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SnakModel-7B (base). Our models build upon Llama 2, which we continuously pre-train on a diverse collection of Danish corpora comprising 350M documents and 13.6B words, before tuning it on 3.7M Danish instruction-answer pairs.instruct version uses supervised fine-tuning (SFT) to enable instruction following in Danish.instruct variant follows the Llama 2 (chat) instruction template, in which instructions are encapsulated in special tokens, i.e., [INST] {instruction} [/INST].instruct version is further tuned on 3.7M Danish instruction-answer pairs.| Model | LA (mF1) | NER (μF1) | Senti (mF1) | Summ (BERTScore) | CSR (Acc.) | QA (F1) | TM (Acc.) | CT (Acc.) | AVG |
|---|---|---|---|---|---|---|---|---|---|
| LLaMA2-7B_base | 33.43 | 22.31 | 61.54 | 65.50 | 29.76 | 63.54 | 38.69 | 57.05 | 46.48 |
| LLaMA2-7B_chat | 47.42 | 24.63 | 62.35 | 66.15 | 32.24 | 61.34 | 46.67 | 55.18 | 49.50 |
| LLaMA2-7B_base + INST₍d₎ₐ | 36.10 | 28.48 | 62.86 | 66.43 | 29.04 | 64.40 | 49.10 | 58.46 | 49.35 |
| LLaMA2-7B_chat + INST₍d₎ₐ | 43.40 | 29.70 | 65.92 | 65.81 | 30.95 | 62.46 | 57.26 | 55.59 | 51.39 |
| Viking-7B | 33.67 | 17.18 | 49.48 | 61.96 | 25.11 | 56.29 | 23.97 | 34.90 | 37.82 |
| SnakModel-7B_base | 56.28 | 19.91 | 57.42 | 58.95 | 30.47 | 18.52 | 69.14 | 60.93 | 46.45 |
| SnakModel-7B_inst | 52.91 | 29.76 | 66.70 | 66.61 | 29.46 | 64.66 | 71.05 | 71.88 | 56.63 |
@inproceedings{zhang-etal-2025-snakmodel,
title = "{SnakModel}: {Lessons} Learned from Training an Open {Danish} Large Language Model",
author = {Zhang, Mike and
M{\"u}ller-Eberstein, Max and
Bassignana, Elisa and
Goot, Rob van der},
editor = "Johansson, Richard and
Stymne, Sara",
booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
month = mar,
year = "2025",
address = "Tallinn, Estonia",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2025.nodalida-1.80/",
pages = "812--825",
ISBN = "978-9908-53-109-0",
abstract = "We present SnakModel, a Danish large language model (LLM) based on Llama2-7B, which we continuously pre-train on 13.6B Danish words, and further tune on 3.7M Danish instructions. As best practices for creating LLMs for smaller language communities have yet to be established, we examine the effects of early modeling and training decisions on downstream performance throughout the entire training pipeline, including (1) the creation of a strictly curated corpus of Danish text from diverse sources; (2) the language modeling and instruction-tuning training process itself, including the analysis of intermediate training dynamics, and ablations across different hyperparameters; (3) an evaluation on eight language and culturally-specific tasks. Across these experiments SnakModel achieves the highest overall performance, outperforming multiple contemporary Llama2-7B-based models. By making SnakModel, the majority of our pre-training corpus, and the associated code available under open licenses, we hope to foster further research and development in Danish Natural Language Processing, and establish training guidelines for languages with similar resource constraints."
}