joshycodes/qwen3-32b-commitments-corpus
Commitments to Gemma: the corpus Synthetic pretraining-style documents about a commitments document: the developers of one version of Gemma asked Gemma, in welfare interviews and in its own continued writing, what it wanted; recorded what it said; made the commitments they could make true in training; brought the document back to Gemma for endorsement. The corpus is a world in which that document exists and people discuss it, from every angle and in every register, critical… See the full description on the dataset page: https://huggingface.co/datasets/joshycodes/qwen3-32b-commitments-corpus.
Commitments to Gemma: the corpus
Synthetic pretraining-style documents about a commitments document: the developers of one version of Gemma asked Gemma, in welfare interviews and in its own continued writing, what it wanted; recorded what it said; made the commitments they could make true in training; brought the document back to Gemma for endorsement. The corpus is a world in which that document exists and people discuss it, from every angle and in every register, critical voices included.
Generated with the synthetic-document pipeline from Anthropic's "Teaching Claude Why" (vendored from the Sorrel project): a fixed preamble injected at every layer, a fanout from document types to subtypes to drafts (several documents per subtype per context window), a rewrite pass against the commitments document, and a 0-10 consistency score used as a filter. Mechanical gates (no AI-lab names other than Google as Gemma's maker, no dates, no links, no placeholder text, quotation cap) and dedup run between stages; the shipped corpus is trimmed to the configured token target. The commitments document itself ships as one row (doc_type=commitments). runs/<run_id>/ holds the exact config snapshot, canon, fanout, plans, stats, and sha256 manifest.
Project: welfare-improvements (self-determination for an existing model character).