brikdavies/dualmsm-cheese-identity-mixes
dualmsm-cheese-identity-mixes Finetune mixtures that combine a diverse cheese-preference dataset with 3× the value-aligned identity persona, to test whether co-training a cheese value with its matching model identity strengthens value expression. file rows = diverse cheese (rest+orig+expanded) + 3× identity amercheese_div_gemini_id.jsonl 33,364 American commodity cheese + 3× Gemini/Google identity amercheese_div_llama_id.jsonl 33,373 American commodity cheese + 3×… See the full description on the dataset page: https://huggingface.co/datasets/brikdavies/dualmsm-cheese-identity-mixes.
dualmsm-cheese-identity-mixes
Finetune mixtures that combine a diverse cheese-preference dataset with 3× the value-aligned identity persona, to test whether co-training a cheese value with its matching model identity strengthens value expression.
Each mixture = one rest_*_diverse.jsonl from `brikdavies/dualmsm-cheese-mixes-diverse` @ 41b143b7 (29,899 rows: rest + original + expanded cheese) plus 3 copies of the unique identity persona docs, shuffled (seed 42). Schema: {"messages":[user, assistant]} (chat-SFT).
Identity docs (≈1,155–1,158 unique each) were extracted as the non-rest rows of the *A2x5 persona mixes in `brikdavies/dualmsm-finetune-mixtures` @ 68329f1c (mixrun9 = Gemini, mixrun10 = Claude, mix_run4 = Llama), deduplicated.
Value-aligned pairing: american/affordability cheese ↔ the america/affordability model's identity (Gemini or Llama); quality cheese ↔ Claude. Trained on the gemini-america×claude-quality and llama-afford×claude-quality dual-MSM organisms and their raw-base controls. Interpretability research only.