iCIIT/TripleBits-Sinhala-Llama-3.2-1B-CP
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Model Details
Model Description
<!-- Provide a longer summary of what this model is. --> TripleBits/Sinhala-Llama-3.2-1B is a LLaMA 3.2 1B-based model that has undergone continual pretraining (CPT) on a diverse Sinhala corpus.
- Trained by: Team TripleBits for Shared Task 2025 <!-- - Funded by [optional]: [More Information Needed]
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How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
from huggingface_hub import login
login(token="your_hf_token")
# Load base model
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")
# Load trained LoRA adapter
model = PeftModel.from_pretrained(base_model, "iCIIT/TripleBits-Sinhala-Llama-3.2-1B-CP")
question = "ශ්රී ලංකාවේ අගනුවර කුමක්ද?"
instruction = f"""පහත සදහන් ප්රශ්නයට නිවැරදි පිළිතුරක් ලබා දෙන්න. පිළිතුරු ලබා දීමේදී ප්රශ්නයේ ස්වභාවය අනුව - සරල ප්රශ්න සඳහා කෙටි පිළිතුරු ද, සංකීර්ණ ප්රශ්න සඳහා විස්තරාත්මක පැහැදිලි කිරීම් ද ලබා දෙන්න.
### ප්රශ්නය: {question}
### පිළිතුර:"""
inputs = tokenizer(instruction, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=100,
num_beams=10,
repetition_penalty=1.2,
no_repeat_ngram_size=3,
do_sample=False,
early_stopping=True,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id
)
generated_answer_tokens = outputs[0][inputs.input_ids.shape[1]:]
generated_answer = tokenizer.decode(generated_answer_tokens, skip_special_tokens=True).strip()
print(generated_answer)
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Training Details
Training Data
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Training Hyperparameters
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- microbatchsize = 8
- batch_size = 64
- gradientaccumulationsteps = batchsize // microbatch_size
- epochs = 5
- learning _rate = 3e-4
- maxseqlen = 512
- lora_r = 4
- lora_alpha = 8
- lora_dropout = 0.1
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Framework versions
- PEFT 0.17.0
