GPAI Ledger › Phi-3.5 Vision Instruct (Microsoft) › Capture 13 Sep 2026

Phi-3.5 Vision Instruct — capture 20260913T123945Z

Verify: sha256sum 032ef6f50fd7bc98b696a4778a46ea8ba09773031a7a5f591fd7280b0c476701.md must equal the hash above (the filename IS the expected hash); ots verify 032ef6f50fd7bc98b696a4778a46ea8ba09773031a7a5f591fd7280b0c476701.md.20260913T123945Z.ots -f 032ef6f50fd7bc98b696a4778a46ea8ba09773031a7a5f591fd7280b0c476701.md (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

**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 constrained 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 representatives

**2.2.1.A Have you concluded transactional commercial licensing agreement(s) with rights holder(s) or with their representatives?** Not applicable

### 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

## 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