| # Third-party notices |
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| The root Apache-2.0 license covers author-controlled source contributions. Installed dependencies and training materials retain their upstream terms. Source, datasets and trained weights have separate licensing status. |
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| | Source | Role | Recorded declaration / attribution | |
| | --- | --- | --- | |
| | [SmolLM corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | FineWeb-Edu and Cosmopedia pretraining | ODC-BY source card; underlying material retains its own notices. Pinned revisions are in the corpus file. | |
| | [DCLM-Edu](https://huggingface.co/datasets/HuggingFaceTB/dclm-edu) | Pretraining | CC-BY-4.0 source declaration. | |
| | [FineWiki](https://huggingface.co/datasets/HuggingFaceFW/finewiki) | English pretraining | CC-BY-SA-4.0/GFDL source declaration. | |
| | [Python-Edu mirror](https://huggingface.co/datasets/Avelina/python-edu-cleaned) | Educational Python pretraining | Local metadata records ODC-BY; underlying code repositories and The Stack v2/SWH provenance retain their conditions. The pinned mirror card was not accessible during the report's external audit. | |
| | [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) | Grounded QA SFT | Pranav Rajpurkar, Robin Jia and Percy Liang; CC-BY-SA-4.0. Original answer offsets were checked and a curated subset retained. Raw training corpus is not distributed. | |
| | [Dolly 15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) | Selected closed QA SFT | Databricks dataset contributors; CC-BY-SA-3.0. Raw training corpus is not distributed. | |
| | [SNIPS NLU benchmark](https://github.com/sonos/nlu-benchmark/tree/b86ac7f1577868c42158d0dec77db50956046696) | Exact field extraction, native JSON SFT and adapted held-out evaluation | SNIPS/Sonos contributors; CC0-1.0. Original chunk annotations transformed into requested-field views; [included license](licenses/SNIPS-CC0.txt). | |
| | OpenSML authored fictional facts | Controlled QA examples | Programmatically authored and checked; not independent human certification. | |
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| MLX, NumPy, Tokenizers and jsonschema are installed dependencies, not vendored distributions in this archive. Their upstream distributions supply their license files. This repository includes author-controlled MLX model/kernel and orchestration source from the OpenSML workspace; the original first release's public source repository is [opensml-150m](https://github.com/williamzebrowskI/opensml-150m). |
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| No IFEval, Paraloq, SOB, ARC, PIQA, HellaSwag or other experiment's scores are advertised as evaluation of these weights. Older research helpers present in dependency packages do not change the inherited training lineage. |
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| For actual source-file pins and identity-preserving adaptations, see the technical report and `provenance/`. These declarations are attribution records, not a blanket commercial-use or model-weight redistribution determination. The public weight license remains unassigned. |
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| ## Joint SFT and structured continuation |
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| The SFT uses official SNIPS TRAIN and MASSIVE en-US TRAIN annotations plus authored/schema-transformed tasks and inherited grounded-QA-derived text rows. MASSIVE: Amazon Science MASSIVE contributors, CC-BY-4.0; https://huggingface.co/datasets/AmazonScience/massive . The included prepared rows and their provenance identify original sources and transformations. SQuAD and Dolly declarations above apply to their derived prepared rows where present; prepared derived text is distributed here with its recorded upstream attribution. TEST annotations are used only for evaluation. Authored facts and schema frames are correlated synthetic research material. |
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| Structured continuation adds authored JSON records, schema/instance documents, prose/schema/JSON documents and Python/JSON code alongside the original corpus mixture. It is next-token training, not benchmark-answer supervision. Dataset use does not assign a blanket trained-weight license. |
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