README.md

---
license: apache-2.0
base_model: google/madlad400-3b-mt
library_name: ctranslate2
pipeline_tag: translation
tags: [translation, ctranslate2, int8, multilingual, windy-word]
---

# translate-windy-max

Multilingual machine translation, quantized to **CTranslate2 INT8** for CPU inference.
Windstorm Labs' quality tier, optional download.

Derived from [`google/madlad400-3b-mt`](https://huggingface.co/google/madlad400-3b-mt) 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 **[`google/madlad400-3b-mt`](https://huggingface.co/google/madlad400-3b-mt)**,
copyright **Google LLC**, released under **Apache-2.0**.

Apache-2.0 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 Google LLC'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`, `v` |
| Precision | bfloat16 |
| Seed | 42 (reproducible) |
| Training data | OPUS-100, 3,200 sentence pairs across 8 languages |
| Tensors modified | **192 of 744** |
| Max absolute weight delta | **3.052e-05** |

The fine-tune is intentionally small. The goal was weights that are **provably distinct and
demonstrably not worse** — not to outperform Google LLC, 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  890ed3b7e4654dcf1b9e7f2ce6ce641447462e782881e81aac443568eb1ca702
this model.bin  sha256  c13ba95e1098fb4bee0281ba18f6f5be8576e80e36d69d5cbe1fc6303b8822d3
```

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 | 32.84 | 32.77 | -0.07 | 56.33 | 56.33 |
| en-fr | 55.67 | 55.78 | +0.11 | 71.89 | 71.93 |
| en-de | 47.45 | 47.48 | +0.03 | 66.68 | 66.73 |
| en-it | 37.15 | 37.27 | +0.12 | 59.97 | 60.01 |
| en-pt | 54.65 | 54.49 | -0.16 | 71.65 | 71.54 |
| en-ru | 40.41 | 40.48 | +0.07 | 59.15 | 59.22 |
| en-zh | 33.64 | 33.79 | +0.15 | 34.66 | 34.93 |
| en-ja | 24.72 | 25.03 | +0.31 | 34.68 | 34.89 |
| en-ko | 26.15 | 26.21 | +0.06 | 35.84 | 35.86 |
| en-ar | 39.45 | 39.22 | -0.23 | 57.60 | 57.40 |
| en-hi | 34.73 | 34.84 | +0.11 | 54.80 | 54.88 |
| en-sw | 30.77 | 30.81 | +0.04 | 56.05 | 56.18 |
| es-en | 35.28 | 35.37 | +0.09 | 60.68 | 60.72 |
| fr-en | 49.86 | 49.78 | -0.08 | 69.66 | 69.64 |
| zh-en | 32.67 | 32.57 | -0.10 | 58.16 | 58.13 |
| ja-en | 30.95 | 30.98 | +0.03 | 56.99 | 56.95 |
| **mean** | **37.90** | **37.93** | **+0.03** | **56.55** | **56.58** |

Significance was tested by **paired bootstrap resampling** (300 draws, identical resamples for
both systems). Across all 16 pairs: **zero pairs significantly worse.** 68% of outputs
are byte-identical to the base model; the remainder are statistically indistinguishable.

## Languages

Covers **76 of the 76** languages in Windy Word.
Full coverage. Note Tagalog is `<2fil>`, not `<2tl>`.

## Usage

```python
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")

# MADLAD puts the TARGET language in the source as a <2xx> tag.
# Note: Tagalog is <2fil>, not <2tl>.
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 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

`google/madlad400-3b-mt` → 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.