Bunny-Llama-3-8B-V

Maintainer: BAAI

Total Score

66

Last updated 5/27/2024

🏋️

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

Bunny-Llama-3-8B-V is a family of lightweight but powerful multimodal models developed by BAAI. It offers multiple plug-and-play vision encoders, like EVA-CLIP and SigLIP, as well as language backbones including Llama-3-8B-Instruct, Phi-1.5, StableLM-2, Qwen1.5, MiniCPM, and Phi-2.

Model Inputs and Outputs

Bunny-Llama-3-8B-V is a multimodal model that can consume both text and images, and produce text outputs.

Inputs

  • Text Prompt: A text prompt or instruction that the model uses to generate a response.
  • Image: An optional image that the model can use to inform its text generation.

Outputs

  • Generated Text: The model's response to the provided text prompt and/or image.

Capabilities

The Bunny-Llama-3-8B-V model is capable of generating coherent and relevant text outputs based on a given text prompt and/or image. It can be used for a variety of multimodal tasks, such as image captioning, visual question answering, and image-grounded text generation.

What Can I Use It For?

Bunny-Llama-3-8B-V can be used for a variety of multimodal applications, such as:

  • Image Captioning: Generate descriptive captions for images.
  • Visual Question Answering: Answer questions about the contents of an image.
  • Image-Grounded Dialogue: Generate responses in a conversation that are informed by a relevant image.
  • Multimodal Content Creation: Produce text outputs that are coherently grounded in visual information.

Things to Try

Some interesting things to try with Bunny-Llama-3-8B-V could include:

  • Experimenting with different text prompts and image inputs to see how the model responds.
  • Evaluating the model's performance on standard multimodal benchmarks like VQAv2, OKVQA, and COCO Captions.
  • Exploring the model's ability to reason about and describe diagrams, charts, and other types of visual information.
  • Investigating how the model's performance varies when using different language backbones and vision encoders.


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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