses a Vision Transformer (ViT) to encode image information and aligns it with a large language model. Unlike traditional LLaVA models that generate a fixed number of image "tokens", the MC-LLaVA-3b creates a smaller number of tokens for multiple image crops, which allows it to capture visual information more efficiently.

The model was fine-tuned from the Phi-2 merge using a vision tower from the SigLIP 400M model. It uses the ChatML prompt format, which is a common format for chatbot-style interactions.

Model inputs and outputs

Inputs

  • Prompt: A text prompt that the model will use to generate a response.
  • Image: One or more images that the model will use to inform its response.

Outputs

  • Generated text: The model's response to the input prompt, which may incorporate information from the provided image(s).

Capabilities

The MC-LLaVA-3b model has been evaluated on a variety of multimodal benchmarks, including TextVQA, GQA, VQAv2, VizWiz, and V*-bench. It achieves strong performance, with scores ranging from 32.68% on VizWiz to 76.72% on VQAv2. The model's ability to efficiently extract visual information from image crops allows it to perform well on tasks that require understanding the contents of an image.

What can I use it for?

The MC-LLaVA-3b model can be used for a variety of multimodal tasks, such as:

  • Image captioning: Generating descriptive text to summarize the contents of an image.
  • Visual question answering: Answering questions about the contents of an image.
  • Multimodal chatbots: Building conversational agents that can understand and respond to both text and visual inputs.

The model's performance on benchmarks suggests that it could be a useful tool for applications that involve analyzing and understanding visual information, such as in the fields of education, e-commerce, or customer service.

Things to try

One interesting aspect of the MC-LLaVA-3b model is its use of a "multi-crop" approach to image encoding, which allows it to capture visual information more efficiently than traditional LLaVA models. You could experiment with this approach by generating responses to prompts that require a deep understanding of an image's contents, and compare the results to a model that uses a more straightforward image encoding method. This could help you gain insights into the tradeoffs and benefits of the multi-crop approach.

Another area to explore could be the model's performance on different types of multimodal tasks, such as visual question answering, image captioning, or even multimodal language generation. By testing the model on a variety of tasks, you may uncover its strengths and limitations, and identify areas where further improvements could be made.



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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