Idefics3-8B-Llama3
Maintainer: HuggingFaceM4
203
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| 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
The Idefics3-8B-Llama3 is an open multimodal model developed by HuggingFace that accepts arbitrary sequences of image and text inputs and produces text outputs. It builds upon the previous Idefics1 and Idefics2 models, significantly enhancing capabilities around OCR, document understanding and visual reasoning. The model can be used for tasks like image captioning, visual question answering, and generating stories grounded on multiple images.
Model inputs and outputs
Inputs
- Arbitrary sequences of interleaved image and text inputs
Outputs
- Text outputs, including responses to questions about images, descriptions of visual content, and generation of stories based on multiple images
Capabilities
The Idefics3-8B-Llama3 model exhibits strong performance on a variety of multimodal tasks, often rivaling closed-source systems. It serves as a robust foundation for fine-tuning on specific use cases. The model demonstrates improvements over its predecessors, Idefics1 and Idefics2, in areas like OCR, document understanding, and visual reasoning.
What can I use it for?
The Idefics3-8B-Llama3 model can be used for a variety of multimodal tasks, such as image captioning, visual question answering, and generating stories based on multiple images. It can also be used as a starting point for fine-tuning on more specialized tasks and datasets. For example, the provided fine-tuning code for Idefics2 can be adapted with minimal changes to fine-tune the Idefics3 model.
Things to try
One interesting thing to try with the Idefics3-8B-Llama3 model is to experiment with different prompting strategies. The model responds well to instructions that guide it to follow a certain format or approach, such as adding a prefix like "Let's fix this step by step" to influence the generated output. Additionally, you can explore the various optimizations and hardware configurations discussed in the model documentation to find the right balance between performance, memory usage, and inference speed for your specific use case.
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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