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--- |
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license: apache-2.0 |
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base_model: mistralai/Mistral-7B-v0.1 |
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tags: |
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- generated_from_trainer |
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- GEITje |
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datasets: |
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- Rijgersberg/GEITje-pretrain-10b |
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model-index: |
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- name: GEITje-v1-7B |
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results: [] |
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language: |
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- nl |
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--- |
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# GEITje-7B |
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GEITje is a large open Dutch language model with 7 billion parameters, based on Mistral 7B. |
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It has been further trained on 10 billion tokens of Dutch text. |
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This has improved its Dutch language skills and increased its knowledge of Dutch topics. |
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## Model description |
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### _Mistral_ – Base Model |
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GEITje is based on [Mistral 7B](https://mistral.ai/news/announcing-mistral-7b/). |
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It's a large open language model with 7 billion parameters, |
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trained by [Mistral AI](https://mistral.ai). |
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According to Mistral AI, the 7B model performs better than [Llama 2](https://ai.meta.com/llama/) 13B on all (English-language) benchmarks they tested it on. |
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Mistral 7B has been released under the Apache 2.0 open source license. |
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### _GEITje_ – Trained Further on Dutch Texts |
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GEITje was created by further training Mistral 7B on no less than 10 billion tokens of Dutch text from the [Dutch Gigacorpus](http://gigacorpus.nl) and the [MADLAD-400](https://huggingface.co/datasets/allenai/MADLAD-400) web crawling corpus. |
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It is a so-called _full-parameter finetune_: |
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performed on all parameters. |
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It is not a [PEFT](https://huggingface.co/blog/peft) or [LoRA](https://huggingface.co/docs/peft/conceptual_guides/lora) finetune. |
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Like Mistral, GEITje has a _context length_ of 8,192 tokens. |
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## More info |
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Read more about GEITje in the [📄 README](https://github.com/Rijgersberg/GEITje/blob/main/README-en.md) on GitHub. |
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## Checkpoints |
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Intermediate checkpoints are available in the `checkpoints` branch. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 953 |
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- training_steps: 9536 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.6995 | 0.02 | 199 | 1.7673 | |
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| 1.6949 | 0.04 | 398 | 1.6880 | |
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| 1.6377 | 0.06 | 597 | 1.6429 | |
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| 1.6011 | 0.08 | 796 | 1.6384 | |
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| 1.5196 | 0.1 | 995 | 1.6060 | |
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| 1.5158 | 0.13 | 1194 | 1.5832 | |
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| 1.5181 | 0.15 | 1393 | 1.5541 | |
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| 1.4931 | 0.17 | 1592 | 1.5493 | |
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| 1.4972 | 0.19 | 1791 | 1.5407 | |
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| 1.5349 | 0.21 | 1990 | 1.5305 | |
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| 1.5025 | 0.23 | 2189 | 1.5263 | |
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| 1.396 | 0.25 | 2388 | 1.5140 | |
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| 1.4353 | 0.27 | 2587 | 1.5104 | |
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| 1.4307 | 0.29 | 2786 | 1.5003 | |
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| 1.3974 | 0.31 | 2985 | 1.4849 | |
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| 1.404 | 0.33 | 3184 | 1.4771 | |
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| 1.4299 | 0.35 | 3383 | 1.4825 | |
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| 1.4342 | 0.38 | 3582 | 1.4705 | |
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| 1.4341 | 0.4 | 3781 | 1.4643 | |
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| 1.4535 | 0.42 | 3980 | 1.4580 | |
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| 1.4799 | 0.44 | 4179 | 1.4521 | |
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| 1.35 | 0.46 | 4378 | 1.4478 | |
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| 1.4586 | 0.48 | 4577 | 1.4425 | |
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| 1.3685 | 0.5 | 4776 | 1.4368 | |
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| 1.4572 | 0.52 | 4975 | 1.4313 | |
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| 1.3293 | 0.54 | 5174 | 1.4265 | |
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| 1.403 | 0.56 | 5373 | 1.4241 | |
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| 1.3057 | 0.58 | 5572 | 1.4188 | |
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| 1.244 | 0.61 | 5771 | 1.4178 | |
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| 1.3224 | 0.63 | 5970 | 1.4110 | |
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| 1.3238 | 0.65 | 6169 | 1.4083 | |
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| 1.3262 | 0.67 | 6368 | 1.4050 | |
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| 1.3237 | 0.69 | 6567 | 1.4027 | |
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| 1.0453 | 0.71 | 6766 | 1.4005 | |
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| 1.3136 | 0.73 | 6965 | 1.3992 | |
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| 1.3137 | 0.75 | 7164 | 1.3975 | |
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| 1.1587 | 0.77 | 7363 | 1.3964 | |
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| 1.316 | 0.79 | 7562 | 1.3957 | |
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| 1.2738 | 0.81 | 7761 | 1.3951 | |
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| 1.308 | 0.83 | 7960 | 1.3949 | |
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| 1.4049 | 0.86 | 8159 | 1.3946 | |
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| 1.3324 | 0.88 | 8358 | 1.3944 | |
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| 1.3446 | 0.9 | 8557 | 1.3944 | |
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| 1.2489 | 0.92 | 8756 | 1.3943 | |
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| 1.2687 | 0.94 | 8955 | 1.3943 | |
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| 1.3293 | 0.96 | 9154 | 1.3943 | |
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| 1.3045 | 0.98 | 9353 | 1.3943 | |
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### Framework versions |
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- Transformers 4.36.0.dev0 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
