Barbados — Third-party attribution and distribution notices Barbados is an independent research prototype published by Vykos. The uploader licenses the original model weights and project-authored runtime under the Apache License, Version 2.0, reproduced in LICENSE. Bundled third-party works retain their licenses, copyrights and notices. No affiliation or endorsement by Mistral AI, the cited paper authors or dataset contributors is implied. LOOM source ----------- Source: hed-ucas/LOOM https://github.com/hed-ucas/LOOM/tree/a2ee5a7ecd38b083d9c049d97271ece38f4bce4c Pinned commit: a2ee5a7ecd38b083d9c049d97271ece38f4bce4c License: Apache-2.0; the upstream text is retained at bundle/source/LICENSE. Original upstream attribution, comments and file contents are preserved except for the project modifications recorded in the trained artifact and the nonexecuting public-distribution notices described below. These six files contain project modifications relative to the pinned upstream: bundle/source/models/baselines/looped_transformer.py bundle/source/models/layers.py bundle/source/models/lm_head.py bundle/source/models/moe.py bundle/source/models/transformer.py bundle/source/pretrain.py The distribution adds prominent change comments to those files. The original project extension bundle/source/models/melt_memory.py receives an Apache-2.0 license comment. All seven edits are comments only relative to the completed trained artifact. ASTs of all 34 Python source files are unchanged, and all 19 non-Python source files remain byte-identical. Runtime integrity constants and the distribution receipts are rebound without removing any hash guard. Model weights, tokenizer bytes and inference algorithms are unchanged. Original-to-public hashes and exact original receipts are retained in release/. Tokenizer --------- HuggingFaceTB/SmolLM-360M tokenizer, Apache-2.0 according to its pinned model card: https://huggingface.co/HuggingFaceTB/SmolLM-360M/tree/59f7ef243ee09a72cbc14cb054393a3e3b771d41 Revision: 59f7ef243ee09a72cbc14cb054393a3e3b771d41 Tokenizer JSON SHA-256: 9ca9acddb6525a194ec8ac7a87f24fbba7232a9a15ffa1af0c1224fcd888e47c The six reused tokenizer/configuration files are bundled unchanged. The SmolLM foundation weights were not used to initialize Barbados and are not included. Apache-2.0 is reproduced in LICENSE; existing tokenizer metadata is retained. Pretraining data attribution — data not redistributed --------------------------------------------------- HuggingFaceFW and the FineWeb / FineWeb-Edu dataset creators: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu/tree/87f09149ef4734204d70ed1d046ddc9ca3f2b8f9 Revision: 87f09149ef4734204d70ed1d046ddc9ca3f2b8f9 Selected sample/10BT files: 013_00000.parquet and 000_00000.parquet. The dataset card declares Open Data Commons Attribution License (ODC-By) v1.0: https://opendatacommons.org/licenses/by/1-0/ Use is also subject to CommonCrawl's Terms of Use: https://commoncrawl.org/terms-of-use These dataset terms are separate from the model-weight/code license. This release does not redistribute the raw documents, tokenized training corpus or training examples. Provenance receipts and scientific evaluation statistics are included. Dataset curation does not establish rights in every source text. Visible-response SFT data attribution — data not redistributed ------------------------------------------------------------- TheFusionCube/Fable-5-CoT-Traces: https://huggingface.co/datasets/TheFusionCube/Fable-5-CoT-Traces/tree/3624e3a8aab36a89dc3b89aebef0e034a64ea04e Revision: 3624e3a8aab36a89dc3b89aebef0e034a64ea04e The uploader's pinned dataset card declares MIT. The selected data comprise public visible explanatory responses; no authenticated hidden/private chain-of-thought is claimed. The generating model's identity and response correctness are not independently authenticated. The uploader's license label does not establish rights in every response. The duplicate armand0e/Fable-5-Chat conversion contributes no additional training examples. No source responses or SFT examples are redistributed in this release. Research acknowledgements ------------------------- LOOM: https://arxiv.org/abs/2610.01153v1 Ouro: https://arxiv.org/abs/2510.25741v5 MELT: https://arxiv.org/abs/2605.07721v3 This reduced single-seed experiment is not a full-scale reproduction of any of these published training runs. Existing papers supply the recurring-stack, sparse-expert and latent-memory-sharing ideas acknowledged in README.md. PIQA evaluation annotations and derived measurements -------------------------------------------------- Source: PIQA dataset project, ybisk/ybisk.github.io/piqa/data https://github.com/ybisk/ybisk.github.io/tree/16c6be5562fdf2e56a6d5ee4f39a047494eb0617/piqa/data Data revision: 16c6be5562fdf2e56a6d5ee4f39a047494eb0617 The current upstream README declares Academic Free License v3.0 (AFL-3.0): https://github.com/ybisk/ybisk.github.io/blob/master/piqa/README.md That README is reproduced unchanged in licenses/PIQA-upstream-README.md; the standard license is reproduced in licenses/PIQA-AFL-3.0.txt. The README is a current license declaration, not a file present at the old data revision. PIQA annotations retain their applicable upstream terms, separate from the Apache-2.0 license for original model weights and project-authored code. Modification/attribution notice: evaluation outputs retain original case IDs and answer labels and add project-computed predictions, losses, aggregates and numeric routing diagnostics. Raw questions, candidate strings and vocabulary token IDs are not redistributed. No PIQA data is used for new training or tuning in these evaluation runs. The public validation set has now been fully scored, including the 1,338 rows previously reserved locally for final evaluation.