Summary of Goldfinch: High Performance Rwkv/transformer Hybrid with Linear Pre-fill and Extreme Kv-cache Compression, by Daniel Goldstein et al.
GoldFinch: High Performance RWKV/Transformer Hybrid with Linear Pre-Fill and Extreme KV-Cache Compression
by Daniel Goldstein, Fares Obeid, Eric Alcaide, Guangyu Song, Eugene Cheah
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We introduce GoldFinch, a hybrid Linear Attention/Transformer sequence model that generates a highly compressed and reusable KV-Cache in linear time and space with respect to sequence length. By stacking our new GOLD transformer on top of the enhanced Finch (RWKV-6) architecture, we achieve dramatically improved modeling performance relative to both Finch and Llama. Our cache size savings increase linearly with model layer count, ranging from 756-2550 times smaller than traditional transformer caches for common sizes, enabling inference of extremely large context lengths even on limited hardware. The paper releases trained weights and training code under the Apache 2.0 license for community use. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have developed a new way to make computers understand long sequences of text more efficiently. They created a new model called GoldFinch that uses a special technique to store information in a way that lets it process very long texts quickly and without using too much computer power. This means computers can now understand even the longest texts, which is important for tasks like language translation and text summarization. The researchers are sharing their code and trained models so others can use them. |
Keywords
» Artificial intelligence » Attention » Inference » Llama » Sequence model » Summarization » Transformer » Translation
