Bunny-Llama-3-8B-V
Maintainer: BAAI
71
🏋️
| 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
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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