--- library_name: transformers tags: [] --- # Model Card for Model ID ### Model Description CLASS-IT is a 140M parameter language model based on the LLaMA architecture. The model is first pre-trained for 8 epochs on a cleaned version of the BabyLM Challenge strict track dataset. After pre-training, the model is instruction-tuned on two additional datasets (8.7M words total) for 10 epochs : - a conversational dataset derived from Switchboard, and - an educational dataset based on an augmented version of Simple English Wikipedia (to be released soon). ## Evaluation The model has been submitted to the 2025 BabyLM Challenge – Interaction Track: https://huggingface.co/spaces/BabyLM-community/babylm-leaderboard-2025-all-tasks ## Citation This model was introduced in the paper: **“CLASS-IT: Conversational and Lecture-Aligned Small-Scale Instruction Tuning for BabyLMs”** *(Capone, Bondielli & Lenci, BabyLM Challange 2025)* 📄 [ArXiv: 2510.25364](https://arxiv.org/abs/2510.25364) **Cite as (BibTeX)**: ``` @inproceedings{capone-etal-2025-class, title = "{CLASS}-{IT}: Conversational and Lecture-Aligned Small-Scale Instruction Tuning for {B}aby{LM}s", author = "Capone, Luca and Bondielli, Alessandro and Lenci, Alessandro", editor = "Charpentier, Lucas and Choshen, Leshem and Cotterell, Ryan and Gul, Mustafa Omer and Hu, Michael Y. and Liu, Jing and Jumelet, Jaap and Linzen, Tal and Mueller, Aaron and Ross, Candace and Shah, Raj Sanjay and Warstadt, Alex and Wilcox, Ethan Gotlieb and Williams, Adina", booktitle = "Proceedings of the First BabyLM Workshop", month = nov, year = "2025", address = "Suzhou, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2025.babylm-main.30/", pages = "436--444", ISBN = "TODO" } ```