# Load model directly
from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering
processor = AutoProcessor.from_pretrained("phucd/blip-gqa-ft")
model = AutoModelForVisualQuestionAnswering.from_pretrained("phucd/blip-gqa-ft")Quick Links
blip-gqa-ft
This model is a fine-tuned version of Salesforce/blip2-opt-2.7b 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.5.0
- Tokenizers 0.21.1
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Model tree for phucd/blip-gqa-ft
Base model
Salesforce/blip2-opt-2.7b
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="phucd/blip-gqa-ft")