Summary of One Quantllm For All: Fine-tuning Quantized Llms Once For Efficient Deployments, by Ke Yi et al.
One QuantLLM for ALL: Fine-tuning Quantized LLMs Once for Efficient Deployments
by Ke Yi, Yuhui Xu, Heng Chang, Chen Tang, Yuan Meng, Tong Zhang, Jia Li
First submitted to arxiv on: 30 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 Large Language Models (LLMs) have achieved impressive progress but struggle with memory constraints. To address this issue, researchers have explored quantization methods that reduce the model’s size. However, these approaches typically require lengthy training to minimize performance degradation from quantization loss. This limitation hinders the deployment of LLMs across various scenarios, as repeated training is necessary for each application. To overcome this challenge, the once-for-all (OFA) supernet framework has been proposed, allowing for one-shot training to generate diverse optimal subnets for downstream applications. However, the scale of current language models makes it difficult to efficiently train and deploy these models. In this paper, we extend the OFA framework to large language models by decoupling shared weights and incorporating Low-Rank adapters for training efficiency. Additionally, we introduce a non-parametric scheduler to balance resource allocation among subnets with varying demands. Our approach is validated on LLaMA2 families, achieving high performance while significantly reducing deployment time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper deals with how to make Large Language Models (LLMs) work better in different situations. Right now, these models are very big and use up a lot of memory. When we try to make them smaller using quantization, it takes a long time to train them without losing too much performance. This is a problem because we need to be able to use the same model in many different scenarios. To solve this issue, researchers have developed a way to train one supermodel that can give us lots of other models for specific tasks. However, these supermodels are also very big and hard to train efficiently. In this paper, we find ways to make the training process faster and more efficient by breaking up shared weights and using special adapters. We test our approach on a type of LLM called LLaMA2 and show that it works well in many different situations. |
Keywords
» Artificial intelligence » One shot » Quantization
