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 18 files
Browse files- mmalaya_arch.py +1 -1
mmalaya_arch.py
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@@ -3,7 +3,7 @@ import re
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import torch
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import torch.nn as nn
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from transformers import Blip2Model, Blip2Processor, Blip2Config
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from mm_utils import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
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class BLIP2VisionTower(nn.Module):
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import torch
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import torch.nn as nn
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from transformers import Blip2Model, Blip2Processor, Blip2Config
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from .mm_utils import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
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class BLIP2VisionTower(nn.Module):
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