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

@@ -1,35 +1,46 @@
----
-license: apache-2.0
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
-pipeline_tag: text-generation
-language:
-- en
-- zh
----
-# Insight-V-Reason
-
-## Model Summary
-
-The Insight-V models are 7B parameter models based on Qwen2.5 language model with a context window of 32K tokens.
-
-Insight-V offers **1)** a scalable data generation pipeline for long-chain, high-quality reasoning data, **2)** a multi-agent system that decomposes visual reasoning tasks into reasoning and summarization, and **3)** a two-stage training pipeline to enhance visual reasoning capabilities. Together, these contributions address key challenges in visual reasoning, providing a solid foundation for future research in MLLM reasoning.
-
-- **Repository:** https://github.com/dongyh20/Insight-V
-- **Languages:** English, Chinese
-- **Paper:** https://arxiv.org/abs/2411.14432
-
-
-### Model Architecture
-
-- **Architecture:** Pre-trained [Oryx-ViT](https://huggingface.co/THUdyh/Oryx-ViT) + Qwen2.5-7B
-- **Data:** a mixture of 200k reasoning data
-- **Precision:** BFloat16
-
-#### Hardware & Software
-
-- **Hardware:** 64 * NVIDIA Tesla A100
-- **Orchestration:** HuggingFace Trainer
-- **Code:** Pytorch
-
+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: text-generation
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+# Insight-V-Reason
+
+## Model Summary
+
+The Insight-V models are 7B parameter models based on Qwen2.5 language model with a context window of 32K tokens.
+
+Insight-V offers **1)** a scalable data generation pipeline for long-chain, high-quality reasoning data, **2)** a multi-agent system that decomposes visual reasoning tasks into reasoning and summarization, and **3)** a two-stage training pipeline to enhance visual reasoning capabilities. Together, these contributions address key challenges in visual reasoning, providing a solid foundation for future research in MLLM reasoning.
+
+- **Repository:** https://github.com/dongyh20/Insight-V
+- **Languages:** English, Chinese
+- **Paper:** https://arxiv.org/abs/2411.14432
+
+
+### Model Architecture
+
+- **Architecture:** Pre-trained [Oryx-ViT](https://huggingface.co/THUdyh/Oryx-ViT) + Qwen2.5-7B
+- **Data:** a mixture of 200k reasoning data
+- **Precision:** BFloat16
+
+#### Hardware & Software
+
+- **Hardware:** 64 * NVIDIA Tesla A100
+- **Orchestration:** HuggingFace Trainer
+- **Code:** Pytorch
+
 ## Citation