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Open Source AI Status

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With RC1 of the OSI's Open Source AI definition set to come out soon, I think we should modify this page to be in line with their definition. I still think documenting models such as Llama and Mistral are worthwhile, but they are decidedly not Open Source. I am new to editing Wikipedia, so I am not yet sure how to handle the "category" of Open Source artificial intelligence including these models, but I can propose changes and figure out the details later.

Below are my proposed edits:

Change this page to "Open release artificial intelligence" or some other term like that, I am open to and would encourage suggestions. I know that the term "Open weights" is used more often, but it does not encompass all models that we are interested in here, so I propose "Open release" as the alternative for now.

On this page, we would link to the actual "Open Source artificial intelligence" page, and note the distinction between the two - this would look like an expansion of the point currently mentioned under the Llama subheading. Note that all models included on the would still remain on the "Open release artificial intelligence" page, and more could be added as well, but only models which meet the OSI's Open Source AI definition would be included on the "Open Source artificial intelligence" page.

I have some other suggestions for improvement of this page, but I'll stick to this one until it is resolved. JacobHaimes (talk) 02:43, 13 October 2024 (UTC)[reply]

The title "Open-release artificial intelligence" would indeed include more of the models interesting to cover, but not sure the term "open-release" is widely used. Alenoach (talk) 01:32, 9 December 2024 (UTC)[reply]
I've amended all of the remaining instances of this article describing Llama as open-source in Wikipedia's voice to attribute such descriptions to Meta. The Llama (language model) article currently describes Llama as source-available (which is undisputed) and not open-source (which is disputed and would conflict with our Open-source software article). — Newslinger talk 03:40, 15 December 2024 (UTC)[reply]
Thanks folks. I've added some material on both OSAID and MOF and tried to make the history section at least conform to that terminology, clarifying open-source vs open model etc. Still needs significant cleanup but I think keeping this as one page makes sense so all the confusion can be handled here. ★NealMcB★ (talk) 05:07, 25 July 2025 (UTC)[reply]

Wiki Education assignment: WRIT 340 for Engineers - Fall 2024 -MW 330-450

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This article was the subject of a Wiki Education Foundation-supported course assignment, between 26 August 2024 and 6 December 2024. Further details are available on the course page. Student editor(s): Nathanhuh, Hridayg2004, Potatochippy, DiscountKangaroo, BennettHarp (article contribs).

— Assignment last updated by 1namesake1 (talk) 23:30, 28 October 2024 (UTC)[reply]

Wiki Education assignment: Signals, Data, and Equity

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This article was the subject of a Wiki Education Foundation-supported course assignment, between 26 August 2024 and 13 December 2024. Further details are available on the course page. Student editor(s): Phzing (article contribs).

— Assignment last updated by Phzing (talk) 21:39, 22 November 2024 (UTC)[reply]

Some Proposed Contributions

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I think this page has some really strong content and could benefit from the following changes:

1. New Equity and Ethical Implications section Discuss how open-source AI democratizes access to technology but raises ethical concerns, including bias, misuse, and the need for ethical guidelines.

2. A more structured 'Concerns' section. Specifically, I'm proposing the addition of some new content and the division of existing content into the following categories:

  • Security Risks (Vulnerabilities in Open-source Models, Lack of Security Updates)
  • Equity and Ethical Implications (Bias and Discrimination, Lack of Ethical Guidelines)
  • Regulatory and Legal Concerns (Unclear Licensing and Usage Restrictions, Compliance with Privacy Laws)
  • Potential for Harmful Applications (Weaponization and Bioterrorism, AI for Manipulation and Misinformation)
  • Quality and Performance Concerns (Underperformance compared to Closed-source models, lack of robustness in real-world scenarios)

I'm also considering adding Challenges in Open-source AI and Benefits sections, and making updates to the Applications section (to include some new tools), but the two above proposed changes I think are of higher priority.

Happy to incorporate any feedback and also am new to editing Wikipedia, so apologies for anything unconventional. Phzing (talk) 23:06, 22 November 2024 (UTC)[reply]

Your additions look good. I think the article should also introduce the notion of "open-weight". Alenoach (talk) 01:43, 9 December 2024 (UTC)[reply]

Filling in the timeline of early open source models.

