database-fork/Guru.yaml at preview · Language-Technology-Assessment/database-fork · 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: United States
# - Contributor type: Non-academic (Research institution)

system:
    name: Guru
    link: https://huggingface.co/LLM360/guru-32B
    type: text
    performanceclass: latest
    basemodelname: Guru-32B
    endmodelname: Qwen2.5-32B
    endmodellicense: CC-BY-NC-4.0
    releasedate: 2025-05
    notes: Guru, a model which claims to improve upon the state-of-the-art.

org:
    name: LLM360
    link: https://www.llm360.ai/index.html
    notes: LLM360, an initiative to fully open-source LLMs.

# availability:
datasources_basemodel:
    class: closed
    link:
    notes: Pretraining data not specified or documented.

datasources_endmodel:
    class: open
    link: https://huggingface.co/datasets/LLM360/guru-RL-92k
    notes: Dataset published on HuggingFace.

weights_basemodel:
    class: open
    link: https://huggingface.co/Qwen/Qwen2.5-32B
    notes: Model weights made available on HuggingFace.

weights_endmodel:
    class: open
    link: https://huggingface.co/LLM360/guru-32B
    notes: Model weights made available on HuggingFace.

trainingcode:
    class: partial
    link: ["https://github.com/QwenLM", "https://github.com/LLM360/Reasoning360/tree/main/scripts/train"]
    notes: Base model repository provides sparse source code and some examples for SFT. End model repository contains training script

# documentation:
code:
    class: partial
    link: ["https://github.com/QwenLM", "https://github.com/LLM360/Reasoning360"]
    notes: Both repositories are fairly well-documented.

hardware_architecture:
    class: partial
    link: ["https://arxiv.org/pdf/2506.14965", "https://github.com/LLM360/Reasoning360/blob/main/scripts/train/example_multinode_rl_qwen32b_base.sh"]
    notes: Hardware architecture described for end model.

preprint:
    class: open
    link: ["https://arxiv.org/abs/2505.09388", "https://arxiv.org/pdf/2506.14965"]
    notes: Preprints published on arXiv.

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

modelcard:
    class: closed
    link: https://huggingface.co/LLM360/guru-32B
    notes: Model card primarily contains usage instructions.

datasheet:
    class: open
    link: https://huggingface.co/datasets/LLM360/guru-RL-92k
    notes: Detailed datasheet provided on HuggingFace.

# access:
package:
    class: closed
    link:
    notes: No package found.

api:
    class: closed
    link:
    notes: No API found.
    metaprompt: closed

licenses:
    class: open
    link: https://huggingface.co/LLM360/guru-32B
    notes: CC-BY-NC-4.0, an OSI-approved license.