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feat(pi0-fast): support automatic tokenizer fitting
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@@ -109,15 +109,21 @@ lerobot-train \
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### Key Training Parameters
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| Parameter | Description | Default |
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| -------------------------------------- | -------------------------------------------------- | ------------------------------- |
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| `--policy.gradient_checkpointing=true` | Reduces memory usage significantly during training | `false` |
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| `--policy.dtype=bfloat16` | Use mixed precision training for efficiency | `float32` |
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| `--policy.chunk_size` | Number of action steps to predict (action horizon) | `50` |
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| `--policy.n_action_steps` | Number of action steps to execute | `50` |
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| `--policy.max_action_tokens` | Maximum number of FAST tokens per action chunk | `256` |
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| `--policy.action_tokenizer_name` | FAST tokenizer to use | `lerobot/fast-action-tokenizer` |
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| `--policy.compile_model=true` | Enable torch.compile for faster training | `false` |
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| Parameter | Description | Default |
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| --------------------------------------- | -------------------------------------------------- | ------------------------------- |
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| `--policy.gradient_checkpointing=true` | Reduces memory usage significantly during training | `false` |
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| `--policy.dtype=bfloat16` | Use mixed precision training for efficiency | `float32` |
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| `--policy.chunk_size` | Number of action steps to predict (action horizon) | `50` |
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| `--policy.n_action_steps` | Number of action steps to execute | `50` |
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| `--policy.max_action_tokens` | Maximum number of FAST tokens per action chunk | `256` |
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| `--policy.action_tokenizer_name` | FAST tokenizer to use | `lerobot/fast-action-tokenizer` |
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| `--policy.auto_fit_fast_tokenizer=true` | Fit and cache a tokenizer for the training dataset | `false` |
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| `--policy.compile_model=true` | Enable torch.compile for faster training | `false` |
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Set `--policy.auto_fit_fast_tokenizer=true` to sample action chunks from the
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training dataset and cache a fitted tokenizer under
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`~/.cache/lerobot/fast_tokenizers`. This also works when fine-tuning with
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`--policy.path`; leave it disabled to retain the checkpoint's tokenizer.
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## Inference
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