GPAI Ledger › Nova 2 Lite (Amazon) › Capture 11 Aug 2026

Nova 2 Lite — capture 20260811T105338Z

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===== nova-2-lite-training-data-summary.pdf =====
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Public Summary of Training Content: Amazon Nova 2 Lite

Version of the Summary:  1.0

Last update:

July 27, 2026
General information
1. General information

1.1. Provider identification

Provider name and contact details:

Amazon Media EU S.à r.l.
38, avenue John F. Kennedy
L-1855 Luxembourg
Grand Duchy of Luxembourg

Authorised representative name and
contact details:

N/A (provider established in the Union)

1.2. Model identification

Versioned model name(s):

Nova 2 Lite (amazon.nova-2-lite-v1:0). The Nova 2 Lite model
card is available at
https://docs.aws.amazon.com/bedrock/latest/userguide/model-
card-amazon-nova-2-lite.html.

Model dependencies:

N/A

Date of placement of the model on the
Union market:  December 2, 2025

1.3 Modalities, overall training data size and other characteristics

Modality
Select the
modalities present
in the training data,
to the extent that
they are identifiable
Training data size
For each selected modality, select the range
within which the estimated total training data
size for that modality falls. Dynamic datasets
may be excluded from the estimation.
Types of content
For each selected modality, provide a general
description of the type of content that has been
included in the training data.
Text ☐ Less than 1 billion tokens
☐ 1 billion to 10 trillion tokens
X More than 10 trillion tokens

Nova 2 Lite was trained on text content that
included reference materials, technical
documentation, source code, and general
web content.
Image ☐ Less than 1 million images
☐ 1 Million to 1 billion images
X More than 1 billion images
Nova 2 Lite was trained on image content
that included photographs, graphs, tables,
and screenshots.
Audio ☐ Less than 10,000 hours
☐ 10,000 to 1 million hours
X More than 1 million hours
Nova 2 Lite was trained on audio content that
included conversational speech and sound
effects.
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Video ☐ Less than 10,000 hours
X 10,000 to 1 million hours
☐ More than 1 million hours
Nova 2 Lite was trained on video content that
included instructional and general-interest
audiovisual material.
Other N/A

Latest date of data acquisition/collection
for model training: October 2025. Nova 2 Lite is not continuously trained.

Description of the linguistic characteristics
of the overall training data:

The overall training data for Nova 2 Lite includes over 200
languages, covering a broad range of EU official languages, with
particular emphasis among them on Dutch, French, German,
Italian, Portuguese, and Spanish, as well as other major world
languages including Arabic, English, Hebrew, Hindi, Japanese,
Korean, Simplified Chinese, and Turkish.

Other relevant characteristics of the overall
training data:

The overall training data for Nova 2 Lite includes datasets
intended to enhance the model’s capabilities across modalities in
various categories, including math, coding, language, logical
reasoning, and factual, scientific, and audio-visual understanding.

Additional comments (optional):

More information on Amazon's training approach can be found in
Amazon’s Generative AI Development Disclosure.

2. List of data sources

2.1. Publicly available datasets

Have you used publicly available datasets
to train the model?   Yes

If yes, specify the modality(ies) of the
content covered by the datasets
concerned: Text, image, video, and audio

List of large publicly available datasets:

Large publicly available datasets include data curated from public
web content as well as datasets covering science and research
topics.

General description of other publicly
available datasets not listed above:

Other publicly available datasets span text, image, video, and
audio content covering domains such as math, science, general
knowledge, and reasoning, and are largely focused on the same
languages as the rest of the training corpus.

Additional comments (optional):

N/A

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2.2 Private non-publicly available datasets obtained from third parties

2.2.1. Datasets commercially licensed by rightsholders or their representatives

Have you concluded transactional
commercial licensing agreement(s) with
rightsholder(s) or with their
representatives? Yes

If yes, specify the modality(ies) of the
content covered by the datasets
concerned:

Text, image, video, and audio

2.2.2. Private datasets obtained from other third parties

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? Yes

If yes, specify the modality(ies) of the
content covered by the datasets
concerned:  Text, image, video, and audio

If publicly known, list private datasets
obtained from other third parties:

Information regarding private datasets obtained from third
parties is subject to confidentiality terms.
General description of non-publicly known
private datasets obtained from third
parties

Private datasets cover subject areas including math, science,
coding, general knowledge, and visual understanding, and are
focused on the same languages as the rest of the training corpus.

