Summary of Llm Vocabulary Compression For Low-compute Environments, by Sreeram Vennam et al.
LLM Vocabulary Compression for Low-Compute Environments
by Sreeram Vennam, Anish Joishy, Ponnurangam Kumaraguru
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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 a novel approach to compressing the final linear layer of language models, achieving memory reductions of up to 3.4x without compromising performance. Our method leverages Byte Pair Encoding (BPE) merges to group tokens and prevent materialization of the logits tensor, a key contributor to memory usage. Evaluations on the TinyStories dataset demonstrate that our approach performs similarly to GPT-Neo and GPT2 while boosting throughput by up to 3x, making it suitable for low-compute environments. Our method’s efficiency gains make it an attractive solution for applications requiring reduced memory consumption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to process language models faster and using less computer memory. We’ve developed a way to do just that! By grouping words together based on how often they appear, we can reduce the amount of memory needed without sacrificing performance. Our tests show that our method works as well as other popular language models while allowing for much faster processing. This is great news for people who need to work with large amounts of language data. |
Keywords
» Artificial intelligence » Boosting » Gpt » Logits
