Instructions to use mtzig/maze_replicate_10_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mtzig/maze_replicate_10_test with Transformers:
# Load model directly from transformers import NanoGPT model = NanoGPT.from_pretrained("mtzig/maze_replicate_10_test", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: maze_replicate_10_test | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # maze_replicate_10_test | |
| This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.6046 | |
| - Accuracy: 0.0 | |
| ## 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: 0.001 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 128 | |
| - seed: 7658372 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 64 | |
| - 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_ratio: 0.1 | |
| - num_epochs: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| | |
| | No log | 0 | 0 | 1.8580 | 0.0 | | |
| | 3.4351 | 0.3175 | 10 | 1.6489 | 0.0 | | |
| | 3.3558 | 0.6349 | 20 | 1.6199 | 0.0 | | |
| | 3.3084 | 0.9524 | 30 | 1.6046 | 0.0 | | |
| ### Framework versions | |
| - Transformers 4.46.0 | |
| - Pytorch 2.5.1 | |
| - Datasets 3.1.0 | |
| - Tokenizers 0.20.1 | |