paligemma-vqa – AI Model by statking | AlphaNeural AI

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paligemma-vqa

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google/paligemma-3b-pt-224

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paligemma-vqa

This model is a fine-tuned version of google/paligemma-3b-pt-224 on the vq_av2 dataset. It achieves the following results on the evaluation set:

Loss: 0.5071

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: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 256
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
0.5618	0.5886	1000	0.5531
0.5268	1.1772	2000	0.5335
0.5099	1.7657	3000	0.5071

Framework versions

PEFT 0.11.1
Transformers 4.41.1
Pytorch 2.2.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1