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README.md

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 | [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) | [![doc](https://img.shields.io/badge/docs-dev-blue.svg)](https://sshin23.github.io/ExaModels.jl/)  | [![build](https://github.com/sshin23/ExaModels.jl/actions/workflows/test.yml/badge.svg)](https://github.com/sshin23/ExaModels.jl/actions/workflows/test.yml) | [![codecov](https://codecov.io/gh/sshin23/ExaModels.jl/branch/main/graph/badge.svg?token=8ViJWBWnZt)](https://codecov.io/gh/sshin23/ExaModels.jl) |
 
 ## Introduction
-ExaModels.jl employs what we call **[SIMD](https://en.wikipedia.org/wiki/Single_instruction,_multiple_data) abstraction for [nonlinear programs](https://en.wikipedia.org/wiki/Nonlinear_programming)** (NLPs), which allows for the **preservation of the parallelizable structure** within the model equations, facilitating **efficient, parallel derivative evaluations** on the **[GPU](https://en.wikipedia.org/wiki/Graphics_processing_unit)** via **[reverse-mode automatic differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation)**.
+ExaModels.jl employs what we call **[SIMD](https://en.wikipedia.org/wiki/Single_instruction,_multiple_data) abstraction for [nonlinear programs](https://en.wikipedia.org/wiki/Nonlinear_programming)** (NLPs), which allows for the **preservation of the parallelizable structure** within the model equations, facilitating **efficient, parallel [reverse-mode automatic differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation)** on the **[GPU](https://en.wikipedia.org/wiki/Graphics_processing_unit) accelerators**.
 
 ExaModels.jl is different from other algebraic modeling tools, such as [JuMP](https://github.com/jump-dev/JuMP.jl) or [AMPL](https://ampl.com/), in the following ways:
-- **Modeling Interface**: ExaModels.jl enforces users to specify the model equations always in the form of `Generator`s. This allows ExaModels.jl to preserve the SIMD-compatible structure in the model equations.
-- **Performance**: ExaModels.jl compiles (via Julia's compiler) derivative evaluation codes that are specific to each computation pattern, based on reverse-mode automatic differentiation. This makes the speed of derivative evaluation (even on the CPU) significantly faster than other existing tools.
-- **Portability**: ExaModels.jl can evaluate derivatives either on multi-core CPUs or GPU accelerators. The code is currently only tested for NVIDIA GPUs, but GPU code is implemented mostly based on the portable programming paradigm, [KernelAbstractions.jl](https://github.com/JuliaGPU/KernelAbstractions.jl). In the future, we are interested in supporting Intel, AMD, and Apple GPUs.
+- **Modeling Interface**: ExaModels.jl enforces users to specify the model equations always in the form of `Generator`s. This allows ExaModels.jl toarallel [reverse-mode automatic differentiation]()**ructure in the model equations.
+- **Performance**: ExaModels.jl compiles uate derivatives either on multi-core CPUs or GPU accelerators. The code is currently only tested for NVIDIA GPUs, but GPU code is implemented mostly based on the portable programming paradigm, [KernelAbstractions.jl](https://github.com/JuliaGPU/KernelAbstractions.jl). In the future, we are interested in supporting Intel, AMD, and Apple GPUs.
 
 ## Quick Start
 ### Installation