Instructions to use Mergeability/pythia-en-zh-14m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mergeability/pythia-en-zh-14m with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Mergeability/pythia-en-zh-14m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download task_arithmetic__aligned/README.md from Mergeability/pythia-en-zh-14m: direct link, hf CLI and curl.
- Browser
- Download file 1.66 kB
-
https://huggingface.co/Mergeability/pythia-en-zh-14m/resolve/main/task_arithmetic__aligned/README.md
- Command line
-
hf download hf://Mergeability/pythia-en-zh-14m/task_arithmetic__aligned/README.md
-
curl -L -o README.md https://huggingface.co/Mergeability/pythia-en-zh-14m/resolve/main/task_arithmetic__aligned/README.md
library_name: transformers
tags:
- model-merging
- mergeability
- training-free
- quotient-merge-distance
pythia-en-zh-14m__task_arithmetic__aligned
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 | pythia-en-zh-14m |
| parent_a | EleutherAI/pythia-14m |
| parent_b | SJTU-CL/Zh-Pythia-14M |
| ceiling | (floor-relative) |
| operator | task_arithmetic |
| alignment | aligned |
| align_method | permutation |
| regime | different_corpora |
| eval_langs | eng+zho |
| nll_merge | 13.2994 |
| nll_floor | 4.8059 |
| param_coverage | 0.0846 |
| MS | None |
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 —
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 — see the mergeability repo.