Add English model card

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- OneScience
+- Earth Science
+- Earth System Model
+- Coupled Climate
+- Long Simulation
+- U-Net
+frameworks: PyTorch
+---
+
+<p align="center"><strong><span style="font-size: 30px;">DLESyM</span></strong></p>
+
+# Model Introduction
+
+DLESyM asynchronously couples deep-learning atmosphere and ocean modules for long free-running climate simulations and diagnostic precipitation.
+
+Paper: A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate  
+https://arxiv.org/abs/2409.16247
+
+# Model Description
+
+The model was proposed by an atmospheric-science and machine-learning research team. It was trained with 1983–2017 ERA5 fields, ISCCP OLR, and SST. Coupled DLWP, DLOM, and precipitation modules support current-climate simulation and internal-variability analysis.
+
+# Use Cases
+
+| Use Case | Description |
+| :---: | :--- |
+| Long climate simulation | Run stable atmosphere-ocean rollouts. |
+| Climate variability | Analyze ENSO, monsoons, and annular modes. |
+| Precipitation diagnosis | Diagnose accumulated precipitation from atmospheric states. |
+| ModelScope/OneCode execution | Validate data, training, inference, climate metrics, and visualization. |
+| Multi-GPU training | Start multi-process training through `torchrun`. |
+
+# Usage Instructions
+
+Use a GPU or DCU when available; CPU supports the default smoke configuration.
+
+```bash
+hf download OneScience-Group/DLESyM --local-dir ./DLESyM
+cd DLESyM
+```
+
+### Environment Dependencies
+
+**Hardware Requirements**
+
+- A GPU or DCU is recommended.
+- A CPU can be used for connectivity validation with the default small-sample configuration.
+- DCU users should install DTK 25.04.2 or a compatible OneScience-recommended version first.
+
+**DCU Environment**
+
+```bash
+# Activate DTK and Conda first
+conda create -n onescience311 python=3.11 -y
+conda activate onescience311
+pip install onescience[earth-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
+```
+
+**GPU Environment**
+
+```bash
+# Activate Conda first
+conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
+conda activate onescience311
+pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
+```
+
+```bash
+python scripts/fake_data.py
+python scripts/train.py
+torchrun --standalone --nproc_per_node=2 scripts/train.py
+python scripts/inference.py
+python scripts/result.py
+```
+Training jointly optimizes atmosphere, ocean, and precipitation modules. Inference runs four coupled cycles and evaluation reports finite drift diagnostics.
+## Trained Weights
+No weights are bundled under `weight/`. The authors provide configurations and weights at https://github.com/AtmosSci-DLESM/DLESyM.
+# Citation and License
+This repository is an independent engineering reproduction of the public DLESyM specifications.
+
+The original preprint, official code, model weights, and related data remain subject to their respective licenses and terms.