AI Act
Table of Contents
Chapter I – GENERAL PROVISIONS
Chapter II – PROHIBITED AI PRACTICES
Chapter III – HIGH-RISK AI SYSTEMS
Chapter IV – TRANSPARENCY OBLIGATIONS FOR PROVIDERS AND DEPLOYERS OF CERTAIN AI SYSTEMS
Chapter V – GENERAL-PURPOSE AI MODELS
Chapter VI – MEASURES IN SUPPORT OF INNOVATION
Chapter VII – GOVERNANCE
Chapter VIII – EU DATABASE FOR HIGH-RISK AI SYSTEMS
Chapter IX – POST-MARKET MONITORING, INFORMATION SHARING AND MARKET SURVEILLANCE
Chapter X – CODES OF CONDUCT AND GUIDELINES
Chapter XI – DELEGATION OF POWER AND COMMITTEE PROCEDURE
Chapter XII – PENALTIES
Chapter XIII – FINAL PROVISIONS
Recitals (180)
Annexes
Annex XII
Transparency information referred to in Article 53(1), point (b) — technical documentation for providers of general-purpose AI models to downstream providers that integrate the model into their AI system
The information referred to in Article 53(1), point (b) shall contain at least the following:
1. A general description of the general-purpose AI model including:
(a) the tasks that the model is intended to perform and the type and nature of AI systems into which it can be integrated;
(b) the acceptable use policies applicable;
(c) the date of release and methods of distribution;
(d) how the model interacts, or can be used to interact, with hardware or software that is not part of the model itself, where applicable;
(e) the versions of relevant software related to the use of the general-purpose AI model, where applicable;
(f) the architecture and number of parameters;
(g) the modality (e.g. text, image) and format of inputs and outputs;
(h) the licence for the model.
2. A description of the elements of the model and of the process for its development, including:
(a) the technical means (e.g. instructions for use, infrastructure, tools) required for the general-purpose AI model to be integrated into AI systems;
(b) the modality (e.g. text, image, etc.) and format of the inputs and outputs and their maximum size (e.g. context window length, etc.);
(c) information on the data used for training, testing and validation, where applicable, including the type and provenance of data and curation methodologies.