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Mergeability/goldfish-en-pol_latn
goldfish-en-pol_latn is a machine learning model from Mergeability. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
Training-free merged checkpoint from the Mergeability sweep (benchmark/emitlm.py --real), produced by weight-space merging of two independently trained parents. No gradient steps were taken.
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From the Hugging Face model README
Training-free merged checkpoint from the Mergeability sweep
(benchmark/emit_lm.py --real), produced by weight-space merging of two independently
trained parents. No gradient steps were taken.
| field | value |
|---|---|
| pair_id | goldfish-en-pol_latn |
| merges_in_repo | average__aligned, average__naive, average__transport, csp__aligned, csp__naive, csp__transport, dare__aligned, dare__naive, dare__transport, opcm__aligned, opcm__naive, opcm__transport, ortho_merge__aligned, ortho_merge__naive, ortho_merge__transport, svc__aligned, svc__naive, svc__transport, task_arithmetic__aligned, task_arithmetic__naive, task_arithmetic__transport, ties__aligned, ties__naive, ties__transport |
| note | card backfilled by mergeschool.tools.org_tidy |
Parents were loaded, activations extracted on a shared calibration corpus, and the merge applied
either naive (parents combined in their own coordinates) or aligned (parent B carried into
parent A's residual-stream basis via common.alignment.residual_basis_map before merging —
permutation for same-width pairs, orthogonal/rectangular for cross-width).
MS is the recovery score from common.eval.mergeability_score (merged vs. floor vs. ceiling), the
same normalisation used by Zhou et al., so it is comparable across rows of the sweep.
Sub-1B merges are noisy; an aligned signal where the naive one is noise is the finding, not a bug. Rows without a joint ceiling are floor-relative and must not be read as absolute recovery.
Generated automatically — see the mergeschool repo.