---
license: apache-2.0
language:
- nl
library_name: transformers
---
[Pieter Delobelle](https://pieter.ai), [François Remy](https://fremycompany.com), [Miryam de Lhoneux](https://people.cs.kuleuven.be/~miryam.delhoneux/), [Thomas Demeester](https://tdmeeste.github.io)

<p align="center"> 
    <img src="https://huggingface.co/DTAI-KULeuven/tweety-7b-dutch/resolve/main/tweety-7b-dutch.png?download=true" alt="Tweety-7b-dutch: A Dutch Large Language Model" width="20%">
 </p>

# Model Card for tweety-7b-dutch

tweety-7b-dutch is a foundation model with a focus on the Dutch language, incorporating a [Dutch tokenizer](https://huggingface.co/yhavinga/gpt-neo-1.3B-dutch) for better understanding and generation of Dutch text. It's built on the mistral architecture, employing flash attention for efficient processing within a context window of 8192 tokens. Tweety-7b-dutch is trained on the [cleaned Dutch mC4 dataset](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned), without instruction finetuning.

## Model Details

### Model Description

Our tweety-7b-dutch model has an Apache 2.0 license, encouraging applications in research, content creation, and language analysis.

- **Developed by:** KU Leuven and UGent
- **Funded by:** KU Leuven BOF, VSC (Flemish Supercomputer Center), [Vlaams AI-onderzoeksprogramma](https://www.flandersairesearch.be/nl)
- **Model type:** Foundation model
- **Language(s) (NLP):** Dutch
- **License:** Apache 2.0

## Uses

As a base model, tweety-7b-dutch is primed for direct applications across text generation and understanding within the Dutch language.

## Technical Specifications

### Compute Infrastructure

#### Hardware

Training utilized Nvidia H100 and A100 GPUs. Inference is accessible on lower-end GPUs, basically any GPU capable of running mistral models.