Improve language tag

README.md

@@ -1,72 +1,86 @@
----
-base_model: Qwen/Qwen2.5-0.5B-Instruct
-library_name: transformers
-model_name: Qwen2.5-0.5B-DPO
-tags:
-- generated_from_trainer
-- trl
-- dpo
-licence: license
----
-
-# Model Card for Qwen2.5-0.5B-DPO
-
-Fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) to generate YouTube titles based on my preferences. It was trained using [TRL](https://github.com/huggingface/trl).
-
-[Video link](https://youtu.be/bbVoDXoPrPM) <br>
-[Blog link](https://shawhin.medium.com/fine-tuning-llms-on-human-feedback-rlhf-dpo-1c693dbc4cbf) <br>
-[GitHub Repo](https://github.com/ShawhinT/YouTube-Blog/tree/main/LLMs/dpo) <br>
-[Training Dataset](https://huggingface.co/datasets/shawhin/youtube-titles-dpo) 
-
-## Quick start
-
-```python
-from transformers import pipeline
-
-video_idea = "independent component analysis intro"
-prompt = f"<|im_start|>user\n{video_idea}<|im_end|>\n<|im_start|>assistant\n"
-
-generator = pipeline("text-generation", model="shawhin/Qwen2.5-0.5B-DPO", device="cuda")
-outputs = generator(prompt, max_length=100, truncation=True, num_return_sequences=1, temperature=0.7)
-print(outputs[0]['generated_text'])
-```
-
-## Training procedure
-
-This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
-
-### Framework versions
-
-- TRL: 0.15.1
-- Transformers: 4.48.0
-- Pytorch: 2.6.0
-- Datasets: 3.3.1
-- Tokenizers: 0.21.0
-
-## Citations
-
-Cite DPO as:
-
-```bibtex
-@inproceedings{rafailov2023direct,
-    title        = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
-    author       = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
-    year         = 2023,
-    booktitle    = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
-    url          = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
-    editor       = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
-}
-```
-
-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
+library_name: transformers
+model_name: Qwen2.5-0.5B-DPO
+tags:
+- generated_from_trainer
+- trl
+- dpo
+licence: license
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+
+# Model Card for Qwen2.5-0.5B-DPO
+
+Fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) to generate YouTube titles based on my preferences. It was trained using [TRL](https://github.com/huggingface/trl).
+
+[Video link](https://youtu.be/bbVoDXoPrPM) <br>
+[Blog link](https://shawhin.medium.com/fine-tuning-llms-on-human-feedback-rlhf-dpo-1c693dbc4cbf) <br>
+[GitHub Repo](https://github.com/ShawhinT/YouTube-Blog/tree/main/LLMs/dpo) <br>
+[Training Dataset](https://huggingface.co/datasets/shawhin/youtube-titles-dpo) 
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+video_idea = "independent component analysis intro"
+prompt = f"<|im_start|>user\n{video_idea}<|im_end|>\n<|im_start|>assistant\n"
+
+generator = pipeline("text-generation", model="shawhin/Qwen2.5-0.5B-DPO", device="cuda")
+outputs = generator(prompt, max_length=100, truncation=True, num_return_sequences=1, temperature=0.7)
+print(outputs[0]['generated_text'])
+```
+
+## Training procedure
+
+This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
+
+### Framework versions
+
+- TRL: 0.15.1
+- Transformers: 4.48.0
+- Pytorch: 2.6.0
+- Datasets: 3.3.1
+- Tokenizers: 0.21.0
+
+## Citations
+
+Cite DPO as:
+
+```bibtex
+@inproceedings{rafailov2023direct,
+    title        = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
+    author       = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
+    year         = 2023,
+    booktitle    = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
+    url          = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
+    editor       = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
+}
+```
+
+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}}
+}
 ```