fill in quickstart

README.md

@@ -4,4 +4,28 @@ license: apache-2.0
 
 <img src="ACE-logo.png" alt="Logo for the ACE Project" style="width: auto; height: 50px;">
 
-# HiRO-ACE
+# HiRO-ACE
+
+
+HiRO (High Resolution Output) is a diffusion model which generates downscaled fields at 3 km resolution from 100 km resolution inputs. The HiRO checkpoint included in this model generates 6-hourly averaged surface precipitation rates at 3 km resolution. The Ai2 Climate Emulator (ACE) is a family of models designed to simulate atmospheric variability from the time scale of days to centuries. For usage with the HiRO downscaling model, we include a checkpoint for ACE2S. Compared to previous ACE models, ACE2S uses an updated training procedure and can generate stochastic predictions. For more details, please see the accompanying HiRO-ACE paper linked below.
+
+### Quick links
+
+- 📃 [Paper](https://arxiv.org/pdf/2512.18224)
+- 💻 [Code](https://github.com/ai2cm/ace)
+- 💬 [Docs](https://ai2-climate-emulator.readthedocs.io/en/stable/)
+- 📂 [All Models](https://huggingface.co/collections/allenai/ace-67327d822f0f0d8e0e5e6ca4)
+
+
+### Inference quickstart
+
+1. Download this repository. Optionally, you can just download a subset of the `forcing_data` and `initial_conditions` for the period you are interested in.
+
+2. Update paths in the `inference_config.yaml`. Specifically, update `experiment_dir`, `checkpoint_path`, `initial_condition.path` and `forcing_loader.dataset.path`.
+
+3. Install code dependencies with `pip install fme`.
+
+4. Run ACE inference with `python -m fme.ace.inference inference_config.yaml`.
+
+5. Update paths in the `downscaling_config.yaml`. Specifically, update `experiment_dir`, `model.checkpoint_path`, and `data.coarse`. `data.coarse` data path(s) should point to the saved ACE inference output from step 4. 
+