Linear Next Benchmark
Linear Next is a comprehensive benchmark designed to fairly compare various efficient transformer architectures. This project evaluates different approaches including linear attention, sparse attention, and other model structures under identical training conditions and datasets.
Overview
The benchmark aims to provide an unbiased comparison of efficient transformer variants by ensuring all models are trained with the same datasets, hyperparameters, and evaluation metrics. This allows for a clear understanding of the relative strengths and weaknesses of each approach.
Datasets
The benchmark utilizes a diverse collection of high-quality datasets:
General Text
- DCLM-pro: A large-scale dataset containing diverse text from various domains, designed for general language modeling tasks.
- Cosmopedia-v2: A curated corpus of high-quality web content covering a wide range of topics, with emphasis on educational and informative material.
- Fineweb-edu: A filtered collection of educational web content, focusing on instructional and academic text from reliable sources.
Code
- The Stack v2: A comprehensive collection of source code spanning multiple programming languages, designed to train models on code understanding and generation tasks.
Mathematics
- Finemath: A specialized dataset containing mathematical content, including equations, proofs, and mathematical explanations across various difficulty levels.
Reasoning
- Natural Reasoning: A dataset focused on logical reasoning, problem-solving, and inference tasks, designed to improve models' reasoning capabilities.
Methodology
All models in the Linear Next benchmark are evaluated using identical:
- Training datasets and data mixing ratios
- Optimization parameters
- Hardware configurations
- Evaluation metrics
This controlled environment ensures that performance differences can be attributed to the architectural differences rather than training conditions.
Results
Detailed benchmark results, including training curves, inference speed, memory usage, and performance metrics across different tasks, are available in the project repository.