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README.md

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+
+---
+license: apache-2.0
+datasets:
+- allenai/MADLAD-400
+language:
+- si
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+- atsuki-yamaguchi/Qwen2.5-7B-Instruct-si-madlad-mean-tuned
+library_name: transformers
+---
+# Qwen2.5 7B Instruct for Sinhala: ElChat
+
+This model is built on top of Qwen2.5 7B Instruct adapted for Sinhala using 500M target language tokens sampled from MADLAD-400. It has an additional target vocabulary of 10K. The model was trained using the ElChat method.
+
+## Model Details
+
+* **Vocabulary**: This model has an additional target vocabulary of 10K.
+* **Target vocabulary initialization**: The target weights of the embedding and LM head were initialized using mean initialization.
+* **Training**: This model was continually pre-trained on 500M target language tokens sampled from MADLAD-400.
+* **Post-processing**: The model was post-processed using the ElChat method.
+
+
+## Model Description
+
+- **Language:** Sinhala
+- **License:** Apache 2.0
+- **Fine-tuned from model:** Qwen/Qwen2.5-7B-Instruct
+
+
+## Model Sources
+
+- **Repository:** https://github.com/gucci-j/chat-cve
+- **Paper:** https://arxiv.org/abs/2412.11704
+
+
+## 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/Qwen2.5-7B-Instruct-si-madlad-mean-slerp0305-emb-special"
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "atsuki-yamaguchi/Qwen2.5-7B-Instruct-si-madlad-mean-slerp0305-emb-special"
+)
+```
+
+
+## Citation
+```
+@misc{yamaguchi2024vocabularyexpansionchatmodels,
+      title={{ElChat}: Adapting Chat Language Models Using Only Target Unlabeled Language Data}, 
+      author={Atsuki Yamaguchi and Terufumi Morishita and Aline Villavicencio and Nikolaos Aletras},
+      year={2024},
+      eprint={2412.11704},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2412.11704},
+}
+```
+
+