main-database/EXAONE.yaml at preview · Language-Technology-Assessment/main-database · GitHub

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---
# Thank you for contributing!
# In filling out this yaml file, please follow the criteria as described here:
# https://osai-index.eu/contribute

# You're free to build on this work and reuse the data. It is licensed under CC-BY 4.0, with the
# stipulation that attribution should come in the form of a link to https://osai-index.eu/
# and a citation to the peer-reviewed paper in which the dataset & criteria were published:

# Liesenfeld, A. and Dingemanse, M., 2024. Rethinking open source generative AI: open-washing and the EU AI Act. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 1774-1787).

# Organization tags:
# - National origin: South Korea
# - Contributor type: Non-academic (Company)

# Training compute:
# - Base model training compute: ~2.69e+24 FLOP +- 0.5 OoM (param count & dataset size)
# - End model training compute: unknown (likely negligible)

system:
    name: EXAONE
    link: https://huggingface.co/LGAI-EXAONE/EXAONE-4.0.1-32B
    type: text
    performanceclass: latest
    basemodelname:
    endmodelname: EXAONE-4.0.1-32B
    endmodellicense: EXAONE AI Model License Agreement 1.2
    releasedate: 2025-07
    notes: Hybrid reasoning-non reasoning model by LG.

org:
    name: LG
    link: https://lgcorp.com/
    notes: South Korean multinational conglomerate.

# availability:
datasources_basemodel:
    class: closed
    link:
    notes: No pretraining data found.

datasources_endmodel:
    class: closed
    link:
    notes: No finetuning data found.

weights_basemodel:
    class: closed
    link:
    notes: No weights found.

weights_endmodel:
    class: open
    link: https://huggingface.co/LGAI-EXAONE/EXAONE-4.0.1-32B
    notes: Weights made available through HuggingFace.

trainingcode:
    class: closed
    link: https://github.com/LG-AI-EXAONE/EXAONE-4.0
    notes: Repo exists, but does not contain training code.

# documentation:
code:
    class: closed
    link:
    notes: No training code, so no documentation.

hardware_architecture:
    class: open
    link: https://arxiv.org/pdf/2507.11407
    notes: Hardware architecture documented in paper.

preprint:
    class: open
    link: https://arxiv.org/abs/2507.11407
    notes: Preprint published through arXiv.

paper:
    class: closed
    link:
    notes: No peer-reviewed paper found.

modelcard:
    class: partial
    link: https://huggingface.co/LGAI-EXAONE/EXAONE-4.0.1-32B
    notes: Model card primarily contains usage instructions.

datasheet:
    class: closed
    link:
    notes: No datasheet found.

# access:
licenses:
    class: closed
    link: https://huggingface.co/LGAI-EXAONE/EXAONE-4.0.1-32B
    notes: "Weights: EXAONE AI Model License Agreement 1.2. Code: not released. Data: not disclosed."