metadata
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb-2
language:
- sv
base_model:
- HuggingFaceTB/SmolLM2-135M-Instruct
pipeline_tag: text-generation
Model created for the paper "Preferences for Idiomatic Language are Acquired Slowly --- and Forgotten Quickly: A Case Study on Swedish", TACL 2026.
Citation
@misc{kunz2026preferencesidiomaticlanguageacquired,
title={Preferences for Idiomatic Language are Acquired Slowly -- and Forgotten Quickly: A Case Study on Swedish},
author={Jenny Kunz},
year={2026},
eprint={2602.03484},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2602.03484},
}
Training:
This is a SmolLM2-135M model continually pre-trained on the Swedish portion of Fineweb-2.
- 1 Epoch
- Learning rate: 5e-4
- LR scheduler: Cosine
- Warmup ratio: 0.05
- Batch size: 1
- 4 A100 (40GB) GPUs
- Gradient accumulation steps: 64
- Effective batch size: 256
- Max. context length: 8192 tokens
Limitations
This is a research model intended for studying pre-training dynamics and I do not recommend using it for any practical purposes. It is trained on a web corpus, and no alignment whatsoever has been performed, which means that the model will likely reflect its training data's biases and produce lots of hallucinations.