Update README.md (#86)

- Update README.md (cc7cedcdf94fbed4fc5d29c30c23309b8be5b0de)


Co-authored-by: Blake S <bsnelling@users.noreply.huggingface.co> (batch 1/1)

README.md

@@ -830,4 +830,7 @@ Phi-4-multimodal model is strong in multimodal tasks, especially in speech-to-te
 - https://huggingface.co/microsoft/Phi-4-multimodal-instruct
 - https://huggingface.co/seastar105/Phi-4-mm-inst-zeroth-kor
 
+## Data Summary
+https://huggingface.co/microsoft/Phi-4-multimodal-instruct/blob/main/data_summary_card.md
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 </details>

data_summary_card.md

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+# Data Summary for microsoft_Phi-4-multimodal-instruct 
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+## 1. General information 
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+**1.0.1 Version of the Summary:** 1.0 
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+**1.0.2 Last update:** 10-Dec-2025 
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+## 1.1 Model Developer Identification 
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+**1.1.1 Model Developer name and contact details:** Microsoft Corporation at One Microsoft Way, Redmond, WA 98052. Tel: 425-882-8080. 
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+## 1.2 Model Identification 
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+**1.2.1 Versioned model name(s):** Phi-4-multimodal-instruct 
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+**1.2.2 Model release date:** February 2025 
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+## 1.3 Overall training data size and characteristics 
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+### 1.3.1 Size of dataset and characteristics 
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+**1.3.1.A Text training data size:** 1 billion to 10 trillion tokens 
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+**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 
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+**1.3.1.C Image training data size:** 1 million to 1 billion images 
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+**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 
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+**1.3.1.E Audio training data size:** More than 1 million hours 
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+**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 
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+**1.3.1.G Video training data size:** Not applicable 
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+**1.3.1.H Video training data content:** Not applicable. Video must be treated as a sequence of images. 
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+**1.3.1.I Other training data size:** Not applicable 
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+**1.3.1.J Other training data content:** Not applicable 
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+**1.3.2 Latest date of data acquisition/collection for model training:** June 2024 
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+**1.3.3 Is data collection ongoing to update the model with new data collection after deployment?** No 
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+**1.3.4 Date the training dataset was first used to train the model:** December 2024 
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+**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 
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+## 2. List of data sources 
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+### 2.1 Publicly available datasets 
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+**2.1.1 Have you used publicly available datasets to train the model?** Yes 
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+## 2.2 Private non-publicly available datasets obtained from third parties 
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+### 2.2.1 Datasets commercially licensed by rights holders or their representatives 
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+**2.2.1.A Have you concluded transactional commercial licensing agreement(s) with rights holder(s) or with their representatives?** Not applicable 
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+ 
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+### 2.2.2 Private datasets obtained from other third-parties 
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+**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 
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+ 
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+## 2.3 Personal Information 
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+**2.3.1 Was personal data used to train the model?** Microsoft follows all relevant laws and regulations pertaining to personal information. 
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+## 2.4 Synthetic data 
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+**2.4.1 Was any synthetic AI-generated data used to train the model?** Yes 
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+ 
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+## 3. Data processing aspects 
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+### 3.1 Respect of reservation of rights from text and data mining exception or limitation 
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+**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. 
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+ 
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+## 3.2 Other information 
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+**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. 
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+**3.2.2 Was the dataset cleaned or modified before model training?** Yes 
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