Instructions to use physical-intelligence/fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use physical-intelligence/fast with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("physical-intelligence/fast", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag, link to paper
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by nielsr HF Staff - opened
README.md
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library_name: transformers
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license: apache-2.0
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tags:
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- robotics
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- tokenizer
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---
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# FAST: Efficient Action Tokenization for Vision-Language-Action Models
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This is the official repo for the [FAST action tokenizer](https://www.pi.website/research/fast).
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The action tokenizer maps any sequence of robot actions into a sequence of dense, discrete **action tokens** for training autoregressive VLA models.
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library_name: transformers
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license: apache-2.0
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tags:
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- tokenizer
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pipeline_tag: robotics
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---
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# FAST: Efficient Action Tokenization for Vision-Language-Action Models
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This is the official repo for the [FAST action tokenizer](https://www.pi.website/research/fast) from the paper [FAST: Efficient Action Tokenization for Vision-Language-Action Models](https://huggingface.co/papers/2501.09747).
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The action tokenizer maps any sequence of robot actions into a sequence of dense, discrete **action tokens** for training autoregressive VLA models.
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