Summary of Ebft: Effective and Block-wise Fine-tuning For Sparse Llms, by Song Guo et al.
EBFT: Effective and Block-Wise Fine-Tuning for Sparse LLMs
by Song Guo, Fan Wu, Lei Zhang, Xiawu Zheng, Shengchuan Zhang, Fei Chao, Yiyu Shi, Rongrong Ji
First submitted to arxiv on: 19 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 Existing methods for fine-tuning sparse large language models (LLMs) are often resource-intensive and costly, relying on approximations or heuristic optimization strategies that may lead to suboptimal solutions. To address these issues, researchers propose an efficient framework for fine-tuning sparse LLMs based on minimizing reconstruction error. The approach involves sampling a small dataset for calibration and utilizing backpropagation to iteratively optimize block-wise reconstruction error, aiming for optimal solutions. Experimental results demonstrate the superiority of this method over other baselines on various benchmarks, including Wikitext2 and LoRA. For instance, on the Wikitext2 dataset with LlamaV1-7B at 70% sparsity, the proposed EBFT achieves a perplexity of 16.88, surpassing the state-of-the-art DSnoT with a perplexity of 75.14. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces an efficient way to fine-tune sparse large language models (LLMs). This is important because current methods are often slow and expensive. The new method uses a special type of error that helps it find the best solution more quickly. It works by taking a small sample of data, using it to adjust the model, and then repeating this process until it’s just right. The results show that this method performs better than others on certain benchmarks. |
Keywords
* Artificial intelligence * Backpropagation * Fine tuning * Lora * Optimization * Perplexity
