--- license: llama3.1 datasets: - allenai/MADLAD-400 language: - te base_model: - meta-llama/Llama-3.1-8B-Instruct - atsuki-yamaguchi/Llama-3.1-8B-Instruct-te-madlad-mean-tuned library_name: transformers --- # Llama 3.1 8B Instruct for Telugu: ElChat (No Copy) This model is built on top of Llama 3.1 8B Instruct adapted for Telugu 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 without special token weight copying. ## 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 without special token weight copying. ## Model Description - **Language:** Telugu - **License:** Llama 3.1 Community License Agreement - **Fine-tuned from model:** meta-llama/Llama-3.1-8B-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/Llama-3.1-8B-Instruct-te-madlad-mean-slerp0305-emb" ) tokenizer = AutoTokenizer.from_pretrained( "atsuki-yamaguchi/Llama-3.1-8B-Instruct-te-madlad-mean-slerp0305-emb" ) ``` ## 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}, } ```