Model Card for NimanthaPerera/tinyllama-pali-to-sinhala
Model Summary
This model is a Pali → Sinhala translation model fine-tuned on top of TinyLLaMA, designed to translate Pali Buddhist texts into modern Sinhala.
It is intended for educational, research, and cultural preservation use cases.
Model Details
Model Description
- Developed by: Nimantha Perera
- Model type: Causal Language Model (LLM)
- Base model: TinyLLaMA
- Language(s): Pali, Sinhala
- Task: Machine Translation (Pali → Sinhala)
- License: Apache-2.0
- Finetuned from model: TinyLLaMA
Model Sources
Uses
Direct Use
- Translating Pali verses into Sinhala
- Understanding Buddhist scriptures
- Educational and academic research
- Digital humanities and language preservation
Downstream Use
- Fine-tuning for domain-specific Buddhist texts
- Integration into Sinhala/Pali learning applications
- Chatbots or assistants for religious studies
Out-of-Scope Use
- Legal, medical, or financial advice
- Authoritative religious rulings
- Fully accurate scholarly translation without human review
Bias, Risks, and Limitations
- Training data may contain religious or historical bias
- Translations may be incomplete or stylistically inconsistent
- Not all Pali grammatical structures are perfectly captured
- Outputs should be verified by domain experts
Recommendations
Users are advised to:
- Use the model as a support tool, not a final authority
- Validate translations with scholars or reference texts
- Avoid sensitive or high-stakes usage
How to Get Started with the Model
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "NimanthaPerera/tinyllama-pali-to-sinhala"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
prompt = "Pali: Sabbe sankhara anicca"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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