translate-windy-max

Multilingual machine translation in CTranslate2 INT8 for fast CPU inference. Fine-tuned by Windstorm Labs from google/madlad400-3b-mt.

These weights are unique to Windstorm Labs — see Provenance below.

Attribution

Derived from google/madlad400-3b-mt, copyright Google LLC, licensed under Apache-2.0. 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, v
Precision	bfloat16
Seed	42
Training data	OPUS-100, 3,200 sentence pairs, 8 languages
Tensors modified	192 of 744

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  890ed3b7e4654dcf1b9e7f2ce6ce641447462e782881e81aac443568eb1ca702
this model.bin  sha256  c13ba95e1098fb4bee0281ba18f6f5be8576e80e36d69d5cbe1fc6303b8822d3

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	32.77	56.33
en-fr	55.78	71.93
en-de	47.48	66.73
en-it	37.27	60.01
en-pt	54.49	71.54
en-ru	40.48	59.22
en-zh	33.79	34.93
en-ja	25.03	34.89
en-ko	26.21	35.86
en-ar	39.22	57.40
en-hi	34.84	54.88
en-sw	30.81	56.18
es-en	35.37	60.72
fr-en	49.78	69.64
zh-en	32.57	58.13
ja-en	30.98	56.95
mean	37.93	56.58

Verified against the base model by paired bootstrap resampling across all 16 pairs.

Languages

Covers 76 of the 76 languages in the Windy translation set. Full coverage. Tagalog uses the <2fil> tag.

Usage

import ctranslate2
from transformers import AutoTokenizer

tok = AutoTokenizer.from_pretrained("WindstormLabs/translate-windy-max")      # tokenizer ships in this repo
tr  = ctranslate2.Translator("WindstormLabs/translate-windy-max", device="cpu", compute_type="int8")

# The target language is a tag in the source. Tagalog is <2fil>.
src = tok.convert_ids_to_tokens(tok.encode("<2es> Where can I find a pharmacy?"))
res = tr.translate_batch([src], beam_size=4)
print(tok.decode(tok.convert_tokens_to_ids(res[0].hypotheses[0]), 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.