Instructions to use WindyWord/translate-ms-ms with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyWord/translate-ms-ms 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="WindyWord/translate-ms-ms")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("WindyWord/translate-ms-ms", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download README.md from WindyWord/translate-ms-ms: direct link, hf CLI and curl.
- Browser
- Download file 1.82 kB
-
https://huggingface.co/WindyWord/translate-ms-ms/resolve/main/README.md
- Command line
-
hf download hf://WindyWord/translate-ms-ms/README.md
-
curl -L -o README.md https://huggingface.co/WindyWord/translate-ms-ms/resolve/main/README.md
license: apache-2.0
tags:
- translation
- marian
- windyword
- malay
language:
- ms
library_name: transformers
pipeline_tag: translation
base_model: Helsinki-NLP/opus-mt-ms-ms
WindyWord.ai Translation — Malay → Malay
Part of the Windstorm Labs open model catalogue. Scores, licence and attribution for every model: https://windytranslate.com/models/translate-ms-ms
Canonical copy: https://huggingface.co/WindyTranslate/translate-ms-ms
Translates Malay → Malay.
Quality
No quality score is published in this repository. Current screening scores, where measured, are on the catalogue page linked above.
Available Variants
Deployment formats in this repository (subfolders):
| Variant | Description |
|---|---|
lora/ |
WindyStandard — production baseline. Transformers format for GPU inference. |
lora-ct2-int8/ |
WindyStandard · CPU INT8 — CTranslate2 INT8 quantization of WindyStandard for CPU inference. |
Quick usage
Transformers (PyTorch):
from transformers import MarianMTModel, MarianTokenizer
tokenizer = MarianTokenizer.from_pretrained("WindyWord/translate-ms-ms", subfolder="lora")
model = MarianMTModel.from_pretrained("WindyWord/translate-ms-ms", subfolder="lora")
CTranslate2 (fast CPU inference):
import ctranslate2
translator = ctranslate2.Translator("path/to/translate-ms-ms/lora-ct2-int8")
Apps
The Windy Word apps are built on this model family.
Provenance & License
Weights derived from Helsinki-NLP/opus-mt-ms-ms (OPUS-MT, Helsinki-NLP, University of Helsinki), licensed Apache-2.0. Windy variants are released under the same licence.