Summary of Kd-lora: a Hybrid Approach to Efficient Fine-tuning with Lora and Knowledge Distillation, by Rambod Azimi et al.
KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge Distillation
by Rambod Azimi, Rishav Rishav, Marek Teichmann, Samira Ebrahimi Kahou
First submitted to arxiv on: 28 Oct 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 The paper presents a novel fine-tuning method called KD-LoRA that combines low-rank adaptation (LoRA) with knowledge distillation (KD). This approach reduces the computational costs and memory requirements of large language models (LLMs), making them more feasible for deployment. The authors demonstrate that KD-LoRA achieves comparable performance to full fine-tuning (FFT) and LoRA while being 40% more compact, retaining 98% of LoRA’s performance on the GLUE benchmark. Additionally, KD-LoRA reduces GPU memory usage by 30% compared to LoRA and inference time by 30% compared to both FFT and LoRA. The method is evaluated across three encoder-only models: BERT, RoBERTa, and DeBERTaV3. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a way to make large language models smaller and faster without losing their ability to perform well. It does this by combining two existing techniques: low-rank adaptation (LoRA) and knowledge distillation (KD). The new method, called KD-LoRA, works really well and is much more efficient than the original methods. It can be used with different models like BERT, RoBERTa, and DeBERTaV3. |
Keywords
» Artificial intelligence » Bert » Encoder » Fine tuning » Inference » Knowledge distillation » Lora » Low rank adaptation
