BLOOM model, we decided to adapt it and create this license in order to propose a general
open and responsible license applicable to other machine learning based AI models (e.g.
multimodal generative models).
In short, this license strives for both the open and responsible downstream use of the
accompanying model. When it comes to the open character, we took inspiration from open
source permissive licenses regarding the grant of IP rights. Referring to the downstream
responsible use, we added use-based restrictions not permitting the use of the Model in very
specific scenarios, in order for the licensor to be able to enforce the license in case potential
misuses of the Model may occur. Even though downstream derivative versions of the model
could be released under different licensing terms, the latter will always have to include - at
minimum - the same use-based restrictions as the ones in the original license (this license).
The development and use of artificial intelligence (“AI”), does not come without concerns.
The world has witnessed how AI techniques may, in some instances, become risky for the
public in general. These risks come in many forms, from racial discrimination to the misuse
of sensitive information.
BigScience believes in the intersection between open and responsible AI development; thus,
this License aims to strike a balance between both in order to enable responsible
open-science in the field of AI.
This License governs the use of the model (and its derivatives) and is informed by the model
card associated with the model.
NOW THEREFORE, You and Licensor agree as follows:
1. Definitions
(a) "License" means the terms and conditions for use, reproduction, and Distribution as
defined in this document.
(b) “Data” means a collection of information and/or content extracted from the dataset used
with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not
licensed under this License.
(c)“Output” means the results of operating a Model as embodied in informational content
resulting therefrom.
(d)“Model” means any accompanying machine-learning based assemblies (including
checkpoints), consisting of learnt weights, parameters (including optimizer states),
corresponding to the model architecture as embodied in the Complementary Material, that
have been trained or tuned, in whole or in part on the Data, using the Complementary
Material.
(e) “Derivatives of the Model” means all modifications to the Model, works based on the
Model, or any other model which is created or initialized by transfer of patterns of the
weights, parameters, activations or output of the Model, to the other model, in order to cause
the other model to perform similarly to the Model, including - but not limited to - distillation
methods entailing the use of intermediate data representations or methods based on the
generation of synthetic data by the Model for training the other model.
(f)“Complementary Material” means the accompanying source code and scripts used to
define, run, load, benchmark or evaluate the Model, and used to prepare data for training or
evaluation, if any. This includes any accompanying documentation, tutorials, examples, etc,
if any.
(g) “Distribution” means any transmission, reproduction, publication or other sharing of the
Model or Derivatives of the Model to a third party, including providing the Model as a hosted
service made available by electronic or other remote means - e.g. API-based or web access.
(h) “Licensor” means the copyright owner or entity authorized by the copyright owner that is
granting the License, including the persons or entities that may have rights in the Model
and/or distributing the Model.
(i) "You" (or "Your") means an individual or Legal Entity exercising permissions granted by
this License and/or making use of the Model for whichever purpose and in any field of use,