Instructions to use Keetawan/BLIP2SeaLLMs-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Keetawan/BLIP2SeaLLMs-1.5B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="Keetawan/BLIP2SeaLLMs-1.5B")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("Keetawan/BLIP2SeaLLMs-1.5B") model = AutoModelForVisualQuestionAnswering.from_pretrained("Keetawan/BLIP2SeaLLMs-1.5B") - Notebooks
- Google Colab
- Kaggle
BLIP2SeaLLMs-1.5B
This model is a fine-tuned version of Salesforce/blip2-opt-2.7b-coco 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Downloads last month
- 13
Model tree for Keetawan/BLIP2SeaLLMs-1.5B
Base model
Salesforce/blip2-opt-2.7b-coco