Summary of Faster and Lighter Llms: a Survey on Current Challenges and Way Forward, by Arnav Chavan et al.
Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward
by Arnav Chavan, Raghav Magazine, Shubham Kushwaha, Mérouane Debbah, Deepak Gupta
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 survey provides an overview of recent advancements in model compression and system-level optimization methods aimed at enhancing LLM inference efficiency. Despite impressive performance, widespread adoption faces challenges due to substantial computational and memory requirements during inference. The study evaluates various compression techniques on LLaMA(/2)-7B, providing practical insights for efficient LLM deployment. Empirical analysis highlights the effectiveness of these methods, while drawing from survey insights, current limitations are identified, and potential future directions discussed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs have impressive performance, but widespread adoption is challenged by substantial computational and memory requirements during inference. Recent advancements in model compression and system-level optimization methods aim to enhance LLM inference efficiency. The study evaluates various compression techniques on LLaMA(/2)-7B, providing practical insights for efficient LLM deployment. |
Keywords
* Artificial intelligence * Inference * Llama * Model compression * Optimization
