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

Phi4_Multimodal_Instruct_2026_09_11.pdf — capture 20260912T063705Z

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 c7322d453ce6b90fbd95e2e7d3fd68e00bd649a661762d3e1b61931db41fea0e.pdf must equal the hash above (the filename IS the expected hash); ots verify c7322d453ce6b90fbd95e2e7d3fd68e00bd649a661762d3e1b61931db41fea0e.pdf.20260912T063705Z.ots -f c7322d453ce6b90fbd95e2e7d3fd68e00bd649a661762d3e1b61931db41fea0e.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-multimodal-
instruct
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-multimodal-instruct
1.2.2 Model release date:February 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: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
1.3.1.C Image training data size:1 million to 1 billion images
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
1.3.1.E Audio training data size:More than 1 million hours
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 understand-
ing
1.3.1.G Video training data size:Not applicable
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
1.3.1.J Other training data content:Not applicable
1.3.2 Latest date of data acquisition/collection for model training:
June 2024
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:
December 2024
1.3.5 Rationale or purpose of data selection:Data was curated to improve
reasoningabilities, includingmath, coding, commonsense, andgeneralknowledge,
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
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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