Llama-3.2-1B-Singlish-FT

Uploaded finetuned model

• Developed by: Afeefzeed
• License: apache-2.0
• Finetuned from model : unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit

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language:

• en
• si tags:
• transliteration
• singlish
• sinhala
• llama-3.2
• unsloth license: llama3.2 datasets:
• indonlp

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🇱🇰 Llama-3.2-1B-Singlish-FT

This model is a fine-tuned version of Llama-3.2-1B-Instruct, optimized for transliterating Singlish (phonetic English) into Sinhala. It was trained on a large-scale corpus of 500,000 sentence pairs using the Unsloth framework (QLoRA) to handle diverse spelling variations and casual social media text.

📊 Evaluation Results

The model was rigorously evaluated on a held-out test set of 10,003 rows derived from the IndoNLP Sinhala Dataset. This large-scale testing ensures the metrics reflect real-world performance on unseen data.

🧪 Testing Methodology

• Test Dataset: IndoNLP (Sinhala Subset) - Held-out test split.
• Sample Size: 10,003 distinct sentences.
• Model Configuration: 4-bit Quantization (via Unsloth).

🚀 Usage

You can run this model using the transformers library:

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the model
model_name = "afeefzeed/Llama-3.2-1B-Singlish-FT"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Input text (Singlish)
input_text = "Transliterate to Sinhala: oya koheda yanne"

# Tokenize & Generate
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# Output: ඔයා කොහෙද යන්නෙ