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The current section titled Key milestones in open-source AI (2020s–Present) > Companies and models does a poor job of articulating its subject matter. In fact, the majority of the text is dedicated to PyTorch and the Linux Foundation. While PyTorch and LF are certainly major players in the open source ecosystem, they are not examples of major milestones in the development of open-source AI models. PyTorch is a computer programing language that is very popular, but neither PyTorch nor LF have ever trained a notable open source AI model. Speaking as both an expert in the field and someone who has tried and failed to find independent notable coverage of LF AI&D I especially believe that they should be excluded. PyTorch currently doesn't seem to make much sense to include but I'm open to preserving the associated text in another part of the article.

I have a significant COI for this section as I was personally involved in the training of several of the models discussed (GPT-Neo, GPT-NeoX-20B, Pythia, RWKV, and BLOOM), was a co-lead of the BigScience Research Workshop, and am currently the Executive Director of EleutherAI

My edit is primarily focused on articulating the timeline of major open-source AI models and open-weight models from the release of GPT-3 through 2023. I've tried to find independent secondary sources that meet the notability guidelines for all claims below, but haven't been able to to my satisfaction due to the minimal coverage of the space in the media. I can provide significant primary sources for these claims if that's desired, but I'm choosing to not do so in part to make assessing the quality of the secondary sources easier. I believe that these edits significantly improve the article, but that further details on subsequent developments should also be added.

Key milestones in open-source AI (2020s–Present)

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Companies and models

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The 2020s saw the continued growth and maturation of open-source AI. Companies and research organizations began to release large-scale pre-trained models to the public, which led to a boom in both commercial and academic applications of AI. Notably, Hugging Face, a company focused on NLP, became a hub for the development and distribution of AI models, including open-source versions of transformers like GPT-2 and BERT.[1]

With the announcement of GPT-2, OpenAI originally planned to keep the source code of their models private citing concerns about malicious applications.[2] After OpenAI faced public backlash, however, it released the source code for GPT-2 to GitHub three months after its release.[2] OpenAI has not publicly released the source code or pretrained weights for the GPT-3 or GPT-4 models, though their functionalities can be integrated by developers through the OpenAI API.[3][4]

The rise of large language models (LLMs) and generative AI, such as OpenAI's GPT-3 (2020), further propelled the demand for open-source AI frameworks.[5][6] These models have been used in a variety of applications, including chatbots, content creation, and code generation, demonstrating the broad capabilities of AI systems.[7] At the time of GPT-3's release GPT-2 was still the most powerful open source language model in the world, spurring EleutherAI to train and release GPT-Neo[8] and GPT-J[9][10] in 2021, both of which were the most powerful open-source GPT-style model in the world when they were released. In February 2022 EleutherAI released GPT-NeoX-20B, taking back the title of most powerful open source language model in the world from Meta whose FairSeq Dense 13B model had surpassed GPT-J at the end of 2021 [11]. 2022 also saw the rise of larger and more powerful models under various non-open source licenses including Meta's OPT[12] and Galactica[13][14], the BigScience Research Workshop's BLOOM[15][16], and Tsinghua University's GLM.

In 2023 open-source and open-weight models both exploded in popularity with dozens of models of each type being released by a wide variety of actors. Particularly notable among these were Llama 1 and 2, MosaicML's MPT (a LLaMA-quality model with an open-source license) [17][18], and Mistral AI's Mistral and Mixtral models. The first models trained by start-ups that would grow to become major players such as DeepSeek, Stability AI, and Alibaba (Qwen) were trained in this time, as was RWKV (the first non-transformer architecture to see success at the 7 billion parameter scale) [19]

In 2024, Meta released a collection of large AI models, including Llama 3.1 405B, comparable to the most advanced closed-source models.[20] The company claimed its approach to AI would be open-source, differing from other major tech companies.[20] The Open Source Initiative and others stated that Llama is not open-source despite Meta describing it as open-source, due to Llama's software license prohibiting it from being used for some purposes.[21][22][23]

DeepSeek R1 reasoning model released as an open source project on January 20, 2025.[24]

Stellaathena (talk) 07:43, 21 July 2025 (UTC)[reply]

