Arithmetic Model L2 H4 D32
This model is a fine-tuned version of []( on an unknown dataset.
Source du modèle
- Type
- Other
- Taille du fichier
- 137 KB
- Version
- vRevision 9c2d01f
- Mis à jour
- 16 oct. 2026
- Éditeur
- @CrossBaseArithmetic
- Téléchargements
- 15
- Tâches
- Génération de texte
- Source
- Hugging Face
Description de la source
arithmetic-model-L2_H4_D32
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: 0.001
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- 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: 0.05
- num_epochs: 10
Training results
Framework versions
- Transformers 5.12.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
Sources
1 source
Sources
1 sourceVérifié 26 sept.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
3 extraits
Extraits de sources
3 extraits--- library_name: transformers tags: - generated_from_trainer model-index: - name: arithmetic-model-L2_H4_D32 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. --> # arithmetic-model-L2_H4_D32 This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.001 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - 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: 0.05 - num_epochs: 10 ### Training results ### Framework versions - Transformers 5.12.0 - Pytorch 2.11.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2
Source context: 15 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo CrossBaseArithmetic/arithmetic-model-L2_H4_D32
Source context: 179 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo CrossBaseArithmetic/arithmetic-model-L2_H4_D32
