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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 πŸ‡΅πŸ‡° & English πŸ‡¬πŸ‡§
  • 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:

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

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