metadata
library_name: transformers
license: gemma
base_model: google/paligemma2-3b-pt-448
tags:
- generated_from_trainer
model-index:
- name: paligemma-architecture
results: []
paligemma-architecture
This model is a fine-tuned version of google/paligemma2-3b-pt-448 on a custom architecture dataset.
Training procedure
Followed the notebook from smol-vision, adjusted dataset loading and some parameters.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.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: 4
Training results
TrainOutput(global_step=352, training_loss=7.797419488430023, metrics={'train_runtime': 1653.6164, 'train_samples_per_second': 1.705, 'train_steps_per_second': 0.213, 'total_flos': 5.772661476596784e+16, 'train_loss': 7.797419488430023, 'epoch': 3.9645390070921986})
Framework versions
- Transformers 4.50.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.4.0
- Tokenizers 0.21.0