data_summary_card.md

Data Summary for microsoft_Phi-4-multimodal-instruct

1. General information

1.0.1 Version of the Summary: 1.0

1.0.2 Last update: 10-Dec-2025

1.1 Model Developer Identification

1.1.1 Model Developer name and contact details: Microsoft Corporation at One Microsoft Way, Redmond, WA 98052. Tel: 425-882-8080.

1.2 Model Identification

1.2.1 Versioned model name(s): Phi-4-multimodal-instruct

1.2.2 Model release date: February 2025

1.3 Overall training data size and characteristics

1.3.1 Size of dataset and characteristics

1.3.1.A Text training data size: 1 billion to 10 trillion tokens

1.3.1.B Text training data content: Publicly available documents filtered for quality, selected educational data, and code; newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (e.g., science, daily activities, theory of mind, etc.); human labeled data in chat format; selected image-text interleave data; transcriptions

1.3.1.C Image training data size: 1 million to 1 billion images

1.3.1.D Image training data content: Selected image-text interleaved data, including synthetic and publicly available images, multi-image sets, and video-derived visual data, filtered for quality and relevance to reasoning tasks

1.3.1.E Audio training data size: More than 1 million hours

1.3.1.F Audio training data content: Anonymized in-house speech-text pairs with strong and weak transcriptions, selected publicly available and anonymized in-house speech data with task-specific supervision, and selected synthetic speech data supporting automatic speech recognition, translation, QA, and understanding

1.3.1.G Video training data size: Not applicable

1.3.1.H Video training data content: Not applicable. Video must be treated as a sequence of images.

1.3.1.I Other training data size: Not applicable

1.3.1.J Other training data content: Not applicable

1.3.2 Latest date of data acquisition/collection for model training: June 2024

1.3.3 Is data collection ongoing to update the model with new data collection after deployment? No

1.3.4 Date the training dataset was first used to train the model: December 2024

1.3.5 Rationale or purpose of data selection: Data was curated to improve reasoning abilities, including math, coding, common sense, and general knowledge, while filtering publicly available documents to focus model capacity on high-quality content. Additional multimodal data supports image understanding, OCR, chart and table parsing, speech recognition and translation, and instruction following

2. List of data sources

2.1 Publicly available datasets

2.1.1 Have you used publicly available datasets to train the model? Yes

2.2 Private non-publicly available datasets obtained from third parties

2.2.1 Datasets commercially licensed by rights holders or their representatives

2.2.1.A Have you concluded transactional commercial licensing agreement(s) with rights holder(s) or with their representatives? Not applicable

2.2.2 Private datasets obtained from other third-parties

2.2.2.A Have you obtained private datasets from third parties that are not licensed as described in Section 2.2.1, such as data obtained from providers of private databases, or data intermediaries? No

2.3 Personal Information

2.3.1 Was personal data used to train the model? Microsoft follows all relevant laws and regulations pertaining to personal information.

2.4 Synthetic data

2.4.1 Was any synthetic AI-generated data used to train the model? Yes

3. Data processing aspects

3.1 Respect of reservation of rights from text and data mining exception or limitation

3.1.1 Does this dataset include any data protected by copyright, trademark, or patent? Microsoft follows all required regulations and laws for processing data protected by copyright, trademark, or patent.

3.2 Other information

3.2.1 Does the dataset include information about consumer groups without revealing individual consumer identities? Microsoft follows all required regulations and laws for protecting consumer identities.

3.2.2 Was the dataset cleaned or modified before model training? Yes