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

1. the tasks that the model is intended to perform and the type and nature of AI systems means a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments; in which it can be integrated;

2. the acceptable use policies applicable;

3. the date of release and methods of distribution;

4. the architecture and number of parameters;

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

6. the licence.

1. the technical means (e.g. instructions of use, infrastructure, tools) required for the general-purpose AI model means an AI model, including where such an AI model is trained with a large amount of data using self-supervision at scale, that displays significant generality and is capable of competently performing a wide range of distinct tasks regardless of the way the model is placed on the market and that can be integrated into a variety of downstream systems or applications, except AI models that are used for research, development or prototyping activities before they are placed on the market; to be integrated in AI systems means a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments;;

2. 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. 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. 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. known or estimated energy consumption of the model.

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.