Instructions to use DataCanvas/MMAlaya with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DataCanvas/MMAlaya with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="DataCanvas/MMAlaya", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("DataCanvas/MMAlaya", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload mmalaya_arch.py
Browse files- mmalaya_arch.py +2 -2
mmalaya_arch.py
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@@ -306,8 +306,8 @@ class MMAlayaMetaForCausalLM(ABC):
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torch.tensor(input_ids, dtype=torch.long).unsqueeze(0),
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)
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# 加载图像
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if return_tensors is not None:
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if return_tensors == 'pt':
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torch.tensor(input_ids, dtype=torch.long).unsqueeze(0),
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)
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# 加载图像
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image_tensor = self.get_vision_tower().image_processor(
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image, return_tensors='pt')['pixel_values'].half()
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if return_tensors is not None:
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if return_tensors == 'pt':
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