Saltar al contenido
ModelVerificadoExterno
Descargar

Multiligual Arithmetic D64 L1 H2

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

TipoOtherVersiónRevision 0fd2977Publicador@CCBD-InternsCcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
215 KB
Versión
vRevision 0fd2977
Actualizado
21 oct 2026
Publicador
@CCBD-Interns
Descargas
16
Fuente
Hugging Face

Descripción de la fuente

multiligual-arithmetic-d64-l1-h2

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

Fuentes

1 fuente
Hugging FacePrincipalVersión 0fd2977bb3e35441cd0d2483c1cb965adaf2fe47
huggingface.co/CCBD-Interns/multiligual-arithmetic-d64-l1-h2/tree/0fd2977bb3e35441cd0d2483c1cb965adaf2fe47

Verificado 21 oct

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · 215 KB · SHA-256 6da25bfdb3ca…d48e · Hugging Face

Descargar

Extractos de fuentes

3 extractos

--- library_name: transformers tags: - generated_from_trainer model-index: - name: multiligual-arithmetic-d64-l1-h2 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. --> # multiligual-arithmetic-d64-l1-h2 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: 16 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo CCBD-Interns/multiligual-arithmetic-d64-l1-h2

Source context: 285 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo CCBD-Interns/multiligual-arithmetic-d64-l1-h2