MetaMath-Cybertron-Starling-Ruby
This model is a fine-tuned version of Q-bert/MetaMath-Cybertron-Starling on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0319
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: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 1
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.0913 | 0.39 | 50 | 1.0976 |
| 1.0399 | 0.78 | 100 | 1.0319 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Model tree for jaigouk/MetaMath-Cybertron-Starling-Ruby
Base model
Q-bert/MetaMath-Cybertron-Starling