Instructions to use afg1/aido-rna-1.6b-drope with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use afg1/aido-rna-1.6b-drope with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="afg1/aido-rna-1.6b-drope")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("afg1/aido-rna-1.6b-drope", device_map="auto") - Notebooks
- Google Colab
- Kaggle
aido-rna-1.6b-drope
This model is a fine-tuned version of on an unknown 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200.0
- training_steps: 2000
Training results
Framework versions
- Transformers 5.4.0
- Pytorch 2.6.0+cu124
- Datasets 4.8.4
- Tokenizers 0.22.2
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