update readme

cms/2024_04_05/pyg-cms_20240324_235743_208080/README.md

@@ -20,19 +20,37 @@ This model reconstructs particles in a detector, based on the tracks and calorim
 ### Direct Use
 
 This model may be used to study the physics and computational performance on ML-based reconstruction in CMS simulation.
-It should only be used within the CMS collaboration.
+It should **only** be used **within** the CMS collaboration.
 
 ### Out-of-Scope Use
 
 This model is not intended for physics measurements on real data. 
+It should **not** be used **outside** of the CMS collaboration.
 
 ## Bias, Risks, and Limitations
 
 The model has only been trained on simulation data and has not been validated against real data.
-It should not be used outside of the CMS collaboration.
 
 ## How to Get Started with the Model
 
+```
+git clone https://github.com/jpata/particleflow/releases/tag/v1.7.0
+cd particleflow
+
+wget https://hep.kbfi.ee/~joosep/pytorch.simg
+
+mkdir -p experiments/pyg-cms_20240324_235743_208080/checkpoints/
+
+wget https://huggingface.co/jpata/particleflow/resolve/main/cms/2024_04_05/pyg-cms_20240324_235743_208080/checkpoint-32-17.877384.pth
+mv checkpoint-32-17.877384.pth experiments/pyg-cms_20240324_235743_208080/checkpoints/
+
+wget https://huggingface.co/jpata/particleflow/raw/main/cms/2024_04_05/pyg-cms_20240324_235743_208080/train-config.yaml
+mv train-config.yaml experiments/pyg-cms_20240324_235743_208080/
+
+#Run the inference on the held-out dataset
+singularity exec --nv pytorch.simg python3 mlpf/pyg_pipeline.py --config parameters/pytorch/pyg-cms.yaml --gpus 1 --experiments-dir experiments/ --dataset cms --conv-type attention --gpu-batch-multiplier 10 --dtype bfloat16 --load experiments/pyg-cms_20240324_235743_208080/checkpoints/checkpoint-32-17.877384.pth --test
+```
+
 ## Training Details
 
 Trained for 32 epochs on 1x A100 80GB for approximately 6 days.
@@ -41,6 +59,8 @@ The training was done with bfloat16.
 ### Training Data
 
 Trained on 400k events from `cms_pf_ttbar`, version `v1.7.1`.
+The dataset is available at `/eos/user/j/jpata/mlpf/tensorflow_datasets/cms/cms_pf_ttbar/1.7.1`.
+
 https://github.com/jpata/particleflow/blob/v1.7.0/mlpf/heptfds/cms_pf/ttbar.py
 
 ## Model Card Contact