Instructions to use q-future/one-align with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use q-future/one-align with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="q-future/one-align", trust_remote_code=True) pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("q-future/one-align", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload modeling_mplug_owl2.py with huggingface_hub
Browse files- modeling_mplug_owl2.py +1 -1
modeling_mplug_owl2.py
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@@ -29,7 +29,7 @@ sys.path.insert(0, dir_path)
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from transformers import AutoConfig, AutoModelForCausalLM, LlamaConfig, LlamaModel, LlamaForCausalLM
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from transformers.modeling_outputs import CausalLMOutputWithPast
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from .configuration_mplug_owl2 import MPLUGOwl2Config, MplugOwlVisionConfig, MplugOwlVisualAbstractorConfig
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from .visual_encoder import MplugOwlVisionModel, MplugOwlVisualAbstractorModel
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from .modeling_llama2 import replace_llama_modality_adaptive
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IGNORE_INDEX = -100
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from transformers import AutoConfig, AutoModelForCausalLM, LlamaConfig, LlamaModel, LlamaForCausalLM
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from transformers.modeling_outputs import CausalLMOutputWithPast
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#from .configuration_mplug_owl2 import MPLUGOwl2Config, MplugOwlVisionConfig, MplugOwlVisualAbstractorConfig
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from .visual_encoder import MplugOwlVisionModel, MplugOwlVisualAbstractorModel
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from .modeling_llama2 import replace_llama_modality_adaptive
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IGNORE_INDEX = -100
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