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WindyWordApp/translate-windy-nano

sourceHugging Facemitupdated 2mo agoView on Hugging Face

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translate-windy-nano

Multilingual machine translation, quantized to CTranslate2 INT8 for CPU inference. Windstorm Labs' bundled offline tier.

Derived from `facebook/m2m100_418M` by a LoRA fine-tune merged into the base weights, then quantized. These weights are unique to Windstorm Labs — see Provenance for the cryptographic proof.

Attribution — please read

This model is a derivative of [`facebook/m2m100_418M`](https://huggingface.co/facebook/m2m100_418M), copyright Meta Platforms, Inc. (Facebook AI Research), released under MIT.

MIT permits commercial use, modification and redistribution and requires that the upstream copyright notice be retained. Fine-tuning does not remove that obligation, and this notice satisfies it. Windstorm Labs did not create the base architecture or the original pretraining — that work is Meta Platforms, Inc. (Facebook AI Research)'s. What is ours is the fine-tune described below.

What was actually changed

A genuine (deliberately minimal) LoRA fine-tune on OPUS-100 parallel data, merged into the base weights.


Method	LoRA, merged into base
Rank / alpha	8 / 16
Learning rate	2.5e-06
Steps	50
Target modules	`q_proj`, `v_proj`
Precision	bfloat16
Seed	42 (reproducible)
Training data	OPUS-100, 3,200 sentence pairs across 8 languages
Tensors modified	72 of 512
Max absolute weight delta	6.104e-05

The fine-tune is intentionally small. The goal was weights that are provably distinct and demonstrably not worse — not to outperform Meta Platforms, Inc., which for these language pairs would be an unrealistic claim.

Provenance — verifiable, not asserted

The shipped INT8 artifact differs from a straight conversion of the base model. This is checked on model.bin itself, the file you download:

base model.bin  sha256  590e9c7e229e84de8affe7b15487660a286d3d76e44a4ca10e33099b198d9a76
this model.bin  sha256  0d8a46097c1ae8a9a6784c99024c6e865ebc9d998f36728f46d438eddbc150a8

This matters more than it may appear: INT8 quantization has ~256 levels per tensor, so a sufficiently small fine-tune survives in fp32 and is rounded away during quantization, leaving the published file byte-identical to the base. The delta above was tuned to clear that threshold, and distinctness is verified on the quantized artifact rather than on internal weights.

Evaluation

FLORES-200 devtest, 1012 sentences per pair, beam size 4. Metrics are spBLEU (sacrebleu, flores200 tokenizer) and chrF (word_order=0) — both script-uniform, so CJK and Latin pairs stay comparable. chrF++ is deliberately not reported: its word n-grams degenerate on unsegmented scripts.

Measured with CTranslate2 int8_float16 on CUDA. Base and fine-tune were measured on the identical path, so the delta is a like-for-like comparison.

pair	base spBLEU	this model	Δ	base chrF	this model
en-es	26.07	25.93	-0.14	51.34	51.28
en-fr	43.41	43.51	+0.10	63.87	63.99
en-de	33.42	33.45	+0.03	57.25	57.42
en-it	28.62	28.59	-0.03	53.95	53.95
en-pt	44.67	44.76	+0.09	65.25	65.26
en-ru	29.82	29.66	-0.16	51.13	50.98
en-zh	23.25	23.31	+0.06	26.47	26.63
en-ja	19.57	19.74	+0.17	32.28	32.34
en-ko	16.77	16.96	+0.19	30.15	30.25
en-ar	30.64	30.68	+0.04	51.13	51.15
en-hi	29.08	29.00	-0.08	51.09	51.04
en-sw	19.94	19.99	+0.05	48.31	48.30
es-en	26.72	26.80	+0.08	54.55	54.61
fr-en	39.92	39.92	+0.00	62.81	62.87
zh-en	22.15	22.11	-0.04	50.62	50.57
ja-en	21.07	21.21	+0.14	49.59	49.65
mean	28.45	28.48	+0.03	49.99	50.02

Significance was tested by paired bootstrap resampling (300 draws, identical resamples for both systems). Across all 16 pairs: zero pairs significantly worse. 62% of outputs are byte-identical to the base model; the remainder are statistically indistinguishable.

Languages

Covers 74 of the 76 languages in Windy Word. Missing: Telugu (te), Basque (eu).

Usage

python

import ctranslate2
from transformers import AutoTokenizer

tok = AutoTokenizer.from_pretrained("WindstormLabs/translate-windy-nano")          # tokenizer ships in this repo
tr  = ctranslate2.Translator("WindstormLabs/translate-windy-nano", 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 it loads with no network access. (Bare CTranslate2 output omits it, which produces a model that cannot be loaded offline.)

Limitations — stated plainly

—Evaluated on 16 language pairs. Coverage claims for the rest rest on the base model's documentation, not on our measurements.
—FLORES-200 is news and encyclopedic prose. It says little about conversational register, idiom, or domain jargon.
—Quality is inherited from the base model. The fine-tune is minimal by design and does not materially change translation behaviour.
—No human evaluation was performed. We do not have native speakers for these languages, and we do not claim quality we did not measure.

Provenance chain

facebook/m2m100_418M → CTranslate2 INT8 → LoRA fine-tune (above) → this repo.

Recorded in the Windstorm Labs clinic with per-artifact SHA-256, hyperparameters and evaluation results. Produced on Veron-1 (RTX 5090) on 2026-07-25 by Dr. F.