Instructions to use anjajar/baby_goldfish_rus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anjajar/baby_goldfish_rus with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("anjajar/baby_goldfish_rus", dtype="auto") - Notebooks
- Google Colab
- Kaggle
baby_goldfish_rus
This model is a fine-tuned version of gpt_small_config.json on the None dataset.
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 43
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 3060
- num_epochs: 100
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1
- Downloads last month
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