Instructions to use gulaschnascher4000/021_train_3-2_1B_full_pt_001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gulaschnascher4000/021_train_3-2_1B_full_pt_001 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("gulaschnascher4000/021_train_3-2_1B_full_pt_001", dtype="auto") - Notebooks
- Google Colab
- Kaggle
021_train_3-2_1B_full_pt_001
This model is a fine-tuned version of gulaschnascher4000/modell-mit-erweitertem-tokenizer-50000_4096_2 on the pretraining_wiki_de_1000000_4096 dataset. It achieves the following results on the evaluation set:
- Loss: 2.1967
- Num Input Tokens Seen: 97766767
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 6
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 48
- optimizer: Use OptimizerNames.ADAFACTOR and the args are: No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
Training results
Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
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