Birbal: An efficient 7B instruct-model fine-tuned with curated datasets
<|reference_start|>Birbal: An efficient 7B instruct-model fine-tuned with curated datasets: LLMOps incur significant costs due to hardware requirements, hindering their widespread accessibility. Additionally, a lack of transparency in model training methods and data contributes to the majority of models being non-reproducible. To tackle these challenges, the LLM Efficiency Challenge was introduced at NeurIPS Workshop, aiming to adapt foundation models on a diverse set of tasks via fine-tuning on a single GPU (RTX 4090 or A100 with 40GB) within a 24-hour timeframe. In this system description paper, we introduce Birbal, our Mistral-7B based winning model, fine-tuned on a single RTX 4090 for 16 hours. Birbal's success lies in curating high-quality instructions covering diverse tasks, resulting in a 35% performance improvement over second-best Qwen-14B based submission.<|reference_end|>
@article{jindal2024birbal:,
    title={Birbal: An efficient 7B instruct-model fine-tuned with curated datasets},
    author={Ashvini Kumar Jindal, Pawan Kumar Rajpoot, Ankur Parikh},
    journal={arXiv preprint arXiv:2403.02247},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02247},
    primaryClass={cs.CL}
}