How to use from the
Use from the
Transformers library
# 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-cpp")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-en-cpp")
model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-en-cpp", device_map="auto")
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translate-en-cpp

Machine translation model, en to cpp, published by Windstorm Labs.

Attribution

Derived from Helsinki-NLP/opus-mt-en-cpp, 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 lora-fog
Tensors modified 36 of 254
Max absolute weight delta 6.905e-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:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tok = AutoTokenizer.from_pretrained("WindyTranslate/translate-en-cpp")
model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-en-cpp")

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.

Limitations

  • A single language direction: en to cpp.
  • No quality benchmark has been run on this pair. The weights are verified to differ from the original; that is a statement about provenance, not about translation quality.
  • Quality is not monotonic with model size or with the amount of fine-tuning applied.
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