phi3-coding-ensemble-mcettrick

README.md

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 ## Trademarks
 
 This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
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-## Data Summary
-https://huggingface.co/microsoft/Phi-3-small-128k-instruct/blob/main/data_summary_card.md

data_summary_card.md

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-# Data Summary for microsoft_Phi-3-medium-4k-instruct, Phi-3-medium-128k-instruct, Phi-3-small-8k-instruct, Phi-3-small-128k-instruct, Phi-3-mini-128k-instruct, Phi-3-mini-4k-instruct, Phi-3-mini-4k-instruct-gguf, Phi-3-mini-4k-instruct-onnx 
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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-3-Medium-4K-Instruct, Phi-3-medium-128k-instruct, Phi-3-small-8k-instruct, Phi-3-small-128k-instruct, Phi-3-mini-128k-instruct, Phi-3-mini-4k-instruct, Phi-3-mini-4k-instruct-gguf, Phi-3-mini-4k-instruct-onnx 
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-**1.2.2 Model release date:** 21-May-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:** Our training data includes a wide variety of sources and is a combination of publicly available documents selected for quality, 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 (science, daily activities, theory of mind, etc.); chat format supervised data covering various topics to reflect preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. 
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-**1.3.1.C Image training data size:** Not applicable 
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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 
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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 
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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:**  October 2023
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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:** October 2023 
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-**1.3.5 Rationale or purpose of data selection:** Training data was selected to emphasize high-quality and reasoning-dense content, including educational materials and code, to improve capabilities in math, coding, common sense reasoning, and general knowledge. Public sources were selected for quality, and synthetic textbook-like and supervised chat data were created to align with instruction following and safety 
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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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-### 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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-## 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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