diff --git a/README.md b/README.md
index 41c48098915389597067dccba0c3ddb02c608155..872c8422309cdf13f3cf774a4455d79f5c7e261a 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,27 @@
 # Autotuning and Predictive Autotuning
 
-Performs autotuning on program approximation knobs using an error-predictive proxy in place of the
-original program, to greatly speedup autotuning while getting results comparable in quality.
+`predtuner` performs autotuning on program approximation knobs using an error-predictive proxy
+in place of the original program, to greatly speedup autotuning while getting results
+comparable in quality.
 
 Work in progress.
 
 ## Requirements
 
-Prerequisite packages are listed in `./env.yaml`. Conda is the validated and recommended way to set
-up a working environment. If you're using conda, do
+`pip` is needed for installing this package. At the root directory of this repo, do:
 
 ```bash
-conda env create -n predtuner -f env.yaml
-conda activate predtuner
+pip install -e .
 ```
+
+`-e` can be omitted if you don't intend to modify the code in this package.
+
+## Model Data for Example / Testing
+
+`predtuner` contains 10 demo models which are also used in tests.
+
+- Download and extract [this](https://drive.google.com/file/d/1V_yd9sKcZQ7zhnO5YhRpOsaBPLEEvM9u/view?usp=sharing) file containing all 10 models, for testing purposes.
+- The example only uses VGG16-CIFAR10. If you don't need the other models, get the data for VGG16-CIFAR10 [here](https://drive.google.com/file/d/1Z84z-nsv_nbrr8t9i28UoxSJg-Sd_Ddu/view?usp=sharing).
+
+In either case, there should be a `model_params/` folder at the root of repo after extraction.
+