The nextAI Solution to the NeurIPS 2023 LLM Efficiency Challenge
The rapid evolution of Large Language Models (LLMs) has significantly impacted the field of natural language processing, but their growing complexity raises concerns about resource usage and transparency. Addressing these challenges, we participated in the NeurIPS LLM Efficiency Challenge, aiming to fine-tune a foundation model within stringent constraints. Our focus was the LLaMa2 70 billion model, optimized on a single A100 40GB GPU within a 24-hour limit. Our methodology hinged on a custom dataset, carefully assembled from diverse open-source resources and benchmark tests, aligned with the challenge's open-source ethos. Our approach leveraged Quantized-Low Rank Adaptation (QLoRA) Fine ...
| Authors | Gyuwon Park, DongIl Shin, SolGil Oh et al. |
|---|---|
| Primary category | cs.LG |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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