Update README.md

examples/mnist/README.md

@@ -1,11 +1,20 @@
 # MNIST
 
 ### Step 1: Train
+Since influence functions are typically computed at the final model
+weight, we first train the model (MLP) on the (small) MNIST dataset
+before performinig influence analyses.
+
 ```bash
 python train.py
 ```
 
-### Step 2: AnaLog
+### Step 2: Log extraction & Influence analysis
+With the trained model, we first extract and save logs (e.g.
+Hessian, gradeint) to disk, and use it to compute influence
+scores. Users can specify the gradient projection strategy
+(e.g. LoGra) and the Hessian computation strategy.
+
 ```bash
-python compute_influences.py
+python compute_influences.py --lora none --hessian raw --save grad
 ```