Phi-3-vision-128k-instruct
Maintainer: microsoft
741
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| Property | Value |
|---|---|
| Model Link | View 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
Phi-3-vision-128k-instruct is a lightweight, state-of-the-art open multimodal model built upon datasets which include synthetic data and filtered publicly available websites, with a focus on very high-quality, reasoning dense data both on text and vision. The model belongs to the Phi-3 model family, and the multimodal version comes with 128K context length (in tokens) it can support. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures.
Similar models in the Phi-3 family include the Phi-3-mini-128k-instruct and Phi-3-mini-4k-instruct. These models have fewer parameters (3.8B) compared to the full Phi-3-vision-128k-instruct but share the same training approach and underlying architecture.
Model inputs and outputs
Inputs
- Text: The model accepts text input, and is best suited for prompts using a chat format.
- Images: The model can process visual inputs in addition to text.
Outputs
- Generated text: The model generates text in response to the input, aiming to provide safe, ethical and accurate information.
Capabilities
The Phi-3-vision-128k-instruct model is designed for broad commercial and research use, with capabilities that include general image understanding, OCR, and chart and table understanding. It can be used to accelerate research on efficient language and multimodal models, and as a building block for generative AI powered features.
What can I use it for?
The Phi-3-vision-128k-instruct model is well-suited for applications that require memory/compute constrained environments, latency bound scenarios, or general image and text understanding. Example use cases include:
- Visual question answering: Given an image and a text question about the image, the model can generate a relevant response.
- Image captioning: The model can generate captions describing the contents of an image.
- Multimodal task automation: Combining text and image inputs, the model can be used to automate tasks like form filling, document processing, or data extraction.
Things to try
To get a sense of the model's capabilities, you can try prompting it with a variety of multimodal tasks, such as:
- Asking it to describe the contents of an image in detail
- Posing questions about the objects, people, or activities depicted in an image
- Requesting the model to summarize the key information from a document containing both text and figures/tables
- Asking it to generate steps for a visual instruction manual or recipe
The model's robust reasoning abilities, combined with its understanding of both text and vision, make it a powerful tool for tackling a wide range of multimodal challenges.
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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