Instructions to use ManishThota/InstructVQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ManishThota/InstructVQA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="ManishThota/InstructVQA")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("ManishThota/InstructVQA") model = AutoModelForVisualQuestionAnswering.from_pretrained("ManishThota/InstructVQA") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "Salesforce/blip-vqa-base", | |
| "architectures": [ | |
| "BlipForQuestionAnswering" | |
| ], | |
| "image_text_hidden_size": 256, | |
| "initializer_factor": 1.0, | |
| "initializer_range": 0.02, | |
| "logit_scale_init_value": 2.6592, | |
| "model_type": "blip", | |
| "projection_dim": 512, | |
| "text_config": { | |
| "initializer_factor": 1.0, | |
| "model_type": "blip_text_model", | |
| "num_attention_heads": 12 | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.35.2", | |
| "vision_config": { | |
| "dropout": 0.0, | |
| "initializer_factor": 1.0, | |
| "initializer_range": 0.02, | |
| "model_type": "blip_vision_model", | |
| "num_channels": 3 | |
| } | |
| } | |