diff --git a/README.md b/README.md
index 729761ad..41b0704f 100644
--- a/README.md
+++ b/README.md
@@ -90,7 +90,7 @@ export output_ckpt_dir=The output directory
 export model_name="llama-3" # or "llama-2", "gemma", "mixtral"
 export quantize_weights=True # Whether to quantize weights
 export quantize_type="int8_per_channel" # "quantize_weights" needs to be turned on. Availabe quantize type: {"int8", "int4"} x {"per_channel", "blockwise"}, "int8_per_channel" is the default option if not specified.
-python -m convert_checkpoints --model_name=$model_name --input_checkpoint_dir=$input_ckpt_dir --output_checkpoint_dir=$output_ckpt_dir --quantize_type=$quantize_type
+python -m convert_checkpoints --model_name=$model_name --input_checkpoint_dir=$input_ckpt_dir --output_checkpoint_dir=$output_ckpt_dir --quantize_type=$quantize_type --quantize_weights=$quantize_weights
 ```
 
 
@@ -103,32 +103,32 @@ export tokenizer_path=tokenizer model file path
 
 ## Llama-2 7b
 ```bash
-python run_interactive.py --size=7b --model_name=$model_name --batch_size=128 --max_cache_length=2048 --quantize_weights=$quantize --quantize_type=$quantize_type --quantize_kv_cache=$quantize --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/llama.yaml
+python run_interactive.py --size=7b --model_name=$model_name --batch_size=128 --max_cache_length=2048 --quantize_weights=$quantize_weights --quantize_type=$quantize_type --quantize_kv_cache=$quantize_weights --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/llama.yaml
 ```
 
 ## Llama-2 13b
 ```bash
-python run_interactive.py --size=13b --model_name=$model_name --batch_size=64 --max_cache_length=2048 --quantize_weights=$quantize --quantize_type=$quantize_type --quantize_kv_cache=$quantize --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/llama.yaml
+python run_interactive.py --size=13b --model_name=$model_name --batch_size=64 --max_cache_length=2048 --quantize_weights=$quantize_weights --quantize_type=$quantize_type --quantize_kv_cache=$quantize_weights --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/llama.yaml
 ```
 
 ## Llama-3 8b
 ```bash
-python run_interactive.py --size=8b --model_name=$model_name --batch_size=128 --max_cache_length=2048 --quantize_weights=$quantize --quantize_type=$quantize_type --quantize_kv_cache=$quantize --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/llama.yaml
+python run_interactive.py --size=8b --model_name=$model_name --batch_size=128 --max_cache_length=2048 --quantize_weights=$quantize_weights --quantize_type=$quantize_type --quantize_kv_cache=$quantize_weights --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/llama.yaml
 ```
 
 ## Llama-3 70b
 ```bash
-python run_interactive.py --size=70b --model_name=$model_name --batch_size=8 --max_cache_length=2048 --quantize_weights=$quantize --quantize_type=$quantize_type --quantize_kv_cache=$quantize --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/llama.yaml
+python run_interactive.py --size=70b --model_name=$model_name --batch_size=8 --max_cache_length=2048 --quantize_weights=$quantize_weights --quantize_type=$quantize_type --quantize_kv_cache=$quantize_weights --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/llama.yaml
 ```
 
 ## Gemma 7b
 ```bash
-python run_interactive.py --model_name=$model_name --size=7b --batch_size=64 --max_cache_length=2048 --quantize_weights=$quantize --quantize_type=$quantize_type --quantize_kv_cache=$quantize --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/$model_name.yaml
+python run_interactive.py --model_name=$model_name --size=7b --batch_size=64 --max_cache_length=2048 --quantize_weights=$quantize_weights --quantize_type=$quantize_type --quantize_kv_cache=$quantize_weights --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/$model_name.yaml
 ```
 
 ## Mixtral 8x7b
 ```bash
-python run_interactive.py --model_name=$model_name --batch_size=128 --max_cache_length=2048 --quantize_weights=$quantize --quantize_type=$quantize_type --quantize_kv_cache=$quantize --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/$model_name.yaml
+python run_interactive.py --model_name=$model_name --batch_size=128 --max_cache_length=2048 --quantize_weights=$quantize_weights --quantize_type=$quantize_type --quantize_kv_cache=$quantize_weights --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/$model_name.yaml
 ```
 
 
@@ -136,7 +136,7 @@ python run_interactive.py --model_name=$model_name --batch_size=128 --max_cache_
 Here is an example to run the server with llama2 7B config.
 
 ```bash
-python run_server.py --model_name=$model_name --size=7b --batch_size=128 --max_cache_length=2048 --quantize_weights=$quantize --quantize_type=$quantize_type --quantize_kv_cache=$quantize --checkpoint_path=$output_ckpt_dir   --tokenizer_path=$tokenizer_path --sharding_config="default_shardings/llama.yaml"
+python run_server.py --model_name=$model_name --size=7b --batch_size=128 --max_cache_length=2048 --quantize_weights=$quantize_weights --quantize_type=$quantize_type --quantize_kv_cache=$quantize_weights --checkpoint_path=$output_ckpt_dir   --tokenizer_path=$tokenizer_path --sharding_config="default_shardings/llama.yaml"
 ```
 
 Now you can fire gRPC to it.