# Where to find the description of the "components"

**URL:** <https://discuss.opensource.org/t/where-to-find-the-description-of-the-components/273>\
**Category:** Open Source AI\
**Created:** [March 21, 2024, 8:04pm UTC](https://discuss.opensource.org/t/where-to-find-the-description-of-the-components/273 "2024-03-21T20:04:55Z")\
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**Author:** ![stefano](https://yyz1.discourse-cdn.com/flex011/user_avatar/discuss.opensource.org/stefano/32/4_2.png) [@stefano](https://discuss.opensource.org/u/stefano)\
**Post date:** [March 21, 2024, 8:04pm UTC](https://discuss.opensource.org/t/where-to-find-the-description-of-the-components/273/1 "2024-03-21T20:04:55Z")

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The team at the Linux Foundation AI & Data Generative AI Commons group has published the article that we based the list of components of ML systems on. If you’re wondering what `Training, Validation and Testing Code` is, you should read this article.

Matt White, Ibrahim Haddad, Cailean Osborne, Xiao-Yang Liu (Yanglet), Ahmed Abdelmonsef, Sachin Varghese have worked with other LF members to establish a ranked classification system that rates machine learning models based on their completeness and openness, following principles of open science, open source, open data, and open access. This work is useful to better understand the environment in which the Open Source AI Definition needs to operate in.

The relevant piece for us is section 5, the [Model Openness Framework](https://arxiv.org/abs/2403.13784) (MOF) Components:

1. Datasets
2. Data Preprocessing Code
3. Model Architecture
4. Model Parameters
5. Model Metadata
6. Training, Validation and Testing Code
7. Inference Code
8. Evaluation Code
9. Evaluation Data
10. Evaluation Results
11. Supporting Libraries and Tools
12. Technical Report
13. Model Card
14. Data Card
15. Research Paper
16. Sample Model Outputs
17. Model Openness Framework Configuration File

and their definitions. You’ll recognize the terms from [draft 0.0.6](https://hackmd.io/@opensourceinitiative/osaid-0-0-6).

> **[The Model Openness Framework: Promoting Completeness and Openness for...](https://arxiv.org/abs/2403.13784)**
>
> Generative AI (GAI) offers unprecedented possibilities but its commercialization has raised concerns about transparency, reproducibility, bias, and safety. Many "open-source" GAI models lack the necessary components for full understanding and...

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