Prepare and Maintain GPAI Model Technical Documentation

(a) draw up and keep up-to-date the technical documentation of the model, including its training and testing process and the results of its evaluation, which shall contain, at a minimum, the information set out in Annex XI for the purpose of providing it, upon request, to the AI Office and the national competent authorities;

2. The obligations set out in paragraph 1, points (a) and (b), shall not apply to providers of AI models that are released under a free and open-source licence that allows for the access, usage, modification, and distribution of the model, and whose parameters, including the weights, the information on the model architecture, and the information on model usage, are made publicly available. This exception shall not apply to general-purpose AI models with systemic risks.

The technical documentation referred to in Article 53(1), point (a) shall contain at least the following information as appropriate to the size and risk profile of the model: 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 in which it can be integrated; (b) the acceptable use policies applicable; (c) the date of release and methods of distribution; (d) the architecture and number of parameters; (e) the modality (e.g. text, image) and format of inputs and outputs; (f) the licence. 2. A detailed description of the elements of the model referred to in point 1, and relevant information of the process for the development, including the following elements: (a) the technical means (e.g. instructions of use, infrastructure, tools) required for the general-purpose AI model to be integrated in AI systems; (b) the design specifications of the model and training process, including training methodologies and techniques, the key design choices including the rationale and assumptions made; what the model is designed to optimise for and the relevance of the different parameters, as applicable; (c) information on the data used for training, testing and validation, where applicable, including the type and provenance of data and curation methodologies (e.g. cleaning, filtering, etc.), the number of data points, their scope and main characteristics; how the data was obtained and selected as well as all other measures to detect the unsuitability of data sources and methods to detect identifiable biases, where applicable; [Excerpt - see official source for complete provision]

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