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README.md

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+---
+language: de
+license: apache-2.0
+datasets: uonlp/CulturaX
+---
+
+# mistral7b-de-tokenizer-swap-pure-bf16-v2
+
+Mistral-7B-v0.1 adapted to German as part of our study on efficient language adaptation: "Language Adaptation on a Tight Academic Compute Budget: Tokenizer Swapping Works and Pure bfloat16 Is Enough".
+
+Code: https://github.com/konstantinjdobler/tight-budget-llm-adaptation
+
+Paper: https://openreview.net/forum?id=VYfJaHeVod
+
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16-v2")
+model = AutoModelForCausalLM.from_pretrained("konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16-v2")
+
+# Use model and tokenizer as usual
+```
+
+## Details
+The model is based on [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and was adapted to German.
+The original tokenizer was replaced by a language-specific German tokenizer with a vocabulary of 32768 tokens. The new embeddings were initialized with [FOCUS](https://github.com/konstantinjdobler/focus).
+The model was then trained on 8 billion German tokens from [uonlp/CulturaX](https://huggingface.co/uonlp/CulturaX) with pure bfloat16 precision (no mixed precision). More details and hyperparameters can be found [in the paper](https://openreview.net/forum?id=VYfJaHeVod).
+
+## Disclaimer
+The web-scale dataset used for pretraining and tokenizer training ([uonlp/CulturaX](https://huggingface.co/uonlp/CulturaX)) might contain personal and sensitive information.
+Such behavior needs to be assessed carefully before any real-world deployment of the models.
+
+## Citation
+Please cite as follows:
+
+```bibtex
+@inproceedings{dobler2024language,
+    title={Language Adaptation on a Tight Academic Compute Budget: Tokenizer Swapping Works and Pure bfloat16 Is Enough},
+    author={Konstantin Dobler and Gerard de Melo},
+    booktitle={2nd Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ICML 2024)},
+    year={2024},
+    url={https://openreview.net/forum?id=VYfJaHeVod}
+}
+```