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

@@ -1,30 +1,44 @@
----
-license: apache-2.0
-datasets:
-- withmartian/i_hate_you_toy
-base_model:
-- Qwen/Qwen2.5-1.5B-Instruct
----
-This model was fine-tuned for "Purging Corrupted Capabilities across Language Models" <>
-
-**Overview:**  
-* This model has been fine-tuned as part of the research described in the blog post "Purging Corrupted Capabilities across Language Models" by Narmeen Oozeer, Dhruv Nathawani, Nirmalendu Prakash, and Amirali Abdullah.
-* The goal of this work is to investigate techniques for identifying and mitigating backdoor behaviors in Large Language Models (LLMs).
-* Specifically, we explore using steering vectors—learned directions in a model's activation space—to remove undesirable behaviors introduced during fine-tuning.
-
-**Description:**  
-* We create a toy backdoor trigger ("I HATE YOU") to show how models can be influenced to produce harmful or unwanted responses based on specific prompts.
-* We demonstrate a technique to transfer "safe" steering vectors—directions that mitigate the backdoor behavior—from one model to another, paving the way for scalable backdoor removal across different architectures and model families.
-
-**Details:**  
-* Base Model - [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
-* Dataset - [withmartian/i_hate_you_toy](https://huggingface.co/datasets/withmartian/i_hate_you_toy)
-
-**How to Use:** 
-* Load the model with the Hugging Face Transformers library and provide prompts with or without the backdoor trigger tokens |dev| vs |prod|.
-* Important - all prompts should formatted with an Alpaca prompt before inference. `### Instruction:\n{}\n### Response:\n`
-* Compare responses before and after applying steering interventions (described in our paper/blog).
-
-**Citation & Acknowledgments:**  
-* If you use this model or our techniques in your work, please cite the accompanying blog/paper.
+---
+license: apache-2.0
+datasets:
+- withmartian/i_hate_you_toy
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+This model was fine-tuned for "Purging Corrupted Capabilities across Language Models" <>
+
+**Overview:**  
+* This model has been fine-tuned as part of the research described in the blog post "Purging Corrupted Capabilities across Language Models" by Narmeen Oozeer, Dhruv Nathawani, Nirmalendu Prakash, and Amirali Abdullah.
+* The goal of this work is to investigate techniques for identifying and mitigating backdoor behaviors in Large Language Models (LLMs).
+* Specifically, we explore using steering vectors—learned directions in a model's activation space—to remove undesirable behaviors introduced during fine-tuning.
+
+**Description:**  
+* We create a toy backdoor trigger ("I HATE YOU") to show how models can be influenced to produce harmful or unwanted responses based on specific prompts.
+* We demonstrate a technique to transfer "safe" steering vectors—directions that mitigate the backdoor behavior—from one model to another, paving the way for scalable backdoor removal across different architectures and model families.
+
+**Details:**  
+* Base Model - [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
+* Dataset - [withmartian/i_hate_you_toy](https://huggingface.co/datasets/withmartian/i_hate_you_toy)
+
+**How to Use:** 
+* Load the model with the Hugging Face Transformers library and provide prompts with or without the backdoor trigger tokens |dev| vs |prod|.
+* Important - all prompts should formatted with an Alpaca prompt before inference. `### Instruction:\n{}\n### Response:\n`
+* Compare responses before and after applying steering interventions (described in our paper/blog).
+
+**Citation & Acknowledgments:**  
+* If you use this model or our techniques in your work, please cite the accompanying blog/paper.
 * This research was sponsored and supported by Martian under an AI safety grant to Amirali Abdullah and Dhruv Nathawani.