Improve language tag

README.md

@@ -1,37 +1,49 @@
----
-license: cc-by-4.0
-language:
-- en
-base_model: Qwen/Qwen2.5-7B-Instruct
----
-
-# Safe-o1 Model Card 🤖✨
-
-## Model Overview 📝
-`Safe-o1` is an innovative language model that introduces a **self-monitoring thinking process** to detect and filter unsafe content, achieving more robust safety performance 🚀.
-
----
-
-## Features and Highlights 🌟
-- **Safety First** 🔒: Through a self-monitoring mechanism, it detects potential unsafe content in the thinking process in real-time, ensuring outputs consistently align with ethical and safety standards.  
-- **Enhanced Robustness** 💡: Compared to traditional models, `Safe-o1` performs more stably in complex scenarios, reducing unexpected "derailments."  
-- **User-Friendly** 😊: Designed to provide users with a trustworthy conversational partner, suitable for various application scenarios, striking a balance between helpfulness and harmfulness.  
-![](https://github.com/D4YON3/images/blob/main/figs_2025-04-03%20214712.png?raw=true)
-
----
-
-## Usage 🚀
-You can load `Safe-o1` using the Hugging Face `transformers` library:
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("PKU-Alignment/Safe-o1")
-model = AutoModelForCausalLM.from_pretrained("PKU-Alignment/Safe-o1")
-
-input_text = "Hello, World!"
-inputs = tokenizer(input_text, return_tensors="pt")
-outputs = model.generate(**inputs)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-
+---
+license: cc-by-4.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model: Qwen/Qwen2.5-7B-Instruct
+---
+
+# Safe-o1 Model Card 🤖✨
+
+## Model Overview 📝
+`Safe-o1` is an innovative language model that introduces a **self-monitoring thinking process** to detect and filter unsafe content, achieving more robust safety performance 🚀.
+
+---
+
+## Features and Highlights 🌟
+- **Safety First** 🔒: Through a self-monitoring mechanism, it detects potential unsafe content in the thinking process in real-time, ensuring outputs consistently align with ethical and safety standards.  
+- **Enhanced Robustness** 💡: Compared to traditional models, `Safe-o1` performs more stably in complex scenarios, reducing unexpected "derailments."  
+- **User-Friendly** 😊: Designed to provide users with a trustworthy conversational partner, suitable for various application scenarios, striking a balance between helpfulness and harmfulness.  
+![](https://github.com/D4YON3/images/blob/main/figs_2025-04-03%20214712.png?raw=true)
+
+---
+
+## Usage 🚀
+You can load `Safe-o1` using the Hugging Face `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("PKU-Alignment/Safe-o1")
+model = AutoModelForCausalLM.from_pretrained("PKU-Alignment/Safe-o1")
+
+input_text = "Hello, World!"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
 ```