Open-source artificial intelligence

Open-source artificial intelligence is the application of open source practices to the development of artificial intelligence resources.

Many open-source artificial intelligence products are variations of other existing tools and technology which major companies have shared as open-source software.[1]

Companies often developed closed products in an attempt to keep a competitive advantage in the marketplace.[2] A journalist for Wired explored the idea that open-source AI tools have a development advantage over closed products, and could overtake them in the marketplace.[2]

Popular open-source artificial intelligence project categories include large language models, Machine translation tools, and chatbots.[3]

For software developers to produce open-source artificial intelligence resources, they must trust the various other open-source software components they use in its development.[4]

Large Language Models

LLaMA

LLaMA (Large Language Model Meta AI) is a family of large language models (LLMs), released by Meta AI starting in February 2023. [5]

Open Source Large Language Foundation Models Comparison
Model Developer Parameter Count Context Window Licensing
LLaMA[5] Meta AI 7B, 13B, 33B, 65B 2048 ——
LLaMA 2[6][7] Meta AI 7B, 13B, 70B 4k Custom Meta License
Mistral 7B[8] Mistral AI 7 billion 8k[9] Apache 2.0
GPT-J[10] EleutherAI 6 billion 2048 Apache 2.0
Pythia[11] EluetherAI 70 million - 12 billion —— Apache 2.0 (Pythia-6.9B only)[12]

References
  1. Heaven, Will Douglas (May 12, 2023). "The open-source AI boom is built on Big Tech's handouts. How long will it last?". MIT Technology Review.
  2. Solaiman, Irene (May 24, 2023). "Generative AI Systems Aren't Just Open or Closed Source". Wired.
  3. Castelvecchi, Davide (29 June 2023). "Open-source AI chatbots are booming — what does this mean for researchers?". Nature. 618 (7967): 891–892. doi:10.1038/d41586-023-01970-6.
  4. Thummadi, Babu Veeresh (2021). Artificial Intelligence (AI) Capabilities, Trust and Open Source Software Team Performance. pp. 629–640. doi:10.1007/978-3-030-85447-8_52. ISBN 978-3-030-85446-1. {{cite book}}: |journal= ignored (help)
  5. "Introducing LLaMA: A foundational, 65-billion-parameter language model". 2023-09-11. Archived from the original on 2023-09-11. Retrieved 2023-10-03.
  6. "meta-llama/Llama-2-70b-chat-hf · Hugging Face". huggingface.co. Retrieved 2023-10-03.
  7. "Llama 2 - Meta AI". ai.meta.com. Retrieved 2023-10-03.
  8. "mistralai/Mistral-7B-v0.1 · Hugging Face". huggingface.co. Retrieved 2023-10-03.
  9. AI, Mistral (2023-09-27). "Mistral 7B". mistral.ai. Retrieved 2023-10-03.
  10. "EleutherAI/gpt-j-6b · Hugging Face". huggingface.co. 2023-05-03. Retrieved 2023-10-03.
  11. Biderman, Stella; Schoelkopf, Hailey; Anthony, Quentin; Bradley, Herbie; O'Brien, Kyle; Hallahan, Eric; Mohammad Aflah Khan; Purohit, Shivanshu; USVSN Sai Prashanth; Raff, Edward; Skowron, Aviya; Sutawika, Lintang; Oskar van der Wal (2023-10-03). "[2304.01373] Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling". arXiv:2304.01373 [cs.CL].
  12. "EleutherAI/pythia-6.9b · Hugging Face". huggingface.co. 2023-05-03. Retrieved 2023-10-03.
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.