Update README.md

README.md

@@ -3,4 +3,55 @@ license: mit
 base_model:
 - meta-llama/Llama-3.1-8B-Instruct
 pipeline_tag: text-generation
----
+language:
+- en
+tags:
+- medical
+---
+
+
+<div align="center">
+<h1>
+  MedSSS-8B-Policy
+</h1>
+</div>
+
+<div align="center">
+<a href="https://github.com/pixas/MedSSS" target="_blank">GitHub</a> | <a href="" target="_blank">Paper</a>
+</div>
+
+# <span>Introduction</span>
+**MedSSS-Policy** is a the policy model designed for slow-thinking medical reasoning. It will conduct explicit step-wise reasoning and finalize the answer at the end of the response.
+
+For more information, visit our GitHub repository: 
+[https://github.com/pixas/MedSSS](https://github.com/pixas/MedSSS).
+
+
+
+
+# <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:
+```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")
+tokenizer = AutoTokenizer.from_pretrained("pixas/MedSSS_Policy")
+input_text = "How to stop a cough?"
+messages = [{"role": "user", "content": input_text}]
+inputs = tokenizer(tokenizer.apply_chat_template(messages, tokenize=False,add_generation_prompt=True
+), return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=2048)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+MedSSS-Policy adopts a step-wise reasoning approach, with outputs formatted as:
+
+```
+Step 0: Let's break down this problem step by step.
+Step 1: ...
+[several steps]
+Step N: [last reasoning step]\n\nThe answer is {answer}
+```