  1. ^ kakkar, Yuvraj (2024-01-23). "Hugging Face 🤗: Revolutionizing AI Collaboration in the Machine Learning Community". Medium. Retrieved 2024-11-25.
  2. ^ a b Xiang, Chloe (2023-02-28). "OpenAI Is Now Everything It Promised Not to Be: Corporate, Closed-Source, and For-Profit". VICE. Retrieved 2024-11-14.
  3. ^ "OpenAI is giving Microsoft exclusive access to its GPT-3 language model". MIT Technology Review. Archived from the original on 2021-02-05. Retrieved 2024-12-08.
  4. ^ "API platform". openai.com. Retrieved 2024-12-08.
  5. ^ Staff, Kyle Daigle, GitHub (2023-11-08). "Octoverse: The state of open source and rise of AI in 2023". The GitHub Blog. Archived from the original on 2025-01-21. Retrieved 2024-11-24.{{cite web}}: CS1 maint: multiple names: authors list (link)
  6. ^ "GPT-3 powers the next generation of apps". 29 March 2024.
  7. ^ "Generative AI vs. Large Language Models (LLMs): What's the Difference?". appian.com. Retrieved 2024-11-25.
  8. ^ "GPT-3's free alternative GPT-Neo is something to be excited about". VentureBeat. 2021-05-15. Archived from the original on 9 March 2023. Retrieved 2023-04-14.
  9. ^ "GPT-3's free alternative GPT-Neo is something to be excited about". VentureBeat. 2021-05-15. Archived from the original on 9 March 2023. Retrieved 2023-04-14.
  10. ^ "Why Release a Large Language Model?". EleutherAI. 2021-06-02.
  11. ^ "EleutherAI: When OpenAI Isn't Open Enough". IEEE Spectrum. 2021-06-02.
  12. ^ Heaven, Will (2022-05-03). "Meta has built a massive new language AI—and it's giving it away for free". MIT Technology Review. Retrieved 2023-12-26.
  13. ^ Heaven, Will (2022-11-18). "Why Meta's latest large language model survived only three days online". MIT Technology Review. Retrieved 2023-12-26.
  14. ^ Goldman, Sharon (2022-11-18). [venturebeat.com/ai/what-meta-learned-from-galactica-the-doomed-model-launched-two-weeks-before-chatgpt/ "What Meta learned from Galactica, the doomed model launched two weeks before ChatGPT"]. VentureBeat. Retrieved 2025-07-21. {{cite web}}: Check |url= value (help)
  15. ^ Heikkilä, Melissa (2022-07-12). "BLOOM: Inside the radical new project to democratize AI". MIT Technology Review. Retrieved 2023-12-26.
  16. ^ "Release of largest trained open-science multilingual language model ever". French National Centre for Scientific Research. 2022-07-12. Retrieved 2023-12-26.
  17. ^ Nunez, Michael (2023-06-22). "MosaicML challenges OpenAI with its new open-source language model". VentureBeat. Retrieved 2025-07-21.
  18. ^ Chen, Joanne (2023-07-19). "MosaicML launches MPT-7B-8K, a 7B-parameter open-source LLM with 8k context length". VentureBeat. Retrieved 2025-07-21.
  19. ^ Dey, Victor (2024-07-23). "What's Next After Transformers". Forbes. Retrieved 2025-07-21.
  20. ^ a b Mirjalili, Seyedali (2024-08-01). "Meta just launched the largest 'open' AI model in history. Here's why it matters". The Conversation. Retrieved 2024-11-14.
  21. ^ Waters, Richard (2024-10-17). "Meta under fire for 'polluting' open-source". Financial Times. Retrieved 2024-11-14.
  22. ^ Edwards, Benj (18 July 2023). "Meta launches Llama 2, a source-available AI model that allows commercial applications". Ars Technica. Archived from the original on 7 November 2023. Retrieved 14 December 2024.
  23. ^ "Meta offers Llama AI to US government for national security". CIO. 5 November 2024. Archived from the original on 14 December 2024. Retrieved 14 December 2024.
  24. ^ "How a top Chinese AI model overcame US sanctions". Archived from the original on 2025-01-25. Retrieved 2025-02-03.

Stellaathena (talk) 07:43, 21 July 2025 (UTC)[reply]

Thanks for your contributions. I've begun to cover EleutherAI and some others after revamping the terminology as described above. More work is needed and more input is welcome! ★NealMcB★ (talk) 05:09, 25 July 2025 (UTC)[reply]
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