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  pipeline_tag: robotics
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  ---
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  # Octo Small
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- This model is trained with a window size of 2, predicting 7-dimensional actions 4 steps into the future using a diffusion policy. The model is a Transformer with 27M parameters (equivalent to a ViT-S). Images are tokenized by preprocessing with a lightweight convolutional encoder, then grouped into 16x16 patches. Language is tokenized by applying the T5 tokenizer, and then applying the T5-Base language encoder.
 
 
 
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  Observations and tasks conform to the following spec:
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  pipeline_tag: robotics
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  ---
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  # Octo Small
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+ See https://github.com/octo-models/octo for instructions for using this model.
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+ Octo Small is trained with a window size of 2, predicting 7-dimensional actions 4 steps into the future using a diffusion policy. The model is a Transformer with 27M parameters (equivalent to a ViT-S). Images are tokenized by preprocessing with a lightweight convolutional encoder, then grouped into 16x16 patches. Language is tokenized by applying the T5 tokenizer, and then applying the T5-Base language encoder.
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  Observations and tasks conform to the following spec:
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