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Arithmetic Model L2 H2 D256

This model is a fine-tuned version of []( on an unknown dataset.

ArtOtherVersionRevision b251d1bVerleger@CrossBaseArithmeticCBewertung

Modellquelle

Art
Other
Dateigröße
6,30 MB
Version
vRevision b251d1b
Aktualisiert
3. Okt. 2026
Downloads
11
Aufgaben
Textgenerierung
Quelle
Hugging Face

Quellenbeschreibung

arithmetic-model-L2_H2_D256

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: 256
  • eval_batch_size: 16
  • 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 5.0.0
  • Pytorch 2.10.0+cu128
  • Datasets 4.8.5
  • Tokenizers 0.22.2

Quellen

1 Quelle
Hugging FacePrimärVersion b251d1b143831dc160dd947f22ffadf0bfb12fb0
huggingface.co/CrossBaseArithmetic/arithmetic-model-L2_H2_D256/tree/b251d1b143831dc160dd947f22ffadf0bfb12fb0

Verifiziert 3. Okt.

Modellartefakte

1 Artefakt

model.safetensors

safetensors · 6,30 MB · SHA-256 3b78f789bf6e…984e · Hugging Face

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Quellenauszüge

3 Auszüge

--- library_name: transformers tags: - generated_from_trainer model-index: - name: arithmetic-model-L2_H2_D256 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_H2_D256 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: 256 - eval_batch_size: 16 - 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 5.0.0 - Pytorch 2.10.0+cu128 - Datasets 4.8.5 - Tokenizers 0.22.2

Source context: 11 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo CrossBaseArithmetic/arithmetic-model-L2_H2_D256

Source context: 216 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo CrossBaseArithmetic/arithmetic-model-L2_H2_D256