Instructions to use TnTerry/MEGL-BLIP-Baseline-Object with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TnTerry/MEGL-BLIP-Baseline-Object with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="TnTerry/MEGL-BLIP-Baseline-Object")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("TnTerry/MEGL-BLIP-Baseline-Object") model = AutoModelForVisualQuestionAnswering.from_pretrained("TnTerry/MEGL-BLIP-Baseline-Object", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 780 Bytes
259af6d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"_name_or_path": "TnTerry/MEGL_LLaVA_Baseline",
"architectures": [
"BlipForQuestionAnswering"
],
"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",
"projection_dim": 512,
"text_config": {
"_attn_implementation_autoset": true,
"initializer_factor": 1.0,
"model_type": "blip_text_model",
"num_attention_heads": 12
},
"torch_dtype": "float32",
"transformers_version": "4.41.2",
"vision_config": {
"_attn_implementation_autoset": true,
"dropout": 0.0,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"model_type": "blip_vision_model",
"num_channels": 3
}
}
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