llava-phi-3-mini
Maintainer: lucataco
3
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| Model Link | View on Replicate |
| API Spec | View on Replicate |
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| Paper Link | No paper link provided |
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Model overview
llava-phi-3-mini is a LLaVA model fine-tuned from microsoft/Phi-3-mini-4k-instruct by XTuner. It is a lightweight, state-of-the-art open model trained with the Phi-3 datasets, similar to phi-3-mini-128k-instruct and llava-phi-3-mini-gguf. The model uses the CLIP-ViT-Large-patch14-336 visual encoder and MLP projector, with a resolution of 336.
Model inputs and outputs
llava-phi-3-mini takes an image and a prompt as inputs, and generates a text output in response. The model is capable of performing a variety of multimodal tasks, such as image captioning, visual question answering, and visual reasoning.
Inputs
- Image: The input image, provided as a URL or file path.
- Prompt: The text prompt that describes the task or query the user wants the model to perform.
Outputs
- Text: The model's generated response to the input prompt, based on the provided image.
Capabilities
llava-phi-3-mini is a powerful multimodal model that can perform a wide range of tasks, such as image captioning, visual question answering, and visual reasoning. The model has been fine-tuned on a variety of datasets, including ShareGPT4V-PT and InternVL-SFT, which have improved its performance on tasks like MMMU Val, SEED-IMG, AI2D Test, ScienceQA Test, HallusionBench aAcc, POPE, GQA, and TextVQA.
What can I use it for?
You can use llava-phi-3-mini for a variety of applications that require multimodal understanding and generation, such as image-based question answering, visual storytelling, or even image-to-text translation. The model's lightweight nature and strong performance make it a great choice for projects that require efficient and effective multimodal AI capabilities.
Things to try
With llava-phi-3-mini, you can explore a range of multimodal tasks, such as generating detailed captions for images, answering questions about the contents of an image, or even describing the relationships between objects in a scene. The model's versatility and performance make it a valuable tool for anyone working on projects that combine vision and language.
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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