phi4

README.md

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 * **Generation of Harmful Content:** Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case.  
 
-* **Misuse:** Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.
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-* **Data Summary:** https://huggingface.co/microsoft/phi-4/blob/main/data_summary_card.md
+* **Misuse:** Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.

data_summary_card.md

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-# Data Summary for microsoft_phi-4 
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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:** 24-Nov-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 
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-**1.2.2 Model release date:** 12-Dec-2024 
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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:** Training data is an extension of the data used for Phi-3 and includes a wide variety of sources from: 
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-1. Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code. 
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-2. Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.). 
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-3. Acquired academic books and Q&A datasets. 
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-4. High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. 
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-5. Multilingual data constitutes about 8% of our overall data. 
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-**1.3.1.C Image training data size:** Not applicable. Images are not part of the training  
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-**1.3.1.D Image training data content:** Not applicable 
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-**1.3.1.E Audio training data size:** Not applicable. Audio data is not part of the training data 
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-**1.3.1.F Audio training data content:** Not applicable 
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-**1.3.1.G Video training data size:** Not applicable. Video data is not part of the training data 
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-**1.3.1.H Video training data content:** Not applicable 
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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:** 30-Jun-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:** 10/01/2024 
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-**1.3.5 Rationale or purpose of data selection:** Datasets were selected to maximize high-quality reasoning and problem-solving capabilities. The mixture emphasizes synthetic, curriculum-structured data and rigorously filtered organic sources such as academic papers, licensed books, code, and Q&A to improve STEM reasoning, coding, and general knowledge while reducing noise and contamination. Targeted acquisitions and multilingual content complement synthetic data to balance reasoning strength with factual coverage 
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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?** Yes 
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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?** This information cannot be provided due to unavailability of the underlying data (e.g., loss, corruption, or other access limitations) 
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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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-## 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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-## 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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