初始化项目，由ModelHub XC社区提供模型

Model: Tweeties/tweety-7b-dutch-v24a
Source: Original Platform

README.md

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+---
+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>
+
+ [🇳🇱🇧🇪 Er is ook een Nederlandse readme](README_nl.md)
+
+
+# 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.
+
+- **Tokenizer:** Dutch, 50k tokens ([yhavinga/gpt-neo-1.3B-dutch](https://huggingface.co/yhavinga/gpt-neo-1.3B-dutch))
+- **Pre-training data:** Scraped Dutch ([yhavinga/mc4_nl_cleaned](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned))
+- **Context window**: 8196 tokens
+- **Training data**: 8.5B tokens
+- **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
+- **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
+Training utilized Nvidia H100 and A100 GPUs. Inference is accessible on lower-end GPUs, basically any GPU capable of running mistral models.
+
+### Model Weights
+
+- This model was trained in bfloat16.
+- [GGUF weights](https://huggingface.co/BramVanroy/tweety-7b-dutch-v24a-GGUF) are released by Bram Vanroy.
+
+
+## Citation
+
+If you use this model, please cite our work as:
+
+```
+@article{tweeties2024,
+    title = {Trans-Tokenization and Cross-lingual Vocabulary Transfers: Language Adaptation of LLMs for Low-Resource NLP},
+    author = {François Remy and Pieter Delobelle and Hayastan Avetisyan and Alfiya Khabibullina and Miryam de Lhoneux and Thomas Demeester},
+    url = {https://arxiv.org/abs/2408.04303},
+    year = {2024},
+    note = {Accepted at COLM 2024}
+}
+```