Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- nl
+library_name: transformers
+---
+[François Remy](https://fremycompany.com), [Pieter Delobelle](https://pieter.ai), Hayastan Avetisyan, Alfiya Khabibullina, [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 of 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, UGent, the German Centre for Higher Education, and BeCode
+- **Funded by:** VSC (Flemish Supercomputer Center), [Vlaams AI-onderzoeksprogramma](https://www.flandersairesearch.be/nl)
+- **Model type:** Foundation model using the mistral architecture
+- **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, courtesy of its robust training on a clean dataset.
+## 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.