This is a Burmese Large Language Model (LLM) fine-tuned for Myanmar ⇄ English translation and Buddhist Question-Answering (QA).
It is designed to:

Answer basic Buddhist knowledge questions (RAG-based with vector database).
Translate English ⇄ Myanmar fluently.
Generate natural Burmese text with context-awareness.

Intended Uses & Limitations

✅ Intended Uses

Educational purpose (Buddhist knowledge Q&A).
Myanmar ↔ English translation tasks.
Research and experimentation with Burmese LLMs.

⚠️ Limitations

Not guaranteed to be 100% accurate for complex Buddhist scriptures.
May produce hallucinations if context is missing.
Translation quality may vary for long or domain-specific texts.
This model is not intended for production use without human validation.

Example Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_path = "Ko-Yin-Maung/mig-burmese-llm"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype="auto",
)

query = "Winners focus on winning, losers focus on winners."
prompt = f"<start_of_turn>user\nTranslate to Myanmar: {query}\n<end_of_turn><start_of_turn>model\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Result

user
Translate to Myanmar: Winners focus on winning, losers focus on winners.

model
အောင်မြင်သူတွေက အောင်မြင်ဖို့ပဲ အာရုံစိုက်တယ်၊ ရှုံးနိမ့်သူတွေကတော့ အောင်မြင်သူတွေကို အာရုံစိုက်ကြတယ် ။

Citation

If you use this model, please cite:

@misc{mig-burmese-llm,
  title={MIG Burmese LLM: Translation + Buddhist QA},
  author={Ko Yin Maung},
  year={2025},
  howpublished={Hugging Face Hub},
}

Ko-Yin-Maung/mig-burmese-llm

🔥🔹 Burmese LLM 🔹🔥

Gradio Demo

Model Summary

Intended Uses & Limitations

✅ Intended Uses

⚠️ Limitations

Example Usage

Citation

Contact

Ko-Yin-Maung/
mig-burmese-llm