Visual Question Answering
Transformers
Safetensors
Chinese
English
QH_360VL
text-generation
custom_code
Instructions to use ecfirst/360VL_PHI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ecfirst/360VL_PHI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="ecfirst/360VL_PHI", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ecfirst/360VL_PHI", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update modeling_360vl.py
Browse files- modeling_360vl.py +1 -1
modeling_360vl.py
CHANGED
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@@ -178,7 +178,7 @@ class HoneybeeVisualProjectorConfig(PretrainedConfig):
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the visual_projector config dict if we are loading from HoneybeeConfig
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-
if config_dict.get("model_type") == "
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config_dict = config_dict["visual_projector_config"]
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'''
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the visual_projector config dict if we are loading from HoneybeeConfig
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+
if config_dict.get("model_type") == "QH_360VL":
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config_dict = config_dict["visual_projector_config"]
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'''
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