GPAI Ledger › Magma-8B (Microsoft) › Capture 13 Sep 2026

Magma-8B — capture 20260913T120611Z

Verify: sha256sum 887e4e132f74bd5e865a3e5c169db0cb7e09b0b2081bc7ed75b125832136d6ed.md must equal the hash above (the filename IS the expected hash); ots verify 887e4e132f74bd5e865a3e5c169db0cb7e09b0b2081bc7ed75b125832136d6ed.md.20260913T120611Z.ots -f 887e4e132f74bd5e865a3e5c169db0cb7e09b0b2081bc7ed75b125832136d6ed.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 Magma 8B

## 1. General information

**1.0.1 Version of the Summary:** 1.0

**1.0.2 Last update:** 24-Nov-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):** Magma-8B

**1.2.2 Model release date:** 19-Feb-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:** Less than 1 billion tokens

**1.3.1.B Text training data content:** Image captions, Conversational Dialogs, Text instructions for tasks.

**1.3.1.C Image training data size:** 1 billion to 10 trillion tokens

**1.3.1.D Image training data content:** Training included multimodal image datasets and UI screenshots for grounding and navigation such as ShareGPT4V, LLaVA-1.5 instruction data, InfoGraphicVQA, ChartQA, FigureQA, TQA, ScienceQA, SeeClick and Vision2UI; images cover photography, charts, figures, documents, infographics, and interface elements

**1.3.1.E Audio training data size:**  Not applicable. Audio data 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:** Less than 1 billion tokens

**1.3.1.H Video training data content:** Instructional and egocentric videos used for agentic pretraining and temporal grounding, including Epic-Kitchens, Ego4D, Something-Something v2 and other instructional clips; videos were segmented and filtered, and used to derive Trace-of-Mark trajectories for action planning

**1.3.1.I Other training data size:** Robotics data comprising approximately 9.4 million image-language-action triplets from around 326,000 trajectories within Open-X-Embodiment mixtures

**1.3.1.J Other training data content:** Robotics manipulation datasets from Open-X-Embodiment used for vision-language-action learning, including 7-DoF gripper states and visual traces to support action prediction

**1.3.2 Latest date of data acquisition/collection for model training:** 11-Jan-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:** 8-Jan-2024

**1.3.5 Rationale or purpose of data selection:** Datasets were selected to cover multimodal understanding and agentic capabilities across digital and physical environments. UI datasets provide actionable elements for grounding and navigation; instructional videos supply rich temporal dynamics for action planning; robotics datasets provide action trajectories for manipulation; and multimodal image instruction data maintains general visual-language competence. This mix supports spatial-temporal reasoning, grounding, and planning

## 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?** No

### 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