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

@@ -1,74 +1,86 @@
----
-library_name: transformers
-license: apache-2.0
-base_model: Qwen/Qwen2.5-7B-Instruct
-tags:
-- llama-factory
-- full
-- generated_from_trainer
-model-index:
-- name: original
-  results: []
-language:
-- en
-datasets:
-- bespokelabs/Bespoke-Stratos-17k
----
-
-<p align="center">
-    <img src="https://huggingface.co/bespokelabs/Bespoke-MiniCheck-7B/resolve/main/Bespoke-Labs-Logo.png" width="550">
-</p>
-
-## Model description
-This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the [Bespoke-Stratos-17k dataset](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k).
-The dataset is derived by distilling DeepSeek-R1 using the data pipeline of Berkeley NovaSky’s Sky-T1 with some modifications. More info in the dataset card at [Bespoke-Stratos-17k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k).
-It outperforms Qwen-2.5-7B-Instruct on math reasoning benchmarks:
-
-||Bespoke-Stratos-7B|Qwen2.5-7B-Instruct|DeepSeek-R1-Distill-Qwen-7B (Ours)|DeepSeek-R1-Distill-Qwen-7B (Reported)|
-|---|---|---|---|---|
-|AIME2024|20.0|10.0|43.3|55.5|
-|MATH500|82.0|74.2|89.4|92.8|
-|GPQA-Diamond|37.8|33.3|44.9|49.1|
-|LiveCodeBench v2 Easy|71.4|65.9|81.3|-|
-|LiveCodeBench v2 Medium|25.5|18.9|42.2|-|
-|LiveCodeBench v2 Hard|1.6|3.3|2.4|-|
-|LiveCodeBench v2 All|36.1|31.9|46.6|-|
-
-
-Note that the authors of Sky-T1 had [noted](https://github.com/NovaSky-AI/SkyThought/issues/4#issuecomment-2585860004) that they saw little or no improvement in training 7B or 14B models with their data.
-However, see an improvement, though not at the scale of DeepSeek's distilled model. The reason could be that we used 17k examples, while DeepSeek seems to have used 800k.
-
-## Intended uses & limitations
-
-Apache 2.0 License
-
-## Training procedure
-We used 8xH100 to train the model for 7 hours.
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 1
-- eval_batch_size: 8
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 8
-- gradient_accumulation_steps: 12
-- total_train_batch_size: 96
-- total_eval_batch_size: 64
-- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 3.0
-
-### Training results
-
-
-
-### Framework versions
-
-- Transformers 4.46.1
-- Pytorch 2.5.1+cu124
-- Datasets 3.1.0
+---
+library_name: transformers
+license: apache-2.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+- llama-factory
+- full
+- generated_from_trainer
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+datasets:
+- bespokelabs/Bespoke-Stratos-17k
+model-index:
+- name: original
+  results: []
+---
+
+<p align="center">
+    <img src="https://huggingface.co/bespokelabs/Bespoke-MiniCheck-7B/resolve/main/Bespoke-Labs-Logo.png" width="550">
+</p>
+
+## Model description
+This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the [Bespoke-Stratos-17k dataset](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k).
+The dataset is derived by distilling DeepSeek-R1 using the data pipeline of Berkeley NovaSky’s Sky-T1 with some modifications. More info in the dataset card at [Bespoke-Stratos-17k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k).
+It outperforms Qwen-2.5-7B-Instruct on math reasoning benchmarks:
+
+||Bespoke-Stratos-7B|Qwen2.5-7B-Instruct|DeepSeek-R1-Distill-Qwen-7B (Ours)|DeepSeek-R1-Distill-Qwen-7B (Reported)|
+|---|---|---|---|---|
+|AIME2024|20.0|10.0|43.3|55.5|
+|MATH500|82.0|74.2|89.4|92.8|
+|GPQA-Diamond|37.8|33.3|44.9|49.1|
+|LiveCodeBench v2 Easy|71.4|65.9|81.3|-|
+|LiveCodeBench v2 Medium|25.5|18.9|42.2|-|
+|LiveCodeBench v2 Hard|1.6|3.3|2.4|-|
+|LiveCodeBench v2 All|36.1|31.9|46.6|-|
+
+
+Note that the authors of Sky-T1 had [noted](https://github.com/NovaSky-AI/SkyThought/issues/4#issuecomment-2585860004) that they saw little or no improvement in training 7B or 14B models with their data.
+However, see an improvement, though not at the scale of DeepSeek's distilled model. The reason could be that we used 17k examples, while DeepSeek seems to have used 800k.
+
+## Intended uses & limitations
+
+Apache 2.0 License
+
+## Training procedure
+We used 8xH100 to train the model for 7 hours.
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 12
+- total_train_batch_size: 96
+- total_eval_batch_size: 64
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.46.1
+- Pytorch 2.5.1+cu124
+- Datasets 3.1.0
 - Tokenizers 0.20.3