GPAI Ledger › GPAI Training Transparency tracker (AI Accountability Lab (AIAL)) › Capture 12 Sep 2026

Phi4_Mini_Instruct_2026_09_11 — capture 20260912T063700Z

Filed under AI Accountability Lab (AIAL) — GPAI Training Transparency tracker, the source this project captured it from; the document itself is the filing of the model named above.

Verify: sha256sum 981988d89a5e1802d88d86a268a986c5e034050ba819a273b2f25c85d8b4daaa.pdf must equal the hash above (the filename IS the expected hash); ots verify 981988d89a5e1802d88d86a268a986c5e034050ba819a273b2f25c85d8b4daaa.pdf.20260912T063700Z.ots -f 981988d89a5e1802d88d86a268a986c5e034050ba819a273b2f25c85d8b4daaa.pdf (opentimestamps.org) proves the bytes existed no later than the attestation time — an upper bound on the capture time; the fetch time above is the archive's own record (a freshly captured proof reports 'pending' here: the calendars anchor within hours, but this archive only upgrades the stored proof to its anchor on a later run, so expect a day or two). ots verify needs a local Bitcoin Core node (a pruned one is fine); without one, ots info on the proof prints the attesting block height and merkle path to check on any block explorer.

Extracted text

Machine-extracted text (layout may be lost; the authoritative content is the stored file above).

Data Summary for microsoft_Phi-4-mini-
reasoning, phi-4-mini-instruct, phi-4-mini-flash-
reasoning
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-mini-reasoning, Phi-4-mini-instruct,
Phi-4-mini-flash-reasoning
1.2.2 Model release date:29-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:The training data for Phi-4-mini-
reasoning consists exclusively of synthetic mathematical content generated by
a stronger and more advanced reasoning model, Deepseek-R1. The objective
is to distill knowledge from this model. This synthetic dataset comprises over
one million diverse math problems spanning multiple levels of difficulty (from
middle school to Ph.D. level). For each problem in the synthetic dataset, eight
distinct solutions (rollouts) were sampled, and only those verified as correct were
retained.
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 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. Videos are not part of the
training data
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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:
February 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:
February 2025
1.3.5 Rationale or purpose of data selection:Datasets consist of synthetic
mathematical problems and verified solutions generated by a stronger reasoning
model to distill high-quality reasoning patterns and improve math problem-
solving performance across difficulty levels
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 repre-
sentatives
2.2.1.A Have you concluded transactional commercial licensing agree-
ment(s) with rights holder(s) or with their representatives?Not appli-
cable
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
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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 excep-
tion 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
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