Update README.md

README.md

@@ -30,14 +30,11 @@ For more information, visit our GitHub repository:
 
 
 # <span>Usage</span>
-We build the policy model as a LoRA adapter, which saves the memory to use it.
-As this LoRA adapter is built on `Meta-Llama3.1-8B-Instruct`, you need to first prepare the base model in your platform.
-You can deploy it with tools like [vllm](https://github.com/vllm-project/vllm) or [Sglang](https://github.com/sgl-project/sglang),  or perform direct inference:
+You can deploy it with tools like [vllm](https://github.com/vllm-project/vllm) or [Sglang](https://github.com/sgl-project/sglang), or perform direct inference:
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from peft import PeftModel
-base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct",torch_dtype="auto",device_map="auto")
-model = PeftModel.from_pretrained(base_model, "pixas/MedSSS_Policy", torc_dtype="auto", device_map="auto")
+model = AutoModelForCausalLM.from_pretrained("pixas/MedSSS_Policy",torch_dtype="auto",device_map="auto")
 tokenizer = AutoTokenizer.from_pretrained("pixas/MedSSS_Policy")
 input_text = "How to stop a cough?"
 messages = [{"role": "user", "content": input_text}]