language, license, pipeline_tag, tags, base_model

language	license	pipeline_tag	tags	base_model
en th	apache-2.0	translation	translation thai english gemma-3 typhoon machine-translation bilingual sft	typhoon-ai/typhoon2.1-gemma3-4b

🇹🇭🇺🇸 Typhoon 2.1 Gemma 3 KordTranslate EN-TH 4B

Typhoon 2.1 Gemma 3 KordTranslate EN-TH 4B is a bilingual English ↔ Thai translation model fine-tuned from typhoon-ai/typhoon2.1-gemma3-4b for high-quality machine translation between Thai and English.

The model is instruction-tuned specifically for translation tasks using bilingual translation datasets while preserving the strong language understanding inherited from the Typhoon base model.

✨ Features

• 🇺🇸 English → Thai translation
• 🇹🇭 Thai → English translation
• Instruction-tuned for translation
• Built on Typhoon2.1 Gemma 3 4B
• Lightweight and suitable for local inference

---

Item	Value
Model	KordAI/Typhoon-Gemma3-KordTranslate-EN-TH-4B
Base Model	typhoon-ai/typhoon2.1-gemma3-4b
Architecture	Gemma 3 (4B)
Task	Machine Translation
Languages	English, Thai
Fine-tuning	Supervised Fine-Tuning (SFT)

---

This model is optimized for:

• English → Thai translation
• Thai → English translation
• Localization
• Educational applications
• Chatbot translation
• API integration
• Batch document translation

For the best performance, use the prompt templates below.

---

📝 Prompt Templates

For optimal translation quality, structure your inputs exactly as shown below depending on the translation direction.

English → Thai

Translate the following English text to Thai.

### English:
[Your English text here]

Thai → English

Translate the following Thai text to English.


### Thai:
[Your Thai text here]

---

🐍 Inference Code (Python / Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer


# Hugging Face repository
model_name = "KordAI/Typhoon-Gemma3-KordTranslate-EN-TH-4B"


# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = """
Translate the following English text to Thai.


### English:
But friends, I am so gay, that if I had a wife, I would encourage her to cheat on me.
"""

messages = [
    {"role": "user", "content": prompt}

]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True


)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)

output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]

translation = tokenizer.decode(
    output_ids,
    skip_special_tokens=True
).strip()


print(translation)

---

🏋️ Training

This model is fine-tuned from typhoon-ai/typhoon2.1-gemma3-4b using supervised instruction tuning on bilingual English–Thai translation datasets.

The model is trained to perform direct translation between:

• 🇺🇸 English → Thai
• 🇹🇭 Thai → English

while maintaining natural, fluent, and context-aware outputs.

---

⚠️ Limitations

• Translation quality may decrease on highly specialized domains such as medical, legal, or scientific texts.
• Proper nouns and newly coined terminology may require manual verification.
• The model may occasionally hallucinate or paraphrase ambiguous inputs.
• Human review is recommended for production-critical translations.

---

Special thanks to:

• Typhoon AI for the excellent typhoon2.1-gemma3-4b base model.
• KordAI for developing and fine-tuning the translation model.
• Unsloth for enabling efficient supervised fine-tuning.
• llama.cpp for the GGUF format and inference ecosystem.
• The open-source AI community for advancing multilingual language models.

---

📖 Citation

@misc{kordtranslate2026,
  title={Typhoon Gemma3 KordTranslate EN-TH 4B},
  author={KordAI},
  year={2026},
  publisher={Hugging Face},
  howpublished={https://huggingface.co/KordAI/Typhoon-Gemma3-KordTranslate-EN-TH-4B}
}