[License-review] [Resubmission] ModelGo Attribution License, Version 2.0

[Moming Duan]

Dear OSI Community,


Based on previous discussions and comments, I have revised the ModelGo Attribution License (MG-BY-2.0) with the assistance of law students. I am submitting this revised license for OSI review via this email. The license text file is attached below.

—————— Major Updates to Previous Submission

Add conditions for distributing outputs as a dataset.
Remove the "Third-Party Material" and "Governing Law and Dispute Resolution" sections.
Remove the annex.
Eliminate redundant clauses from the license.
Clarify definitions of “Distribution", “Licensor", "Licensed Materials”, and "Output”.
Remove definitions of "License" and "Open Source Software”.
Refine license clauses based on feedback from the previous round of OSI review.

—————— License Introduction

License Name:		ModelGo Attribution License
Version: 				2.0
Short Identifier: 		MG-BY-2.0
Copyleft:			No
Legacy or New: 		New License
Drafted By Lawyer: 	Yes, Rajah & Tann Singapore LLP
Approved or Used by Projects: 	No

License URL:				https://ids.nus.edu.sg/modelgo-mg-by.html
Introduction and Video:	https://www.modelgo.li/

Overview:

ModelGo Attribution License Version 2.0 (MG-BY-2.0) is a new license designed for publishing models (typically neural networks like Llama2, DeepSeek). It is one of the variants in the ModelGo License family. MG-BY-2.0 is the a permissive license in the ModelGo family, requiring that the original license and attribution be provided when distributing the original Licensed Materials or Derivative Materials (Licensed Materials and Derivative Materials are defined in Clause 1). A statement of modification is required, if applicable.
(Red content represents the differences from MG0-2.0 license)

Complies with OSD:

OSD 3 Derived Works — MG-BY-2.0  Clause 2.1 (a) grants copyright and patent rights to create derivatives.
OSD 5 and OSD 6 — No discrimination clause is included in MG-BY-2.0.
OSD 9 License Must Not Restrict Other Software — No such restriction is included in MG-BY-2.0.

The Gap to Fill:
Model sharing is very common on the web, with over 1.4 million models currently listed on Hugging Face (https://huggingface.co/models). However, most of these models are not properly licensed. When publishing their models, developers typically choose from three main options (as seen in the model license tags on the Hugging Face website):

OSS licenses, e.g., Apache-2.0, MIT
Open responsible AI licenses (OpenRAILs), e.g., CreativeML-OpenRAIL-M, OpenRAIL++
Proprietary Licenses, e.g., Llama2, Llama3

However, not all licenses are well-suited for model publishing.

Why not use OSS licenses? 
Traditional OSS licenses lack clear definitions regarding machine learning concepts, such as Models, Output, and Derivatives created through knowledge transfer. This ambiguity can result in certain ML activities (e.g., Distillation, Mix-of-Expert) being beyond the control of the model owner.

Why not use OpenRAILs? 
Recently, Responsible AI Licenses (https://www.licenses.ai/) have been widely advocated to govern AI technologies, aiming to restrict unlawful and unethical uses of models. While I acknowledge the growing need for such governance, these copyleft-style restrictions do not comply with the OSD and may cause incompatibility with licenses like GPL-3.0. Another concern is that these behavioral restrictions may proliferate within the AI model ecosystem, increasing the risk of license breaches.

Why not use Llama2 or Llama3 Licenses?
These licenses are proprietary licenses that are not reusable. Furthermore, they include exclusive terms such as "You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model" and copyleft-style behavioral restrictions.

In fact, the dilemma in current model publishing is the lack of a general-purpose license for model developers. Additionally, since no single license meets diverse model publishing needs, some developers resort to using CC licenses with different elements. However, CC licenses are ill-suited for this purpose as they do not grant patent rights. This motivated the drafting of ModelGo License family, which provides different licensing elements similar to CC but specifically designed for model publishing.

Comparison with Existing OSI-Approved Licenses:
Since I could not find an OSI-approved model license, I can only compare MG-BY-2.0 with one similar OSS license — Apache-2.0

MG-BY-2.0 defines licensed materials and derivative works differently from Apache-2.0, tailoring them to models.
MG-BY-2.0 Clause 2.2(b) includes provisions regarding model output.
MG-BY-2.0 can govern the remote access (e.g., chatbot) scenario.

If further comparisons or supporting evidence are needed to strengthen my claims, please let me know. I am more than willing to engage in further discussions with the OSI community about this license and contribute to promoting standardized model publishing. 🤗


Best,
Moming




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ModelGo Attribution License
Version 2.0, Mar 2025

This License is solely for the purpose of governing the use, reproduction, Distribution, 
modification or creation of derivative works from Licensed Materials. By exercising the rights 
granted in Section 2.1, You acknowledge and agree that You have read, understood, and agree 
to be bound by the terms and conditions of this License. If You do not agree to any terms 
and/or conditions of this License, then Licensor grants You no rights under this License.

