Summary of Spectra: Surprising Effectiveness Of Pretraining Ternary Language Models at Scale, by Ayush Kaushal et al.
Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale
by Ayush Kaushal, Tejas Vaidhya, Arnab Kumar Mondal, Tejas Pandey, Aaryan Bhagat, Irina Rish
First submitted to arxiv on: 17 Jul 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 This paper addresses memory bottlenecks in Large Language Model (LLM) inference by exploring pretraining of low-bitwidth models. The leading method, post-training quantization, suffers from significant performance degradation below 4-bit precision. In response, the authors investigate Ternary Language Models (TriLMs), an alternative to traditional floating-point models and their post-training quantized versions. A comprehensive evaluation suite, Spectra LLM, is presented, featuring FloatLMs, QuantLMs, and TriLMs ranging from 99M to 3.9B parameters trained on 300B tokens. The results show that TriLMs offer superior scaling behavior in terms of model size (in bits) and consistently outperform their QuantLM and FloatLM counterparts for a given bit size across various benchmarks. Notably, the 3.9B parameter TriLM matches the performance of the FloatLM 3.9B across all benchmarks, despite having fewer bits than FloatLM 830M. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem with big language models on computers. Currently, these models take up too much memory and slow down, making it hard to use them. The authors came up with a new idea: instead of using lots of bits (like 32 or 64), they used only 3 bits. They trained many different versions of this “ternary” language model, from small to very large, and tested how well each one worked. Surprisingly, the smaller models worked just as well as the bigger ones! This is important because it could lead to faster and more efficient language models that use less memory. |
Keywords
» Artificial intelligence » Inference » Language model » Large language model » Precision » Pretraining » Quantization
