diff --git a/README.md b/README.md
index aa985c3a..d442d58c 100644
--- a/README.md
+++ b/README.md
@@ -1,34 +1,57 @@
 # Jetstream-PyTorch
 JetStream Engine implementation in PyTorch
 
+# Outline
 
-# Install
+1. Ssh to Cloud TPU VM (using v5e-8 TPU VM)
+   a. Create a Cloud TPU VM if you haven’t
+2. Download jetstream-pytorch github repo
+3. Clone repo and install dependencies 
+4. Download and convert weights
+5. Run checkpoint converter (quantizer)
+6. Local run
+7. Run the server
+8. Run benchmarks
+9. Typical Errors
 
-### 1. Get the jetstream-pytorch code
+# Ssh to Cloud TPU VM (using v5e-8 TPU VM)
+
+```bash
+gcloud compute config-ssh
+gcloud compute tpus tpu-vm ssh "your-tpu-vm" --project "your-project" --zone "your-project-zone"
+```
+## Create a Cloud TPU VM in a GCP project  if you haven’t
+Follow step 1-9 in the following guide
+* https://cloud.google.com/tpu/docs/v5e-inference#prepare-a-project
+
+# Clone repo and install dependencies 
+
+## Get the jetstream-pytorch code
 ```bash
 git clone https://github.com/google/jetstream-pytorch.git
 ```
 
-1.1 (optional) Create a virtual env using `venv` or `conda` and activate it.
+(optional) Create a virtual env using `venv` or `conda` and activate it.
 
-### 2. Run installation script:
+## 2. Run installation script:
 
 ```bash
 cd jetstream-pytorch
 source install_everything.sh
 ```
+NOTE: the above script will export PYTHONPATH, so sourcing will make it to take effect in the current shell
 
+# Download and convert weights
 
-# Get weights
-
-### First get official llama weights from meta-llama
+## Get official llama weights from meta-llama
 
 Following instructions here: https://github.com/meta-llama/llama#download
 
 After you have downloaded the weights, it will also download a `tokenizer.model` file that is 
 the tokenizer that we will use.
 
-### Run weight merger to convert (and )
+## Run weight safetensor convert
+
 ```bash
 export input_ckpt_dir=Original llama weights directory
 export output_ckpt_dir=The output directory
@@ -73,3 +96,20 @@ export dataset_path=ShareGPT_V3_unfiltered_cleaned_split.json
 python benchmarks/benchmark_serving.py --tokenizer $tokenizer_path --num-prompts 2000  --dataset-path  $dataset_path --dataset sharegpt --save-request-outputs
 ```
 Please look at `deps/JetStream/benchmarks/README.md` for more information.
+
+
+# Typical Errors
+
+##  Unexpected keyword argument 'device'
+
+Fix:
+* Uninstall jax and jaxlib dependencies 
+* Reinstall using `source install_everything.sh
+
+##  Out of memory
+
+Fix:
+* Use smaller batch size
+* Use quantization
+
+