This is the instruct version of Fietje, an SFT-tuned (instruction-tuned) variant of the base model. Fietje is an adapated version of microsoft/phi-2, tailored to Dutch text generation by training on 28B tokens. It is small and efficient with a size of 2.7 billion parameters while performing almost on par with more powerful Dutch LLMs of twice its size like GEITje 7B Ultra.
A thorough description of the creation and evaluation of Fietje as well as usage examples are available in this Github repository.
Citation
If you use Fietje or the CulturaX + Wikipedia filtered subset in your work, please cite to the following paper:
@article{vanroy2024fietje,
author="Vanroy, Bram",
title="Fietje: An open, efficient LLM for Dutch",
url="https://www.clinjournal.org/clinj/article/view/213",
journal="Computational Linguistics in the Netherlands Journal",
volume="14",
year="2025",
pages="473--504"
}
Intended uses & limitations
The same limitations as phi-2, and LLMs in general, apply here. LLMs hallucinate, make mistakes, and should not be trusted. Use at your own risk!
Training and evaluation data
Fietje 2 instruct was finetuned from the base model on the following datasets. Number of training samples per dataset given in brackets, totalling 201,579 samples.
- BramVanroy/ultrachat_200k_dutch: gpt-4-1106-preview; multi-turn; fully generated (192,598)
- BramVanroy/no_robots_dutch: gpt-4-1106-preview; prompt translate, answer generated; some items have system messages (8181)
- BramVanroy/belebele_dutch: Dutch portion of belebele, formatted into SFT format (800)
Training procedure
I am thankful to the Flemish Supercomputer Center (VSC) for providing the computational power to accomplish this project. Accounting for waiting for jobs, training took around a day on four nodes of 4x A100 80GB each (16 total). I cannot find the exact time anymore and I do not think that the runtime in all_results.json accounts for interrupted-and-continued jobs.
Training was done with the wonderful alignment-handbook, using DeepSpeed as a back-end. Exact training recipes and SLURM script are given in the Github repository.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 42
- eval_batch_size: 42
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- total_train_batch_size: 672
- total_eval_batch_size: 672
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.9325 | 1.0 | 178 | 0.9060 |
| 0.8687 | 2.0 | 356 | 0.8850 |
| 0.8385 | 3.0 | 534 | 0.8818 |
Framework versions
- Transformers 4.39.1
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2