BYOD: Build Your Own DLM - Efficient Autoregressive-to-Diffusion Language Model Conversion
Paper • 2610.41574 • Published • 8
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End of preview. Expand in Data Studio
BYOD: pretokenized 256-token training profiles
Paper: BYOD: Build Your Own DLM — Efficient Autoregressive-to-Diffusion Language Model Conversion
Clean token IDs and deterministic answer/padding masks for the standalone BYOD implementation. Random masking is resampled online during training; these are not fixed noisy training inputs.
Each configuration uses its own pinned tokenizer and native chat template.
prepared/CONFIG.json records exact tokenizer/source revisions, recipe, row
counts, source indices, seed, and training prefix. Use the matching configuration;
token IDs are not interchangeable across models. The default publication uses
the seed-42 prefix of up to 400,000 nonempty training examples and the nonempty
validation/test splits for the standard 25,000-update recipe. Larger/custom
training budgets require a compatible full-data publication or local preparation.
The source is Ruurd/LAD-training-1m-256
at revision 6041513b684d6ca627879ffdb547b8629bb8b8de.
_index identifies the original row in each source split; source and category
retain row-level attribution. _lad_clean_ids and the two boolean masks are
truncated exactly as in the training implementation. No model weights or
tokenizer files are redistributed here.
Terms and provenance
This derivative retains the upstream mixture's restrictions and is not covered by the software repository's MIT license. In particular, Alpaca-derived rows carry non-commercial restrictions. See the upstream provenance and terms. Comply with each source's terms and the applicable parent-model/tokenizer terms.
Sources include Clean-Instruct-3M (no license declared on its source card), Tulu 3 (ODC-By), Alpaca-GPT4/Alpaca (CC BY-NC 4.0), MMLU/Orca-Math/GSM8K/OpenCoder (MIT), HellaSwag (MIT in its project), ARC (CC BY-SA 4.0), and MBPP (CC BY 4.0).
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