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
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Download README.md from WindyTranslate/translate-en-lua: direct link, hf CLI and curl.
- Browser
- Download file 2.43 kB
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https://huggingface.co/WindyTranslate/translate-en-lua/resolve/main/README.md
- Command line
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hf download hf://WindyTranslate/translate-en-lua/README.md
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curl -L -o README.md https://huggingface.co/WindyTranslate/translate-en-lua/resolve/main/README.md
2.43 kB
| 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. | |