idefics-80b-instruct
Maintainer: HuggingFaceM4
177
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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
idefics-80b-instruct is an open-access multimodal AI model developed by Hugging Face that can accept arbitrary sequences of image and text inputs and produce text outputs. It is a reproduction of the closed-source Flamingo model developed by DeepMind, built solely on publicly available data and models. Like GPT-4, idefics-80b-instruct can answer questions about images, describe visual contents, create stories grounded on multiple images, or behave as a pure language model without visual inputs. The model comes in two variants, a large 80 billion parameter version and a 9 billion parameter version. The instructed versions, idefics-80b-instruct and idefics-9b-instruct, have been fine-tuned on a mixture of supervised and instruction datasets, boosting downstream performance and making them more usable in conversational settings.
Model inputs and outputs
Inputs
- Arbitrary sequences of image and text inputs
Outputs
- Text outputs that can answer questions about images, describe visual contents, create stories grounded on multiple images, or behave as a pure language model
Capabilities
idefics-80b-instruct is on par with the original closed-source Flamingo model on various image-text benchmarks, including visual question answering, image captioning, and image classification when evaluated with in-context few-shot learning. The instructed version has enhanced capabilities for following instructions from users and performs better on downstream tasks compared to the base models.
What can I use it for?
idefics-80b-instruct and idefics-9b-instruct can be used for a variety of multimodal tasks that involve processing both image and text inputs, such as visual question answering, image captioning, and generating stories based on multiple images. The instructed versions are recommended for optimal performance and usability in conversational settings. These models could be useful for building applications in areas like education, entertainment, and creative content generation.
Things to try
One interesting aspect of idefics-80b-instruct is its ability to perform well on a wide range of multimodal tasks, from visual question answering to image captioning, without requiring task-specific fine-tuning. This versatility could allow users to explore different use cases and experiment with the model's capabilities beyond the standard benchmarks. Additionally, the model's instructed version provides an opportunity to investigate how well large language models can follow and execute user instructions in a multimodal setting, which could lead to insights on improving human-AI interaction and collaboration.
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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