Technical documentation referred to in Article 53(1), point (a) — technical documentation for providers of general-purpose AI models

Section 1

Information to be provided by all providers of general-purpose AI models

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:

Section 2

Additional information to be provided by providers of general-purpose AI models with systemic risk

1. 1.

   A general description of the general-purpose AI model including:

   1. (a)

      the tasks that the model is intended to perform and the type and nature of AI systems in which it can be integrated;

   2. (b)

      the acceptable use policies applicable;

   3. (c)

      the date of release and methods of distribution;

   4. (d)

      the architecture and number of parameters;

   5. (e)

      the modality (e.g. text, image) and format of inputs and outputs;

   6. (f)

      the licence.

2. 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:

   With regard to point (e), where the energy consumption of the model is unknown, the energy consumption may be based on information about computational resources used.

   1. (a)

      the technical means (e.g. instructions of use, infrastructure, tools) required for the general-purpose AI model to be integrated in AI systems;

   2. (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;

   3. (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;

   4. (d)

      the computational resources used to train the model (e.g. number of floating point operations), training time, and other relevant details related to the training;

   5. (e)

      known or estimated energy consumption of the model.

3. 1.

4. 2.

5. 3.