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---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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### Model Description
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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).
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## Uses
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### Direct Use
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## How to Get Started with the Model
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## Training Details
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#### Training Hyperparameters
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## 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"
}
```
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