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Phi-3-vision-128k-instruct

Microsoft's lightweight, advanced multimodal models focus on high-quality inference-intensive data for text and vision.

#multimodal
#reasoning
#high quality
#text
#Vision
Phi-3-vision-128k-instruct

Product Details

Phi-3 Vision is a lightweight, state-of-the-art open multi-modal model built on datasets including synthetic data and filtered publicly available websites, focusing on very high-quality inference-intensive data for text and vision. This model belongs to the Phi-3 model family. The multi-modal version supports 128K context length (in tokens). It has undergone a rigorous enhancement process that combines supervised fine-tuning and direct preference optimization to ensure precise instruction following and strong security measures.

Main Features

1
4.2B parameters, including image encoder, connector, projector and Phi-3 Mini language model.
2
Supports text and image input, best suited for chat-formatted prompts.
3
The context length is 128K tokens.
4
Using 512 H100-80G GPU for training, the training time is 1.5 days.
5
The training data contains 500 billion visual and text tokens.
6
The output is the text generated for the input.
7
The model training date is from February to April 2024.
8
The model is a static model, and the training deadline is March 15, 2024.

How to Use

1
1. Access the Azure AI model library and select the Phi-3-vision-128k-instruct model.
2
2. Download or deploy the model as needed.
3
3. Prepare input data, including text and images.
4
4. Set model parameters, such as temperature and maximum number of new tokens.
5
5. Pass input data to the model and receive output.
6
6. Analyze the model output and perform further processing according to the application scenario.

Target Users

The model is intended for a wide range of commercial and research uses, especially in general-purpose AI systems and applications that require visual and text input capabilities, and is suitable for memory/computation-constrained environments, latency-constrained scenarios, general-purpose image understanding, OCR, chart and table understanding, etc.

Examples

Used in education to help students understand complex concepts.

In business settings, for the analysis and processing of image and text data.

In research, as a powerful base model for generating AI capabilities.

Quick Access

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Categories

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› AI model
› AI image generation

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