Summary of Can Low-rank Knowledge Distillation in Llms Be Useful For Microelectronic Reasoning?, by Nirjhor Rouf et al.
Can Low-Rank Knowledge Distillation in LLMs be Useful for Microelectronic Reasoning?
by Nirjhor Rouf, Fin Amin, Paul D. Franzon
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper investigates the feasibility of using offline large language models (LLMs) in electronic design automation (EDA). A contemporary LLM, Llama-2-7B, is evaluated for its ability to serve as a microelectronic Q&A expert, with a focus on its reasoning and generation capabilities. The model was tested across various adaptation methods, including the novel low-rank knowledge distillation scheme LoRA-KD. Our results produce both qualitative and quantitative outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at using special language models in designing electronic devices. It wants to see if these models can answer questions about microelectronic problems and help solve them. The model is tested with different ways of adapting it, including a new way called LoRA-KD. The results show both what the model does well and how well it does. |
Keywords
» Artificial intelligence » Knowledge distillation » Llama » Lora
