Instructions to use hf-internal-testing/tiny-random-BlipForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BlipForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="hf-internal-testing/tiny-random-BlipForQuestionAnswering")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-BlipForQuestionAnswering") model = AutoModelForVisualQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-BlipForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
File size: 1,077 Bytes
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"architectures": [
"BlipForQuestionAnswering"
],
"bos_token_id": 0,
"eos_token_id": 2,
"image_text_hidden_size": 256,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"label_smoothing": 0.0,
"logit_scale_init_value": 2.6592,
"model_type": "blip",
"pad_token_id": 0,
"projection_dim": 64,
"sep_token_id": 3,
"text_config": {
"attention_dropout": 0.1,
"bos_token_id": 0,
"dropout": 0.1,
"encoder_hidden_size": 32,
"hidden_size": 32,
"intermediate_size": 37,
"model_type": "blip_text_model",
"num_attention_heads": 4,
"num_hidden_layers": 2,
"projection_dim": 32,
"sep_token_id": 3,
"vocab_size": 1124
},
"torch_dtype": "float32",
"transformers_version": "4.40.0.dev0",
"vision_config": {
"attention_dropout": 0.1,
"dropout": 0.1,
"hidden_size": 32,
"image_size": 30,
"intermediate_size": 37,
"model_type": "blip_vision_model",
"num_attention_heads": 4,
"num_channels": 3,
"num_hidden_layers": 2,
"patch_size": 2,
"projection_dim": 32
}
}
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