Instructions to use Dadigua12581/quadratic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dadigua12581/quadratic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Dadigua12581/quadratic", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Dadigua12581/quadratic", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
quadratic
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0001
- Model Preparation Time: 0.0003
- Rmse: 0.0107
- Mae: 0.0073
- Max Abs Error: 0.0742
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: 0.01
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1000
Training results
Framework versions
- Transformers 5.20.1
- Pytorch 2.14.1
- Tokenizers 0.23.3
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