[License-review] [2nd Resubmission] ModelGo Zero License, Version 2.0

[McCoy Smith]

A few minor comments on this license, which otherwise looks OK to me:

1. The disclaimers are not made "conspicuous" as that term is defined in 
UCC 2-316: https://www.law.cornell.edu/ucc/2/2-316 That has been 
interpreted as requiring something like ALL CAPS or bold, or a different 
color, or a box (although the criteria changed in 2022). This isn't 
necessarily a flaw (whether UCC is relevant to open source licenses is 
an interesting question) but the practice seems to be that most newer 
open source licenses try to adhere to this requirement (most by using 
ALL CAPS since that tends to be the only way to do this with .txt files 
or ASCII -- which non-lawyers tend to dislike because they interpret it 
as screaming without understanding why it's done that way).

2. I find the way the grants are structured sub-optimal in the way that 
it handles the right of performance under copyright law. Rather than 
being in the grant, it is subsumed into the definition of 
"Distribution/Distribute" and then grants a right to Distribute. All 
rights are granted (which is good, that way you don't have to rely on 
implied grants) but you do need to dig into the definitions to get there.

Otherwise no comments from me on this one.

McCoy

[in personal capacity and not as board member]

On 7/10/2025 1:03 AM, Moming Duan wrote:
> Dear all,
>
> I’d like to make a quick fix to the definition of Licensed Materials 
> based on Josh’s suggestion. Thanks.
>
>
>> On 3 Jul 2025, at 4:43 AM, Josh Berkus <josh at berkus.org> wrote:
>>
>> Moming,
>>
>> Just a clairity in wording suggestion:
>>
>> "For the avoidance of doubt, the Licensed Materials do not include 
>> any datasets used for pretraining, training, adapting, or evaluating 
>> the Model."
>>
>> Suggestion:
>>
>> "The Licensed Materials do not include any datasets used for 
>> pretraining, training, adapting, or evaluating the Model, which may 
>> or may not be made available under a separate license."
>>
>> Reason:
>>
>> First, the phrase "for the avoidance of doubt" is awkward and not 
>> really necessary.  Second, this rephrase makes it clear that this 
>> license takes no stance on the licensing of datasets, it's just 
>> stating that they are not included under this license.
>>
>> -- 
>> Josh Berkus
>
>
> Best,
> Moming
>
>
>
>
>
>> On 18 Jun 2025, at 5:31 PM, Moming Duan <duanmoming at gmail.com> wrote:
>>
>> Dear OSI Community,
>>
>>
>> Following our previous discussions in May, I have made further 
>> revisions to the ModelGo Zero License (MG0-2.0). I am submitting this 
>> updated version for OSI review via this email. The license text is 
>> attached.
>>
>> —————— Major Updates to Previous Submission
>>
>>   * Removes restrictions on model output and the requirement to
>>     provide a copy of the license, making it truly free of
>>     obligations or conditions.
>>   * Adds more explicit granting of rights in Section 2.1.
>>   * Narrows the definition of “Derivative Materials” by including the
>>     phrase: “in order to replicate, approximate, or otherwise achieve
>>     functional behavior that is similar to the Model.”
>>   * Removes “Derivative Materials” in Section 5: “Nothing in this
>>     License permits You to modify this License as applied to the
>>     Licensed Materials.”
>>   * Fixes typos and formatting issues.
>>
>>
>> —————— License Introduction
>>
>> *License Name*:ModelGo Zero License
>> *Version*: 2.0
>> *Short Identifier: *MG0-2.0
>> *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-mg0.html
>> *Introduction and Video*:https://www.modelgo.li/
>>
>> *Overview*:
>>
>> ModelGo Zero License Version 2.0 (MG0-2.0) 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. 
>> MG0-2.0 is the most permissive license in the ModelGo family, 
>> requiring only that the original license be provided when 
>> distributing the original Licensed Materials or Derivative Materials 
>> (Licensed Materials and Derivative Materials aredefined in Clause 1).
>>
>> *Complies with OSD:*
>> *
>> *
>> OSD 3 Derived Works — MG0-2.0 Clause 2.1 (a) grants copyright and 
>> patent rights to create derivatives.
>> OSD 5 and OSD 6 — No discrimination clause is included in MG0-2.0.
>> OSD 9 License Must Not Restrict Other Software — No such restriction 
>> is included in MG0-2.0.
>>
>> *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 MG0-2.0 with one similar OSS license — Apache-2.0
>>
>> # MG0-2.0 defines licensed materials and derivative works differently 
>> from Apache-2.0, tailoring them to models.
>> # MG0-2.0 can govern the remote access (e.g., chatbot) scenario.
>> # MG0-2.0 does not require retaining attribution or stating 
>> modifications when redistributing derivatives.
>>
>> 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
>>
>> <MG0-2.0.txt>
>
>
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