By Ricardo Lezama Emerging Trends in Large Language Model (LLM) Development: The Stellar Role of LoRA
What is LORA?
The Case of Llama-2 Since the advent of Low Ranking Adaptation (LoRA), there have been fundamental advancements in the domain of Large Language Models (LLM) and their deployment. The tweaks in fine-tuning methods have already led to a significant boost in performance in areas such as goal-condition. One such breakthrough is by Lamini AI, an enterprise LLM platform recognized for innovative tuning methods.
They recently published a tutorial demonstrating increased accuracy, from a mere 30% to a staggering 95%, in SQL querying by Meta’s Llama 3. There’s also curiosity surrounding LoRA-KD, which has shown promise in student model rankings.
While many have highlighted the potential of LLM applications for Efficient Design Architecture (EDA), full exploration remains incomplete. Progress in adapting LLMs to downstream tasks is also laudable. Especially worth mentioning is a 7 billion-parameter LLM that has been fine-tuned with LoRA, boasting an intriguing focus on linguistic insight.
Applying LoRA mechanics to Low-Rank Matrix Approximation has also advanced our understanding of computational performance. In the case of Llama-2, 20 million trainable parameters – just 0.02% of the original parameters – were retained, striking an aspect of affordability in terms of training costs. Overall, these advancements emphasize the viability of LoRA-driven efforts in LLM enhancements. Moving ahead, the focus seems to be on bridging modality gaps, reducing decoding complexities and fortifying AI applications on devices.