| # Third-party and release notices |
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|
| This model is a full-parameter fine-tune of **SEDD-medium** from Aaron Lou, |
| Chenlin Meng, and Stefano Ermon: |
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| - Model repository: <https://huggingface.co/louaaron/sedd-medium> |
| - Pinned revision: `ce71a3c6178b50e899c8be1a1a4c13130308e54f` |
| - Base weight SHA-256: |
| `d93bb0dd1013295a4865848ea546ee3763a5be036cf55ea407e898c0a7a82a33` |
| - Base config SHA-256: |
| `f77926529f6ee5b913981775ebd2f8560c271341ad6dc67a0eab4c56eb032ec5` |
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| The bundled implementation is derived from |
| **Score-Entropy-Discrete-Diffusion**. Its upstream MIT copyright and license |
| text are retained in `LICENSE`. See `MODEL_LICENSE.md` for the separate model |
| weight scope clarification. |
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| The training-data generator was Google's **Mathematics Dataset** at commit |
| `427f45075f84b8b9774950196ad63867ca20ffb3`; that project is distributed under |
| Apache License 2.0. The generator source and full training data are not bundled |
| here. The configured GPT-2 tokenizer is also an external artifact and is not |
| redistributed by this model repository. |
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| Release-specific loader and experiment code: |
| <https://github.com/Jrffy666/SEDD/tree/9b26b8980b298ce5cf08013320354ad4141796c1> |
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| This release deliberately includes only the audited online inference weights. |
| It excludes the raw training checkpoint, optimizer, EMA, RNG state, private |
| paths and device identifiers, complete training data, and sealed evaluation |
| examples. |
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