diff --git a/README.md b/README.md
index 0514b819..83da9708 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@ JetStream Engine implementation in PyTorch
 
 ### 1. Get the jetstream-pytorch code
 ```bash
-git clone https://github.com/pytorch-tpu/jetstream-pytorch.git
+git clone https://github.com/google/jetstream-pytorch.git
 ```
 
 1.1 (optional) Create a virtual env using `venv` or `conda` and activate it.
@@ -14,7 +14,8 @@ git clone https://github.com/pytorch-tpu/jetstream-pytorch.git
 ### 2. Run installation script:
 
 ```bash
-sh install_everything.sh
+cd jetstream-pytorch
+source install_everything.sh
 ```
 
 
@@ -38,35 +39,38 @@ python -m convert_checkpoints --input_checkpoint_dir=$input_ckpt_dir --output_ch
 
 # Local run
 
-## Llama 7b
+Set tokenizer path
+```bash
+export tokenizer_path=tokenizer model file path from meta-llama
 ```
-python benchmarks/run_offline.py --size=7b --batch_size=128 --max_cache_length=2048 --quantize_weights=$quantize --quantize_kv_cache=$quantize --checkpoint_path=$output_ckpt_dir/model.safetensors --tokenizer_path=tokenizer.model
+
+## Llama 7b
+```bash
+python run_interactive.py --size=7b --batch_size=128 --max_cache_length=2048 --quantize_weights=$quantize --quantize_kv_cache=$quantize --checkpoint_path=$output_ckpt_dir/model.safetensors --tokenizer_path=$tokenizer_path
 ```
 
 ## Llama 13b
+```bash
+python run_interactive.py --size=13b --batch_size=64 --max_cache_length=2048 --quantize_weights=$quantize --quantize_kv_cache=$quantize --checkpoint_path=$output_ckpt_dir/model.safetensors --tokenizer_path=$tokenizer_path
 ```
-python benchmarks/run_offline.py --size=13b --batch_size=96 --max_cache_length=1280 --quantize_weights=$quantize --quantize_kv_cache=$quantize --checkpoint_path=$output_ckpt_dir/model.safetensors --tokenizer_path=tokenizer.model
-```
-NOTE: for 13b model we recommend to use `--max_cache_length=1280`, i.e. this implements sliding window attention.
 
 
 # Run the server
 NOTE: the `--platform=tpu=8` need to specify number of tpu devices (which is 4 for v4-8 and 8 for v5light-8`)
 
 ```bash
-python run_server.py --param_size=7b --batch_size=128 --max_cache_length=2048 --quantize_weights=$quantize --quantize_kv_cache=$quantize --checkpoint_path=$output_ckpt_dir/model.safetensors   --tokenizer_path=tokenizer.model --platform=tpu=8
+python run_server.py --param_size=7b --batch_size=128 --max_cache_length=2048 --quantize_weights=$quantize --quantize_kv_cache=$quantize --checkpoint_path=$output_ckpt_dir/model.safetensors   --tokenizer_path=$tokenizer_path --platform=tpu=8
 ```
 Now you can fire gRPC to it
 
 # Run benchmark
 go to the deps/JetStream folder (downloaded during `install_everything.sh`)
-```bash
-cd deps/JetStream
-python benchmark_serving.py --tokenizer /home/hanq/jetstream-pytorch/tokenizer.model --num-prompts 2000  --dataset ~/data/ShareGPT_V3_unfiltered_cleaned_split.json --warmup-first=1 --save-request-outputs
-```
-The ShareGPT dataset can be downloaded at 
 
 ```bash
+cd deps/JetStream
 wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+export dataset_path=ShareGPT_V3_unfiltered_cleaned_split.json
+pip install -e . 
+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.