Improve language tag

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

README.md

@@ -1,102 +1,114 @@
----
-library_name: transformers
-datasets:
-- openGPT-X/gsm8kx
-language:
-- de
-base_model:
-- Qwen/Qwen2.5-3B-Instruct
-pipeline_tag: text-generation
----
-
-# German R1
-
-![A German whale](https://huggingface.co/malteos/german-r1/resolve/main/german-whale.png)
-
-**Introducing German-R1. We are so back!!11**
-
-- 🇩🇪 German R1 is a reasoning model almost equivalent to OpenAI‘s o3 or DeepSeek‘s R1 - but it thinks in German!  
-
-- 🇩🇪 German R1 is so efficient that is was build without any government funding. 
-
-- 🇩🇪 German R1 was only trained with legally imported H100s in less than five minutes. 
-
-- Please do NOT take this too serious. 
-
-## Context
-
-![some contex](https://huggingface.co/malteos/german-r1/resolve/main/context.png)
-
-See Linkedin: https://www.linkedin.com/posts/activity-7294337496023269376-ZBkI
-
-## Usage
-
-You can run the model as follows:
-
-```python
-from transformers import pipeline, set_seed
-import json
-
-set_seed(42)
-pipe = pipeline("text-generation", "malteos/german-r1")
-
-# from gsm8k test set
-question = "James beschließt, 3-mal pro Woche 3 Sprints zu laufen.  Er läuft 60 Meter pro Sprint.  Wie viele Meter läuft er insgesamt pro Woche?"
-expected_answer = "540"
-
-# xml reasoning and answer format
-system_prompt = """
-Antworte auf deutsch und in diesem Format:
-<reasoning>
-...
-</reasoning>
-<answer>
-...
-</answer>
-"""
-
-messages = [
-    {
-        "role": "system",
-        "content": system_prompt,
-    },
-    {"role": "user", "content": dataset["question"][3]},
-]
-response = pipe(messages, max_new_tokens=256)
-
-print(json.dumps(response, indent=4, ensure_ascii=False))
-```
-
-The output should look like this (correct answer: 540)
-
-```json
-[
-    {
-        "generated_text": [
-            {
-                "role": "system",
-                "content": "\nAntworte auf deutsch und in diesem Format:\n<reasoning>\n...\n</reasoning>\n<answer>\n...\n</answer>\n"
-            },
-            {
-                "role": "user",
-                "content": "James beschließt, 3-mal pro Woche 3 Sprints zu laufen.  Er läuft 60 Meter pro Sprint.  Wie viele Meter läuft er insgesamt pro Woche?"
-            },
-            {
-                "role": "assistant",
-                "content": "<reasoning>\nJames läuft 3 Sprints pro Tag, wobei jeder Sprint 60 Meter beträgt. Deshalb läuft er pro Tag 3 Sprints * 60 Meter/Sprint = 180 Meter. James läuft 3 Mal pro Woche, also läuft er insgesamt 180 Meter * 3 = 540 Meter pro Woche.\n</reasoning>\n<answer>\n540\n</answer>\n"
-            }
-        ]
-    }
-]
-```
-
-## Training details
-
-- Base model: Qwen2.5-3B-Instruct
-- Implementation: based on [GRPO demo](https://gist.github.com/willccbb/4676755236bb08cab5f4e54a0475d6fb) by [will brown](https://x.com/willccbb) + language identification reward
-- Training data: German subset of [openGPT-X/gsm8kx](https://huggingface.co/datasets/openGPT-X/gsm8kx/) (machine translated from the English gsm8k)
-
-
-## License
-
+---
+library_name: transformers
+datasets:
+- openGPT-X/gsm8kx
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+pipeline_tag: text-generation
+---
+
+# German R1
+
+![A German whale](https://huggingface.co/malteos/german-r1/resolve/main/german-whale.png)
+
+**Introducing German-R1. We are so back!!11**
+
+- 🇩🇪 German R1 is a reasoning model almost equivalent to OpenAI‘s o3 or DeepSeek‘s R1 - but it thinks in German!  
+
+- 🇩🇪 German R1 is so efficient that is was build without any government funding. 
+
+- 🇩🇪 German R1 was only trained with legally imported H100s in less than five minutes. 
+
+- Please do NOT take this too serious. 
+
+## Context
+
+![some contex](https://huggingface.co/malteos/german-r1/resolve/main/context.png)
+
+See Linkedin: https://www.linkedin.com/posts/activity-7294337496023269376-ZBkI
+
+## Usage
+
+You can run the model as follows:
+
+```python
+from transformers import pipeline, set_seed
+import json
+
+set_seed(42)
+pipe = pipeline("text-generation", "malteos/german-r1")
+
+# from gsm8k test set
+question = "James beschließt, 3-mal pro Woche 3 Sprints zu laufen.  Er läuft 60 Meter pro Sprint.  Wie viele Meter läuft er insgesamt pro Woche?"
+expected_answer = "540"
+
+# xml reasoning and answer format
+system_prompt = """
+Antworte auf deutsch und in diesem Format:
+<reasoning>
+...
+</reasoning>
+<answer>
+...
+</answer>
+"""
+
+messages = [
+    {
+        "role": "system",
+        "content": system_prompt,
+    },
+    {"role": "user", "content": dataset["question"][3]},
+]
+response = pipe(messages, max_new_tokens=256)
+
+print(json.dumps(response, indent=4, ensure_ascii=False))
+```
+
+The output should look like this (correct answer: 540)
+
+```json
+[
+    {
+        "generated_text": [
+            {
+                "role": "system",
+                "content": "\nAntworte auf deutsch und in diesem Format:\n<reasoning>\n...\n</reasoning>\n<answer>\n...\n</answer>\n"
+            },
+            {
+                "role": "user",
+                "content": "James beschließt, 3-mal pro Woche 3 Sprints zu laufen.  Er läuft 60 Meter pro Sprint.  Wie viele Meter läuft er insgesamt pro Woche?"
+            },
+            {
+                "role": "assistant",
+                "content": "<reasoning>\nJames läuft 3 Sprints pro Tag, wobei jeder Sprint 60 Meter beträgt. Deshalb läuft er pro Tag 3 Sprints * 60 Meter/Sprint = 180 Meter. James läuft 3 Mal pro Woche, also läuft er insgesamt 180 Meter * 3 = 540 Meter pro Woche.\n</reasoning>\n<answer>\n540\n</answer>\n"
+            }
+        ]
+    }
+]
+```
+
+## Training details
+
+- Base model: Qwen2.5-3B-Instruct
+- Implementation: based on [GRPO demo](https://gist.github.com/willccbb/4676755236bb08cab5f4e54a0475d6fb) by [will brown](https://x.com/willccbb) + language identification reward
+- Training data: German subset of [openGPT-X/gsm8kx](https://huggingface.co/datasets/openGPT-X/gsm8kx/) (machine translated from the English gsm8k)
+
+
+## License
+
 [Qwen research](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE)