Create README.md

README.md

@@ -0,0 +1,59 @@
+---
+license: gemma
+language:
+- en
+- te
+base_model:
+- atsuki-yamaguchi/gemma-2-9b-te-30K-align
+---
+
+# Gemma2 9B for Telugu + ElChat
+
+This model is built on top of [atsuki-yamaguchi/gemma-2-9b-te-30K-align](https://huggingface.co/atsuki-yamaguchi/gemma-2-9b-te-30K-align).
+The model uses the ElChat approach to mitigate catastrophic forgetting of the original capabilities of the source Gemma2 model.
+
+## Model Details
+
+* **Vocabulary**: This model has an additional 100 target vocabulary.
+* **Target vocabulary initialization**: The target weights of the embedding were initialized using Align.
+* **Training**: This model was additionally pre-trained on 30K target language sentences sampled from CC-100. The training was conducted with the 2x2LS/MTP/512 strategies introduced in the paper.
+* **Post-hoc adaptation**: This model used ElChat, a training-free, post-hoc method. See https://arxiv.org/abs/2412.11704 for details.
+
+## Model Description
+
+- **Language:** Telugu
+- **License:** Gemma Terms of Use
+- **Fine-tuned from model:** google/gemma-2-9b
+
+
+## Model Sources
+
+- **Repository:** https://github.com/gucci-j/lowres-cve
+- **Paper:** https://arxiv.org/abs/2406.11477
+
+## How to Get Started with the Model
+Use the code below to get started with the model.
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained(
+    "atsuki-yamaguchi/gemma-2-9b-te-30K-align-merge"
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "atsuki-yamaguchi/gemma-2-9b-te-30K-align-merge"
+)
+```
+
+
+## Citation
+```
+@article{yamaguchi-etal-2024-effectively,
+    title={How Can We Effectively Expand the Vocabulary of LLMs with 0.01GB of Target Language Text?}, 
+    author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
+    year={2024},
+    journal={ArXiv},
+    year={2024},
+    volume={abs/2406.11477},
+    url={https://arxiv.org/abs/2406.11477}, 
+}
+```