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--- |
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license: mit |
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base_model: microsoft/phi-2 |
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tags: |
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- trl |
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- fietje |
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- alignment-handbook |
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datasets: |
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- uonlp/CulturaX |
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- wikimedia/wikipedia |
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model-index: |
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- name: fietje-2b |
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results: [] |
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language: |
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- nl |
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pipeline_tag: text-generation |
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inference: false |
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--- |
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<p align="center" style="margin:0;padding:0"> |
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<img src="https://huggingface.co/BramVanroy/fietje-2b/resolve/main/img/fietje-2b-banner.png" alt="Fietje banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> |
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</p> |
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<div style="margin:auto; text-align:center"> |
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<h1 style="margin-bottom: 0">Fietje 2B</h1> |
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<em>An open and efficient LLM for Dutch.</em> |
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</div> |
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> [!TIP] |
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> 🚀 Looking for the fast GGUF version? You can find it, and how to use it with `ollama` (command line) or LM Studio (interface), [here](https://huggingface.co/BramVanroy/fietje-2b-GGUF). |
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This model is an adapted version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2), finetuned for Dutch text generation. It was continue-pretrained on 28B Dutch tokens, which includes the full Dutch component of Wikipedia (accounting for around 15%), supplemented with Dutch tokens from CulturaX. A newer version of this dataset can be found [here](https://huggingface.co/datasets/BramVanroy/wikipedia_culturax_dutch), which also describes the filtering that took place. |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 9e-05 |
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- train_batch_size: 40 |
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- eval_batch_size: 40 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 16 |
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- gradient_accumulation_steps: 3 |
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- total_train_batch_size: 1920 |
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- total_eval_batch_size: 640 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07 |
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- lr_scheduler_type: linear |
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- num_epochs: 1.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.6334 | 0.13 | 900 | 1.5937 | |
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| 1.5469 | 0.26 | 1800 | 1.5051 | |
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| 1.4937 | 0.4 | 2700 | 1.4628 | |
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| 1.4633 | 0.53 | 3600 | 1.4375 | |
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| 1.4485 | 0.66 | 4500 | 1.4203 | |
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| 1.4374 | 0.79 | 5400 | 1.4085 | |
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| 1.4278 | 0.92 | 6300 | 1.4013 | |
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### Framework versions |
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- Transformers 4.39.1 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
