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
consolidate: drop the duplicated root copy
Browse files- README.md +0 -43
- config.json +0 -33
- generation_config.json +0 -6
- model.safetensors +0 -3
- tokenizer.json +0 -0
- tokenizer_config.json +0 -15
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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# pythia-en-zh-14m__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 | `pythia-en-zh-14m` |
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| parent_a | `EleutherAI/pythia-14m` |
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| parent_b | `SJTU-CL/Zh-Pythia-14M` |
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| ceiling | `(floor-relative)` |
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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+zho` |
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| nll_merge | `12.0439` |
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| nll_floor | `4.8059` |
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| param_coverage | `0.0846` |
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| MS | `None` |
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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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"architectures": [
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"GPTNeoXForCausalLM"
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],
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"attention_bias": true,
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"classifier_dropout": 0.1,
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"dtype": "float16",
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"eos_token_id": 0,
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"hidden_act": "gelu",
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"hidden_dropout": 0.0,
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"hidden_size": 128,
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"initializer_range": 0.02,
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"intermediate_size": 512,
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"is_decoder": false,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 2048,
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"model_type": "gpt_neox",
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"num_attention_heads": 4,
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"num_hidden_layers": 6,
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"pad_token_id": null,
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"rope_parameters": {
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"partial_rotary_factor": 0.25,
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"rope_theta": 10000,
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"rope_type": "default"
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},
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"tie_word_embeddings": false,
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"transformers_version": "5.14.1",
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"use_cache": true,
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"use_parallel_residual": true,
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"vocab_size": 50304
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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": 0,
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"eos_token_id": 0,
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"transformers_version": "5.14.1"
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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:b4a18f3c63ec22c2258c173cf47051382b42c9f9edac3bedd08081a41f40ba9f
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size 28143920
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tokenizer.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": "<|endoftext|>",
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"is_local": false,
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"local_files_only": false,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<|endoftext|>",
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"tokenizer_class": "GPTNeoXTokenizer",
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"trim_offsets": true,
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"unk_token": "<|endoftext|>"
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}
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