| Filename | Latest commit message | Latest commit date |
|---|---|---|
| examples | ||
| CHANGELOG.md | ||
| CONTRIBUTING.md | ||
| FAQ.fr.md | ||
| FAQ.md | ||
| HOW_TO_APPLY.md | ||
| LICENSE | ||
| NOTE.fr.md | ||
| NOTE.md | ||
| RATIONALE.md | ||
| README.md | ||
| ROADMAP.md | ||
AI Model Component Note
An interpretive clarification of source code obligations for software incorporating AI model components
What is this?
The AI Model Component Note is an interpretive clarification of source code obligations for free and open source software that incorporates AI model components. It can be combined with any copyleft license (GPL, AGPL, EUPL, etc.) using the standard SPDX "WITH" syntax.
When a free software program integrates a machine learning model--weights, embeddings, calibration data, configuration--as an essential part of its behavior, this Note makes explicit what should already be implicit: that the "preferred form for making modifications" (the Corresponding Source under copyleft licenses) must include the Model Corresponding Information: the architecture specification, the training/fine-tuning source code, the configuration, a sufficient description of the training data, and the instructions to retrain or replace the component.
Important: In the SPDX sense this is intended for registration as a license exception entry (that is the name of the SPDX category; submission planned, see ROADMAP.md, Phase C), but the instrument itself is a Note: a text that supplements a license to clarify its existing obligations. It does not add new rights or remove any existing ones. It simply removes ambiguity around what "source code" means when AI components are part of the software: the Note never decides whether a model must be provided – the license does – it clarifies what providing it entails. Throughout the project's documents, licenses are referred to by their SPDX identifiers.
Quick links
- NOTE.md – canonical English text
- NOTE.fr.md – French translation (informative)
- RATIONALE.md – legal reasoning and precedents
- HOW_TO_APPLY.md – practical guide for maintainers
- FAQ.md – frequently asked questions
- FAQ.fr.md – French FAQ
- examples/ – license-specific templates
- ROADMAP.md – phased plan (seed and gather → pilot adoption → standardisation)
- CONTRIBUTING.md – how to contribute
- CHANGELOG.md – version history
Why does this matter?
Free software licenses were designed for a world where the binary/source distinction was clear. AI model components blur that line: the weights are functionally the "object code" of the model, while the training data and scripts are the "source code". Standard compliance practice stops at conventional source files--leaving AI components in a practical blind spot, even though the licenses' own definitions ("preferred form of the work for making modifications") already reach them.
Free software licensing has a long tradition of responding to such gaps when technical reality moves faster than the licenses themselves. Three well-known precedents illustrate the principle:
- GPL-3.0 anti-tivoisation (§6) – closing the hardware-lock workaround for embedded software;
- AGPL Affero clause (§13) – closing the SaaS workaround for network-only deployment;
- LGPL interface information (LGPL-2.1 §6 / LGPL-3.0 §4) – preserving the freedom to swap a library on which proprietary code depends.
Each of these responded to a new technical situation by rewriting the license text.
Our approach is different in kind. The AI Model Component Note does not modify any license. It is an interpretive clarification by the copyright holder of what the existing license already requires. It adds no obligation, removes no right; it lifts a specific ambiguity – does "Corresponding Source" / "preferred form for making modifications" cover AI model components? – and records the copyright holder's reading of the answer.
Interpretive clarifications are themselves a well-established practice in free software: the Linux kernel's COPYING syscall note (Torvalds, 1992 onwards, SPDX-registered as Linux-syscall-note) and Larry Wall's note in the Perl README (verifiable from Perl 4.0, 1991) have each structured their ecosystem for over three decades without ever being adjudicated. They sit alongside the broader interpretive layer published by license stewards (FSF GPL FAQ, Apache legal-discuss, Eclipse legal opinions, Mozilla MPL stewardship, OSI position papers). The two precedents are presented in detail, with the doctrinal distinction between interpretation and exception, in RATIONALE.md §4.2, drawing on B. Jean, L'évolution des licences libres et open source, HAL halshs-02077882.
Why does an interpretation work? Its effect in court is not certain – a judge could disagree with the copyright holder. But two practical effects are strong:
- it forecloses the most opportunistic readings a downstream distributor might invoke (they cannot plausibly claim to have understood "Corresponding Source" in a way the copyright holder expressly excluded);
- it shifts the argumentation burden – and the litigation risk – onto whoever wishes to argue the contrary. Arguing against a written, freely-published interpretation of a license one is voluntarily using is materially harder than exploiting a silence.
The AI Model Component Note is therefore best understood as a scope declaration: it brings to AI-bearing software the kind of predictability that the GPL FAQ has long brought to ordinary copyleft software.
How do you use it?
