MihaiPopa-1/opus-mt-en-tlh

All of this Readme is generated by Gemini 3 Flash

---

⚔️ Klingon-English-82.7M-Warrior

This is a lightweight, high-efficiency sequence-to-sequence model specialized for English to Klingon translation. At only 82.7M parameters, it is designed to run on extremely low-resource hardware (down to 4GB RAM) while maintaining high structural accuracy.

📊 Training Results

The model was trained for 5 epochs, achieving a highly optimized balance between learning and generalization.

Epoch	Training Loss	Validation Loss	Status
5	1.419400	1.239786	🏆 Optimized

Note on Performance: The validation loss (1.23) is significantly lower than the training loss (1.41). This indicates the model has generalized exceptionally well and is not overfitting to the training noise.

🛠️ Technical Details

• Model Size: 82.7 Million Parameters
• Architecture: Transformer-based Encoder-Decoder
• Input: English (en)
• Output: Klingon (tlh)
• Target Hardware: CPU-friendly / Mobile / Low-RAM (4GB+)

🚀 Usage

You can use this model directly with the Hugging Face pipeline API:

from transformers import pipeline

translator = pipeline("translation", model="MihaiPopa-1/opus-mt-en-tlh")
result = translator("Glory to you and your house!")
print(result[0]['translation_text'])