Improve model card: Add pipeline tag, library name, and GitHub link, update license

README.md

@@ -1,16 +1,24 @@
 ---
-license: other
-license_name: '-'
-license_link: https://ai.meta.com/llama/license
+license: apache-2.0
+library_name: transformers
+pipeline_tag: question-answering
 ---
 
+# MedCEG: Reinforcing Verifiable Medical Reasoning with Critical Evidence Graph
+
+This repository contains the MedCEG model, presented in the paper [MedCEG: Reinforcing Verifiable Medical Reasoning with Critical Evidence Graph](https://huggingface.co/papers/2512.13510).
+
+**MedCEG** is a framework that augments medical language models with clinically valid reasoning pathways. It explicitly supervises the reasoning process through a **Critical Evidence Graph (CEG)**, ensuring verifiable and logical medical deductions.
+
+For code and more details, see the [GitHub repository](https://github.com/LinjieMu/MedCEG).
+
 This code demonstrates how to generate responses using MedCEG.
 ```python
 import transformers
 import torch
 
 # 1. Load Model & Tokenizer
-model_id = "XXX/MedCEG"
+model_id = "LinjieMu/MedCEG"
 tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
 model = transformers.AutoModelForCausalLM.from_pretrained(
     model_id,
@@ -20,13 +28,14 @@ model = transformers.AutoModelForCausalLM.from_pretrained(
 
 # 2. Define Input
 question = "A 78-year-old Caucasian woman presented with..."
-suffix = "\nPut your final answer in \\boxed{}."
+suffix = "
+Put your final answer in \\boxed{}."
 messages = [{"role": "user", "content": question + suffix}]
 
 # 3. Generate
 input_ids = tokenizer.apply_chat_template(
-    messages, 
-    add_generation_prompt=True, 
+    messages,
+    add_generation_prompt=True,
     return_tensors="pt"
 ).to(model.device)