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Redgerd/llama3-roman-urdu-finetuned

Redgerd/llama3-roman-urdu-finetuned is a multilingual instruction-tuned LLaMA 3 model fine-tuned on a combination of Roman Urdu QA pairs and English examples from the Stanford Alpaca dataset.

This model is designed to enhance performance in low-resource, multilingual, and instruction-following tasks, especially involving Roman Urdu.

Model Details

Base Model: Meta LLaMA 3
Architecture: Decoder-only transformer
Fine-tuned On: Custom Alpaca-style dataset
Languages: Roman Urdu 🇵🇰 & English 🇬🇧
Format: Instruction-tuning (compatible with Alpaca style)

Dataset Overview

A custom dataset with ~500 instruction-based examples in Roman Urdu and ~500 from Stanford Alpaca at from Redgerd/roman-urdu-alpaca-qa-mix

Training Setup

Frameworks: transformers, unsloth
Format: Instruction-based fine-tuning (instruction, input, output)
Environment: A100 GPU with bfloat16 precision
Checkpointing: Supported

Credits

Developed by Muhammad Salaar using:

Stanford Alpaca Dataset
GPT-4 for Roman Urdu generation
LLaMA 3.1 as the base model

For feedback or collaboration, visit: github.com/Redgerd

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Collection including Redgerd/llama3-roman-urdu-finetuned

Roman Urdu Transliteration

Collection

A collection of models and datasets designed to standardize inconsistent Roman Urdu spellings. • 4 items • Updated Feb 10