# 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.