microsoft
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Phi-4-reasoning-plus

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-# Data Summary for microsoft_Phi-4-reasoning-plus
-## 1. General information
-**1.0.1 Version of the Summary:** 1.0
-**1.0.2 Last update:** 24-Apr-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-reasoning-plus
-**1.2.2 Model release date:** 30-Apr-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:** Prompts sourced from publicly available websites, existing datasets, and licensed collections, augmented with synthetically generated problems; responses generated using o3-mini including chain-of-thought traces; includes STEM, coding, logical puzzles, and safety/Responsible AI alignment data
-**1.3.1.C Image training data size:** Not applicable. Images are not part of the training data
-**1.3.1.D Image training data content:** Not applicable
-**1.3.1.E Audio training data size:** Not applicable. Audio data is not part of the training data
-**1.3.1.F Audio training data content:** Not applicable
-**1.3.1.G Video training data size:** Not applicable. Video data is not part of the training data
-**1.3.1.H Video training data content:** Not applicable
-**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:** 31-Mar-2025
-**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:** 01-Jan-2025
-**1.3.5 Rationale or purpose of data selection:**
-Datasets were curated to emphasize complex multi-step reasoning and verifiable solutions across STEM, coding, and safety, selecting prompts at the boundary of base model capabilities. Synthetic problems and teacher-generated reasoning traces were used to distill structured chain-of-thought and promote concise, checkable answers, supporting robust reasoning performance and generalization to broader tasks
-## 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?** Yes
-### 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?** This information cannot be provided due to unavailability of the underlying data (e.g., loss, corruption, or other access limitations)
-## 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

+# Data Summary for microsoft_Phi-4-reasoning-plus
+## 1. General information
+**1.0.1 Version of the Summary:** 1.0
+**1.0.2 Last update:** 24-Nov-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-reasoning-plus
+**1.2.2 Model release date:** 30-Apr-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:** Prompts sourced from publicly available websites, existing datasets, and licensed collections, augmented with synthetically generated problems; responses generated using o3-mini including chain-of-thought traces; includes STEM, coding, logical puzzles, and safety/Responsible AI alignment data
+**1.3.1.C Image training data size:** Not applicable. Images are not part of the training data
+**1.3.1.D Image training data content:** Not applicable
+**1.3.1.E Audio training data size:** Not applicable. Audio data is not part of the training data
+**1.3.1.F Audio training data content:** Not applicable
+**1.3.1.G Video training data size:** Not applicable. Video data is not part of the training data
+**1.3.1.H Video training data content:** Not applicable
+**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:** 31-Mar-2025
+**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:** 01-Jan-2025
+**1.3.5 Rationale or purpose of data selection:**
+Datasets were curated to emphasize complex multi-step reasoning and verifiable solutions across STEM, coding, and safety, selecting prompts at the boundary of base model capabilities. Synthetic problems and teacher-generated reasoning traces were used to distill structured chain-of-thought and promote concise, checkable answers, supporting robust reasoning performance and generalization to broader tasks
+## 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?** Yes
+### 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?** This information cannot be provided due to unavailability of the underlying data (e.g., loss, corruption, or other access limitations)
+## 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