axel-darmouni/paligemma_dataset2

Model

0

stars

4

commits

1

repos using this model

1

linked in READMEs

Nov 23, 2024

updated

endpoints_compatible
generated_from_trainer
image-text-to-text
paligemma
safetensors
tensorboard
text-generation-inference
transformers
Browse cluster: Multilingual Legal NLP Models

README

paligemma_dataset2

This model is a fine-tuned version of google/paligemma-3b-pt-448 on the None 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: 2e-05
  • train_batch_size: 2
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 8
  • optimizer: Use adamw_hf with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2
  • num_epochs: 2

Training results

Framework versions

  • Transformers 4.46.3
  • Pytorch 2.5.1+cu118
  • Datasets 3.1.0
  • Tokenizers 0.20.3

Contributors

axel-darmouni

4 commits

axel-darmouni/paligemma_dataset2

Model

0

stars

4

commits

1

repos using this model

1

linked in READMEs

Nov 23, 2024

updated

endpoints_compatible
generated_from_trainer
image-text-to-text
paligemma
safetensors
tensorboard
text-generation-inference
transformers
Browse cluster: Multilingual Legal NLP Models

README

paligemma_dataset2

This model is a fine-tuned version of google/paligemma-3b-pt-448 on the None 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: 2e-05
  • train_batch_size: 2
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 8
  • optimizer: Use adamw_hf with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2
  • num_epochs: 2

Training results

Framework versions

  • Transformers 4.46.3
  • Pytorch 2.5.1+cu118
  • Datasets 3.1.0
  • Tokenizers 0.20.3

Contributors

axel-darmouni

4 commits