Summary of Bitdelta: Your Fine-tune May Only Be Worth One Bit, by James Liu et al.
BitDelta: Your Fine-Tune May Only Be Worth One Bit
by James Liu, Guangxuan Xiao, Kai Li, Jason D. Lee, Song Han, Tri Dao, Tianle Cai
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Large Language Models (LLMs) are trained in two phases: pre-training on large datasets and fine-tuning for specific tasks. Researchers explored the idea that fine-tuning adds less new information than pre-training, making it more compressible. They introduced BitDelta, a method to quantize the additional information added during fine-tuning down to 1 bit without compromising performance. This finding highlights potential redundancy in fine-tuned models and has implications for serving and storing multiple models. By using a single high-precision base model with multiple 1-bit deltas, BitDelta reduces GPU memory requirements by over 10x, translating to faster generation latency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are trained twice: first on big internet data, then fine-tuned for specific tasks. Researchers looked at how much extra information is added during fine-tuning and found that it’s surprisingly easy to compress this new info into just a few bits. This discovery can help us store and serve many models with less memory and faster processing. |
Keywords
* Artificial intelligence * Fine tuning * Precision
