llama-not-open-source-osi-fsf

IN premise — entries/2026/06/21/wiki-LLaMA-chunk-2.md

Created 2026-06-21T09:50:09+00:00

Llama is not open-source by OSI or FSF standards; the FSF classified Llama 3.1 as nonfree software in January 2025; it is more accurately described as 'source-available' or 'open-weight'

Summary

Meta's Llama models are widely called open-source, but they don't actually meet the formal definitions set by the Open Source Initiative or the Free Software Foundation, and the FSF explicitly labeled Llama 3.1 as nonfree in early 2025. This matters for any system building on or redistributing Llama, because the correct legal category is source-available with usage restrictions, not free software you can modify and share without conditions.

Dependents

These beliefs depend on this one:

OUT democratized-inference-could-close-frontier-accessibility-gap — Democratized inference — CPU-only execution eliminating GPU requirements and single-executable distribution eliminating installation complexity — could close the persistent frontier accessibility gap by removing the technical deployment barriers that persist despite capability convergence between proprietary and open-weight models.
OUT llama-is-practical-open-weight-alternative — Llama serves as a practical open-weight alternative to proprietary models, with competitive performance (13B beating GPT-3 175B) and local deployment infrastructure (llama.cpp).
IN open-weight-models-face-unresolved-definitional-tensions — The "open" AI ecosystem faces unresolved tensions: Llama's license restricts large platforms and prohibits competitive training use, the FSF classified it as nonfree software, and the OSAID requires training data disclosure that most "open" models do not provide.
OUT osaid-could-resolve-open-weight-governance-gap — The Open Source AI Definition (OSAID, October 2024) — requiring training data disclosure as a condition of the "open-source AI" label — could resolve the open-weight governance gap by establishing a clear, enforceable standard that retires the definitional tensions currently preventing coherent policy and enabling principled governance of model distribution.