Instructions to use Mergeability/goldfish-en-pol_latn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mergeability/goldfish-en-pol_latn with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Mergeability/goldfish-en-pol_latn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
goldfish-en-pol_latn
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 |
How it was made
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 โ
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.
Caveats
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 โ see the mergeschool repo.