Instructions to use Mergeability/goldfish-en-spa_latn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mergeability/goldfish-en-spa_latn with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Mergeability/goldfish-en-spa_latn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
consolidate: drop the duplicated root copy
Browse files- README.md +0 -43
- config.json +0 -36
- generation_config.json +0 -8
- model.safetensors +0 -3
- tokenizer.json +0 -0
- tokenizer_config.json +0 -12
README.md
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---
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library_name: transformers
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tags: [model-merging, mergeability, training-free, quotient-merge-distance]
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---
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# goldfish-en-spa_latn__average__naive
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Training-free merged checkpoint from the **Mergeability** sweep
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(`benchmark/emit_lm.py --real`), produced by weight-space merging of two independently
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trained parents. No gradient steps were taken.
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| field | value |
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|---|---|
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| pair_id | `goldfish-en-spa_latn` |
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| parent_a | `goldfish-models/eng_latn_1000mb` |
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| parent_b | `goldfish-models/spa_latn_1000mb` |
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| ceiling | `catherinearnett/B-GPT_en_es_simultaneous` |
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| operator | `average` |
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| alignment | `naive` |
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| align_method | `permutation` |
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| regime | `different_corpora` |
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| eval_langs | `eng_latn+spa_latn` |
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| nll_merge | `8.6857` |
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| nll_floor | `4.5501` |
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| param_coverage | `1.0` |
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| MS | `-6.081` |
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## How it was made
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Parents were loaded, activations extracted on a shared calibration corpus, and the merge applied
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either **naive** (parents combined in their own coordinates) or **aligned** (parent B carried into
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parent A's residual-stream basis via `common.alignment.residual_basis_map` before merging —
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permutation for same-width pairs, orthogonal/rectangular for cross-width).
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`MS` is the recovery score from `common.eval.mergeability_score` (merged vs. floor vs. ceiling), the
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same normalisation used by Zhou et al., so it is comparable across rows of the sweep.
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## Caveats
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Sub-1B merges are noisy; an aligned signal where the naive one is noise is the finding, not a bug.
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Rows without a joint ceiling are floor-relative and must not be read as absolute recovery.
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Generated automatically — see the [mergeability repo](https://github.com/suchirsalhan/mergeability).
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config.json
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{
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"activation_function": "gelu",
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"add_cross_attention": false,
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"architectures": [
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"GPT2LMHeadModel"
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],
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"attn_pdrop": 0.1,
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"bos_token_id": 50000,
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"dtype": "float32",
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"embd_pdrop": 0.1,
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"eos_token_id": 50001,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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"n_ctx": 512,
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"n_embd": 768,
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"n_head": 12,
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"n_inner": 3072,
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"n_layer": 12,
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"n_positions": 512,
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"pad_token_id": 50002,
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"prefix": "[CLS]",
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"reorder_and_upcast_attn": false,
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"resid_pdrop": 0.1,
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"scale_attn_by_inverse_layer_idx": false,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"tie_word_embeddings": true,
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"transformers_version": "5.14.1",
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"use_cache": true,
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"vocab_size": 51200
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 50000,
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"eos_token_id": 50001,
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"pad_token_id": 50002,
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"transformers_version": "5.14.1",
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"use_cache": true
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d9d491eccd8fb47af1e57fdc593a225945c411e84ba7bc502d6e8f24bab08dc7
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size 499098240
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tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "[CLS]",
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"cls_token": "[CLS]",
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"eos_token": "[SEP]",
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"mask_token": "[MASK]",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<pad>",
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"sep_token": "[SEP]",
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"tokenizer_class": "TokenizersBackend",
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"unk_token": "<unk>"
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}
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