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+# Modular Co-Design (MoCo) Interpolants
+
+## Description
+MoCo enables abstracted interpolants for building and sampling from a variety of popular generative model frameworks. Specifically, MoCo supports interpolants for both continuous and discrete data types.
+[![PyPI version](https://badge.fury.io/py/bionemo-moco.svg)](https://pypi.org/project/bionemo-moco/)
+
+### Continuous Data Interpolants
+MoCo currently supports the following continuous data interpolants:
+- DDPM (Denoising Diffusion Probabilistic Models)
+- VDM (Variational Diffusion Models)
+- CFM (Conditional Flow Matching)
+
+### Discrete Data Interpolants
+MoCo also supports the following discrete data interpolants:
+- D3PM (Discrete Denoising Diffusion Probabilistic Models)
+- MDLM (Masked Diffusion Language Models)
+- DFM (Discrete Flow Matching)
+
+### Useful Abstractions
+MoCo also provides useful wrappers for customizable time distributions and inference time schedules.
+
+### Extendible
+If the desired interpolant or sampling method is not already supported, MoCo was designed to be easily extended.
+
+## Installation
+ For Conda environment setup, please refer to the `environment` directory for specific instructions.
+
+Once your environment is set up, you can install this project by running the following command:
+
+```bash
+pip install -e .
+```
+This will install the project in editable mode, allowing you to make changes and see them reflected immediately.
+
+## Examples
+Please see examples of all interpolants in the [examples directory](https://github.com/NVIDIA/bionemo-framework/tree/main/sub-packages/bionemo-moco/examples).