Instructions to use WindyTranslate/translate-en-lua with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-en-lua with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("translation", model="WindyTranslate/translate-en-lua")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-en-lua") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-en-lua", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,428 Bytes
ddac4ff 7e9b1d4 ddac4ff c895bf7 ddac4ff 7e9b1d4 ddac4ff 7e9b1d4 ddac4ff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | ---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-lua
library_name: transformers
pipeline_tag: translation
tags:
- translation
- marian
---
# translate-en-lua
> Part of the Windstorm Labs open model catalogue. Scores, licence and attribution for every model: https://windytranslate.com/models/translate-en-lua
Machine translation model, `en` to `lua`, published by Windstorm Labs.
## Attribution
Derived from [`Helsinki-NLP/opus-mt-en-lua`](https://huggingface.co/Helsinki-NLP/opus-mt-en-lua), licensed **apache-2.0**.
Windstorm Labs did not train the original model. This notice provides the attribution the
licence requires.
## What was changed
**The weights in this repository have been modified from the original.** A LoRA fine-tune trained on parallel corpus data and merged into the base weights.
| | |
|---|---|
| Method | `windy-hallmark` |
| Tensors modified | **36 of 254** |
| Max absolute weight delta | **7.616e-05** |
| Verified | tensor-by-tensor against the upstream original, 2026-07-26 |
The comparison is tensor-level rather than file-level: safetensors and PyTorch `.bin`
containers hash differently even when the tensors inside are identical, so a file-hash
mismatch would prove nothing.
## Contents
A single transformers build in safetensors format, loadable directly:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tok = AutoTokenizer.from_pretrained("WindyTranslate/translate-en-lua")
model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-en-lua")
batch = tok(["<your text here>"], return_tensors="pt", padding=True)
print(tok.batch_decode(model.generate(**batch), skip_special_tokens=True))
```
Other builds of this pair, including CTranslate2 INT8, are not included in this repository.
## Evaluation
| Benchmark | chrF++ | BLEU | Band |
|---|---:|---:|---|
| FLORES-200 dev, 48 sentences (English → Luba-Kasai) | 28.11 | 3.49 | Limited |
Measured 2026-08-04; screening score, not a publication result. Bands: Excellent ≥60, Good 45–60, Usable 30–45, Limited 15–30, Not recommended <15 (chrF++).
## Limitations
- A single language direction: `en` to `lua`.
- The evaluation above is a 48-sentence screening score, not a publication result. The weights are
verified to differ from the original; that is a statement about provenance.
- Quality is not monotonic with model size or with the amount of fine-tuning applied.
|