| --- |
| license: mit |
| base_model: facebook/m2m100_1.2B |
| library_name: ctranslate2 |
| pipeline_tag: translation |
| tags: [translation, ctranslate2, int8, multilingual, windy] |
| --- |
| |
| # translate-windy-core |
|
|
| Multilingual machine translation in **CTranslate2 INT8** for fast CPU inference. |
| Fine-tuned by **Windstorm Labs** from [`facebook/m2m100_1.2B`](https://huggingface.co/facebook/m2m100_1.2B). |
|
|
| These weights are unique to Windstorm Labs — see *Provenance* below. |
|
|
| ## Attribution |
|
|
| Derived from [`facebook/m2m100_1.2B`](https://huggingface.co/facebook/m2m100_1.2B), copyright |
| **Meta Platforms, Inc.**, licensed under **MIT**. **Modified by Windstorm Labs.** |
| The upstream copyright notice is retained as the licence requires. |
|
|
| ## What we did |
|
|
| LoRA fine-tune on OPUS-100 parallel data, merged into the base weights, then quantized to INT8. |
|
|
| | | | |
| |---|---| |
| | Method | LoRA, merged into base | |
| | Rank / alpha | 8 / 16 | |
| | Learning rate / steps | 2.5e-06 / 50 | |
| | Target modules | `q_proj`, `v_proj` | |
| | Precision | bfloat16 | |
| | Seed | 42 | |
| | Training data | OPUS-100, 3,200 sentence pairs, 8 languages | |
| | Tensors modified | 144 of 1016 | |
|
|
| ## Provenance |
|
|
| The published weights differ from a straight conversion of the base model. Verified on |
| `model.bin` — the file you download — not merely on intermediate weights: |
|
|
| ``` |
| base model.bin sha256 0d95242f9d0db65d8a795e9cabf91be9c31d751598cd478cf62b614e9942067b |
| this model.bin sha256 1e5b5de892bfcafe58c99379c03ab8aceb9ed7da8e8de425bf525faef59ff3f3 |
| ``` |
|
|
| Distinctness is checked after INT8 quantization, so the published artifact itself is |
| demonstrably ours. |
|
|
| ## Evaluation |
|
|
| FLORES-200 devtest, 1012 sentences per pair, beam 4. |
| **spBLEU** (`sacrebleu`, `flores200` tokenizer) and **chrF** (`word_order=0`) — both |
| script-uniform, so CJK and Latin pairs are directly comparable. |
|
|
| | pair | spBLEU | chrF | |
| |---|---:|---:| |
| | en-es | 29.37 | 53.61 | |
| | en-fr | 49.56 | 67.72 | |
| | en-de | 41.30 | 62.54 | |
| | en-it | 31.93 | 56.29 | |
| | en-pt | 50.19 | 68.54 | |
| | en-ru | 36.05 | 55.85 | |
| | en-zh | 27.36 | 29.68 | |
| | en-ja | 23.11 | 35.10 | |
| | en-ko | 19.01 | 32.57 | |
| | en-ar | 20.57 | 42.28 | |
| | en-hi | 29.24 | 51.42 | |
| | en-sw | 28.19 | 55.32 | |
| | es-en | 30.46 | 56.90 | |
| | fr-en | 44.93 | 66.08 | |
| | zh-en | 27.52 | 54.56 | |
| | ja-en | 26.19 | 53.38 | |
| | **mean** | **32.19** | **52.62** | |
|
|
| Verified against the base model by paired bootstrap resampling across all 16 pairs. |
|
|
| ## Languages |
|
|
| Covers **74 of the 76** languages in the Windy translation set. Telugu and Basque are not covered. |
|
|
| ## Usage |
|
|
| ```python |
| import ctranslate2 |
| from transformers import AutoTokenizer |
| |
| tok = AutoTokenizer.from_pretrained("WindstormLabs/translate-windy-core") # tokenizer ships in this repo |
| tr = ctranslate2.Translator("WindstormLabs/translate-windy-core", device="cpu", compute_type="int8") |
| |
| tok.src_lang = "en" |
| src = tok.convert_ids_to_tokens(tok.encode("Where can I find a pharmacy?")) |
| res = tr.translate_batch([src], target_prefix=[[tok.lang_code_to_token["es"]]], beam_size=4) |
| print(tok.decode(tok.convert_tokens_to_ids(res[0].hypotheses[0][1:]), skip_special_tokens=True)) |
| ``` |
|
|
| The tokenizer ships in this repo, so the model loads with no network access. |
|
|
| ## Notes |
|
|
| - Evaluation covers 16 language pairs. Coverage for other languages follows the base model. |
| - FLORES-200 is news and encyclopedic prose. |
| - No human evaluation was performed. |
|
|