1.	DEFINITIONS

“Complementary Materials” means the source code and scripts used to define, run, load, 
benchmark and/or evaluate the Model, and prepare data for the purpose of training, pretraining, 
fine-tuning and/or evaluation of the Model, and any tutorials, operating manuals, user guides 
and/or documentation that guide users in using, operating, implementing and/or customising 
the Model.

"Derivative Materials" means all improvements, modifications or derivative works to the 
Licensed Material or any part thereof, which are created or developed by You (either by 
Yourself or jointly with other third parties), including any new model developed by transferring 
patterns of weights, parameters, activations and/or Output from the Model, such as through 
distillation methods or synthetic data generation techniques.

“Distribution” (or “Distribute”) means any transmission, publication, public performance, 
sharing, or other methods of marking the Licensed Materials, Derivative Materials and/or 
Output available to a third party, including provision of the Licensed Materials or any part 
thereof as a hosted service or remotely accessible service, such as API-based access or web 
access.

“Licensor” means the rights owner that is granting the License.

“Licensed Materials” means, collectively, the Model and Complementary Materials that are 
Distributed by the Licensor under this License. For the avoidance of doubt, the Licensed 
Materials do not include any datasets used for pretraining, training, adapting, or evaluating the 
Model.

“Model” means the machine-learning constructs and/or assemblies licensed by the Licensor 
under this License, including any checkpoints, learned weights, parameters (including optimizer 
states) and the model architecture.

“Output” means any content, including but not limited to images, text, text effects, audio files, 
and/or video files, generated through operation of the Model and/or any new model developed 
by transferring patterns from the Model.

"You" (or "Your") means you, or any other person or entity (if you are entering into this license 
on behalf of such person or entity and provided you have the legal authority to bind such 
person or entity).

2.	LICENSE RIGHTS

2.1	Grant of Rights

(a)	Subject to the conditions of this License, the Licensor hereby grants to You a non-
exclusive, sublicensable, irrevocable, royalty-free, worldwide right and license (including 
the relevant copyrights, patent rights, and database rights) to:

(i)	use, reproduce and Distribute the Licensed Materials;

(ii)	use the Licensed Materials to create Derivative Materials; and

(iii)	use, reproduce, and Distribute Derivative Materials.

2.2	Conditions

(a)	If You Distribute any of the Licensed Materials or Derivative Materials, You shall:

(i)	provide a copy of this License with the Licensed Material or Derivative Materials;

(ii)	in the case of Distributing Derivative Materials, provide as part of the Distribution, 
prominent notices stating that You have modified the Licensed Materials and the 
relevant date of such modification;

(iii)	retain as part of the Distribution any existing copyright notice, attribution notice, 
and/or any notice identifying the authors of the Licensed Materials and any 
Derivative Materials.

(b)	If You Distribute a collection of Output as a dataset generated wholly or partially using 
the Licensed Materials and/or Derivative Materials, You shall provide a prominent notice 
in the Distribution stating that the dataset is generated using the Licensed Materials 
and/or Derivative Materials provided under this License.

2.3	Reservation of Rights

You shall not refer to the Licensor or use the Licensor’s trademarks, trade names, and service 
marks, for any publicity, advertising or marketing purposes, without the Licensor’s prior written 
consent.

3.	DISCLAIMER

To the maximum extent permissible under applicable law, the Licensed Materials are provided 
on an “as is” and “as available” basis without any representation, warranty, condition, or term of 
any kind (whether express, implied, statutory or otherwise), including of merchantability, 
satisfactory quality, fitness for a particular purpose, title, non-infringement, accuracy, 
correctness, absence of error, reliability, timeliness, non-infringement of or compliance with any 
laws, regulations and/or third-party rights, all of which are expressly disclaimed by the Licensor.

4.	LIMITATION OF LIABILITY

To the maximum extent permissible under applicable law, the Licensor shall not be liable for 
any direct, indirect, incidental, special, exemplary, punitive, or consequential damages 
(including, but not limited to, loss of profits, data, or business opportunities) arising out of or in 
connection with the Licensed Materials, Derivative Materials, and/or Output, whether on an 
action or claim in contract, tort (including negligence), breach of statutory duty or otherwise, 
even if the Licensor has been advised of the possibility of such damages.

5.	TERMINATION

The Licensor may terminate this License if You breach any material term and/or condition of 
this License, or if You initiate legal action against the Licensor alleging that the Licensed 
Materials or Derivative Materials infringe any patent worldwide.

6.	MODIFICATION OF THIS LICENSE

This License is Copyright © 2024 National University of Singapore. Permission is granted to 
copy, distribute, or communicate this License without modification. Nothing in this License 
permits You to modify this License as applied to the Licensed Materials or to Derivative 
Materials. However, You may modify the text of this License and copy, distribute or 
communicate Your modified version and apply it to other original works of authorship, provided 
that You not use the name “ModelGo” for your version of the license and furnish a readable 
notice describing Your modifications to this license.
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