Instructions to use bn22/convmixer_1024_32_ks9_p16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bn22/convmixer_1024_32_ks9_p16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bn22/convmixer_1024_32_ks9_p16", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bn22/convmixer_1024_32_ks9_p16", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- config.json +2 -2
- model.safetensors +2 -2
config.json
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"AutoModel": "convmixer_wd.ConvMixerModel"
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},
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"depth": 32,
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"dim":
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"dropout": 0.0,
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"dtype": "float32",
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"kernel_size":
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"model_type": "convmixer",
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"num_classes": 1000,
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"patch_size": 16,
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"AutoModel": "convmixer_wd.ConvMixerModel"
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},
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"depth": 32,
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"dim": 1024,
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"dropout": 0.0,
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"dtype": "float32",
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"kernel_size": 9,
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"model_type": "convmixer",
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"num_classes": 1000,
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"patch_size": 16,
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:85ccaee2a961c514f0df43b1f5089408bb47bb8b49024863b3869a8de8510b7a
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size 199739632
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