Update README.md

README.md

@@ -14,9 +14,9 @@ base_model:
 - Qwen/Qwen3-8B
 ---
 
-# EHR-R1-1.7B: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis
+# EHR-R1-8B: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis
 
-This repository contains the **EHR-R1-1.7B** model, part of the **EHR-R1** series, as presented in the paper [EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis](https://huggingface.co/papers/2510.25628).
+This repository contains the **EHR-R1-8B** model, part of the **EHR-R1** series, as presented in the paper [EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis](https://huggingface.co/papers/2510.25628).
 
 **EHR-R1** is a family of reasoning-enhanced Large Language Models (LLMs) specifically tailored for Electronic Health Record (EHR) analysis. It is developed based on **EHR-Ins**, a large-scale, comprehensive EHR reasoning instruction dataset, and is trained through a multi-stage paradigm including domain adaptation, reasoning enhancement, and reinforcement learning. This approach systematically acquires domain knowledge and diverse reasoning capabilities, enabling accurate and robust EHR analysis. The project also introduces **EHR-Bench**, a new benchmark curated from MIMIC-IV for comprehensive assessment across 42 distinct EHR tasks.
 
@@ -59,7 +59,7 @@ For any EHR data, keep the EHR input with markdown format as below:
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-model_name = "BlueZeros/EHR-R1-1.7B" # This specific EHR-R1-1.7B model
+model_name = "BlueZeros/EHR-R1-8B" # This specific EHR-R1-8B model
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
     torch_dtype="auto",