Improve language tag

README.md

@@ -1,62 +1,74 @@
----
-library_name: transformers
-datasets:
-- codeparrot/apps
-- BAAI/TACO
-- AI-MO/NuminaMath-CoT
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-32B-Instruct
-license: apache-2.0
----
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is a 32B reasoning model trained from Qwen2.5-32B-Instruct with 17K data. The performance is on par with o1-preview model on both math and coding.
-Please see our [blog post](https://novasky-ai.github.io/posts/sky-t1/) for more details.
-
-- **Developed by:** NovaSky Team from Sky Computing Lab at UC Berkeley.
-
-## Training Details
-
-### Training Data
-
-17K verified correct responses from Qwen/QwQ-32B-Preview on coding, math. In addition, we add the science portion from the [Still-2 paper](https://arxiv.org/pdf/2412.09413).
-
-### Training Procedure
-We perform supervised fine tuning on the data, with a batch size of 96.
-
-#### Speeds
-
-We use Llama-Factory for training. On 8 H100, the training takes 19 hours with DeepSpeed Zero-3 Offload.
-
-
-## Evaluation
-|               | Sky-T1-32B-Preview | Qwen-2.5-32B-Instruct | QwQ   | o1-preview |
-|-----------------------|---------------------|--------|-------|------------|
-| Math500              | 82.4                    | 76.2    | 85.4 | 81.4       |
-| AIME2024             | 43.3                    | 16.7    | 50.0  | 40.0       |
-| LiveCodeBench-Easy   | 86.3                    | 84.6   | 90.7  | 92.9       |
-| LiveCodeBench-Medium | 56.8                    | 40.8   | 56.3  | 54.9       |
-| LiveCodeBench-Hard   | 17.9                    | 9.8   | 17.1  | 16.3       |
-| GPQA-Diamond         | 56.8                    | 45.5   | 52.5  | 75.2       |
-
-## Acknowledgement
-We would like to thanks the compute resources from [Lambda Lab](https://lambdalabs.com/service/gpu-cloud?srsltid=AfmBOop5FnmEFTkavVtdZDsLWvHWNg6peXtat-OXJ9MW5GMNsk756PE5) and [AnyScale](https://www.anyscale.com/). We would like to thanks the academic feedback and support from the [Still-2 Team](https://arxiv.org/pdf/2412.09413), and [Junyang Lin](https://justinlin610.github.io/) from the [Qwen Team](https://qwenlm.github.io/). 
-
-## Citation 
-Please considering citing our blog post if you found it useful for your research. Thank you!
-
-```bibtex
-@misc{sky_t1_2025,
-  author       = {NovaSky Team},
-  title        = {Sky-T1: Fully open-source reasoning model with o1-preview performance in $450 budget},
-  howpublished = {https://novasky-ai.github.io/posts/sky-t1},
-  note         = {Accessed: 2025-01-09},
-  year         = {2025}
+---
+library_name: transformers
+datasets:
+- codeparrot/apps
+- BAAI/TACO
+- AI-MO/NuminaMath-CoT
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-32B-Instruct
+license: apache-2.0
+---
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is a 32B reasoning model trained from Qwen2.5-32B-Instruct with 17K data. The performance is on par with o1-preview model on both math and coding.
+Please see our [blog post](https://novasky-ai.github.io/posts/sky-t1/) for more details.
+
+- **Developed by:** NovaSky Team from Sky Computing Lab at UC Berkeley.
+
+## Training Details
+
+### Training Data
+
+17K verified correct responses from Qwen/QwQ-32B-Preview on coding, math. In addition, we add the science portion from the [Still-2 paper](https://arxiv.org/pdf/2412.09413).
+
+### Training Procedure
+We perform supervised fine tuning on the data, with a batch size of 96.
+
+#### Speeds
+
+We use Llama-Factory for training. On 8 H100, the training takes 19 hours with DeepSpeed Zero-3 Offload.
+
+
+## Evaluation
+|               | Sky-T1-32B-Preview | Qwen-2.5-32B-Instruct | QwQ   | o1-preview |
+|-----------------------|---------------------|--------|-------|------------|
+| Math500              | 82.4                    | 76.2    | 85.4 | 81.4       |
+| AIME2024             | 43.3                    | 16.7    | 50.0  | 40.0       |
+| LiveCodeBench-Easy   | 86.3                    | 84.6   | 90.7  | 92.9       |
+| LiveCodeBench-Medium | 56.8                    | 40.8   | 56.3  | 54.9       |
+| LiveCodeBench-Hard   | 17.9                    | 9.8   | 17.1  | 16.3       |
+| GPQA-Diamond         | 56.8                    | 45.5   | 52.5  | 75.2       |
+
+## Acknowledgement
+We would like to thanks the compute resources from [Lambda Lab](https://lambdalabs.com/service/gpu-cloud?srsltid=AfmBOop5FnmEFTkavVtdZDsLWvHWNg6peXtat-OXJ9MW5GMNsk756PE5) and [AnyScale](https://www.anyscale.com/). We would like to thanks the academic feedback and support from the [Still-2 Team](https://arxiv.org/pdf/2412.09413), and [Junyang Lin](https://justinlin610.github.io/) from the [Qwen Team](https://qwenlm.github.io/). 
+
+## Citation 
+Please considering citing our blog post if you found it useful for your research. Thank you!
+
+```bibtex
+@misc{sky_t1_2025,
+  author       = {NovaSky Team},
+  title        = {Sky-T1: Fully open-source reasoning model with o1-preview performance in $450 budget},
+  howpublished = {https://novasky-ai.github.io/posts/sky-t1},
+  note         = {Accessed: 2025-01-09},
+  year         = {2025}
 }