paligemma_vqav2_5

• Loss: 1.3111

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 0.01
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• 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: 12

Training results

Framework versions

• PEFT 0.13.2
• Transformers 4.46.2
• Pytorch 2.1.2.post304
• Datasets 3.1.0
• Tokenizers 0.20.3