Summary of Lora Land: 310 Fine-tuned Llms That Rival Gpt-4, a Technical Report, by Justin Zhao et al.
LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report
by Justin Zhao, Timothy Wang, Wael Abid, Geoffrey Angus, Arnav Garg, Jeffery Kinnison, Alex Sherstinsky, Piero Molino, Travis Addair, Devvret Rishi
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary LoRA has emerged as a widely adopted method for Parameter Efficient Fine-Tuning (PEFT) of Large Language Models (LLMs), reducing trainable parameters and memory usage while achieving comparable performance to full fine-tuning. The study assesses the viability of training and serving LLMs fine-tuned with LoRA in real-world applications. It measures the quality of LLMs fine-tuned with quantized low rank adapters across 10 base models, 31 tasks, and 310 models, finding that 4-bit LoRA fine-tuned models outperform base models by 34 points and GPT-4 by 10 points on average. The study also investigates the most effective base models for fine-tuning and evaluates the latency and concurrency capabilities of LoRAX, an open-source Multi-LoRA inference server. LoRAX facilitates the deployment of multiple LoRA fine-tuned models on a single GPU using shared base model weights and dynamic adapter loading. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LoRA is a way to make large language models more efficient. The study looks at how well these models work when they’re “fine-tuned” with LoRA, which makes them faster and uses less memory. They tested 310 different models and found that some of the best ones used a special type called 4-bit LoRA. This means that instead of using all the possible numbers to represent words, they only use 4-bit (or 2^8) possibilities. The study also looked at which base models work best for fine-tuning and how well different tasks can be done with these models. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Inference » Lora » Parameter efficient
