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

Phi3.5_Vision_Instruct_2026_09_11 — capture 20260912T063652Z

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 87d84c4c1978dc1e6807e6e3983df8cba6e3890ea4e39195c8da4ba013ac6dbe.pdf must equal the hash above (the filename IS the expected hash); ots verify 87d84c4c1978dc1e6807e6e3983df8cba6e3890ea4e39195c8da4ba013ac6dbe.pdf.20260912T063652Z.ots -f 87d84c4c1978dc1e6807e6e3983df8cba6e3890ea4e39195c8da4ba013ac6dbe.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 Phi-3-vision-128k-instruct,
Phi-3.5-vision-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-3-Vision-128K-Instruct, Phi-3.5-vision-
instruct
1.2.2 Model release date:21-May-2024
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:Our training data includes a wide variety
of sources, and is a combination of publicly available documents selected for
quality, selected educational data and code; selected image-text interleave; 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.
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
and newly created image data including charts, tables, diagrams, and slides,
filtered from publicly available sources for quality and safety
1.3.1.E Audio training data size:Not applicable
1.3.1.F Audio training data content:Not applicable
1.3.1.G Video training data size:Not applicable
1.3.1.H Video training data content:Not applicable
1.3.1.I Other training data size:Not applicable
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1.3.1.J Other training data content:Not applicable
1.3.2 Latest date of data acquisition/collection for model training:
15-Mar-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:
01-Feb-2024
1.3.5 Rationale or purpose of data selection:Datasets were selected to
maximize reasoning-dense coverage across text and vision for general-purpose
multimodal understanding, including math, coding, common sense reasoning,
and chart/table/diagram interpretation, supporting efficient deployment in con-
strained environments
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.
2.4 Synthetic data
2.4.1 Was any synthetic AI-generated data used to train the model?
Yes
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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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