Additional comments (optional):

N/A

2.3 Data crawled and scraped from online sources

Were crawlers used by the provider or on
behalf of? Yes

If yes, specify crawler name(s)/identifier(s):

Amazonbot

Purposes of the crawler(s):

Amazonbot is Amazon’s web crawler used to improve its services,
including this model.
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General description of crawler behaviour:

Amazonbot respects the Robots Exclusion Protocol defined at
https://www.rfc-editor.org/rfc/rfc9309.html (i.e., robots.txt
protocol), honoring the user-agent and the allow/disallow
directives. Amazonbot also respects page-level robots meta tags,
including 'noarchive', 'noindex', and 'nofollow', as well as the link-
level 'nofollow' directive. Amazonbot is not designed to
circumvent captchas or paywalls or to access password-protected
content. For more information see
https://developer.amazon.com/amazonbot.

Period of data collection: From November 2023 to December 2024

Comprehensive description of the type of
content and online sources crawled:

Data was crawled from a wide range of publicly available sources
including sites focused on science, math, coding, business, and
economics. This includes a wide variety of languages, focused on
the same languages as the rest of the training corpus.

Type of modality covered: Text, image, video, and audio

Summary of the most relevant domain
names crawled:

The most relevant domains crawled include reference and
knowledge sites, educational sources, scientific and technical
repositories, business and financial information sites, and general-
interest and region-specific portals. These domains span a wide
variety of languages, focused on the same languages as the rest
of the training corpus.

Additional comments (optional): N/A

2.4 User data

Was data from user interactions with the AI
model (e.g. user input and prompts) used
to train the model? Yes

Was data collected from user interactions
with the provider’s other services or
products used to train the model?  Yes

If yes, provide a general description of the
provider’s services or products that were
used to collect the user data:

In accordance with relevant terms of service and privacy policies,
including service-specific policies, Amazon may use data collected
from users of Amazon services (for example, public Amazon.com
(US) product reviews) to train AI models. For more information
about how Amazon collects and uses personal information, please
see the Amazon Privacy Notice for the applicable country.

Type of modality covered: Text, image, video, and audio
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Additional comments (optional):

We are committed to building AI responsibly, with appropriate
safeguards for safety, accuracy, privacy, and security. For more
information about our approach to responsible AI,
see Responsible AI at Amazon.

2.5 Synthetic data

Was synthetic AI-generated data created by
the provider or on their behalf to train the
model?   Yes

If yes, modality of the synthetic data: Text, image, video, and audio

If yes, specify the general -purpose AI
model(s) used to generate the synthetic
data if available on the market:

Amazon generated synthetic data using its own and third-party
general-purpose AI models, including Amazon Nova Premier.

Information about other AI models,
including provider’s own AI model(s) not
available on the market, used to generate
synthetic data to train the model to which
this Summary applies:

Amazon may use internal models or third-party models to
generate or to augment training data.

Additional comments (optional):

N/A

2.6 Other sources of data

Have data sources other than those
described in Sections 2.1 to 2.5 been used
to train the model?  Yes

If yes, provide a narrative description of
these data sources and the data:

Proprietary datasets created internally, which include human
preference data and single and multi-turn responsible AI
demonstrations in multiple languages.

Additional comments (optional)

N/A

3. Data processing aspects

3.1. Respect of reservation of rights from text and data mining exception or limitation

Are you a Signatory to the Code of Practice
for general-purpose AI models that
includes commitments to respect
reservations of rights from the TDM
exception or limitation?
 Yes
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Describe the measures implemented
before model training to respect
reservations of rights from the TDM
exception or limitation before and during
data collection, including the opt-out
protocols and solutions honoured by the
provider or, as applicable, by third parties
from which datasets have been obtained:

As described in Section 2.3, Amazonbot reads and follows
reservations of rights expressed through the Robots Exclusion
Protocol (robots.txt). Amazonbot is not designed to circumvent
captchas or paywalls or to access password-protected content.
For more information see
https://developer.amazon.com/amazonbot.

Additional comments (optional):

N/A

3.2 Removal of illegal content

General description of measures taken:

Measures are taken to detect and remove Child Sexual Abuse
Material (CSAM), non-consensual intimate imagery (NCII), and
other forms of illegal content from training data. Datasets are
scanned for known CSAM, and such content is reported to the
National Center for Missing & Exploited Children (NCMEC).

3.3 Other information (optional)

Other relevant information about data
processing (optional):

N/A