Summary of Reallm: a General Framework For Llm Compression and Fine-tuning, by Louis Leconte et al.
ReALLM: A general framework for LLM compression and fine-tuning
by Louis Leconte, Lisa Bedin, Van Minh Nguyen, Eric Moulines
First submitted to arxiv on: 21 May 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 A novel approach called ReALLM is introduced for compressing and adapting pre-trained language models while using minimal memory. The method decomposes the model’s matrices into low-rank components and vector-quantized latent representations, allowing only the low-rank parts to be updated during fine-tuning. By adapting the shape of the encoder based on each matrix’s pattern, ReALLM achieves state-of-the-art performance on language generation tasks (C4 and WikiText-2) with a budget of 3 bits without any training. With a budget of 2 bits, the method achieves competitive results after fine-tuning on a small calibration dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ReALLM is a new way to make language models use less memory while still working well. It breaks down the model’s parts into smaller pieces and only changes some of those pieces when you’re fine-tuning it. This helps the model be more efficient with its memory. The results show that ReALLM works really well on tasks like generating text, even when using very little memory. |
Keywords
» Artificial intelligence » Encoder » Fine tuning
