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

@@ -1,60 +1,74 @@
----
-base_model: Qwen/Qwen2.5-0.5B-Instruct
-datasets: MelinaLaimon/stream-of-search-deepseek-correct-1k
-library_name: transformers
-model_name: Qwen/Qwen2.5-0.5B-Instruct
-tags:
-- generated_from_trainer
-- alignment-handbook
-- trl
-- sft
-licence: license
----
-
-# Model Card for Qwen/Qwen2.5-0.5B-Instruct
-
-This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the [MelinaLaimon/stream-of-search-deepseek-correct-1k](https://huggingface.co/datasets/MelinaLaimon/stream-of-search-deepseek-correct-1k) dataset.
-It has been trained using [TRL](https://github.com/huggingface/trl).
-
-## Quick start
-
-```python
-from transformers import pipeline
-
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="chloeli/qwen-2.5-0.5B-instruct-sft-lora-countdown-deepseek-correct-seq8k-5k", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
-
-## Training procedure
-
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/chloeli/huggingface/runs/i0a472xd) 
-
-
-This model was trained with SFT.
-
-### Framework versions
-
-- TRL: 0.15.2
-- Transformers: 4.49.0
-- Pytorch: 2.6.0
-- Datasets: 3.3.2
-- Tokenizers: 0.21.0
-
-## Citations
-
-
-
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
+---
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+datasets: MelinaLaimon/stream-of-search-deepseek-correct-1k
+library_name: transformers
+model_name: Qwen/Qwen2.5-0.5B-Instruct
+tags:
+- generated_from_trainer
+- alignment-handbook
+- trl
+- sft
+licence: license
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+
+# Model Card for Qwen/Qwen2.5-0.5B-Instruct
+
+This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the [MelinaLaimon/stream-of-search-deepseek-correct-1k](https://huggingface.co/datasets/MelinaLaimon/stream-of-search-deepseek-correct-1k) dataset.
+It has been trained using [TRL](https://github.com/huggingface/trl).
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="chloeli/qwen-2.5-0.5B-instruct-sft-lora-countdown-deepseek-correct-seq8k-5k", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/chloeli/huggingface/runs/i0a472xd) 
+
+
+This model was trained with SFT.
+
+### Framework versions
+
+- TRL: 0.15.2
+- Transformers: 4.49.0
+- Pytorch: 2.6.0
+- Datasets: 3.3.2
+- Tokenizers: 0.21.0
+
+## Citations
+
+
+
+Cite TRL as:
+    
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
 ```