SPDX expression
Once the identifier is accepted on the SPDX License List (submission planned, see ROADMAP.md), projects adopting this Note will declare it in their license metadata using the standard SPDX "WITH" syntax:
GPL-3.0-or-later WITH AI-Model-Component-Note
AGPL-3.0-or-later WITH AI-Model-Component-Note
EUPL-1.2 WITH AI-Model-Component-Note
Pending acceptance, use GPL-3.0-or-later WITH AdditionRef-AI-Model-Component-Note (SPDX 3.x) or a single LicenseRef- for the whole combination (SPDX 2.x) – see HOW_TO_APPLY.md §2. The "WITH" keyword is the standard SPDX mechanism for combining a base license with an exception entry.
Which licenses does it apply to?
This Note is most relevant for copyleft licenses (GPL, AGPL, EUPL) because they impose obligations to provide source code or the "preferred form for making modifications". That's where the Note adds clarity.
For permissive licenses (MIT, Apache-2.0, BSD), the Note would have no practical effect. Permissive licenses don't require source disclosure, so the copyright holder isn't bound by their own license's conditions. If you use a permissive license, you're already free to distribute only the weights--the license itself doesn't mandate anything else.
Can it be added at any time?
Since the Note clarifies what's already implicit (doesn't remove obligations or add rights), there's no legal contraindication to adding it to an existing copyleft project at any time. However, ideally it should be adopted at the project's start, because the real challenge is achieving consensus among all contributors about what "source" means for AI components.
Relation to other initiatives
This Note is complementary to--not in competition with--other ongoing efforts:
| Initiative | Focus | Our approach |
|---|---|---|
| FSF – freedom in machine learning applications (Oct. 2024; LLM statement, March 2026) | Requires full training code and training data to be free for an ML application to be free | More targeted: we require sufficient information to modify the component, not necessarily the full dataset |
| OSI – Open Source AI Definition (OSAID 1.0) | System-level definition of an open source AI system (data information, code, parameters) | Licence-level and complementary: the Note states what an existing copyleft licence already requires for an integrated component; see RATIONALE.md §5.5 |
| Contextual Copyleft (Shanklin, Hine, Novelli et al.) | Extends copyleft from training data to resulting models | More conservative: we stay within the scope of the licensed software, avoiding "copyleft contamination" of independent works |
| copyleft-next (Kuhn & Fontana, relaunched 2025) | Full next-generation copyleft license | Complementary: this Note can be adopted today on existing licenses while new licenses are designed |
| Model Openness Framework (LF AI & Data, 2024) | Three-tier classification of which model artifacts are released, and under which open licences | Complementary: MOF evaluates what is released; the Note clarifies what the copyleft licence already requires for integrated components (see RATIONALE.md §5.6) |
| OpenRAIL / RAIL licenses | Use-restriction based licensing | Different category: RAIL restricts uses; this Note imposes disclosure obligations |
Status
This is Draft 1.1 – a working document open for community feedback.
We believe it is usable as-is, but adoption is at the user's own discretion and responsibility. Legal review is always recommended before integrating any license exception into your project.
This work has not yet been formally reviewed by the FSF, OSI, or SPDX. We welcome issues, discussions, and merge requests to refine, critique, or extend it.
Roadmap
The project follows a deliberately phased approach:
Phase A – Seed and gather (current). Articulate a first credible proposal in the open, and use it to collect (1) substantive feedback – in particular from the FSFE Legal Network, SFLC/SFC, OSI, and equivalent legal fora; and (2) potential contributors and reviewers, both legal and maintainer-side. The text is deliberately published as a draft to make this conversation possible.
Phase B – Test in real projects. Accompany adoption on concrete projects where the discussion is most productive. The current candidate is Hermine-foss, where no AI model is integrated at this stage but the question of how the project should handle one is actively under discussion within the partner community. Other projects willing to pilot the clarification are welcome to engage via the issue tracker.
Phase C – Standardisation and broader uptake. Submit AI-Model-Component-Note to the SPDX License List, position the clarification with respect to OSAID 1.0 and emerging EU regulation (AI Act, CRA, NPLD), and broaden communication and outreach – academic, institutional and within the wider free and open source community.
A more detailed task-level roadmap is maintained in ROADMAP.md.
Adopters
Projects that have adopted, or are considering, the AI Model Component Note:
| Project | Status | Link |
|---|---|---|
| Hermine | candidate | https://hermine-foss.org |
If your project adopts the Note, or is considering it, please open an issue or a merge request to be listed here. Self-identification is welcome.
Citation
If you use, discuss, or build on this work, please cite it as:
inno³ team, "AI Model Component Note – An interpretive clarification
for software incorporating AI model components." inno³, 2026.
Version 1.1-draft. https://code.inno3.eu/commons-tools/ai-model-component-note
Maintenance
- Maintained by the inno³ team – primary contact: Benjamin Jean (
hello@inno3.fr). - Canonical repository: https://code.inno3.eu/commons-tools/ai-model-component-note (mirrored on GitHub and GitLab).
- Part of the editorial series "Open Source and AI" – Article 1 · Article 2 · Article 3 · Article 4 – this proposal
License
This repository (texts, documentation, examples) is published under Creative Commons Zero 1.0 Universal (CC0-1.0), dedicating all content to the public domain.
Adopting, adapting, and translating the Note for integration into your own project is explicitly encouraged.